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Kavzoglu, T., M. Yildiz Erdemir, and H. Tonbul. "A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 241–47. http://dx.doi.org/10.5194/isprsarchives-xli-b7-241-2016.
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Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.
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Kavzoglu, T., M. Yildiz Erdemir, and H. Tonbul. "A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 241–47. http://dx.doi.org/10.5194/isprs-archives-xli-b7-241-2016.
Full textAbstract:
Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.
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Sofyan, Agus. "Classification of Land Cover by Using Aerial Photo At CV. Alaska Prima Coal, Cooling Village, Sanga-Sanga Sub-district, Kutai Kartanegara District, East Kalimantan Province." AGRIFOR 17, no. 1 (March 9, 2018): 1. http://dx.doi.org/10.31293/af.v17i1.3090.
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Remote sensing can be done visually and digitally. one of the advantages of airborne photography data generated by drone (phantom-3) compared to satellite imagery with optical sensitivity is its ability to obtain cloud-free images and freedom of recording time and the displayed area shows clearly defined objects corresponding to land cover. characteristics. To limit the object-based area of this research method applied is Object Based Image Analysis (OBIA).This study aims to classify land cover using highly resolved aerial photography with the help of Object Based Image Analysis (OBIA) technique and calculate the accuracy and accuracy, land cover classification by using Objeck Based Image (OBIA) analysis through examination of field conditions.classifying land cover, the classification includes shrubs, young shrubs, plantations (oil palms), shrubs, mines, open land, roads and water bodies with Accuracy of Overcome 0.86.
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Kavzoglu, T., and M. Yildiz. "Parameter-Based Performance Analysis of Object-Based Image Analysis Using Aerial and Quikbird-2 Images." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-7 (September 19, 2014): 31–37. http://dx.doi.org/10.5194/isprsannals-ii-7-31-2014.
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Opening new possibilities for research, very high resolution (VHR) imagery acquired by recent commercial satellites and aerial systems requires advanced approaches and techniques that can handle large volume of data with high local variance. Delineation of land use/cover information from VHR images is a hot research topic in remote sensing. In recent years, object-based image analysis (OBIA) has become a popular solution for image analysis tasks as it considers shape, texture and content information associated with the image objects. The most important stage of OBIA is the image segmentation process applied prior to classification. Determination of optimal segmentation parameters is of crucial importance for the performance of the selected classifier. In this study, effectiveness and applicability of the segmentation method in relation to its parameters was analysed using two VHR images, an aerial photo and a Quickbird-2 image. Multi-resolution segmentation technique was employed with its optimal parameters of scale, shape and compactness that were defined after an extensive trail process on the data sets. Nearest neighbour classifier was applied on the segmented images, and then the accuracy assessment was applied. Results show that segmentation parameters have a direct effect on the classification accuracy, and low values of scale-shape combinations produce the highest classification accuracies. Also, compactness parameter was found to be having minimal effect on the construction of image objects, hence it can be set to a constant value in image classification.
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Zatelli, P., S. Gobbi, C. Tattoni, N. La Porta, and M. Ciolli. "OBJECT-BASED IMAGE ANALYSIS FOR HISTORIC MAPS CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W14 (August 23, 2019): 247–54. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w14-247-2019.
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<p><strong>Abstract.</strong> Heritage maps represent fundamental information for the study of the evolution of a region, especially in terms of landscape and ecologic features. Historical maps present two kinds of hurdle before they can be used in a modern GIS: they must be geometrically corrected to correspond to the datum in use and they must be classified to exploit the information they contain. This study deals the latter problem: the Historical Cadaster Map, created between 1851 and 1861, for the Trentino region in the North of Italy is available as a collection of maps in the ETRS89/UTM 32N datum. The map is a high resolution scan (230 DPI, 24 bit) of the original map and has been used in several ecological studies, since it provides detailed information not only about land property but also about land use. In the past the cadaster map has been manually digitized and for each area a set of attributes has been recorded. Since this approach is time consuming and prone to errors, automatic and semi-automatic procedures have been tested. Traditional image classification techniques, such as maximum likelihood classification, supervised or un-supervised, pixelwise and contextual, do not provide satisfactory results for many reasons: map colors are very variable within the same area, symbols and characters are used to identify cadaster parcels and locations, lines, drawn by hand on the original map, have variable thickness and colors. The availability of FOSS tools for the Object-based Image Analysis (OBIA) has made possible the application of this technique to the cadaster map. This paper describes the use of GRASS GIS and R for the implementation of the OBIA approach for the supervised classification of the historic cadaster map. It describes the determination of the optimal segments, the choice of their attributes and relevant statistics, and their classification. The result has been evaluated with respect to a manually digitized map using Cohens Kappa and the analysis of the confusion matrix. The result of the OBIA classification has also been compared to the classification of the same map using maximum likelihood classification, un-supervised and supervised, both pixelwise and contextual. The OBIA approach has provided very satisfactory results with the ability to automatically remove the background and symbols and characters, creating a ready to be used classified map. This study highlights the effectiveness of the OBIA processing chain available in the FOSS4G ecosystem, and in particular the added value of the interoperability between GRASS GIS and R.</p>
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Blaschke, T., S. Lang, D. Tiede, M. Papadakis, and A. Györi. "OBJECT-BASED IMAGE ANALYSIS BEYOND REMOTE SENSING – THE HUMAN PERSPECTIVE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 879–82. http://dx.doi.org/10.5194/isprs-archives-xli-b7-879-2016.
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We introduce a prototypical methodological framework for a place-based GIS-RS system for the spatial delineation of place while incorporating spatial analysis and mapping techniques using methods from different fields such as environmental psychology, geography, and computer science. The methodological lynchpin for this to happen - when aiming to delineate <i>place</i> in terms of objects - is object-based image analysis (OBIA).
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Blaschke, T., S. Lang, D. Tiede, M. Papadakis, and A. Györi. "OBJECT-BASED IMAGE ANALYSIS BEYOND REMOTE SENSING – THE HUMAN PERSPECTIVE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 879–82. http://dx.doi.org/10.5194/isprsarchives-xli-b7-879-2016.
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We introduce a prototypical methodological framework for a place-based GIS-RS system for the spatial delineation of place while incorporating spatial analysis and mapping techniques using methods from different fields such as environmental psychology, geography, and computer science. The methodological lynchpin for this to happen - when aiming to delineate <i>place</i> in terms of objects - is object-based image analysis (OBIA).
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Fernandez Galarreta, J., N. Kerle, and M. Gerke. "UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning." Natural Hazards and Earth System Sciences 15, no. 6 (June 1, 2015): 1087–101. http://dx.doi.org/10.5194/nhess-15-1087-2015.
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Abstract. Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAVs). 3-D point-cloud assessment for the entire building is combined with detailed object-based image analysis (OBIA) of façades and roofs. This research focuses not on automatic damage assessment, but on creating a methodology that supports the often ambiguous classification of intermediate damage levels, aiming at producing comprehensive per-building damage scores. We identify completely damaged structures in the 3-D point cloud, and for all other cases provide the OBIA-based damage indicators to be used as auxiliary information by damage analysts. The results demonstrate the usability of the 3-D point-cloud data to identify major damage features. Also the UAV-derived and OBIA-processed oblique images are shown to be a suitable basis for the identification of detailed damage features on façades and roofs. Finally, we also demonstrate the possibility of aggregating the multi-perspective damage information at building level.
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Fernandez Galarreta, J., N. Kerle, and M. Gerke. "UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning." Natural Hazards and Earth System Sciences Discussions 2, no. 9 (September 2, 2014): 5603–45. http://dx.doi.org/10.5194/nhessd-2-5603-2014.
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Abstract. Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAVs). 3-D point-cloud assessment for the entire building is combined with detailed object-based image analysis (OBIA) of façades and roofs. This research focuses not on automatic damage assessment, but on creating a methodology that supports the often ambiguous classification of intermediate damage levels, aiming at producing comprehensive per-building damage scores. We identify completely damaged structures in the 3-D point cloud, and for all other cases provide the OBIA-based damage indicators to be used as auxiliary information by damage analysts. The results demonstrate the usability of the 3-D point-cloud data to identify major damage features. Also the UAV-derived and OBIA-processed oblique images are shown to be a suitable basis for the identification of detailed damage features on façades and roofs. Finally, we also demonstrate the possibility of aggregating the multi-perspective damage information at building level.
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Apriyanto, Dwi Putra, I. Nengah Surati Jaya, and Nining Puspaningsih. "Examining the object-based and pixel-based image analyses for developing stand volume estimator model." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 3 (September 1, 2019): 1586. http://dx.doi.org/10.11591/ijeecs.v15.i3.pp1586-1596.
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In the last two decades there has been significant leap on the spatial resolution of the satellite digital images which may be very useful for estimating stand parameter required for forest as well as environment management. This paper describes development of stand volume estimator models using SPOT 6 panchromatic and multispectral images with an object-based digital image analysis (OBIA) and conventional pixel-based approaches. The data used include panchromatic band with1.5m spatial resolution, and multispectral band with6m spatial resolution. The proposed OBIA technique with mean-shift algorithm was functioned to derive a canopy cover variable from the fusion of the panchromatic and multispectral, while the pixel-based vegetation index was used to develop model with an original pixel-size of 6 m. The estimator models were established based on 65 sample plots both measured in the field and images. The study found that the OBIA provides more accurate identification with Kappa Accuracy (KA) of 71% and Overall Accuracy (OA) of 86%. The study concluded that the best stand volume estimation model is the model that developed from the canopy cover (C) derived from OBIA i.e., v = 13.47e<sup>0.032C</sup> with mean deviation of only 0.92%, better than the model derived from conventional pixel-based approach, i.e., v = 0.0000067e<sup>16.48TNDVI</sup> with a mean deviation of 5.37%.
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Yuan, Weitao, Wangle Zhang, Zhongping Lai, and Jingxiong Zhang. "Extraction of Yardang Characteristics Using Object-Based Image Analysis and Canny Edge Detection Methods." Remote Sensing 12, no. 4 (February 22, 2020): 726. http://dx.doi.org/10.3390/rs12040726.
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Parameters of geomorphological characteristics are critical for research on yardangs. However, methods which are low-cost, accurate, and automatic or semi-automatic for extracting these parameters are limited. We present here semi-automatic techniques for this purpose. They are object-based image analysis (OBIA) and Canny edge detection (CED), using free, very high spatial resolution images from Google Earth. We chose yardang fields in Dunhuang of west China to test the methods. Our results showed that the extractions registered an overall accuracy of 92.26% with a Kappa coefficient of agreement of 0.82 at a segmentation scale of 52 using the OBIA method, and the exaction of yardangs had the highest accuracy at medium segmentation scales (138, 145). Using CED, we resampled the experimental image subset to a series of lower spatial resolutions for eliminating noise. The total length of yardang boundaries showed a logarithmically decreasing (R2 = 0.904) trend with decreasing spatial resolution, and there was also a linear relationship between yardang median widths and spatial resolutions (R2 = 0.95). Despite the difficulty of identifying shadows, the CED method achieved an overall accuracy of 89.23% with a kappa coefficient of agreement of 0.72, similar to that of the OBIA method at medium segmentation scale (138).
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Yu, Huai, Tianheng Yan, Wen Yang, and Hong Zheng. "AN INTEGRATIVE OBJECT-BASED IMAGE ANALYSIS WORKFLOW FOR UAV IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 7, 2016): 1085–91. http://dx.doi.org/10.5194/isprsarchives-xli-b1-1085-2016.
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In this work, we propose an integrative framework to process UAV images. The overall process can be viewed as a pipeline consisting of the geometric and radiometric corrections, subsequent panoramic mosaicking and hierarchical image segmentation for later Object Based Image Analysis (OBIA). More precisely, we first introduce an efficient image stitching algorithm after the geometric calibration and radiometric correction, which employs a fast feature extraction and matching by combining the local difference binary descriptor and the local sensitive hashing. We then use a Binary Partition Tree (BPT) representation for the large mosaicked panoramic image, which starts by the definition of an initial partition obtained by an over-segmentation algorithm, i.e., the simple linear iterative clustering (SLIC). Finally, we build an object-based hierarchical structure by fully considering the spectral and spatial information of the super-pixels and their topological relationships. Moreover, an optimal segmentation is obtained by filtering the complex hierarchies into simpler ones according to some criterions, such as the uniform homogeneity and semantic consistency. Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake demonstrate the effectiveness and efficiency of our proposed method.
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Yu, Huai, Tianheng Yan, Wen Yang, and Hong Zheng. "AN INTEGRATIVE OBJECT-BASED IMAGE ANALYSIS WORKFLOW FOR UAV IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 7, 2016): 1085–91. http://dx.doi.org/10.5194/isprs-archives-xli-b1-1085-2016.
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In this work, we propose an integrative framework to process UAV images. The overall process can be viewed as a pipeline consisting of the geometric and radiometric corrections, subsequent panoramic mosaicking and hierarchical image segmentation for later Object Based Image Analysis (OBIA). More precisely, we first introduce an efficient image stitching algorithm after the geometric calibration and radiometric correction, which employs a fast feature extraction and matching by combining the local difference binary descriptor and the local sensitive hashing. We then use a Binary Partition Tree (BPT) representation for the large mosaicked panoramic image, which starts by the definition of an initial partition obtained by an over-segmentation algorithm, i.e., the simple linear iterative clustering (SLIC). Finally, we build an object-based hierarchical structure by fully considering the spectral and spatial information of the super-pixels and their topological relationships. Moreover, an optimal segmentation is obtained by filtering the complex hierarchies into simpler ones according to some criterions, such as the uniform homogeneity and semantic consistency. Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake demonstrate the effectiveness and efficiency of our proposed method.
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Song, Ahram, Yongil Kim, and Youkyung Han. "Uncertainty Analysis for Object-Based Change Detection in Very High-Resolution Satellite Images Using Deep Learning Network." Remote Sensing 12, no. 15 (July 22, 2020): 2345. http://dx.doi.org/10.3390/rs12152345.
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Object-based image analysis (OBIA) is better than pixel-based image analysis for change detection (CD) in very high-resolution (VHR) remote sensing images. Although the effectiveness of deep learning approaches has recently been proved, few studies have investigated OBIA and deep learning for CD. Previously proposed methods use the object information obtained from the preprocessing and postprocessing phase of deep learning. In general, they use the dominant or most frequently used label information with respect to all the pixels inside an object without considering any quantitative criteria to integrate the deep learning network and object information. In this study, we developed an object-based CD method for VHR satellite images using a deep learning network to denote the uncertainty associated with an object and effectively detect the changes in an area without the ground truth data. The proposed method defines the uncertainty associated with an object and mainly includes two phases. Initially, CD objects were generated by unsupervised CD methods, and the objects were used to train the CD network comprising three-dimensional convolutional layers and convolutional long short-term memory layers. The CD objects were updated according to the uncertainty level after the learning process was completed. Further, the updated CD objects were considered as the training data for the CD network. This process was repeated until the entire area was classified into two classes, i.e., change and no-change, with respect to the object units or defined epoch. The experiments conducted using two different VHR satellite images confirmed that the proposed method achieved the best performance when compared with the performances obtained using the traditional CD approaches. The method was less affected by salt and pepper noise and could effectively extract the region of change in object units without ground truth data. Furthermore, the proposed method can offer advantages associated with unsupervised CD methods and a CD network subjected to postprocessing by effectively utilizing the deep learning technique and object information.
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Zollini, Sara, Maria Alicandro, Donatella Dominici, Raimondo Quaresima, and Marco Giallonardo. "UAV Photogrammetry for Concrete Bridge Inspection Using Object-Based Image Analysis (OBIA)." Remote Sensing 12, no. 19 (September 28, 2020): 3180. http://dx.doi.org/10.3390/rs12193180.
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Monitoring infrastructures is becoming an important and challenging issue. In Italy, the heritage consists of more than 60,000 bridges, which need to be inspected and detected in order to guarantee their strength and durability function during nominal lifespan. In this paper, a non-destructive survey methodology for study concrete bridges surface deterioration and viaducts is presented. Terrestrial and unmanned aerial vehicle (UAV) photogrammetry has been used for visual inspection of a standard concrete overpass in L’Aquila (Italy). The obtained orthomosaic has been processed by means of Object-Based Image Analysis (OBIA) to identify and classify deteriorated areas and decay forms. The results show a satisfactory identification and survey of deteriorated areas. It has also been possible to quantify metric information, such as width and length of cracks and extension of weathered areas. This allows to perform easy and fast periodic inspections over time in order to evaluate the evolution of deterioration and plan urgency of preservation or maintenance measures.
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Cornett, Reagan L., and Eileen G. Ernenwein. "Object-Based Image Analysis of Ground-Penetrating Radar Data for Archaic Hearths." Remote Sensing 12, no. 16 (August 7, 2020): 2539. http://dx.doi.org/10.3390/rs12162539.
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Object-based image analysis (OBIA) has been increasingly used to identify terrain features of archaeological sites, but only recently to extract subsurface archaeological features from geophysical data. In this study, we use a semi-automated OBIA to identify Archaic (8000–1000 BC) hearths from Ground-Penetrating Radar (GPR) data collected at David Crockett Birthplace State Park in eastern Tennessee in the southeastern United States. The data were preprocessed using GPR-SLICE, Surfer, and Archaeofusion software, and amplitude depth slices were selected that contained anomalies ranging from 0.80 to 1.20 m below surface (BS). Next, the data were segmented within ESRI ArcMap GIS software using a global threshold and, after vectorization, classified using four attributes: area, perimeter, length-to-width ratio, and Circularity Index. The user-defined parameters were based on an excavated Archaic circular hearth found at a depth greater than one meter, which consisted of fire-cracked rock and had a diameter greater than one meter. These observations were in agreement with previous excavations of hearths at the site. Features that had a high probability of being Archaic hearths were further delineated by human interpretation from radargrams and then ground-truthed by auger testing. The semi-automated OBIA successfully predicted 15 probable Archaic hearths at depths ranging from 0.85 to 1.20 m BS. Observable spatial clustering of hearths may indicate episodes of seasonal occupation by small mobile groups during the Archaic Period.
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Bilotta, Giuliana, Rossella Nocera, and Pier Matteo Barone. "Cultural Heritage and Obia." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 17 (May 5, 2021): 449–65. http://dx.doi.org/10.37394/232015.2021.17.44.
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The historic centre of a town is its oldest and original core. It needs special protection in order to ensure the conservation of its historical, artistic and environmental heritage. In Italy, the definition of historic centres and the protection of their cultural heritage evolved in time, up to the current special attention for the historical aspects. The main threats to historical centres are real estate speculation and mass tourism. The purpose of this study is to catalog and monitor historic centers over time, in the context of urban planning. High-resolution satellite images and geographic information systems (GIS) offer new tools for urban planning and also for cultural heritage themes. “Real time” evaluation of urban structures, cartographic updating, monitoring of the progress of major works, with particular regard to cultural heritage, are made possible by the use of high-resolution images, which facilitate the identification of changes in urban and non-urban areas. The technique of Object Based Image Analysis (OBIA) has been used for image analysis and interpretation. OBIA allows a good interpretation of the scene captured by sensors thanks to classification-based segmentation and extraction of complete objects and their topological relations. This yields to a classification similar to the output of human photo-interpreter, but with a better reproducibility and homogeneity. In this paper we describe, through an application example, the potentiality and the difficulties of this technique and some results. The whole information obtained from segmented and categorized satellite images has been structured in a proper GIS, so that it can be overlaid with other environmental data. Information structuring and special metaheuristic analyses allow to study and monitor historic centers and cultural heritage. This methodology allows to identify the places at risk that need priority restoration; moreover it allows to keep track of changes that occurred over time
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Joshi, Abhasha, Janak Raj Joshi, Nawaraj Shrestha, Saroj Shrestha, and Sudarshan Gautam. "Object Based Land Cover Extraction Using Open Source Software." Journal on Geoinformatics, Nepal 12 (October 31, 2013): 26–30. http://dx.doi.org/10.3126/njg.v12i0.9070.
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Land cover is observed bio-physical cover of the earth’s surface and is an important resource for global monitoring studies, resource management, and planning activities. Traditionally these land resources were obtained from imagery using pixel based image analysis. But with the advent of High resolution satellite imagery and computation techniques these data are now widely being prepared using Object based Image Analysis (OBIA) techniques. But mostly only algorithm provided in commercial software and Ecognition in particular is being used to study OBIA. This paper aims to assess the application of an open source software Spring for OBIA. In this Study 0.5 meter pan sharpened Geo-Eye image was classified using spring software. The image was first segmented using region growing algorithm with similarity and area parameter. Using hit and trail method best parameter for segmentation for the study area was found. These objects were subsequently classified using Bhattacharya Distance. In this classification method spectral derivatives of the segment such as mean, median, standard deviation etc. were used which make this method useful. However the shape, size and context of the segment can’t be accounted during classification. i.e. rule based classification is not possible in spring. This classification method provides satisfactory overall accuracy of 78.46% with kappa coefficient 0.74. This classification method gave smooth land cover classes without salt and pepper effect and good appearance of land cover classes. However image segmentation and classification based on additional parameters such as shape and size of the segment, contextual information, pixel topology etc may give better classification result. Nepalese Journal on Geoinformatics -12, 2070 (2013AD): 26-30
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Fitriana, Hana Listi, Suwarsono Suwarsono, Eko Kusratmoko, and Supriatna Supriatna. "MAPPING BURNT AREAS USING THE SEMI-AUTOMATIC OBJECT-BASED IMAGE ANALYSIS METHOD." International Journal of Remote Sensing and Earth Sciences (IJReSES) 17, no. 1 (August 20, 2020): 57. http://dx.doi.org/10.30536/j.ijreses.2020.v17.a3281.
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Forest and land fires in Indonesia take place almost every year, particularly in the dry season and in Sumatra and Kalimantan. Such fires damage the ecosystem, and lower the quality of life of the community, especially in health, social and economic terms. To establish the location of forest and land fires, it is necessary to identify and analyse burnt areas. Information on these is necessary to determine the environmental damage caused, the impact on the environment, the carbon emissions produced, and the rehabilitation process needed. Identification methods of burnt land was made both visually and digitally by utilising satellite remote sensing data technology. Such data were chosen because they can identify objects quickly and precisely. Landsat 8 image data have many advantages: they can be easily obtained, the archives are long and they are visible to thermal wavelengths. By using a combination of visible, infrared and thermal channels through the semi-automatic object-based image analysis (OBIA) approach, the study aims to identify burnt areas in the geographical area of Indonesia. The research concludes that the semi-automatic OBIA approach based on the red, infrared and thermal spectral bands is a reliable and fast method for identifying burnt areas in regions of Sumatra and Kalimantan.
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Bengoufa, S., S. Niculescu, M. K. Mihoubi, R. Belkessa, and K. Abbad. "ROCKY SHORELINE EXTRACTION USING A DEEP LEARNING MODEL AND OBJECT-BASED IMAGE ANALYSIS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 23–29. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-23-2021.
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Abstract. In the context of the increasing anthropogenic influence on the coastal areas that are subject to high climate variability, the main challenge is to understand its current dynamics and to predict its future evolution. Therefore, monitoring of the shoreline kinematics is a key factor for the coastal erosion assessment and an essential feature for the sustainable management of these naturally vulnerable areas.This work focuses on the detection and extraction of the shoreline, basing on a specific remote sensing methodology using Very High Resolution (VHR) optical images. Indeed, an integrated approach based on a Deep Learning model, which is the Convolutional Neural Network (CNN) and Object Based Image Analysis (OBIA) has been developed. This study aims to evaluate the methodological contribution of this integrated approach for the (semi)-automatic extraction of the rocky shoreline, for which the botanical indicator has been chosen. Therefore the upper limit of black marine lichen has been detected and extracted as the target shoreline. It is the first indication of a (semi)-automatic detection of such a complex type of shoreline.The classification results derived from the combined CNN model and OBIA methods had achieved a high overall accuracy of 0.94. The extracted shoreline have been compared to a shoreline of reference derived from a traditional method that is a manual digitizing. The distances between the two shorelines has been calculated in order to assess the accuracy of the extraction method. This comparison revealed that 76% of the extracted shoreline lies within 1 m, and 35% lies within 0.5 m of reference one. Therefore, the CNN model integrated to OBIA was successfully shown to be a good method for shoreline extraction and could offer an immediate insight regarding rocky shoreline position, providing an alternative to its monitoring.
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Maboudi, M., J. Amini, and M. Hahn. "OBJECTS GROUPING FOR SEGMENTATION OF ROADS NETWORK IN HIGH RESOLUTION IMAGES OF URBAN AREAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 897–902. http://dx.doi.org/10.5194/isprsarchives-xli-b7-897-2016.
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Updated road databases are required for many purposes such as urban planning, disaster management, car navigation, route planning, traffic management and emergency handling. In the last decade, the improvement in spatial resolution of VHR civilian satellite sensors – as the main source of large scale mapping applications – was so considerable that GSD has become finer than size of common urban objects of interest such as building, trees and road parts. This technological advancement pushed the development of “Object-based Image Analysis (OBIA)” as an alternative to pixel-based image analysis methods. <br><br> Segmentation as one of the main stages of OBIA provides the image objects on which most of the following processes will be applied. Therefore, the success of an OBIA approach is strongly affected by the segmentation quality. In this paper, we propose a purpose-dependent refinement strategy in order to group road segments in urban areas using maximal similarity based region merging. For investigations with the proposed method, we use high resolution images of some urban sites. The promising results suggest that the proposed approach is applicable in grouping of road segments in urban areas.
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Maboudi, M., J. Amini, and M. Hahn. "OBJECTS GROUPING FOR SEGMENTATION OF ROADS NETWORK IN HIGH RESOLUTION IMAGES OF URBAN AREAS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 897–902. http://dx.doi.org/10.5194/isprs-archives-xli-b7-897-2016.
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Updated road databases are required for many purposes such as urban planning, disaster management, car navigation, route planning, traffic management and emergency handling. In the last decade, the improvement in spatial resolution of VHR civilian satellite sensors – as the main source of large scale mapping applications – was so considerable that GSD has become finer than size of common urban objects of interest such as building, trees and road parts. This technological advancement pushed the development of “Object-based Image Analysis (OBIA)” as an alternative to pixel-based image analysis methods. <br><br> Segmentation as one of the main stages of OBIA provides the image objects on which most of the following processes will be applied. Therefore, the success of an OBIA approach is strongly affected by the segmentation quality. In this paper, we propose a purpose-dependent refinement strategy in order to group road segments in urban areas using maximal similarity based region merging. For investigations with the proposed method, we use high resolution images of some urban sites. The promising results suggest that the proposed approach is applicable in grouping of road segments in urban areas.
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Medhi, Ankita, and Ashis Kumar Saha. "Rural Road Extraction using Object Based Image Analysis (OBIA): A case study from Assam, India." Advances in Cartography and GIScience of the ICA 1 (July 3, 2019): 1–8. http://dx.doi.org/10.5194/ica-adv-1-13-2019.
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<p><strong>Abstract.</strong> Rural roads in India have been considered as significant component for overall rural development. In India, the status of rural road connectivity is not up to the mark in some of the states. For providing better connectivity in the rural areas the information on roads are considered important. Detailed mapping of the roads can be useful for planning further road connectivity and proving access to facilities in the rural areas. For detailed mapping of roads higher resolution satellite imageries are required. Object based Image Analysis (OBIA) has emerged as a promising map analysis approach using high and very high resolution imageries. Feature extraction is one of the important aspect in OBIA extracting features such as roads, buildings, water bodies and other important features of interest from the high resolution imageries. In the present study, an attempt has been made to extract rural roads of Titabor in Jorhat district of Assam (India). Various OBIA based extraction methods have been used for extracting roads using high & very high resolution Resourcesat-II (5.8&thinsp;m) and Kompsat imagery (2.8&thinsp;m MS &amp; 0.7&thinsp;m PAN). The results have been compared and relative advantages were evaluated.</p>
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Wijesingha, Jayan, Thomas Astor, Damian Schulze-Brüninghoff, and Michael Wachendorf. "Mapping Invasive Lupinus polyphyllus Lindl. in Semi-natural Grasslands Using Object-Based Image Analysis of UAV-borne Images." PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 88, no. 5 (August 7, 2020): 391–406. http://dx.doi.org/10.1007/s41064-020-00121-0.
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Abstract Knowledge on the spatio-temporal distribution of invasive plant species is vital to maintain biodiversity in grasslands which are threatened by the invasion of such plants and to evaluate the effect of control activities conducted. Manual digitising of aerial images with field verification is the standard method to create maps of the invasive Lupinus polyphyllus Lindl. (Lupine) in semi-natural grasslands of the UNESCO biosphere reserve “Rhön”. As the standard method is labour-intensive, a workflow was developed to map lupine coverage using an unmanned aerial vehicle (UAV)-borne remote sensing (RS) along with object-based image analysis (OBIA). UAV-borne red, green, blue and thermal imaging, as well as photogrammetric canopy height modelling (CHM) were applied. Images were segmented by unsupervised parameter optimisation into image objects representing lupine plants and grass vegetation. Image objects obtained were classified using random forest classification modelling based on objects’ attributes. The classification model was employed to create lupine distribution maps of test areas, and predicted data were compared with manually digitised lupine coverage maps. The classification models yielded a mean prediction accuracy of 89%. The maximum difference in lupine area between classified and digitised lupine maps was 5%. Moreover, the pixel-wise map comparison showed that 88% of all pixels matched between classified and digitised maps. Our results indicated that lupine coverage mapping using UAV-borne RS data and OBIA provides similar results as the standard manual digitising method and, thus, offers a valuable tool to map invasive lupine on grasslands.
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Bashit, Nurhadi, Novia Sari Ristianti, Yudi Eko Windarto, and Desyta Ulfiana. "The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency." E3S Web of Conferences 202 (2020): 06036. http://dx.doi.org/10.1051/e3sconf/202020206036.
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Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.
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Widayani, Prima. "Aplikasi object-based image analysis untuk identifikasi awal permukiman kumuh menggunakan Citra satelit worldview-2." Majalah Geografi Indonesia 32, no. 2 (September 30, 2018): 162. http://dx.doi.org/10.22146/mgi.32306.
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Permukiman kumuh adalah perumahan yang mengalami penurunan kualitas fungsi sebagai tempat hunian. Tidak layak huni karena ketidakteraturan bangunan, tingkat kepadatan bangunan yang tinggi, dan kualitas bangunan serta sarana dan prasarana yang tidak memenuhi syarat, (UU No.1 Tahun 2011). Permukiman kumuh banyak ditemukan di kota-kota besar termasuk di sebagian Kota Yogyakarta, karena tidak layak dari sisi keaman, kesehatan dan tidak sesuai dengan tata ruang kota, maka perlu penanganan kawasan permukiman kumuh ini. Sebagai upaya penanganan kawasan kumuh, dibutuhkan pemantauan kawasan permukiman kumuh secara berkelanjutan, sehingga perlu suatu identifikasi cepat untuk membantu pemetaan kawasan kumuh. Penelitian ini bertujuan untuk identifikasi awal permukiman kumuh menggunakan pendekatan Object Base Image Analysis (OBIA) serta menguji kemampuan interpretasi OBIA dalam melakukan pengenalan permukiman kumuh berdasarkan ciri fisik permukiman. Data yang digunakan berupa Citra Satelit Worldview-2 tahun perekaman 2016, data kawasan kumuh Kota Yogyakarta dari program KOTAKU Yogyakarta, dan data survey lapangan. Alat yang digunakan berupa GPS, computer yang dilengkapi dengan software Ecognition, ENVI dan ArcGIS.10.2. Langkah pertama yang dilakukan sebelum menjalankan proses OBIA adalah mengenali karakteristik permukiman kumuh baik dari studi literatur, perundang-undangan maupun pengamatan lapangan. Berdasarkan studi sebelumnya dapat disusun aturan/kunci interpretasi untuk mendeteksi permukiman kumuh. Hasil identifikasi awal permukiman kumuh menggunakan OBIA dapat dilakukan berdasarkan analisis pola permukiman, kondisi jalan, tekstur, vegetasi dan jarak dengan sungai. Identifikasi permukiman kumuh di wilayah pinggiran sungai berdasarkan kondisi fisik permukiman menggunakan citra Wordview-2 mengasilkan ketelitian sebesar 82,14%. Ketelitian ini dapat dikatakan baik sehingga kedepannya diharapkan dapat membantu identifikasi awal dalam rangka pemetaan permukiman kumuh terutama di wilayah pinggiran sungaiABSTRACTSlums are housing that have decreased the quality of function as dwellings. Uninhabitable due to building irregularities, high levels of building density, and the quality of buildings and facilities and infrastructure that do not meet the requirements, (Act No.1 of 2011). Slum settlements are found in large cities including in parts of Yogyakarta City, because they are not feasible in terms of security, health and are not in accordance with the urban spatial structure, it is necessary to deal with these slums. As an effort to deal with slum areas, it is necessary to monitor slum areas in a sustainable manner, so that a quick identification is needed to assist in mapping the slums. This study aims to initial identification of slums using the Object Base Image Analysis (OBIA) approach and to test the ability of OBIA's interpretation of the introduction of slums based on physical characteristics of settlements. The data used are recording Worldview-2 years Satellite Image 2016, data from Yogyakarta City slum area from Yogyakarta KOTAKU program, and field survey data. The tools used in the form of GPS, computers equipped with Ecognition, ENVI and ArcGIS software.10.2. The first step taken before carrying out the OBIA process is to recognize the characteristics of slums both from literature studies, legislation and field observations. Based on previous studies, rules / key interpretations can be prepared to detect slums. The results of the initial identification of slums using OBIA can be done based on the analysis of settlement patterns, road conditions, texture, vegetation and distance to the river. Identification of slums in the riverside area based on the physical conditions of settlements using Wordview-2 imagery resulted in accuracy of 82.14%. This accuracy can be said to be good so that in the future it is expected to be able to help initial identification in the framework of mapping slum settlements, especially in the riverside areas
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Zou, Xiaoliang, Guihua Zhao, Jonathan Li, Yuanxi Yang, and Yong Fang. "OBJECT BASED IMAGE ANALYSIS COMBINING HIGH SPATIAL RESOLUTION IMAGERY AND LASER POINT CLOUDS FOR URBAN LAND COVER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 733–39. http://dx.doi.org/10.5194/isprsarchives-xli-b3-733-2016.
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With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.
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Zou, Xiaoliang, Guihua Zhao, Jonathan Li, Yuanxi Yang, and Yong Fang. "OBJECT BASED IMAGE ANALYSIS COMBINING HIGH SPATIAL RESOLUTION IMAGERY AND LASER POINT CLOUDS FOR URBAN LAND COVER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 10, 2016): 733–39. http://dx.doi.org/10.5194/isprs-archives-xli-b3-733-2016.
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With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.
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Liu, Shengjie, Zhixin Qi, Xia Li, and Anthony Yeh. "Integration of Convolutional Neural Networks and Object-Based Post-Classification Refinement for Land Use and Land Cover Mapping with Optical and SAR Data." Remote Sensing 11, no. 6 (March 22, 2019): 690. http://dx.doi.org/10.3390/rs11060690.
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Object-based image analysis (OBIA) has been widely used for land use and land cover (LULC) mapping using optical and synthetic aperture radar (SAR) images because it can utilize spatial information, reduce the effect of salt and pepper, and delineate LULC boundaries. With recent advances in machine learning, convolutional neural networks (CNNs) have become state-of-the-art algorithms. However, CNNs cannot be easily integrated with OBIA because the processing unit of CNNs is a rectangular image, whereas that of OBIA is an irregular image object. To obtain object-based thematic maps, this study developed a new method that integrates object-based post-classification refinement (OBPR) and CNNs for LULC mapping using Sentinel optical and SAR data. After producing the classification map by CNN, each image object was labeled with the most frequent land cover category of its pixels. The proposed method was tested on the optical-SAR Sentinel Guangzhou dataset with 10 m spatial resolution, the optical-SAR Zhuhai-Macau local climate zones (LCZ) dataset with 100 m spatial resolution, and a hyperspectral benchmark the University of Pavia with 1.3 m spatial resolution. It outperformed OBIA support vector machine (SVM) and random forest (RF). SVM and RF could benefit more from the combined use of optical and SAR data compared with CNN, whereas spatial information learned by CNN was very effective for classification. With the ability to extract spatial features and maintain object boundaries, the proposed method considerably improved the classification accuracy of urban ground targets. It achieved overall accuracy (OA) of 95.33% for the Sentinel Guangzhou dataset, OA of 77.64% for the Zhuhai-Macau LCZ dataset, and OA of 95.70% for the University of Pavia dataset with only 10 labeled samples per class.
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Prampolini, Mariacristina, Lorenzo Angeletti, Giorgio Castellan, Valentina Grande, Tim Le Bas, Marco Taviani, and Federica Foglini. "Benthic Habitat Map of the Southern Adriatic Sea (Mediterranean Sea) from Object-Based Image Analysis of Multi-Source Acoustic Backscatter Data." Remote Sensing 13, no. 15 (July 24, 2021): 2913. http://dx.doi.org/10.3390/rs13152913.
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A huge amount of seabed acoustic reflectivity data has been acquired from the east to the west side of the southern Adriatic Sea (Mediterranean Sea) in the last 18 years by CNR-ISMAR. These data have been used for geological, biological and habitat mapping purposes, but a single and consistent interpretation of them has never been carried out. Here, we aimed at coherently interpreting acoustic data images of the seafloor to produce a benthic habitat map of the southern Adriatic Sea showing the spatial distribution of substrates and biological communities within the basin. The methodology here applied consists of a semi-automated classification of acoustic reflectivity, bathymetry and bathymetric derivatives images through object-based image analysis (OBIA) performed by using the ArcGIS tool RSOBIA (Remote Sensing OBIA). This unsupervised image segmentation was carried out on each cruise dataset separately, then classified and validated through comparison with bottom samples, images, and prior knowledge of the study areas.
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Kim, Yun-Ki. "A Study on Urban Land Cover Classification Using Object-based Image Analysis (OBIA) Techniques." Journal of the Korean Cadastre Information Association 22, no. 1 (April 30, 2020): 122–44. http://dx.doi.org/10.46416/jkcia.2020.04.22.1.122.
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Huang, Huasheng, Yubin Lan, Aqing Yang, Yali Zhang, Sheng Wen, and Jizhong Deng. "Deep learning versus Object-based Image Analysis (OBIA) in weed mapping of UAV imagery." International Journal of Remote Sensing 41, no. 9 (January 6, 2020): 3446–79. http://dx.doi.org/10.1080/01431161.2019.1706112.
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Pandey, Sanjay Kumar, Narendra Chand, Subrata Nandy, Abulqosim Muminov, Anchit Sharma, Surajit Ghosh, and Ritika Srinet. "High-Resolution Mapping of Forest Carbon Stock Using Object-Based Image Analysis (OBIA) Technique." Journal of the Indian Society of Remote Sensing 48, no. 6 (June 2020): 865–75. http://dx.doi.org/10.1007/s12524-020-01121-8.
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Norman, Masayu, Hanani Mohd Shahar, Zuraihan Mohamad, Ashnita Rahim, Fazly Amri Mohd, and Helmi Zulhaidi Mohd Shafri. "Urban building detection using object-based image analysis (OBIA) and machine learning (ML) algorithms." IOP Conference Series: Earth and Environmental Science 620 (January 9, 2021): 012010. http://dx.doi.org/10.1088/1755-1315/620/1/012010.
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Lin, Shih-Yuan, Cheng-Wei Lin, and Stephan van Gasselt. "Processing Framework for Landslide Detection Based on Synthetic Aperture Radar (SAR) Intensity-Image Analysis." Remote Sensing 13, no. 4 (February 10, 2021): 644. http://dx.doi.org/10.3390/rs13040644.
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We present an object-based image analysis (OBIA) approach to identify temporal changes in radar-intensity images and to locate land-cover changes caused by mass-wasting processes at small to large scales, such as landslides. Our approach is based upon change detection in SAR intensity images that remain in their original imaging coordinate system rather than being georeferenced and map-projected, in order to reduce accumulation of filtering artifacts and other unwanted effects that would deteriorate the detection efficiency. Intensity images in their native slant-range coordinate frame allow for a consistent level of detection of land-cover changes. By analyzing intensity images, a much faster response can be achieved and images can be processed as soon as they are made publicly available. In this study, OBIA was introduced to systematically and semiautomatically detect landslides in image pairs with an overall accuracy of at least 60% when compared to in-situ landslide inventory data. In this process, the OBIA feature extraction component was supported by derived data from a polarimetric decomposition as well as by texture indices derived from the original image data. The results shown here indicate that most of the landslide events could be detected when compared to a closer visual inspection and to established inventories, and that the method could therefore be considered as a robust detection tool. Significant deviations are caused by the limited geometric resolution when compared to field data and by an additional detection of stream-related sediment redeposition in our approach. This overdetection, however, turns out to be potentially beneficial for assessing the risk situation after landslide events.
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Aguilar, M. A., F. J. Aguilar, A. García Lorca, E. Guirado, M. Betlej, P. Cichon, A. Nemmaoui, A. Vallario, and C. Parente. "ASSESSMENT OF MULTIRESOLUTION SEGMENTATION FOR EXTRACTING GREENHOUSES FROM WORLDVIEW-2 IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 20, 2016): 145–52. http://dx.doi.org/10.5194/isprsarchives-xli-b7-145-2016.
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The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In this way, object based image analysis (OBIA) approach has been proved as the best option when working with VHR satellite imagery. OBIA considers spectral, geometric, textural and topological attributes associated with meaningful image objects. Thus, the first step of OBIA, referred to as segmentation, is to delineate objects of interest. Determination of an optimal segmentation is crucial for a good performance of the second stage in OBIA, the classification process. The main goal of this work is to assess the multiresolution segmentation algorithm provided by eCognition software for delineating greenhouses from WorldView- 2 multispectral orthoimages. Specifically, the focus is on finding the optimal parameters of the multiresolution segmentation approach (i.e., Scale, Shape and Compactness) for plastic greenhouses. The optimum Scale parameter estimation was based on the idea of local variance of object heterogeneity within a scene (ESP2 tool). Moreover, different segmentation results were attained by using different combinations of Shape and Compactness values. Assessment of segmentation quality based on the discrepancy between reference polygons and corresponding image segments was carried out to identify the optimal setting of multiresolution segmentation parameters. Three discrepancy indices were used: Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR) and Euclidean Distance 2 (ED2).
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Aguilar, M. A., F. J. Aguilar, A. García Lorca, E. Guirado, M. Betlej, P. Cichon, A. Nemmaoui, A. Vallario, and C. Parente. "ASSESSMENT OF MULTIRESOLUTION SEGMENTATION FOR EXTRACTING GREENHOUSES FROM WORLDVIEW-2 IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 20, 2016): 145–52. http://dx.doi.org/10.5194/isprs-archives-xli-b7-145-2016.
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The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In this way, object based image analysis (OBIA) approach has been proved as the best option when working with VHR satellite imagery. OBIA considers spectral, geometric, textural and topological attributes associated with meaningful image objects. Thus, the first step of OBIA, referred to as segmentation, is to delineate objects of interest. Determination of an optimal segmentation is crucial for a good performance of the second stage in OBIA, the classification process. The main goal of this work is to assess the multiresolution segmentation algorithm provided by eCognition software for delineating greenhouses from WorldView- 2 multispectral orthoimages. Specifically, the focus is on finding the optimal parameters of the multiresolution segmentation approach (i.e., Scale, Shape and Compactness) for plastic greenhouses. The optimum Scale parameter estimation was based on the idea of local variance of object heterogeneity within a scene (ESP2 tool). Moreover, different segmentation results were attained by using different combinations of Shape and Compactness values. Assessment of segmentation quality based on the discrepancy between reference polygons and corresponding image segments was carried out to identify the optimal setting of multiresolution segmentation parameters. Three discrepancy indices were used: Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR) and Euclidean Distance 2 (ED2).
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Ma, Lei, Michael Schmitt, and Xiaoxiang Zhu. "Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data." Remote Sensing 12, no. 22 (November 19, 2020): 3798. http://dx.doi.org/10.3390/rs12223798.
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Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification.
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Villareal, Marife Kung, and Alejandro Fernandez Tongco. "Sugarcane Classification Using Spectral Signature and Object-Based Image Analysis (OBIA) in LiDAR Data Sets." International Journal of Agriculture and Environmental Science 6, no. 4 (August 25, 2019): 9–16. http://dx.doi.org/10.14445/23942568/ijaes-v6i4p103.
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Lahousse, T., K. T. Chang, and Y. H. Lin. "Landslide mapping with multi-scale object-based image analysis – a case study in the Baichi watershed, Taiwan." Natural Hazards and Earth System Sciences 11, no. 10 (October 10, 2011): 2715–26. http://dx.doi.org/10.5194/nhess-11-2715-2011.
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Abstract. We developed a multi-scale OBIA (object-based image analysis) landslide detection technique to map shallow landslides in the Baichi watershed, Taiwan, after the 2004 Typhoon Aere event. Our semi-automated detection method selected multiple scales through landslide size statistics analysis for successive classification rounds. The detection performance achieved a modified success rate (MSR) of 86.5% with the training dataset and 86% with the validation dataset. This performance level was due to the multi-scale aspect of our methodology, as the MSR for single scale classification was substantially lower, even after spectral difference segmentation, with a maximum of 74%. Our multi-scale technique was capable of detecting landslides of varying sizes, including very small landslides, up to 95 m2. The method presented certain limitations: the thresholds we established for classification were specific to the study area, to the landslide type in the study area, and to the spectral characteristics of the satellite image. Because updating site-specific and image-specific classification thresholds is easy with OBIA software, our multi-scale technique is expected to be useful for mapping shallow landslides at watershed level.
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Najafi, Payam, Bakhtiar Feizizadeh, and Hossein Navid. "A Comparative Approach of Fuzzy Object Based Image Analysis and Machine Learning Techniques Which Are Applied to Crop Residue Cover Mapping by Using Sentinel-2 Satellite and UAV Imagery." Remote Sensing 13, no. 5 (March 3, 2021): 937. http://dx.doi.org/10.3390/rs13050937.
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Conservation tillage methods through leaving the crop residue cover (CRC) on the soil surface protect it from water and wind erosions. Hence, the percentage of the CRC on the soil surface is very critical for the evaluation of tillage intensity. The objective of this study was to develop a new methodology based on the semiautomated fuzzy object based image analysis (fuzzy OBIA) and compare its efficiency with two machine learning algorithms which include: support vector machine (SVM) and artificial neural network (ANN) for the evaluation of the previous CRC and tillage intensity. We also considered the spectral images from two remotely sensed platforms of the unmanned aerial vehicle (UAV) and Sentinel-2 satellite, respectively. The results indicated that fuzzy OBIA for multispectral Sentinel-2 image based on Gaussian membership function with overall accuracy and Cohen’s kappa of 0.920 and 0.874, respectively, surpassed machine learning algorithms and represented the useful results for the classification of tillage intensity. The results also indicated that overall accuracy and Cohen’s kappa for the classification of RGB images from the UAV using fuzzy OBIA method were 0.860 and 0.779, respectively. The semiautomated fuzzy OBIA clearly outperformed machine learning approaches in estimating the CRC and the classification of the tillage methods and also it has the potential to substitute or complement field techniques.
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Zaabar, N., S. Niculescu, and M. K. Mihoubi. "ASSESSMENT OF COMBINING CONVOLUTIONAL NEURAL NETWORKS AND OBJECT BASED IMAGE ANALYSIS TO LAND COVER CLASSIFICATION USING SENTINEL 2 SATELLITE IMAGERY (TENES REGION, ALGERIA)." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 383–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-383-2021.
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Abstract. Land cover maps can provide valuable information for various applications, such as territorial monitoring, environmental protection, urban planning and climate change prevention. In this purpose, remote sensing based on image classification approaches undergoing a high revolution can be dedicated to land cover mapping tasks. Similarly, deep learning models are considerably applied in remote sensing applications; which can automatically learn features from large amounts of data. Prevalently, the Convolutional Neural Network (CNN), have been increasingly performed in image classification. The aim of this study is to apply a new approach to analyse land cover, and extract its features. Experiments carried out on a coastal town located in north-western Algeria (Ténès region). The study area is chosen because of its importance as a part of the national strategy to combat natural hazards, specifically floods. As well as, a simple CNN model with two hidden layers was constructed, combined with an Object-Based Image Analysis (OBIA). In this regard, a Sentinel-2 image was used, to perform the classification, using spectral index combinations. Furthermore, to compare the performance of the proposed approach, an OBIA based on machines learning algorithms mainly Random Forest (RF) and Support Vector Machine (SVM), was provided. Results of accuracy assessment of classification showed good values in terms of Overall accuracy and Kappa Index, which reach to 93.1% and 0.91, respectively. As a comparison, CNN-OBIA approach outperformed OBIA based on RF algorithm. Therefore, Final land cover maps can be used as a support tool in regional and national decisions.
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Boldt, M., A. Thiele, K. Schulz, and S. Hinz. "SAR Image Segmentation Using Morphological Attribute Profiles." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3 (August 11, 2014): 39–44. http://dx.doi.org/10.5194/isprsarchives-xl-3-39-2014.
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In the last years, the spatial resolution of remote sensing sensors and imagery has continuously improved. Focusing on spaceborne Synthetic Aperture Radar (SAR) sensors, the satellites of the current generation (TerraSAR-X, COSMO-SykMed) are able to acquire images with sub-meter resolution. Indeed, high resolution imagery is visually much better interpretable, but most of the established pixel-based analysis methods have become more or less impracticable since, in high resolution images, self-sufficient objects (vehicle, building) are represented by a large number of pixels. Methods dealing with Object-Based Image Analysis (OBIA) provide help. Objects (segments) are groupings of pixels resulting from image segmentation algorithms based on homogeneity criteria. The image set is represented by image segments, which allows the development of rule-based analysis schemes. For example, segments can be described or categorized by their local neighborhood in a context-based manner. <br><br> In this paper, a novel method for the segmentation of high resolution SAR images is presented. It is based on the calculation of morphological differential attribute profiles (DAP) which are analyzed pixel-wise in a region growing procedure. The method distinguishes between heterogeneous and homogeneous image content and delivers a precise segmentation result.
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Argyropoulou, E., D. Argialas, P. Nomikou, D. Papanikolaou, and M. Dekavalla. "AUTOMATIC IDENTIFICATION OF SUBMARINE LANDFORMS USING OBJECT BASED IMAGE ANALYSIS IN THE AREA OF NORTH AEGEAN BASIN." Bulletin of the Geological Society of Greece 50, no. 3 (July 27, 2017): 1605. http://dx.doi.org/10.12681/bgsg.11880.
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This paper is focused on the study of the North Aegean seabed, from a Digital Seabed Elevation Model (DSEM), by employing Object Based Image Analysis (OBIA). The goal is the automatic extraction of geomorphological features based on morphological criteria, in the North Aegean Basin. A Digital Seabed Elevation Model (DSEM) of 150x150 meters resolution was employed. At first, slope gradient, profile curvature, and percentile were derived from this DSEM. Four different layers of segmentation were created in order to extract the final geomorphological classes, discontinuities, faults like and fault surface in the final segmentation of level 4. On previous levels, more geomorphological features were also classified such as continental platform and continental slope. The results were evaluated qualitatively and quantitatively, through a tectonic map which has been created manually based on the analysis of seismic profiles. The results of the comparison of the two methods were satisfactory. Thus, the developed OBIA method is considered successful.
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Hölbling, Daniel, Barbara Friedl, and Clemens Eisank. "An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan." Earth Science Informatics 8, no. 2 (April 7, 2015): 327–35. http://dx.doi.org/10.1007/s12145-015-0217-3.
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Abstract Earth observation (EO) data are very useful for the detection of landslides after triggering events, especially if they occur in remote and hardly accessible terrain. To fully exploit the potential of the wide range of existing remote sensing data, innovative and reliable landslide (change) detection methods are needed. Recently, object-based image analysis (OBIA) has been employed for EO-based landslide (change) mapping. The proposed object-based approach has been tested for a sub-area of the Baichi catchment in northern Taiwan. The focus is on the mapping of landslides and debris flows/sediment transport areas caused by the Typhoons Aere in 2004 and Matsa in 2005. For both events, pre- and post-disaster optical satellite images (SPOT-5 with 2.5 m spatial resolution) were analysed. A Digital Elevation Model (DEM) with 5 m spatial resolution and its derived products, i.e., slope and curvature, were additionally integrated in the analysis to support the semi-automated object-based landslide mapping. Changes were identified by comparing the normalised values of the Normalized Difference Vegetation Index (NDVI) and the Green Normalized Difference Vegetation Index (GNDVI) of segmentation-derived image objects between pre- and post-event images and attributed to landslide classes.
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Kouli, R., D. Argialas, P. Nomikou, and V. Lykousis. "AUTOMATIC IDENTIFICATION OF THE GEOMORPHOLOGIC AND MORPHOTECTONIC FEATURES OF THE SOUTH CRETAN MARGIN, USING OBJECT BASED IMAGE ANALYSIS." Bulletin of the Geological Society of Greece 50, no. 3 (July 27, 2017): 1633. http://dx.doi.org/10.12681/bgsg.11883.
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This paper is focused on the study of the South Cretan Margin, from a Digital Seabed Elevation Model, by employing Object Based Image Analysis. The goal is the automatic extraction of geomorphological and morphotectonic features based on morphological criteria and topological relations. A Digital Seabed Elevation Model of 150x150 meters resolution was employed. At first, slope, curvature, planform curvature, profile curvature and Topographic Position Index were derived from this DSEM. Five different layers of segmentation were created in order to extract the final geomorphological classes, Ptolemy trough, intraslope basins, main basins, small basins, continental shelf, plains, continental slope, escarpments, canyons, spurs, discontinuities, fault like and fault surface. The results were evaluated quantitatively, through the established indices Completeness, Correctness and Overall Quality. For computing these indices, it was necessary to digitize the boundaries of the objects derived by photo-interpretation. Then, the computation of the above indices, took place by comparing the results of digitized photo-interpretation boundaries, to the extracted feature boundaries through OBIA analysis (in shapefile). It is worth noting that, the results of the evaluation are quite satisfactory. Thus, the developed OBIA method is considered successful.
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Chaudhary, A. K., A. K. Acharya, and S. Khanal. "Forest type mapping using object-based classification method in Kapilvastu district, Nepal." Banko Janakari 26, no. 1 (August 23, 2016): 38–44. http://dx.doi.org/10.3126/banko.v26i1.15500.
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In the recent years, object-based image analysis (OBIA) approach has emerged with an attempt to overcome limitations inherited in conventional pixel-based approaches. OBIA was performed using Landsat 8 image to map the forest types in Kapilvastu district of Nepal. Systematic sampling design was adopted to establish sample points in the field, and 70% samples were used for classification and 30% samples for accuracy assessment. Landsat image was pre-processed, and the slope and aspect derived from the ASTER DEM were used as additional predictors for classification. Segmentation was done using eCognition v8.0 with the scale parameter of 20, ratios of 0.1 and 0.9 for shape and color, respectively. Classification and Regression Tree (CART) and nearest neighbor classifier (k-NN) methods were used for object-based classification. The major forest types observed in the district were KS (Acacia catechu/ Dalbergia sissoo), Sal (Shorea robusta) and Tropical Mixed Hardwood. The k-NN classification technique showed higher overall accuracy than the CART method. The classification approach used in this study can also be applied to classify forest types in other districts. Improvement in classification accuracy can be potentially obtained through inclusion of sufficient samples from all classes.Banko JanakariA Journal of Forestry Information for NepalVol. 26, No. 1, Page: 38-44, 2016
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Guirado, Emilio, Javier Blanco-Sacristán, Juan Rigol-Sánchez, Domingo Alcaraz-Segura, and Javier Cabello. "A Multi-Temporal Object-Based Image Analysis to Detect Long-Lived Shrub Cover Changes in Drylands." Remote Sensing 11, no. 22 (November 13, 2019): 2649. http://dx.doi.org/10.3390/rs11222649.
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Climate change and human actions condition the spatial distribution and structure of vegetation, especially in drylands. In this context, object-based image analysis (OBIA) has been used to monitor changes in vegetation, but only a few studies have related them to anthropic pressure. In this study, we assessed changes in cover, number, and shape of Ziziphus lotus shrub individuals in a coastal groundwater-dependent ecosystem in SE Spain over a period of 60 years and related them to human actions in the area. In particular, we evaluated how sand mining, groundwater extraction, and the protection of the area affect shrubs. To do this, we developed an object-based methodology that allowed us to create accurate maps (overall accuracy up to 98%) of the vegetation patches and compare the cover changes in the individuals identified in them. These changes in shrub size and shape were related to soil loss, seawater intrusion, and legal protection of the area measured by average minimum distance (AMD) and average random distance (ARD) analysis. It was found that both sand mining and seawater intrusion had a negative effect on individuals; on the contrary, the protection of the area had a positive effect on the size of the individuals’ coverage. Our findings support the use of OBIA as a successful methodology for monitoring scattered vegetation patches in drylands, key to any monitoring program aimed at vegetation preservation.
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Nugroho, Udhi C., Dony Kushardono, and Esthi K. Dewi. "Identifikasi Kawasan Pertambangan Timah Menggunakan Data Satelit Sentinel – 1 dengan Metode Object Based Image Analysis (OBIA)." Jurnal Ilmu Lingkungan 17, no. 1 (May 29, 2019): 140. http://dx.doi.org/10.14710/jil.17.1.140-148.
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Berdasarkan data Pendapatan Nasional Indonesia 2017, sektor pertambangan dan penggalian mempunyai peran penting bagi Indonesia. Sektor ini menyumbangkan 7,57% pada produk domestik bruto Indonesia di tahun 2017 . Salah satu sektor pertambangan yang potensial di Indonesia adalah pertambangan mineral Timah di Pulau Bangka dan Belitung. Namun kegiatan pertambangan ini banyak menimbulkan dampak negatif dari sisi lingkungan. Salah satu upaya awal untuk menanggulangi dampak negatif terhadap lingkungan adalah melakukan identifikasi kawasan pertambangan timah secara spasial. Teknologi yang dapat membantu untuk hal ini salah satunya adalah teknologi penginderaan jauh radar. Penelitian ini menggunakan data satelit radar sentinel-1 yang diluncurkan oleh European Space Agency (ESA). Tujuan penelitian ini adalah pemanfaatan data radar Sentinel-1 untuk identifikasi kawasan pertambangan menggunakan metode Object-Base Image Analysis (OBIA). Data sentinel-1 disegmentasi menggunakan algorithma multiresolution segmentation kemudian di klasifikasi menggunakan algorithma nearest neighbor. Masukan data yang digunakan untuk proses klasifikasi dibuat menjadi dua variasi, yang pertama adalah data standar deviasi, mean, dan brightness pada masing – masing segmen di tiap band, kemudian variasi kedua adalah penambahan data tekstur berupa nilai grey level coocurance matrix (GLCM). Hasil klasifikasi menunjukan bahwa masukan data yang menggunakan data tekstur GLCM mempunyai akurasi lebih tinggi dibandingkan dengan yang tanpa data tekstur GLCM. Secara statisktik Hasil klasifikasi dengan type satu menunjukan bahwa total akurasi nya adalah sebesar 89,0 %, dengan nilai kappa sebesar 0,48 sedangkan untuk type dua menunjukan bahwa total akurasinya adalah 89,3%, dengan kappa sebesar 0,50. Hasil klasifikasi kawasan pertambangan dapat digunakan sebagai masukan awal dalam rangka identifikasi spasial kerusakan lingkungan akibat aktivitas pertambangan.
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Pawłuszek, Kamila, Sylwia Marczak, Andrzej Borkowski, and Paolo Tarolli. "Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features." ISPRS International Journal of Geo-Information 8, no. 8 (July 24, 2019): 321. http://dx.doi.org/10.3390/ijgi8080321.
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Landslide identification is a fundamental step enabling the assessment of landslide susceptibility and determining the associated risks. Landslide identification by conventional methods is often time-consuming, therefore alternative techniques, including automatic approaches based on remote sensing data, have captured the interest among researchers in recent decades. By providing a highly detailed digital elevation model (DEM), airborne laser scanning (LiDAR) allows effective landslide identification, especially in forested areas. In the present study, object-based image analysis (OBIA) was applied to landslide detection by utilizing LiDAR-derived data. In contrast to previous investigations, our analysis was performed on forested and agricultural areas, where cultivation pressure has degraded specific landslide geomorphology. A diverse variety of aspects that influence OBIA accuracy in landslide detection have been considered: DEM resolution, segmentation scale, and feature selection. Finally, using DEM delivered layers and OBIA, landslide was identified with an overall accuracy (OA) of 85% and a kappa index (KIA) equal to 0.60, which illustrates the effectiveness of the proposed approach. In the end, a field investigation was performed in order to evaluate the results achieved by applying an automatic OBIA approach. The advantages and challenges of automatic approaches for landslide identification for various land use were also discussed. Final remarks underline that effective landslide detection in forested areas could be achieved while this is still challenging in agricultural areas.