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James, D., A. Collin, A. Mury, and S. Costa. "VERY HIGH RESOLUTION LAND USE AND LAND COVER MAPPING USING PLEIADES-1 STEREO IMAGERY AND MACHINE LEARNING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 675–82. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-675-2020.
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Abstract. Anthropocene is featured with increasing human population and global changes that strongly affect landscapes at an unprecedented pace. As a flagship, the coastal fringe is subject to an accelerated conversion of natural areas into agricultural ones, in turn, into urban ones, generating hazardous soil artificialization. Very high resolution (VHR) technologies such as airborne LiDAR or UAV imageries are good assets to model the topography and classify the land use/land cover (LULC), helping local management. Even if their spatial resolution suits with the management scale, their extent covers a few km2, making large-scale monitoring complex and time-consuming. VHR spaceborne imagery has a great potential to address this spatial challenge given its regional acquisition. This research proposes to evaluate the capabilities of a Pleiades-1 stereo-satellite multispectral imagery (blue, green, red, BGR, and near-infrared, NIR) to both model the surface topography and classify LULC. Horizontal and vertical accuracies of the photogrammetry-driven digital surface model (DSM) attain 0.53 m and 0.65 m, respectively. Nine LULC generic classes are studied using the maximum likelihood (ML) and support vector machine (SVM) algorithms. The classification accuracy of the basic BGR (reaching 84.64 % and 76.13 % with ML and SVM, respectively) is improved by the DSM contribution (5.49 % and 2.91 % for ML and SVM, respectively), and the NIR contribution (6.78 % and 3.89 % for ML and SVM, respectively). The gain of the DSM-NIR combination totals 8.91 % and 8.40 % for ML and SVM, respectively, making the ML-based full combination the best performance (93.55 %).
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Nurtyawan, Rian, and Nadia Fiscarina. "ASSESSMENT OF THE ACCURACY OF DEM FROM PANCHROMATIC PLEIADES IMAGERY (CASE STUDY: BANDUNG CITY. WEST JAVA)." International Journal of Remote Sensing and Earth Sciences (IJReSES) 17, no. 1 (2020): 34. http://dx.doi.org/10.30536/j.ijreses.2020.v17.a3329.
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Pleiades satellite imagery is very high resolution. with 0.5 m spatial resolution in the panchromatic band and 2.5 m in the multispectral band. Digital elevation models (DEM) are digital models that represent the shape of the Earth's surface in three-dimensional (3D) form. The purpose of this study was to assess DEM accuracy from panchromatic Pleaides imagery. The process conducted was orthorectification using ground control points (GCPs) and the rational function model with rational polynomial coefficient (RFC) parameters. The DEM extraction process employed photogrammetric methods with different parallax concepts. Accuracy assessment was made using 35 independent check points (ICPs) with an RMSE accuracy of ± 0.802 m. The results of the Pleaides DEM image extraction were more accurate than the National DEM (DEMNAS) and SRTM DEM. Accuracy testing of DEMNAS results showed an RMSE of ± 0.955 m. while SRTM DEM accuracy was ± 17.740 m. Such DEM extraction from stereo Pleiades panchromatic images can be used as an element on base maps with a scale of 1: 5.000.
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Prabowo, Yudhi, and Kenlo Nishida Nasahara. "DETECTING AND COUNTING COCONUT TREES IN PLEIADES SATELLITE IMAGERY USING HISTOGRAM OF ORIENTED GRADIENTS AND SUPPORT VECTOR MACHINE." International Journal of Remote Sensing and Earth Sciences (IJReSES) 16, no. 1 (2019): 87. http://dx.doi.org/10.30536/j.ijreses.2019.v16.a3089.
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This paper describes the detection of coconut trees using very-high-resolution optical satellite imagery. The satellite imagery used in this study was a panchromatic band of Pleiades imagery with a spatial resolution of 0.5 metres. The authors proposed the use of a histogram of oriented gradients (HOG) algorithm as the feature extractor and a support vector machine (SVM) as the classifier for this detection. The main objective of this study is to find out the parameter combination for the HOG algorithm that could provide the best performance for coconut-tree detection. The study shows that the best parameter combination for the HOG algorithm is a configuration of 3 x 3 blocks, 9 orientation bins, and L2-norm block normalization. These parameters provide overall accuracy, precision and recall of approximately 80%, 73% and 87%, respectively.
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Bley-Dalouman, H., F. Broust, J. Prevost, and A. Tran. "USE OF VERY HIGH SPATIAL RESOLUTION IMAGERY FOR MAPPING WOOD ENERGY POTENTIAL FROM TROPICAL MANAGED FOREST STANDS, REUNION ISLAND." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 189–94. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-189-2021.
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Abstract. The development of a sustainable wood energy chain is an essential part of ecological and energy transition in Reunion Island (Indian Ocean), where Acacia mearnsii is the main potential wood energy resource identified to date. In order to assess future wood biomass supply chain strategies, a major first issue is to gain knowledge of the spatial distribution of this species forest stands.In this study, we assessed the potential of very high spatial resolution multispectral imagery for mapping the main forest stands in a study area located the Western Highlands region, where Acacia mearnsii expands alongside Acacia heterophylla, an endemic forest species and Cryptomeria japonica, an exotic forest stand. A reference database including 150 samples of seven classes (Acacia mearnsii (mature and non-mature), Acacia heterophylla (mature and non-mature), Cryptomeria japonica, ‘herbaceous areas’, and ‘bare soils’) was used to classify a Pleiades image acquired in May 2020. Spectral and textural indices were used in an incremental classification procedure using a random classifier.The best results (Kappa = 0.84, global accuracy = 84%) were obtained for the classification using all spectral and textural bands. The resulting map enables analyzing the spatial distribution of the different forest stands.
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Tsoeleng, Lesiba Thomas, John Odindi, and Paidamwoyo Mhangara. "A Comparison of Two Morphological Techniques in the Classification of Urban Land Cover." Remote Sensing 12, no. 7 (2020): 1089. http://dx.doi.org/10.3390/rs12071089.
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Understanding the often-heterogeneous land cover in urban areas is critical for, among other things, environmental monitoring, spatial planning, and enforcement. Recently, several earth observation satellites were developed with an enhanced spatial resolution that provides for precise and detailed representations of image objects. Morphological image analysis techniques provide useful tools for extracting spatial features from high-resolution, remotely sensed images. This study investigated the efficacy of mathematical morphological (MM) techniques in the land cover classification of a heterogeneous urban landscape using very high-resolution pan-sharpened Pleiades imagery. Specifically, the study evaluated two morphological profiles (MP) techniques (i.e., concatenation of morphological profiles (CMPs) and multi-morphological profiles (MMPs)) in the classification of a heterogeneous urban land cover. The overall accuracies for CMP were 83.14% and 83.19% over the two study areas. Similarly, the MMP overall accuracies were 84.42% and 84.08% for the two study sites. The study concluded that CMP and MMP can greatly improve the classification of heterogeneous landscapes that typify urban areas by effectively representing the structural landscape information necessary for discriminating related land cover classes. In general, similar and visually acceptable results were produced for land cover classification using either CMP or MMP image analysis techniques
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Batsaikhan, B., O. Lkhamjav, G. Batsaikhan, N. Batsaikhan, and B. Norovsuren. "CARBON STOCK ESTIMATION USING REMOTE SENSING DATA AND FIELD MEASUREMENT IN <i>HALOXYLON AMMODENDRON</i> DOMINANT WINTER COLD DESERT REGION OF MONGOLIA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 9–17. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-9-2020.
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Abstract. The UN-REDD Mongolia National Programme has studied about forest carbon emissions, and enhance and sustainably manage its carbon stocks, through the implementation of REDD+ activities since 2011. However, the current assessments seem to remain uncertain, the study for estimating carbon storage based on field survey are still rare. Because the Haloxylon ammodendron, where Gobi desert ecosystems are covering large areas, it is necessary to develop a modelling approach applying remote sensing. The study area is locating in Gobi-Altai province, Trans-Altai area as the south-western part of Mongolia. A total of 32 plots were established on eighth different land cover types to represent the range of variability. The study was used high spatial resolution imagery of Pleiades-1 and both of active and passive data from Sentinel-1 and Sentinel-2. The growing height in 32 plots is ranging from 20 to 460 cm with between 0.002 and 544.9 cm2 for basal area and between 526.5 and 166106.0 cm2 for canopy area, respectively. Shrub density is very high in plot 4 (n=135) and plot 5 (n=117) with low above-ground biomass 12 kg and 10.9 kg. The backscatter (dB) values of vegetated area and non-vegetated were comparable, −27.86 and −17.36 in VH polarisation and −22.72 and −10.61 in VV polarisation, respectively. Model-M1 was best demonstrated when a combination of vegetation coverage area was used as Pleiades-1 and Sentinel-2 derived vegetation cover data. For model-M9, the results were comparable to model-M1 but with lower the coefficient of determination. In this work, NDVI and MSAVI appear as a good indicator of biomass mainly because it does not saturate in sparse shrubs and is more sensitive to canopy parameters.
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Almeida, Luís, Rafael Almar, Erwin Bergsma, et al. "Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery." Remote Sensing 11, no. 5 (2019): 590. http://dx.doi.org/10.3390/rs11050590.
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High spatial resolution coastal Digital Elevation Models (DEMs) are crucial to assess coastal vulnerability and hazards such as beach erosion, sedimentation, or inundation due to storm surges and sea level rise. This paper explores the possibility to use high spatial-resolution Pleiades (pixel size = 0.7 m) stereoscopic satellite imagery to retrieve a DEM on sandy coastline. A 40-km coastal stretch in the Southwest of France was selected as a pilot-site to compare topographic measurements obtained from Pleiades satellite imagery, Real Time Kinematic GPS (RTK-GPS) and airborne Light Detection and Ranging System (LiDAR). The derived 2-m Pleiades DEM shows an overall good agreement with concurrent methods (RTK-GPS and LiDAR; correlation coefficient of 0.9), with a vertical Root Mean Squared Error (RMS error) that ranges from 0.35 to 0.48 m, after absolute coregistration to the LiDAR dataset. The largest errors (RMS error > 0.5 m) occurred in the steep dune faces, particularly at shadowed areas. This work shows that DEMs derived from sub-meter satellite imagery capture local morphological features (e.g., berm or dune shape) on a sandy beach, over a large spatial domain.
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Agrafiotis, P., and A. Georgopoulos. "COMPARATIVE ASSESSMENT OF VERY HIGH RESOLUTION SATELLITE AND AERIAL ORTHOIMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 1–7. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-1-2015.
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This paper aims to assess the accuracy and radiometric quality of orthorectified high resolution satellite imagery from Pleiades-1B satellites through a comparative evaluation of their quantitative and qualitative properties. A Pleiades-B1 stereopair of high resolution images taken in 2013, two adjacent GeoEye-1 stereopairs from 2011 and aerial orthomosaic (LSO) provided by NCMA S.A (Hellenic Cadastre) from 2007 have been used for the comparison tests. As control dataset orthomosaic from aerial imagery provided also by NCMA S.A (0.25m GSD) from 2012 was selected. The process for DSM and orthoimage production was performed using commercial digital photogrammetric workstations. The two resulting orthoimages and the aerial orthomosaic (LSO) were relatively and absolutely evaluated for their quantitative and qualitative properties. Test measurements were performed using the same check points in order to establish their accuracy both as far as the single point coordinates as well as their distances are concerned. Check points were distributed according to JRC Guidelines for Best Practice and Quality Checking of Ortho Imagery and NSSDA standards while areas with different terrain relief and land cover were also included. The tests performed were based also on JRC and NSSDA accuracy standards. Finally, tests were carried out in order to assess the radiometric quality of the orthoimagery. The results are presented with a statistical analysis and they are evaluated in order to present the merits and demerits of the imaging sensors involved for orthoimage production. The results also serve for a critical approach for the usability and cost efficiency of satellite imagery for the production of Large Scale Orthophotos.
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Hashim, H., Z. Abd Latif, and N. A. Adnan. "URBAN VEGETATION CLASSIFICATION WITH NDVI THRESHOLD VALUE METHOD WITH VERY HIGH RESOLUTION (VHR) PLEIADES IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W16 (October 1, 2019): 237–40. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w16-237-2019.
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Abstract. Recently the sensing data for urban mapping used is in high demand together with the accessible of very high resolution (VHR) satellite data such as Worldview and Pleiades. This article presents the use of very high resolution (VHR) remote sensing data for urban vegetation mapping. The research objectives were to assess the use of Pleiades imagery to extricate the data of urban vegetation in urban area of Kuala Lumpur. Normalized Difference Vegetation Index (NDVI) were employs with VHR data to find Vegetation Index for classification process of vegetation and non-vegetation classes. Land use classes are easily determined by computing their Normalized Difference Vegetation Index for Land use land cover classification. Maximum likelihood was conducted for the classification phase. NDVI were extracted from the imagery to assist the process of classification. NDVI method is use by referring to its features such as vegetation at different NDVI threshold values. The result showed three classes of land cover that consist of low vegetation, high vegetation and non-vegetation area. The accuracy assessment gained was then being implemented using the visual interpretation and overall accuracy achieved was 70.740% with kappa coefficient of 0.5. This study gained the proposed threshold method using NDVI value able to identify and classify urban vegetation with the use of VHR Pleiades imagery and need further improvement when apply to different area of interest and different land use land cover characteristics. The information achieved from the result able to help planners for future planning for conservation of vegetation in urban area.
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Hariyanto, Teguh, Akbar Kurniawan, Cherie Bhekti Pribadi, and Rizal Al Amin. "Optimization of Ground Control Point (GCP) and Independent Control Point (ICP) on Orthorectification of High Resolution Satellite Imagery." E3S Web of Conferences 94 (2019): 02008. http://dx.doi.org/10.1051/e3sconf/20199402008.
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In the rapidly evolving technology era, various survey methods have been widely used one of them by remote sensing using satellite. It is known that the satellite image recording process is covered by rides (satellites) moving over the Earth's surface at hundreds of kilometers, causing satellite imagery to have geometric distortion. To reduce the effect of geometric distortion of objects on the image, geometric correction by orthorectification is done. Pleiades is a satellite of high resolution satellite image producer made by Airbus Defense & Space company. The resulting satellite imagery has a 0.5 meter spatial resolution. As a reference for the more detailed space utilization activities of space utilization arranged in the Regional Spatial Plans, Detailed Spatial Plans was created with the 1: 5000 scale map which has been governed by the Geospatial Information Agency. In the process of orthorectifying satellite imagery for this 1: 5000 scale map, ground control or Ground Control Point (GCP) is used for geometric correction and Digital Elevation Model (DEM) data. In this research, the optimal number of GCP usage for orthorectification process in Rational Function method is 21 GCP using 2nd order polynomial
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Palaseanu-Lovejoy, Monica, Marina Bisson, Claudia Spinetti, Maria Fabrizia Buongiorno, Oleg Alexandrov, and Thomas Cecere. "High-Resolution and Accurate Topography Reconstruction of Mount Etna from Pleiades Satellite Data." Remote Sensing 11, no. 24 (2019): 2983. http://dx.doi.org/10.3390/rs11242983.
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The areas characterized by dynamic and rapid morphological changes need accurate topography information with frequent updates, especially if these are populated and involve infrastructures. This is particularly true in active volcanic areas such as Mount (Mt.) Etna, located in the northeastern portion of Sicily, Italy. The Mt. Etna volcano is periodically characterized by explosive and effusive eruptions and represents a potential hazard for several thousands of local people and hundreds of tourists present on the volcano itself. In this work, a high-resolution, high vertical accuracy digital surface model (DSM) of Mt. Etna was derived from Pleiades satellite data using the National Aeronautics and Space Administration (NASA) Ames Stereo Pipeline (ASP) tool set. We believe that this is the first time that the ASP using Pleiades imagery has been applied to Mt. Etna with sub-meter vertical root mean square error (RMSE) results. The model covers an area of about 400 km2 with a spatial resolution of 2 m and centers on the summit portion of the volcano. The model was validated by using a set of reference ground control points (GCP) obtaining a vertical RMSE of 0.78 m. The described procedure provides an avenue to obtain DSMs at high spatial resolution and elevation accuracy in a relatively short amount of processing time, making the procedure itself suitable to reproduce topographies often indispensable during the emergency management case of volcanic eruptions.
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Valente, João, Bilal Sari, Lammert Kooistra, Henk Kramer, and Sander Mücher. "Automated crop plant counting from very high-resolution aerial imagery." Precision Agriculture 21, no. 6 (2020): 1366–84. http://dx.doi.org/10.1007/s11119-020-09725-3.
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Abstract Knowing before harvesting how many plants have emerged and how they are growing is key in optimizing labour and efficient use of resources. Unmanned aerial vehicles (UAV) are a useful tool for fast and cost efficient data acquisition. However, imagery need to be converted into operational spatial products that can be further used by crop producers to have insight in the spatial distribution of the number of plants in the field. In this research, an automated method for counting plants from very high-resolution UAV imagery is addressed. The proposed method uses machine vision—Excess Green Index and Otsu’s method—and transfer learning using convolutional neural networks to identify and count plants. The integrated methods have been implemented to count 10 weeks old spinach plants in an experimental field with a surface area of 3.2 ha. Validation data of plant counts were available for 1/8 of the surface area. The results showed that the proposed methodology can count plants with an accuracy of 95% for a spatial resolution of 8 mm/pixel in an area up to 172 m2. Moreover, when the spatial resolution decreases with 50%, the maximum additional counting error achieved is 0.7%. Finally, a total amount of 170 000 plants in an area of 3.5 ha with an error of 42.5% was computed. The study shows that it is feasible to count individual plants using UAV-based off-the-shelf products and that via machine vision/learning algorithms it is possible to translate image data in non-expert practical information.
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Fundisi, E., and W. Musakwa. "BUILT-UP AREA AND LAND COVER EXTRACTION USING HIGH RESOLUTION PLEIADES SATELLITE IMAGERY FOR MIDRAND, IN GAUTENG PROVINCE, SOUTH AFRICA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1151–56. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1151-2017.
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Urban areas, particularly in developing countries face immense challenges such as climate change, poverty, lack of resources poor land use management systems, and week environmental management practices. Mitigating against these challenges is often hampered by lack of data on urban expansion, urban footprint and land cover. To support the recently adopted new urban agenda 2030 there is need for the provision of information to support decision making in the urban areas. Earth observation has been identified as a tool to foster sustainable urban planning and smarter cities as recognized by the new urban agenda, because it is a solution to unavailability of data. Accordingly, this study uses high resolution EO data Pleiades satellite imagery to map and document land cover for the rapidly expanding area of Midrand in Johannesburg, South Africa. An unsupervised land cover classification of the Pleiades satellite imagery was carried out using ENVI software, whereas NDVI was derived using ArcGIS software. The land cover had an accuracy of 85% that is highly adequate to document the land cover in Midrand. The results are useful because it provides a highly accurate land cover and NDVI datasets at localised spatial scale that can be used to support land use management strategies within Midrand and the City of Johannesburg South Africa.
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McCarthy, Matthew J., Elizabeth J. Merton, and Frank E. Muller-Karger. "Improved coastal wetland mapping using very-high 2-meter spatial resolution imagery." International Journal of Applied Earth Observation and Geoinformation 40 (August 2015): 11–18. http://dx.doi.org/10.1016/j.jag.2015.03.011.
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Falkowski, Michael J., Michael A. Wulder, Joanne C. White, and Mark D. Gillis. "Supporting large-area, sample-based forest inventories with very high spatial resolution satellite imagery." Progress in Physical Geography: Earth and Environment 33, no. 3 (2009): 403–23. http://dx.doi.org/10.1177/0309133309342643.
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Information needs associated with forest management and reporting requires data with a steadily increasing level of detail and temporal frequency. Remote sensing satellites commonly used for forest monitoring (eg, Landsat, SPOT) typically collect imagery with sufficient temporal frequency, but lack the requisite spatial and categorical detail for some forest inventory information needs. Aerial photography remains a principal data source for forest inventory; however, information extraction is primarily accomplished through manual processes. The spatial, categorical, and temporal information requirements of large-area forest inventories can be met through sample-based data collection. Opportunities exist for very high spatial resolution (VHSR; ie, <1 m) remotely sensed imagery to augment traditional data sources for large-area, sample-based forest inventories, especially for inventory update. In this paper, we synthesize the state-of-the-art in the use of VHSR remotely sensed imagery for forest inventory and monitoring. Based upon this review, we develop a framework for updating a sample-based, large-area forest inventory that incorporates VHSR imagery. Using the information needs of the Canadian National Forest Inventory (NFI) for context, we demonstrate the potential capabilities of VHSR imagery in four phases of the forest inventory update process: stand delineation, automated attribution, manual interpretation, and indirect attribute modelling. Although designed to support the information needs of the Canadian NFI, the framework presented herein could be adapted to support other sample-based, large-area forest monitoring initiatives.
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Park, Suyoung, Dongryeol Ryu, Sigfredo Fuentes, Hoam Chung, Mark O’Connell, and Junchul Kim. "Mapping Very-High-Resolution Evapotranspiration from Unmanned Aerial Vehicle (UAV) Imagery." ISPRS International Journal of Geo-Information 10, no. 4 (2021): 211. http://dx.doi.org/10.3390/ijgi10040211.
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There is a growing concern about water scarcity and the associated decline in Australia’s agricultural production. Efficient water use as a natural resource requires more precise and adequate monitoring of crop water use and irrigation scheduling. Therefore, accurate estimations of evapotranspiration (ET) at proper spatial–temporal scales are critical to understand the crop water demand and uptake and to enable optimal irrigation scheduling. Remote sensing (RS)-based ET estimation has been adopted as a method for large-scale applications when the detailed spatial representation of ET is required. This research aimed to estimate instantaneous ET using very-high-resolution (VHR) multispectral and thermal imagery (GSD < 8 cm) collected using a single flight of a UAV over a high-density peach orchard with a discontinuous canopy. The energy balance component estimation was based on the high-resolution mapping of evapotranspiration (HRMET) model. A tree-by-tree ET map was produced using the canopy surface temperature and the leaf area index (LAI) resampled at the corresponding scale via a systematic feature segmentation method based on pure canopy extraction. Results showed a strong linear relationship between the estimated ET and the leaf transpiration (n = 42) measured using a gas exchange sensor, with a coefficient of determination (R2) of 0.89. Daily ET (5.5 mm d−1) derived from the instantaneous ET map was comparable with daily crop ET (6.4 mm d−1) determined by the meteorological approach over the study site. The proposed approach has important implications for mapping tree-by-tree ET over horticultural fields using VHR imagery.
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Kislov, Dmitry E., and Kirill A. Korznikov. "Automatic Windthrow Detection Using Very-High-Resolution Satellite Imagery and Deep Learning." Remote Sensing 12, no. 7 (2020): 1145. http://dx.doi.org/10.3390/rs12071145.
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Wind disturbances are significant phenomena in forest spatial structure and succession dynamics. They cause changes in biodiversity, impact on forest ecosystems at different spatial scales, and have a strong influence on economics and human beings. The reliable recognition and mapping of windthrow areas are of high importance from the perspective of forest management and nature conservation. Recent research in artificial intelligence and computer vision has demonstrated the incredible potential of neural networks in addressing image classification problems. The most efficient algorithms are based on artificial neural networks of nested and complex architecture (e.g., convolutional neural networks (CNNs)), which are usually referred to by a common term—deep learning. Deep learning provides powerful algorithms for the precise segmentation of remote sensing data. We developed an algorithm based on a U-Net-like CNN, which was trained to recognize windthrow areas in Kunashir Island, Russia. We used satellite imagery of very-high spatial resolution (0.5 m/pixel) as source data. We performed a grid search among 216 parameter combinations defining different U-Net-like architectures. The best parameter combination allowed us to achieve an overall accuracy for recognition of windthrow sites of up to 94% for forested landscapes by coniferous and mixed coniferous forests. We found that the false-positive decisions of our algorithm correspond to either seashore logs, which may look similar to fallen tree trunks, or leafless forest stands. While the former can be rectified by applying a forest mask, the latter requires the usage of additional information, which is not always provided by satellite imagery.
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Ziskin, Daniel. "Describing coral reef bleaching using very high spatial resolution satellite imagery: experimental methodology." Journal of Applied Remote Sensing 5, no. 1 (2011): 053531. http://dx.doi.org/10.1117/1.3595300.
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Bjorgo, Einar. "Using very high spatial resolution multispectral satellite sensor imagery to monitor refugee camps." International Journal of Remote Sensing 21, no. 3 (2000): 611–16. http://dx.doi.org/10.1080/014311600210786.
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de Vieilleville, F., T. Ristorcelli, and J. M. Delvit. "DEM RECONSTRUCTION USING LIGHT FIELD AND BIDIRECTIONAL REFLECTANCE FUNCTION FROM MULTI-VIEW HIGH RESOLUTION SPATIAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 503–9. http://dx.doi.org/10.5194/isprsarchives-xli-b3-503-2016.
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This paper presents a method for dense DSM reconstruction from high resolution, mono sensor, passive imagery, spatial panchromatic image sequence. The interest of our approach is four-fold. Firstly, we extend the core of light field approaches using an explicit BRDF model from the Image Synthesis community which is more realistic than the Lambertian model. The chosen model is the Cook-Torrance BRDF which enables us to model rough surfaces with specular effects using specific material parameters. Secondly, we extend light field approaches for non-pinhole sensors and non-rectilinear motion by using a proper geometric transformation on the image sequence. Thirdly, we produce a 3D volume cost embodying all the tested possible heights and filter it using simple methods such as Volume Cost Filtering or variational optimal methods. We have tested our method on a Pleiades image sequence on various locations with dense urban buildings and report encouraging results with respect to classic multi-label methods such as MIC-MAC, or more recent pipelines such as S2P. Last but not least, our method also produces maps of material parameters on the estimated points, allowing us to simplify building classification or road extraction.
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de Vieilleville, F., T. Ristorcelli, and J. M. Delvit. "DEM RECONSTRUCTION USING LIGHT FIELD AND BIDIRECTIONAL REFLECTANCE FUNCTION FROM MULTI-VIEW HIGH RESOLUTION SPATIAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 503–9. http://dx.doi.org/10.5194/isprs-archives-xli-b3-503-2016.
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This paper presents a method for dense DSM reconstruction from high resolution, mono sensor, passive imagery, spatial panchromatic image sequence. The interest of our approach is four-fold. Firstly, we extend the core of light field approaches using an explicit BRDF model from the Image Synthesis community which is more realistic than the Lambertian model. The chosen model is the Cook-Torrance BRDF which enables us to model rough surfaces with specular effects using specific material parameters. Secondly, we extend light field approaches for non-pinhole sensors and non-rectilinear motion by using a proper geometric transformation on the image sequence. Thirdly, we produce a 3D volume cost embodying all the tested possible heights and filter it using simple methods such as Volume Cost Filtering or variational optimal methods. We have tested our method on a Pleiades image sequence on various locations with dense urban buildings and report encouraging results with respect to classic multi-label methods such as MIC-MAC, or more recent pipelines such as S2P. Last but not least, our method also produces maps of material parameters on the estimated points, allowing us to simplify building classification or road extraction.
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Khare, Siddhartha, Hooman Latifi, Sergio Rossi, and Sanjay Kumar Ghosh. "Fractional Cover Mapping of Invasive Plant Species by Combining Very High-Resolution Stereo and Multi-Sensor Multispectral Imageries." Forests 10, no. 7 (2019): 540. http://dx.doi.org/10.3390/f10070540.
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Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38% (for SPOT-6), and 85.27% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8% (for RapidEye) and 7.22% (for SPOT-6), and R2 values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37%, whereas it decreased by up to 37.96% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions.
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Li, Erzhu, Alim Samat, Wei Liu, Cong Lin, and Xuyu Bai. "High-Resolution Imagery Classification Based on Different Levels of Information." Remote Sensing 11, no. 24 (2019): 2916. http://dx.doi.org/10.3390/rs11242916.
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Detailed land use and land cover (LULC) information is one of the important information for land use surveys and applications related to the earth sciences. Therefore, LULC classification using very-high resolution remotely sensed imagery has been a hot issue in the remote sensing community. However, it remains a challenge to successfully extract LULC information from very-high resolution remotely sensed imagery, due to the difficulties in describing the individual characteristics of various LULC categories using single level features. The traditional pixel-wise or spectral-spatial based methods pay more attention to low-level feature representations of target LULC categories. In addition, deep convolutional neural networks offer great potential to extract high-level features to describe objects and have been successfully applied to scene understanding or classification. However, existing studies has paid little attention to constructing multi-level feature representations to better understand each category. In this paper, a multi-level feature representation framework is first designed to extract more robust feature representations for the complex LULC classification task using very-high resolution remotely sensed imagery. To this end, spectral reflection and morphological and morphological attribute profiles are used to describe the pixel-level and neighborhood-level information. Furthermore, a novel object-based convolutional neural networks (CNN) is proposed to extract scene-level information. The object-based CNN method combines advantages of object-based method and CNN method and can perform multi-scale analysis at the scene level. Then, the random forest method is employed to carry out the final classification using the multi-level features. The proposed method was validated on three challenging remotely sensed imageries including a hyperspectral image and two multispectral images with very-high spatial resolution, and achieved excellent classification performances.
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Rahimzadeganasl, Alganci, and Goksel. "An Approach for the Pan Sharpening of Very High Resolution Satellite Images Using a CIELab Color Based Component Substitution Algorithm." Applied Sciences 9, no. 23 (2019): 5234. http://dx.doi.org/10.3390/app9235234.
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Recent very high spatial resolution (VHR) remote sensing satellites provide high spatial resolution panchromatic (Pan) images in addition to multispectral (MS) images. The pan sharpening process has a critical role in image processing tasks and geospatial information extraction from satellite images. In this research, CIELab color based component substitution Pan sharpening algorithm was proposed for Pan sharpening of the Pleiades VHR images. The proposed method was compared with the state-of-the-art Pan sharpening methods, such as IHS, EHLERS, NNDiffuse and GIHS. The selected study region included ten test sites, each of them representing complex landscapes with various land categories, to evaluate the performance of Pan sharpening methods in varying land surface characteristics. The spatial and spectral performance of the Pan sharpening methods were evaluated by eleven accuracy metrics and visual interpretation. The results of the evaluation indicated that proposed CIELab color-based method reached promising results and improved the spectral and spatial information preservation.
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Alkan, M. "INFORMATION CONTENT ANALYSIS FROM VERY HIGH RESOLUTION OPTICAL SPACE IMAGERY FOR UPDATING SPATIAL DATABASE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 25–31. http://dx.doi.org/10.5194/isprs-archives-xlii-4-25-2018.
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<p><strong>Abstract.</strong> High resolution satellite images started with IKONOS imagery. After the launch of the very high resolution IKONOS in the 1990s, a new generation of commercial Earth-imaging satellites have pioneered a new era of space imaging for observations of Earth. The IKONOS satellite image has an important place sampling range with 1<span class="thinspace"></span>m GSD. In the subsequent Quickbird satellite image, the GSD is down to 62<span class="thinspace"></span>cm and the sensitivity is even higher. Advancements in the geometric resolution of space images have improved the conditions for generations of large-scale topographic maps. With using WorldView-1, WorldView-2, and GeoEye-1, images can now be captured from space with a 0.5<span class="thinspace"></span>m ground sampling distance (GSD). The Worldview-4 display with the highest technology and resolution is being used in various application areas. WorldView-4 (formerly GeoEye-2), launched in November 2017, provides a second sensor which is capable of delivering imagery at 30<span class="thinspace"></span>cm resolution, the highest level of detail commercially available from satellite. WorldView-4 greatly expands the 30<span class="thinspace"></span>cm collection capabilities and archive growth in today’s imagery environment. Geometric accuracy and information content are the most significant components of mapping from space images. By using economical, rapid and periodic acquisition, and corresponding ground resolution, these satellites have established an alternative to aerial photos and have been widely used for various applications such as object extraction, change detection, topographic map production, and development of Geographic Information Systems (GIS). The utility of VHR images is dependent on their geometric accuracy and information content. Related with the study, the generally required production scale of 0.05 to 0.1<span class="thinspace"></span>mm GSD in the map scale has been confirmed. This corresponds to a topographic map scale of 1<span class="thinspace"></span>:<span class="thinspace"></span>10,000 respectively 1<span class="thinspace"></span>:<span class="thinspace"></span>5000 for 1<span class="thinspace"></span>m and 0.5<span class="thinspace"></span>m GSD images. In this study, images from IKONOS, QuickBird, WorldView-1, Worldview-2 and WorldView-4 have been used for topographic mapping. For this reason, İstanbul and Zonguldak test fields are an important area for applications of the high resolution imageries. The details which can be identified in the space images dominantly depends upon the ground resolution, available as ground sampling distance (GSD). In this study, high resolution imageries have been tested depending on the GSD and corresponding to the map scales for updating GIS database.</p>
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Leigh, J. R., C. R. Stokes, R. J. Carr, I. S. Evans, L. M. Andreassen, and D. J. A. Evans. "Identifying and mapping very small (<0.5 km2) mountain glaciers on coarse to high-resolution imagery." Journal of Glaciology 65, no. 254 (2019): 873–88. http://dx.doi.org/10.1017/jog.2019.50.
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AbstractSmall mountain glaciers are an important part of the cryosphere and tend to respond rapidly to climate warming. Historically, mapping very small glaciers (generally considered to be <0.5 km2) using satellite imagery has often been subjective due to the difficulty in differentiating them from perennial snowpatches. For this reason, most scientists implement minimum size-thresholds (typically 0.01–0.05 km2). Here, we compare the ability of different remote-sensing approaches to identify and map very small glaciers on imagery of varying spatial resolutions (30–0.25 m) and investigate how operator subjectivity influences the results. Based on this analysis, we support the use of a minimum size-threshold of 0.01 km2 for imagery with coarse to medium spatial resolution (30–10 m). However, when mapping on high-resolution imagery (<1 m) with minimal seasonal snow cover, glaciers <0.05 km2 and even <0.01 km2 are readily identifiable and using a minimum threshold may be inappropriate. For these cases, we develop a set of criteria to enable the identification of very small glaciers and classify them as certain, probable or possible. This should facilitate a more consistent approach to identifying and mapping very small glaciers on high-resolution imagery, helping to produce more comprehensive and accurate glacier inventories.
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Yang, Hui, Penghai Wu, Xuedong Yao, Yanlan Wu, Biao Wang, and Yongyang Xu. "Building Extraction in Very High Resolution Imagery by Dense-Attention Networks." Remote Sensing 10, no. 11 (2018): 1768. http://dx.doi.org/10.3390/rs10111768.
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Building extraction from very high resolution (VHR) imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Compared with the traditional building extraction approaches, deep learning networks have recently shown outstanding performance in this task by using both high-level and low-level feature maps. However, it is difficult to utilize different level features rationally with the present deep learning networks. To tackle this problem, a novel network based on DenseNets and the attention mechanism was proposed, called the dense-attention network (DAN). The DAN contains an encoder part and a decoder part which are separately composed of lightweight DenseNets and a spatial attention fusion module. The proposed encoder–decoder architecture can strengthen feature propagation and effectively bring higher-level feature information to suppress the low-level feature and noises. Experimental results based on public international society for photogrammetry and remote sensing (ISPRS) datasets with only red–green–blue (RGB) images demonstrated that the proposed DAN achieved a higher score (96.16% overall accuracy (OA), 92.56% F1 score, 90.56% mean intersection over union (MIOU), less training and response time and higher-quality value) when compared with other deep learning methods.
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Salgueiro Romero, Luis, Javier Marcello, and Verónica Vilaplana. "Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks." Remote Sensing 12, no. 15 (2020): 2424. http://dx.doi.org/10.3390/rs12152424.
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Sentinel-2 satellites provide multi-spectral optical remote sensing images with four bands at 10 m of spatial resolution. These images, due to the open data distribution policy, are becoming an important resource for several applications. However, for small scale studies, the spatial detail of these images might not be sufficient. On the other hand, WorldView commercial satellites offer multi-spectral images with a very high spatial resolution, typically less than 2 m, but their use can be impractical for large areas or multi-temporal analysis due to their high cost. To exploit the free availability of Sentinel imagery, it is worth considering deep learning techniques for single-image super-resolution tasks, allowing the spatial enhancement of low-resolution (LR) images by recovering high-frequency details to produce high-resolution (HR) super-resolved images. In this work, we implement and train a model based on the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) with pairs of WorldView-Sentinel images to generate a super-resolved multispectral Sentinel-2 output with a scaling factor of 5. Our model, named RS-ESRGAN, removes the upsampling layers of the network to make it feasible to train with co-registered remote sensing images. Results obtained outperform state-of-the-art models using standard metrics like PSNR, SSIM, ERGAS, SAM and CC. Moreover, qualitative visual analysis shows spatial improvements as well as the preservation of the spectral information, allowing the super-resolved Sentinel-2 imagery to be used in studies requiring very high spatial resolution.
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Zhang, J. X., J. H. Yang, and P. Reinartz. "THE OPTIMIZED BLOCK-REGRESSION-BASED FUSION ALGORITHM FOR PANSHARPENING OF VERY HIGH RESOLUTION SATELLITE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 739–46. http://dx.doi.org/10.5194/isprsarchives-xli-b7-739-2016.
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Pan-sharpening of very high resolution remotely sensed imagery need enhancing spatial details while preserving spectral characteristics, and adjusting the sharpened results to realize the different emphases between the two abilities. In order to meet the requirements, this paper is aimed at providing an innovative solution. The block-regression-based algorithm (BR), which was previously presented for fusion of SAR and optical imagery, is firstly applied to sharpen the very high resolution satellite imagery, and the important parameter for adjustment of fusion result, i.e., block size, is optimized according to the two experiments for Worldview-2 and QuickBird datasets in which the optimal block size is selected through the quantitative comparison of the fusion results of different block sizes. Compared to five fusion algorithms (i.e., PC, CN, AWT, Ehlers, BDF) in fusion effects by means of quantitative analysis, BR is reliable for different data sources and can maximize enhancement of spatial details at the expense of a minimum spectral distortion.
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Zhang, J. X., J. H. Yang, and P. Reinartz. "THE OPTIMIZED BLOCK-REGRESSION-BASED FUSION ALGORITHM FOR PANSHARPENING OF VERY HIGH RESOLUTION SATELLITE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 739–46. http://dx.doi.org/10.5194/isprs-archives-xli-b7-739-2016.
Full textAbstract:
Pan-sharpening of very high resolution remotely sensed imagery need enhancing spatial details while preserving spectral characteristics, and adjusting the sharpened results to realize the different emphases between the two abilities. In order to meet the requirements, this paper is aimed at providing an innovative solution. The block-regression-based algorithm (BR), which was previously presented for fusion of SAR and optical imagery, is firstly applied to sharpen the very high resolution satellite imagery, and the important parameter for adjustment of fusion result, i.e., block size, is optimized according to the two experiments for Worldview-2 and QuickBird datasets in which the optimal block size is selected through the quantitative comparison of the fusion results of different block sizes. Compared to five fusion algorithms (i.e., PC, CN, AWT, Ehlers, BDF) in fusion effects by means of quantitative analysis, BR is reliable for different data sources and can maximize enhancement of spatial details at the expense of a minimum spectral distortion.
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Tian, J., R. Qin, D. Cerra, and P. Reinartz. "BUILDING CHANGE DETECTION IN VERY HIGH RESOLUTION SATELLITE STEREO IMAGE TIME SERIES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 149–55. http://dx.doi.org/10.5194/isprsannals-iii-7-149-2016.
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There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satellite image time series (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.
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Tian, J., R. Qin, D. Cerra, and P. Reinartz. "BUILDING CHANGE DETECTION IN VERY HIGH RESOLUTION SATELLITE STEREO IMAGE TIME SERIES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 149–55. http://dx.doi.org/10.5194/isprs-annals-iii-7-149-2016.
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There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satellite image time series (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.
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Li, Peijun, Haiqing Xu, and Jiancong Guo. "Urban building damage detection from very high resolution imagery using OCSVM and spatial features." International Journal of Remote Sensing 31, no. 13 (2010): 3393–409. http://dx.doi.org/10.1080/01431161003727705.
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Corbane, Christina, Nicolas Baghdadi, and Michaël Clairotte. "Tractor Wheel Tracks Detection, Delineation, and Characterization in Very High Spatial Resolution SAR Imagery." IEEE Geoscience and Remote Sensing Letters 8, no. 6 (2011): 1130–34. http://dx.doi.org/10.1109/lgrs.2011.2158184.
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Tuia, D., F. Pacifici, M. Kanevski, and W. J. Emery. "Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines." IEEE Transactions on Geoscience and Remote Sensing 47, no. 11 (2009): 3866–79. http://dx.doi.org/10.1109/tgrs.2009.2027895.
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Zhang, Bing, Shanshan Li, Changshan Wu, Lianru Gao, Wenjuan Zhang, and Man Peng. "A neighbourhood-constrained k-means approach to classify very high spatial resolution hyperspectral imagery." Remote Sensing Letters 4, no. 2 (2012): 161–70. http://dx.doi.org/10.1080/2150704x.2012.713139.
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Lavender, S. J. "MONITORING LAND COVER DYNAMICS AT VARYING SPATIAL SCALES USING HIGH TO VERY HIGH RESOLUTION OPTICAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 937–39. http://dx.doi.org/10.5194/isprsarchives-xli-b8-937-2016.
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Activities have focused on using the Landsat time-series and Sentinel-2 datasets to monitor land cover dynamics across the United Kingdom, with mapping of specific areas including missions such as Worldview and Kompsat. This short conference paper shows some of the preliminary results from the Landsat Operational Land Imager, Thematic Mapper and Enhanced Thematic Mapper data processing that has included the development of a pre-processing system that includes cloud masking and an atmospheric correction. The results are promising, but further research is needed.
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Lavender, S. J. "MONITORING LAND COVER DYNAMICS AT VARYING SPATIAL SCALES USING HIGH TO VERY HIGH RESOLUTION OPTICAL IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 937–39. http://dx.doi.org/10.5194/isprs-archives-xli-b8-937-2016.
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Activities have focused on using the Landsat time-series and Sentinel-2 datasets to monitor land cover dynamics across the United Kingdom, with mapping of specific areas including missions such as Worldview and Kompsat. This short conference paper shows some of the preliminary results from the Landsat Operational Land Imager, Thematic Mapper and Enhanced Thematic Mapper data processing that has included the development of a pre-processing system that includes cloud masking and an atmospheric correction. The results are promising, but further research is needed.
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Kupidura, Przemysław. "The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery." Remote Sensing 11, no. 10 (2019): 1233. http://dx.doi.org/10.3390/rs11101233.
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The paper presents a comparison of the efficacy of several texture analysis methods as tools for improving land use/cover classification in satellite imagery. The tested methods were: gray level co-occurrence matrix (GLCM) features, Laplace filters and granulometric analysis, based on mathematical morphology. The performed tests included an assessment of the classification accuracy performed based on spectro-textural datasets: spectral images with the addition of images generated using different texture analysis methods. The class nomenclature was based on spectral and textural differences and included the following classes: water, low vegetation, bare soil, urban, and two (coniferous and deciduous) forest classes. The classification accuracy was assessed using the overall accuracy and kappa index of agreement, based on the reference data generated using visual interpretation of the images. The analysis was performed using very high-resolution imagery (Pleiades, WorldView-2) and high-resolution imagery (Sentinel-2). The results show the efficacy of selected GLCM features and granulometric analysis as tools for providing textural data, which could be used in the process of land use/cover classification. It is also clear that texture analysis is generally a more important and effective component of classification for images of higher resolution. In addition, for classification using GLCM results, the Random Forest variable importance analysis was performed.
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Ding, Y., M. Wu, Y. Xu, and S. Duan. "P-LINKNET: LINKNET WITH SPATIAL PYRAMID POOLING FOR HIGH-RESOLUTION SATELLITE IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 35–40. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-35-2020.
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Abstract. Automatic extraction of buildings from high-resolution remote sensing imagery is very useful in many applications such as city management, mapping, urban planning and geographic information updating. Although extensively studied in the past years, due to the general texture of the building and the complexity of the image background, high-precision building segmentation from high-resolution sensing image is still a challenging task. Repeated pooling and striding operations used in CNNs reduce feature resolutions and cause the loss of detail information. In order to solve this problem, we proposed a deep learning model with a spatial pyramid pooling module based on the LinkNet. The proposed model called P-LinkNet that takes advantage of a spatial pyramid pooling module to capture and aggregate multi-scale contextual information. We tested it on Inria Building dataset. Experimental results show that the proposed P-LinkNet is superior to the LinkNet.
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Mousakhani, S., M. Eslami, and M. Saadatseresht. "SPATIAL RESOLUTION ASSESSMENT OF THE TELOPS AIRBORNE TIR IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 191–94. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-191-2017.
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Having a high spatial resolution of Thermal InfraRed (TIR) Sensors is a challenge in remote sensing applications. Airborne high spatial resolution TIR is a novel source of data that became available lately. Recent developments in spatial resolution of the TIR sensors have been an interesting topic for scientists. TIR sensors are very sensitive to the energies emitted from objects. Past researches have been shown that increasing the spatial resolution of an airborne image will decrease the spectral content of the data and will reduce the Signal to Noise Ratio (SNR). Therefore, in this paper a comprehensive assessment is adapted to estimate an appropriate spatial resolution of the TIR data (TELOPS TIR data), in consideration of the SNR. So, firstly, a low-pass filter is applied on TIR data and the achieved products fed to a classification method for analysing of the accuracy improvement. The obtained results show that, there is no significant change in classification accuracy by applying low-pass filter. Furthermore, estimation of the appropriate spatial resolution of the TIR data is evaluated for obtaining higher spectral content and SNR. For this purpose, different resolutions of the TIR data are created and fed to the maximum likelihood classification method separately. The results illustrated in the case of using images with ground pixel size four times greater than the original image, the classification accuracy is not reduced. Also, SNR and spectral contents are improved. But the corners sharpening is declined.
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Suresh Kumar, R., and A. R. Mahesh Balaji. "Land use land cover classification using local multiple pattern from very high resolution satellite imagery." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 971–76. http://dx.doi.org/10.5194/isprsarchives-xl-8-971-2014.
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The recent development in satellite sensors provide images with very high spatial resolution that aids detailed mapping of Land Use Land Cover (LULC). But the heterogeneity in the landscapes often results in spectral variation within the same and spectral confusion among different LU/LC classes at finer spatial resolution. This leads to poor classification performances based on traditional spectral-based classification. Many studies have been addressed to improve this classification by incorporating texture information with multispectral images. Although different methods are available to extract textures from the satellite images, only a limited number of studies compared their performance in classification. The major problem with the existing texture measures is either scale/orientation/illumination variant (Haralick textures) or computationally difficult (Gabor textures) or less informative (Local binary pattern). This paper explores the use of texture information captured by Local Multiple Patterns (LMP) for LULC classification in a spectral-spatial classifier framework. LMP preserve more structural information and involves less computational efforts. Thus LMP is expected to be more promising for capturing spatial information from very high spatial resolution images. The proposed method is implemented with spectral bands and LMP derived from WorldView-2 multispectral imagery acquired for Madurai, India study area. The Multi-Layer-Perceptron neural network is used as a classifier. The proposed classification method is compared with LBP and conventional Maximum Likelihood Classification (MLC) separately. The classification results with 89.5% clarify the improvement offered by the LMP for LULC classification in comparison with the conventional approaches.
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Romano, Giovanni, Giovanni Francesco Ricci, and Francesco Gentile. "Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream." Remote Sensing 12, no. 20 (2020): 3376. http://dx.doi.org/10.3390/rs12203376.
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In recent decades, technological advancements in sensors have generated increasing interest in remote sensing data for the study of vegetation features. Image pixel resolution can affect data analysis and results. This study evaluated the potential of three satellite images of differing resolution (Landsat 8, 30 m; Sentinel-2, 10 m; and Pleiades 1A, 2 m) in assessing the Leaf Area Index (LAI) of riparian vegetation in two Mediterranean streams, and in both a winter wheat field and a deciduous forest used to compare the accuracy of the results. In this study, three different retrieval methods—the Caraux-Garson, the Lambert-Beer, and the Campbell and Norman equations—are used to estimate LAI from the Normalized Difference Vegetation Index (NDVI). To validate sensor data, LAI values were measured in the field using the LAI 2200 Plant Canopy Analyzer. The statistical indices showed a better performance for Pleiades 1A and Landsat 8 images, the former particularly in sites characterized by high canopy closure, such as deciduous forests, or in areas with stable riparian vegetation, the latter where stable reaches of riparian vegetation cover are almost absent or very homogenous, as in winter wheat fields. Sentinel-2 images provided more accurate results in terms of the range of LAI values. Considering the different types of satellite imagery, the Lambert-Beer equation generally performed best in estimating LAI from the NDVI, especially in areas that are geomorphologically stable or have a denser vegetation cover, such as deciduous forests.
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Oreti, Loredana, Diego Giuliarelli, Antonio Tomao, and Anna Barbati. "Object Oriented Classification for Mapping Mixed and Pure Forest Stands Using Very-High Resolution Imagery." Remote Sensing 13, no. 13 (2021): 2508. http://dx.doi.org/10.3390/rs13132508.
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The importance of mixed forests is increasingly recognized on a scientific level, due to their greater productivity and efficiency in resource use, compared to pure stands. However, a reliable quantification of the actual spatial extent of mixed stands on a fine spatial scale is still lacking. Indeed, classification and mapping of mixed populations, especially with semi-automatic procedures, has been a challenging issue up to date. The main objective of this study is to evaluate the potential of Object-Based Image Analysis (OBIA) and Very-High-Resolution imagery (VHR) to detect and map mixed forests of broadleaves and coniferous trees with a Minimum Mapping Unit (MMU) of 500 m2. This study evaluates segmentation-based classification paired with non-parametric method K- nearest-neighbors (K-NN), trained with a dataset independent from the validation one. The forest area mapped as mixed forest canopies in the study area amounts to 11%, with an overall accuracy being equal to 85% and K of 0.78. Better levels of user and producer accuracies (85–93%) are reached in conifer and broadleaved dominated stands. The study findings demonstrate that the very high resolution images (0.20 m of spatial resolutions) can be reliably used to detect the fine-grained pattern of rare mixed forests, thus supporting the monitoring and management of forest resources also on fine spatial scales.
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Hu, B., and W. Jung. "INDIVIDUAL TREE CROWN DELINEATION FROM HIGH SPATIAL RESOLUTION IMAGERY USING U-NET." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 61–66. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-61-2021.
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Abstract. The objective of this study was to explore the utilization of deep learning networks in individual tree crown (ITC) delineation, a very important step in individual tree analysis. Even though many traditional machine learning methods have been developed for ITC delineation, the accuracy remains low, especially for dense forests where branches, crowns, and clusters of trees usually have similar characteristics and boundaries of tree crowns are not distinct. Advance in deep learning provides a good opportunity to improve ITC delineation. In this study, U-net, Residual U-net, and attention U-net were implemented for the first time in ITC delineation. In order to ensure that the boundaries of tree crowns were classified correctly, a weight map was generated to give more weights to boundary pixels between two close crowns in the loss function. These three networks were trained and tested using optical imagery obtained over a study site within the Great Lakes-St. Lawrence forest region, Ontario Canada. Based on two test sites dominated by open mixed forest and closed deciduous forests, respectively, the overall accuracies were 0.94 and 0.90, respectively for U-net, 0.89 and 0.62 for Residual U-net, and 0.96 and 0.83 for attention U-net.
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Cai, Guoyin, Huiqun Ren, Liuzhong Yang, Ning Zhang, Mingyi Du, and Changshan Wu. "Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme." Sensors 19, no. 14 (2019): 3120. http://dx.doi.org/10.3390/s19143120.
Full textAbstract:
Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.
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Kumar, Minakshi, and Ashutosh Bhardwaj. "Building Extraction from Very High Resolution Stereo Satellite Images Using OBIA and Topographic Information." Environmental Sciences Proceedings 5, no. 1 (2020): 1. http://dx.doi.org/10.3390/iecg2020-08908.
Full textAbstract:
The availability of very high resolution (VHR) satellite imagery (<1 m) has opened new vistas in large-scale mapping and information management in urban environments. Buildings are the most essential dynamic incremental factor in the urban environment, and hence their extraction is the most challenging activity. Extracting the urban features, particularly buildings using traditional pixel-based classification approaches as a function of spectral tonal value, produces relatively less accurate results for these VHR Imageries. The present study demonstrates building extraction using Pleiades panchromatic (PAN) and multispectral stereo satellite datasets of highly planned and dense urban areas in parts of Chandigarh, India. The stereo datasets were processed in a photogrammetric environment to obtain the digital elevation model (DEM) and corresponding orthoimages. DEM’s were generated at 0.5 m and 2.0 m from stereo PAN and multispectral datasets, respectively. The orthoimages thus generated were segmented using object-based image analysis (OBIA) tools. The object primitives such as scale parameter, shape, textural parameters, and DEM derivatives were used for segmentation and subsequently to determine threshold values for building fuzzy rules for building extraction and classification. The rule-based classification was carried out with defined decision rules based on object primitives and fuzzy rules. Two different methods were utilized for the performance evaluation of the proposed automatic building approach. Overall accuracy, correctness, and completeness were evaluated for extracted buildings. It was observed that overall accuracy was higher (>93%) in areas having larger buildings and that were sparsely built-up as compared to areas having smaller buildings and being densely built-up.
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Arkin, Jeremy, Nicholas C. Coops, Txomin Hermosilla, Lori D. Daniels, and Andrew Plowright. "Integrated fire severity–land cover mapping using very-high-spatial-resolution aerial imagery and point clouds." International Journal of Wildland Fire 28, no. 11 (2019): 840. http://dx.doi.org/10.1071/wf19008.
Full textAbstract:
Fire severity mapping is conventionally accomplished through the interpretation of aerial photography or the analysis of moderate- to coarse-spatial-resolution pre- and post-fire satellite imagery. Although these methods are well established, there is a demand from both forest managers and fire scientists for higher-spatial-resolution fire severity maps. This study examines the utility of high-spatial-resolution post-fire imagery and digital aerial photogrammetric point clouds acquired from an unmanned aerial vehicle (UAV) to produce integrated fire severity–land cover maps. To accomplish this, a suite of spectral, structural and textural variables was extracted from the UAV-acquired data. Correlation-based feature selection was used to select subsets of variables to be included in random forest classifiers. These classifiers were then used to produce disturbance-based land cover maps at 5- and 1-m spatial resolutions. By analysing maps produced using different variables, the highest-performing spectral, structural and textural variables were identified. The maps were produced with high overall accuracies (5m, 89.5±1.4%; 1m, 85.4±1.5%), with the 1-m classification produced at slightly lower accuracies. This reduction was attributed to the inclusion of four additional classes, which increased the thematic detail enough to outweigh the differences in accuracy.
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Ye, Ziran, Yongyong Fu, Muye Gan, Jinsong Deng, Alexis Comber, and Ke Wang. "Building Extraction from Very High Resolution Aerial Imagery Using Joint Attention Deep Neural Network." Remote Sensing 11, no. 24 (2019): 2970. http://dx.doi.org/10.3390/rs11242970.
Full textAbstract:
Automated methods to extract buildings from very high resolution (VHR) remote sensing data have many applications in a wide range of fields. Many convolutional neural network (CNN) based methods have been proposed and have achieved significant advances in the building extraction task. In order to refine predictions, a lot of recent approaches fuse features from earlier layers of CNNs to introduce abundant spatial information, which is known as skip connection. However, this strategy of reusing earlier features directly without processing could reduce the performance of the network. To address this problem, we propose a novel fully convolutional network (FCN) that adopts attention based re-weighting to extract buildings from aerial imagery. Specifically, we consider the semantic gap between features from different stages and leverage the attention mechanism to bridge the gap prior to the fusion of features. The inferred attention weights along spatial and channel-wise dimensions make the low level feature maps adaptive to high level feature maps in a target-oriented manner. Experimental results on three publicly available aerial imagery datasets show that the proposed model (RFA-UNet) achieves comparable and improved performance compared to other state-of-the-art models for building extraction.
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Zhang, Hua, Wenzhong Shi, Yunjia Wang, Ming Hao, and Zelang Miao. "Classification of Very High Spatial Resolution Imagery Based on a New Pixel Shape Feature Set." IEEE Geoscience and Remote Sensing Letters 11, no. 5 (2014): 940–44. http://dx.doi.org/10.1109/lgrs.2013.2282469.
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