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Artykuły w czasopismach na temat „Pixel-Object classification”

Autor: Grafiati

Data publikacji: 5 października 2024

Data aktualizacji: 31 lipca 2025

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Bernardini, A., E. Frontoni, E. S. Malinverni, A. Mancini, A. N. Tassetti, and P. Zingaretti. "Pixel, object and hybrid classification comparisons." Journal of Spatial Science 55, no. 1 (2010): 43–54. http://dx.doi.org/10.1080/14498596.2010.487641.

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Makinde, Esther Oluwafunmilayo, Ayobami Taofeek Salami, James Bolarinwa Olaleye, and Oluwapelumi Comfort Okewusi. "Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria." Geoinformatics FCE CTU 15, no. 2 (2016): 59–70. http://dx.doi.org/10.14311/gi.15.2.5.

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Several studies have been carried out to find an appropriate method to classify the remote sensing data. Traditional classification approaches are all pixel-based, and do not utilize the spatial information within an object which is an important source of information to image classification. Thus, this study compared the pixel based and object based classification algorithms using RapidEye satellite image of Eti-Osa LGA, Lagos. In the object-oriented approach, the image was segmented to homogenous area by suitable parameters such as scale parameter, compactness, shape etc. Classification based

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Martínez Prentice, Ricardo, Miguel Villoslada Peciña, Raymond D. Ward, Thaisa F. Bergamo, Chris B. Joyce, and Kalev Sepp. "Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands." Remote Sensing 13, no. 18 (2021): 3669. http://dx.doi.org/10.3390/rs13183669.

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High-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles (UAVs) are helping to capture the heterogeneity of the environment in images that can be discretized in categories during a classification process. Currently, there is an increasing use of supervised machine learning (ML) classifiers to retrieve accurate results using scarce datasets with samples with non-linear relationships. We compared the accuracies of two ML classifiers using a pixel and object analysis approach in six coastal wetland sites. The results show that the Random Forest (RF) performs be

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Sutanto, Ahmad, Bambang Trisakti, and Aniati Murni Arymurthy. "PERBANDINGAN KLASIFIKASI BERBASIS OBJEK DAN KLASIFIKASI BERBASIS PIKSEL PADA DATA CITRA SATELIT SYNTHETIC APERTURE RADAR UNTUK PEMETAAN LAHAN." Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital 11, no. 1 (2014): 63–75. https://doi.org/10.30536/inderaja.v11i1.3300.

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Utilization of remote sensing data for land mapping has long been developed. In Indonesia, as a tropical region, the cloud becomes a classic problem in observing the Earth’s surface using optical remotely sensor satellite. Synthetic Aperture Radar (SAR) sensor satellite has the ability to penetrate clouds so it can solve cloud cover problems. In this study, the ALOS PALSAR data were used to assess object-based and pixel-based classification techniques. This data was chosen due to its capacity for object recognition based on backscatter characteristics. Object-based classification using the met

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He, Ziqiang, Shaosheng Dai, and Jinsong Liu. "Single-pixel object classification using ordered illumination patterns." Optics Communications 573 (December 2024): 131023. http://dx.doi.org/10.1016/j.optcom.2024.131023.

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Liu, Yanzhu, Yanan Wang, and Adams Wai Kin Kong. "Pixel-wise ordinal classification for salient object grading." Image and Vision Computing 106 (February 2021): 104086. http://dx.doi.org/10.1016/j.imavis.2020.104086.

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Deur, Martina, Mateo Gašparović, and Ivan Balenović. "An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery." Remote Sensing 13, no. 10 (2021): 1868. http://dx.doi.org/10.3390/rs13101868.

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Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three diffe

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Kang, Min Jo, Victor Mesev, and Won Kyung Kim. "Measurements of Impervious Surfaces - per-pixel, sub-pixel, and object-oriented classification -." Korean Journal of Remote Sensing 31, no. 4 (2015): 303–19. http://dx.doi.org/10.7780/kjrs.2015.31.4.3.

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Endo, Yutaka, and Gai Nakajima. "Compressive phase object classification using single-pixel digital holography." Optics Express 30, no. 15 (2022): 28057. http://dx.doi.org/10.1364/oe.463395.

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A single-pixel camera (SPC) is a computational imaging system that obtains compressed signals of a target scene using a single-pixel detector. The compressed signals can be directly used for image classification, thereby bypassing image reconstruction, which is computationally intensive and requires a high measurement rate. Here, we extend this direct inference to phase object classification using single-pixel digital holography (SPDH). Our method obtains compressed measurements of target complex amplitudes using SPDH and trains a classifier using those measurements for phase object classifica

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Powar, Sudhir K., Sachin S. Panhalkar, and Abhijit S. Patil. "An Evaluation of Pixel-based and Object-based Classification Methods for Land Use Land Cover Analysis Using Geoinformatic Techniques." Geomatics and Environmental Engineering 16, no. 2 (2022): 61–75. http://dx.doi.org/10.7494/geom.2022.16.2.61.

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Land use land cover (LULC) classification is a valuable asset for resource managers; in many fields of study, it has become essential to monitor LULC at different scales. As a result, the primary goal of this work is to compare and contrast the performance of pixel-based and object-based categorization algorithms. The supervised maximum likelihood classifier (MLC) technique was employed in pixel-based classification, while multi-resolution segmentation and the standard nearest neighbor (SNN) algorithm were employed in object-based classification. For the urban and suburban parts of Kolhapur, t

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Turissa, Pragunanti, Nababan Bisman, Siregar Vincentius, Kushardono Dony, and Madduppa Hawis. "Evaluation Methods of Change Detection of Seagrass Beds in the Waters of Pajenekang and Gusung Selayar." Trends in Sciences 18, no. 23 (2021): 677. http://dx.doi.org/10.48048/tis.2021.677.

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Knowledge about coastal and small island ecosystems is increasing for the monitoring of marine resources based on remote sensing. Remote sensing data provides up-to-date information with various resolutions when detecting changes in ecosystems. Studies have defined a shift in marine resources but were limited only to pixel or object classification in changes of seagrass area. In the present study, two classification method analysis approaches were compared to obtain optimum results in detecting changes in seagrass extent. It aimed to determine the dynamics of a seagrass ecosystem by comparing

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Aghababaei, Masoumeh, Ataollah Ebrahimi, Ali Asghar Naghipour, Esmaeil Asadi, and Jochem Verrelst. "Classification of Plant Ecological Units in Heterogeneous Semi-Steppe Rangelands: Performance Assessment of Four Classification Algorithms." Remote Sensing 13, no. 17 (2021): 3433. http://dx.doi.org/10.3390/rs13173433.

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Plant Ecological Unit’s (PEUs) are the abstraction of vegetation communities that occur on a site which similarly respond to management actions and natural disturbances. Identification and monitoring of PEUs in a heterogeneous landscape is the most difficult task in medium resolution satellite images datasets. The main objective of this study is to compare pixel-based classification versus object-based classification for accurately classifying PEUs with four selected different algorithms across heterogeneous rangelands in Central Zagros, Iran. We used images of Landsat-8 OLI that were pan-shar

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De Giglio, Michaela, Nicolas Greggio, Floriano Goffo, Nicola Merloni, Marco Dubbini, and Maurizio Barbarella. "Comparison of Pixel- and Object-Based Classification Methods of Unmanned Aerial Vehicle Data Applied to Coastal Dune Vegetation Communities: Casal Borsetti Case Study." Remote Sensing 11, no. 12 (2019): 1416. http://dx.doi.org/10.3390/rs11121416.

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Coastal dunes provide the hinterland with natural protection from marine dynamics. The specialized plant species that constitute dune vegetation communities are descriptive of the dune evolution status, which in turn reveals the ongoing coastal dynamics. The aims of this paper were to demonstrate the applicability of a low-cost unmanned aerial system for the classification of dune vegetation, in order to determine the level of detail achievable for the identification of vegetation communities and define the best-performing classification method for the dune environment according to pixel-based

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Coslu, M., N. K. Sonmez, and D. Koc-San. "OBJECT-BASED GREENHOUSE CLASSIFICATION FROM HIGH RESOLUTION SATELLITE IMAGERY: A CASE STUDY ANTALYA-TURKEY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 183–87. http://dx.doi.org/10.5194/isprs-archives-xli-b7-183-2016.

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Pixel-based classification method is widely used with the purpose of detecting land use and land cover with remote sensing technology. Recently, object-based classification methods have begun to be used as well as pixel-based classification method on high resolution satellite imagery. In the studies conducted, it is indicated that object-based classification method has more successful results than other classification methods. While pixel-based classification method is performed according to the grey value of pixels, object-based classification process is executed by generating imagery segment

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Coslu, M., N. K. Sonmez, and D. Koc-San. "OBJECT-BASED GREENHOUSE CLASSIFICATION FROM HIGH RESOLUTION SATELLITE IMAGERY: A CASE STUDY ANTALYA-TURKEY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 183–87. http://dx.doi.org/10.5194/isprsarchives-xli-b7-183-2016.

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Pixel-based classification method is widely used with the purpose of detecting land use and land cover with remote sensing technology. Recently, object-based classification methods have begun to be used as well as pixel-based classification method on high resolution satellite imagery. In the studies conducted, it is indicated that object-based classification method has more successful results than other classification methods. While pixel-based classification method is performed according to the grey value of pixels, object-based classification process is executed by generating imagery segment

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Karakus, P., and H. Karabork. "EFFECT OF PANSHARPENED IMAGE ON SOME OF PIXEL BASED AND OBJECT BASED CLASSIFICATION ACCURACY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 235–39. http://dx.doi.org/10.5194/isprs-archives-xli-b7-235-2016.

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Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spa

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Karakus, P., and H. Karabork. "EFFECT OF PANSHARPENED IMAGE ON SOME OF PIXEL BASED AND OBJECT BASED CLASSIFICATION ACCURACY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 21, 2016): 235–39. http://dx.doi.org/10.5194/isprsarchives-xli-b7-235-2016.

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Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spa

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Jaber, Hussein Sabah, Muntadher Aidi Shareef, and Zainab Fahkri Merzah. "OBJECT-BASED APPROACHES FOR LAND USE-LAND COVER CLASSIFICATION USING HIGH RESOLUTION QUICK BIRD SATELLITE IMAGERY (A CASE STUDY: KERBELA, IRAQ)." Geodesy and cartography 48, no. 2 (2022): 85–91. http://dx.doi.org/10.3846/gac.2022.14453.

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Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood

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Ubben, Niklas, Maren Pukrop, and Thomas Jarmer. "Spatial Resolution as a Factor for Efficient UAV-Based Weed Mapping—A Soybean Field Case Study." Remote Sensing 16, no. 10 (2024): 1778. http://dx.doi.org/10.3390/rs16101778.

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The influence of spatial resolution on classification accuracy strongly depends on the research object. With regard to unmanned aerial vehicle (UAV)-based weed mapping, contradictory results on the influence of spatial resolution have been attained so far. Thus, this study evaluates the effect of spatial resolution on the classification accuracy of weeds in a soybean field located in Belm, Lower Saxony, Germany. RGB imagery of four spatial resolutions (0.27, 0.55, 1.10, and 2.19 cm ground sampling distance) corresponding to flight altitudes of 10, 20, 40, and 80 m were assessed. Multinomial lo

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Song, M., D. L. Civco, and J. D. Hurd. "A competitive pixel-object approach for land cover classification." International Journal of Remote Sensing 26, no. 22 (2005): 4981–97. http://dx.doi.org/10.1080/01431160500213912.

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Ji, X., and X. Niu. "The Attribute Accuracy Assessment of Land Cover Data in the National Geographic Conditions Survey." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4 (April 23, 2014): 35–40. http://dx.doi.org/10.5194/isprsannals-ii-4-35-2014.

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With the widespread national survey of geographic conditions, object-based data has already became the most common data organization pattern in the area of land cover research. Assessing the accuracy of object-based land cover data is related to lots of processes of data production, such like the efficiency of inside production and the quality of final land cover data. Therefore，there are a great deal of requirements of accuracy assessment of object-based classification map. Traditional approaches for accuracy assessment in surveying and mapping are not aimed at land cover data. It is necessar

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Wu, Nitu, Luís Guilherme Teixeira Crusiol, Guixiang Liu, Deji Wuyun, and Guodong Han. "Comparing Machine Learning Algorithms for Pixel/Object-Based Classifications of Semi-Arid Grassland in Northern China Using Multisource Medium Resolution Imageries." Remote Sensing 15, no. 3 (2023): 750. http://dx.doi.org/10.3390/rs15030750.

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Knowledge of grassland classification in a timely and accurate manner is essential for grassland resource management and utilization. Although remote sensing imagery analysis technology is widely applied for land cover classification, few studies have systematically compared the performance of commonly used methods on semi-arid native grasslands in northern China. This renders the grassland classification work in this region devoid of applicable technical references. In this study, the central Xilingol (China) was selected as the study area, and the performances of four widely used machine lea

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Qu, Le’an, Zhenjie Chen, Manchun Li, Junjun Zhi, and Huiming Wang. "Accuracy Improvements to Pixel-Based and Object-Based LULC Classification with Auxiliary Datasets from Google Earth Engine." Remote Sensing 13, no. 3 (2021): 453. http://dx.doi.org/10.3390/rs13030453.

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The monitoring and assessment of land use/land cover (LULC) change over large areas are significantly important in numerous research areas, such as natural resource protection, sustainable development, and climate change. However, accurately extracting LULC only using the spectral features of satellite images is difficult owing to landscape heterogeneities over large areas. To improve the accuracy of LULC classification, numerous studies have introduced other auxiliary features to the classification model. The Google Earth Engine (GEE) not only provides powerful computing capabilities, but als

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Vu Viet Du, Quan, Tam Minh Pham, Van Manh Pham, et al. "An experimental comparison of pixel-based and object-based classifications with different machine learning algorithms in landscape pattern analysis – Case study from Quang Ngai city, Vietnam." IOP Conference Series: Earth and Environmental Science 1345, no. 1 (2024): 012019. http://dx.doi.org/10.1088/1755-1315/1345/1/012019.

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Abstract In landscape pattern analysis, the choice of an efficient method for image classification is widely studied, but the features are mostly extracted from digital values by the traditional approach (Pixel-based) and modern approach (Object-based). In this study, we compared the performance of two supervised classification algorithms (Maximum likelihood classifier - MLC and Support vector machines - SVM). We used SPOT-5 image data from 2011 to analyze the landscape pattern of a complex territory in Quang Ngai City, Vietnam. We collected 215 ground-truth samples and classified them into se

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Othman, Ainon Nisa, Nurhanisah Hashim, Pauziyah Mohamad Salim, and Puteri Norsarifah Suhada Mohd Zaidi. "Comparative Study of Pixel-Based and Object-Based Classifications in Benthic Mapping." Journal of Advanced Geospatial Science & Technology 3, no. 2 (2023): 51–62. http://dx.doi.org/10.11113/jagst.v3n2.69.

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Coral reefs have been degrading rapidly throughout the last decade due to climate change and other human activities. Classification and mapping of benthic floors and associated ecosystems such as coral reefs are both inefficient and expensive using traditional ground-based methods. New technologies using publicly available and commercial satellite imageries are crucial for accurate classification and mapping of coral reefs' distribution, management and monitoring. The study utilized the medium (Sentinel 2B with 20 m) and high (SPOT 7 with 1.5m) resolution satellite imageries for benthic mappin

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Soffianian, Ali Reza, Neda Bihamta Toosi, Ali Asgarian, Hervé Regnauld, Sima Fakheran, and Lars T. Waser. "Evaluating resampled and fused Sentinel-2 data and machine-learning algorithms for mangrove mapping in the northern coast of Qeshm island, Iran." Nature Conservation 52 (March 20, 2023): 1–22. http://dx.doi.org/10.3897/natureconservation.52.89639.

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Mangrove forests, as an essential component of the coastal zones in tropical and subtropical areas, provide a wide range of goods and ecosystem services that play a vital role in ecology. Mangroves are globally threatened, disappearing, and degraded. Consequently, knowledge on mangroves distribution and change is important for effective conservation and making protection policies. Developing remote sensing data and classification methods have proven to be suitable tools for mapping mangrove forests over a regional scale. Here, we scrutinized and compared the performance of pixel-based and obje

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Soffianian, Ali Reza, Neda Bihamta Toosi, Ali Asgarian, Hervé Regnauld, Sima Fakheran, and Lars T. Waser. "Evaluating resampled and fused Sentinel-2 data and machine-learning algorithms for mangrove mapping in the northern coast of Qeshm island, Iran." Nature Conservation 52 (March 20, 2023): 1–22. https://doi.org/10.3897/natureconservation.52.89639.

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Mangrove forests, as an essential component of the coastal zones in tropical and subtropical areas, provide a wide range of goods and ecosystem services that play a vital role in ecology. Mangroves are globally threatened, disappearing, and degraded. Consequently, knowledge on mangroves distribution and change is important for effective conservation and making protection policies. Developing remote sensing data and classification methods have proven to be suitable tools for mapping mangrove forests over a regional scale. Here, we scrutinized and compared the performance of pixel-based and obje

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Jia, Jie, Yong Jun Yang, Yi Ming Hou, Xiang Yang Zhang, and He Huang. "Adaboost Classification-Based Object Tracking Method for Sequence Images." Applied Mechanics and Materials 44-47 (December 2010): 3902–6. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3902.

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An object tracking framework based on adaboost and Mean-Shift for image sequence was proposed in the manuscript. The object rectangle and scene rectangle in the initial image of the sequence were drawn and then, labeled the pixel data in the two rectangles with 1 and 0. Trained the adaboost classifier by the pixel data and the corresponding labels. The obtained classifier was improved to be a 5 class classifier and employed to classify the data in the same scene region of next image. The confidence map including 5 values was got. The Mean-Shift algorithm is performed in the confidence map area

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SUHAS, B. KALE, and P. DESHMUKH MAYUR. "THE STUDY OF RIVER EXTRACTION TECHNIQUES AND METHODS USING REMOTE SENSING." JournalNX - A Multidisciplinary Peer Reviewed Journal ICACTM (May 4, 2018): 185–87. https://doi.org/10.5281/zenodo.1410320.

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 In this paper, Methods and technology of river Extraction are studied. First DEM with K means, Hill Climbing, and Thresholding. Then different technics like NDWI, pixel-based (supervised and unsupervised) classification and object-based classification. Then a new water body extraction model was developed using the advantages of the OBIA and the NDWI. The need for this kind of method comes from the fact that it is hard for pixel-based classification methods and the NDWI method to separate water from another object that has a low albedo, and since it is impossible to separate them by their

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Castillejo-González, Isabel. "Mapping of Olive Trees Using Pansharpened QuickBird Images: An Evaluation of Pixel- and Object-Based Analyses." Agronomy 8, no. 12 (2018): 288. http://dx.doi.org/10.3390/agronomy8120288.

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This study sought to verify whether remote sensing offers the ability to efficiently delineate olive tree canopies using QuickBird (QB) satellite imagery. This paper compares four classification algorithms performed in pixel- and object-based analyses. To increase the spectral and spatial resolution of the standard QB image, three different pansharpened images were obtained based on variations in the weight of the red and near infrared bands. The results showed slight differences between classifiers. Maximum Likelihood algorithm yielded the highest results in pixel-based classifications with a

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Li, Gang, and Youchuan Wan. "A new combination classification of pixel- and object-based methods." International Journal of Remote Sensing 36, no. 23 (2015): 5842–68. http://dx.doi.org/10.1080/01431161.2015.1109728.

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Hadavand, Ahmad, Mehdi Mokhtarzadeh, Mohammad Javad Valadan Zoej, Saeid Homayouni, and Mohammad Saadatseresht. "USING PIXEL-BASED AND OBJECT-BASED METHODS TO CLASSIFY URBAN HYPERSPECTRAL FEATURES." Geodesy and cartography 42, no. 3 (2016): 92–105. http://dx.doi.org/10.3846/20296991.2016.1226388.

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Object-based image analysis methods have been developed recently. They have since become a very active research topic in the remote sensing community. This is mainly because the researchers have begun to study the spatial structures within the data. In contrast, pixel-based methods only use the spectral content of data. To evaluate the applicability of object-based image analysis methods for land-cover information extraction from hyperspectral data, a comprehensive comparative analysis was performed. In this study, six supervised classification methods were selected from pixel-based category,

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ALİYU, Abdulazeez Onotu, Ebenezer Ayobami AKOMOLAFE, Adamu BALA, Terwase YOUNGU, Hassan MUSA, and Swafiyudeen BAWA. "Integrated Method for Classifying Medium Resolution Satellite Remotely Sensed Imagery into Land Use Map." International Journal of Environment and Geoinformatics 10, no. 2 (2023): 135–44. http://dx.doi.org/10.30897/ijegeo.1150436.

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There are several remotely sensed images of varied resolutions available. As a result, several classification techniques exist, which are roughly classified as pixel-based and object-based classification methods. Based on the foregoing, this study provided an integrated method of deriving land use from a coarse satellite image. Location coordinates of the land uses were acquired with a handheld Global Positioning System (GPS) instrument as primary data. The study classified the image quantitatively (pixel-based) into built-up, water, riparian, cultivated, and uncultivated land cover classes wi

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Sefercik, U. G., T. Kavzoglu, I. Colkesen, et al. "LAND COVER CLASSIFICATION PERFORMANCE OF MULTISPECTRAL RTK UAVs." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 (December 23, 2021): 489–92. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w5-2021-489-2021.

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Abstract. Unmanned air vehicle (UAV) became an alternative airborne remote sensing technique, due to providing very high resolution and low cost spatial data and short processing time. Particularly, optical UAVs are frequently utilized in various applications such as mapping, agriculture, and forestry. Especially for precise agriculture purposes, the UAVs were equipped with multispectral cameras which enables to classify land cover easily. In this study, the land cover classification potential of DJI Phantom IV Multispectral, one of the most preferred agricultural UAVs in the world, was invest

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Alonso-Benito, Alfonso, Lara A. Arroyo, Manuel Arbelo, Pedro Hernández-Leal, and Alejandro González-Calvo. "Pixel and object-based classification approaches for mapping forest fuel types in Tenerife Island from ASTER data." International Journal of Wildland Fire 22, no. 3 (2013): 306. http://dx.doi.org/10.1071/wf11068.

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Four classification algorithms have been assessed and compared with mapped forest fuel types from Terra-ASTER sensor images in a representative area of Tenerife Island (Canary Islands, Spain). A BEHAVE fuel-type map from 2002, together with field data also obtained in 2002 during the Third Spanish National Forest Inventory, was used as reference data. The BEHAVE fuel types of the reference dataset were first converted into the Fire Behaviour Fuel Types described by Scott and Burgan, taking into account the vegetation of the study area. Then, three pixel-based algorithms (Maximum Likelihood, Ne

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Xiao, Xingyuan, Linlong Jiang, Yaqun Liu, and Guozhen Ren. "Limited-Samples-Based Crop Classification Using a Time-Weighted Dynamic Time Warping Method, Sentinel-1 Imagery, and Google Earth Engine." Remote Sensing 15, no. 4 (2023): 1112. http://dx.doi.org/10.3390/rs15041112.

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Reliable crop type classification supports the scientific basis for food security and sustainable agricultural development. However, it still lacks a limited-samples-based crop classification method which is labor- and time-efficient. To this end, we used the Google Earth Engine (GEE) and Sentinel-1A/B SAR time series to develop eight types of crop classification strategies based on different sampling methods of central and scattered, different perspectives of object-based and pixel-based, and different classifiers of the Time-Weighted Dynamic Time Warping (TWDTW) and Random Forest (RF). We ca

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Yao, Manhong, Shujun Zheng, Yuhang Hu, Zibang Zhang, Junzheng Peng, and Jingang Zhong. "Single-Pixel Moving Object Classification with Differential Measuring in Transform Domain and Deep Learning." Photonics 9, no. 3 (2022): 202. http://dx.doi.org/10.3390/photonics9030202.

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Due to limited data transmission bandwidth and data storage space, it is challenging to perform fast-moving objects classification based on high-speed photography for a long duration. Here we propose a single-pixel classification method with deep learning for fast-moving objects. The scene image is modulated by orthogonal transform basis patterns, and the modulated light signal is detected by a single-pixel detector. Thanks to the property that the natural images are sparse in the orthogonal transform domain, we used a small number of basis patterns of discrete-sine-transform to obtain feature

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He, Shi, Hong Tang, Jing Li, Yang Shu, and Li Shen. "Object-Oriented Semisupervised Classification of VHR Images by Combining MedLDA and a Bilateral Filter." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/182439.

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A Bayesian hierarchical model is presented to classify very high resolution (VHR) images in a semisupervised manner, in which both a maximum entropy discrimination latent Dirichlet allocation (MedLDA) and a bilateral filter are combined into a novel application framework. The primary contribution of this paper is to nullify the disadvantages of traditional probabilistic topic models on pixel-level supervised information and to achieve the effective classification of VHR remote sensing images. This framework consists of the following two iterative steps. In the training stage, the model utilize

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Troje, N. F., and T. Vetter. "Pixel-Based versus Correspondence-Based Representations of Human Faces: Implications for Sex Discrimination." Perception 25, no. 1_suppl (1996): 161. http://dx.doi.org/10.1068/v96l1112.

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In human perception, as well as in machine vision, a crucial step in solving any object recognition task is an appropriate description of the object class under consideration. We emphasise this issue when considering the object class ‘human faces’. We discuss different representations that can be characterised by the degree of alignment between the images they provide for. The representations used span the whole range between a purely pixel-based image representation and a sophisticated model-based representation derived from the pixel-to-pixel correspondence between the faces [Vetter and Troj

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Castillejo-González, Isabel, Cristina Angueira, Alfonso García-Ferrer, and Manuel Sánchez de la Orden. "Combining Object-Based Image Analysis with Topographic Data for Landform Mapping: A Case Study in the Semi-Arid Chaco Ecosystem, Argentina." ISPRS International Journal of Geo-Information 8, no. 3 (2019): 132. http://dx.doi.org/10.3390/ijgi8030132.

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This paper presents an object-based approach to mapping a set of landforms located in the fluvio-eolian plain of Rio Dulce and alluvial plain of Rio Salado (Dry Chaco, Argentina), with two Landsat 8 images collected in summer and winter combined with topographic data. The research was conducted in two stages. The first stage focused on basic-spectral landform classifications where both pixel- and object-based image analyses were tested with five classification algorithms: Mahalanobis Distance (MD), Spectral Angle Mapper (SAM), Maximum Likelihood (ML), Support Vector Machine (SVM) and Decision

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Yang, Qiu Xia, Chuan Wen Luo, and Tian Kai Chen. "Remote Sensing Image Classification Based on Object-Oriented Method and Support Vector Machine: A Case Study in Harbin City." Advanced Materials Research 912-914 (April 2014): 1331–34. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1331.

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Remote sensing classification, as an important means of urban planning and construction, has been widely concerned. Urban land use classification is extremely challenging tasks because of some land covers are spectrally too similar to be separated using only the spectral information of remote sensing image. Object-oriented remote sensing image classification method overcomes the drawbacks of traditional pixel-based classification method. It combines the spectral, special structure and texture features of the images, can effectively avoid the phenomenon of "different objects share the same spec

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Liao, Yusen, Yin Cheng, and Jun Ke. "Dual-Task Learning for Long-Range Classification in Single-Pixel Imaging Under Atmospheric Turbulence." Electronics 14, no. 7 (2025): 1355. https://doi.org/10.3390/electronics14071355.

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Unlike traditional imaging, single-pixel imaging (SPI) exhibits greater resistance to atmospheric turbulence. Therefore, we use SPI for long-range classification, in which atmospheric turbulence often cause significant degradation in performance. We propose a dual-task learning method for SPI classification. Specifically, we design the Long-Range Dual-Task Single-Pixel Network (LR-DTSPNet) to perform object classification and image restoration simultaneously, enhancing the model’s generalization and robustness. Attention mechanisms and residual convolutions are used to strengthen feature model

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Barzegar, M., H. Ebadi, and A. Kiani. "COMPARISON OF DIFFERENT VEGETATION INDICES FOR VERY HIGH-RESOLUTION IMAGES, SPECIFIC CASE ULTRACAM-D IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 10, 2015): 97–104. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-97-2015.

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Today digital aerial images acquired with UltraCam sensor are known to be a valuable resource for producing high resolution information of land covers. In this research, different methods for extracting vegetation from semi-urban and agricultural regions were studied and their results were compared in terms of overall accuracy and Kappa statistic. To do this, several vegetation indices were first tested on three image datasets with different object-based classifications in terms of presence or absence of sample data, defining other features and also more classes. The effects of all these cases

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Zhang, Chi, Shiqing Wei, Shunping Ji, and Meng Lu. "Detecting Large-Scale Urban Land Cover Changes from Very High Resolution Remote Sensing Images Using CNN-Based Classification." ISPRS International Journal of Geo-Information 8, no. 4 (2019): 189. http://dx.doi.org/10.3390/ijgi8040189.

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The study investigates land use/cover classification and change detection of urban areas from very high resolution (VHR) remote sensing images using deep learning-based methods. Firstly, we introduce a fully Atrous convolutional neural network (FACNN) to learn the land cover classification. In the FACNN an encoder, consisting of full Atrous convolution layers, is proposed for extracting scale robust features from VHR images. Then, a pixel-based change map is produced based on the classification map of current images and an outdated land cover geographical information system (GIS) map. Both pol

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Ouchra, Hafsa, Abdessamad Belangour, and Allae Erraissi. "A Comparative Study on Pixel-based Classification and Object-Oriented Classification of Satellite Image." International Journal of Engineering Trends and Technology 70, no. 8 (2022): 206–15. http://dx.doi.org/10.14445/22315381/ijett-v70i8p221.

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Manalu, R. Johannes, Ahmad Sutanto, and Bambang Trisakti. "PERBANDINGAN METODE KLASIFIKASI PENUTUP LAHAN BERBASIS PIKSEL DAN BERBASIS OBYEK MENGGUNAKAN DATA PiSAR-L2." Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital 13, no. 1 (2016): 49–60. https://doi.org/10.30536/inderaja.v13i1.3319.

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PiSAR-L2 program is an experimental program for PALSAR-2 sensor installed on ALOS-2. Research collaboration had been conducted between the Japan Aerospace Exploration Agency (JAXA) and Ministry for Research and Technology of Indonesia in 2012 to assess the ability of PiSAR-L2 data for some applications. This paper explores the utilization of PiSAR-L2 data for land cover classification in forest area using pixel-based and object-based methods, then carried out comparison between the two methods. PiSAR-L2 data full polarization with 2.1 level for Riau province was used. Field data conducted by J

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Mahmoud, Ammar Shaker, Mustafa Ridha Mezaal, Mustafa Raad Hameed, and Ahmed Samir Naje. "A Framework for Improving Urban Land Cover Using Object and Pixel-Based Techniques via Remotely Sensed Data." Nature Environment and Pollution Technology 21, no. 5(Suppl) (2022): 2189–200. http://dx.doi.org/10.46488/nept.2022.v21i05.013.

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Recently, the advancement of remote sensing technology played a key role in urban land/cover mapping, planning, tourism, and environmental management. Images with a high spatial resolution for urban classification are widely used. Despite the high spectral resolution of the image, spectral confusion happens among different land covers. Furthermore, the shadow problem also causes poor results in the classification based on traditional per-pixel spectral approaches. This study looks at ways of improving the classification of urban land cover using QuickBird images. Maximum likelihood (ML) pixel-

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Nugroho, Jalu Tejo, Zylshal, Nurwita Mustika Sari, and Dony Kushardono. "A COMPARISON OF OBJECT-BASED AND PIXEL-BASED APPROACHES FOR LAND USE/LAND COVER CLASSIFICATION USING LAPAN-A2 MICROSATELLITE DATA." International Journal of Remote Sensing and Earth Sciences (IJReSES) 14, no. 1 (2017): 27. http://dx.doi.org/10.30536/j.ijreses.2017.v14.a2680.

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In recent years, small satellite industry has been a rapid trend and become important especially when associated with operational cost, technology adaptation and the missions. One mission of LAPAN-A2, the 2nd generation of microsatellite that developed by Indonesian National Institute of Aeronautics and Space (LAPAN), is Earth observation using digital camera that provides imagery with 3.5 m spatial resolution. The aim of this research is to compare between object-based and pixel-based classification of land use/land cover (LU/LC) in order to determine the appropriate classification method in

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Mancera Florez, Juan Ricardo, and Ivan Alberto Lizarazo Salcedo. "Land cover classification at three different levels of detail from optical and radar Sentinel SAR data: a case study in Cundinamarca (Colombia)." DYNA 87, no. 215 (2020): 136–45. http://dx.doi.org/10.15446/dyna.v87n215.84915.

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In this paper, the potential of Sentinel-1A and Sentinel-2A satellite images for land cover mapping is evaluated at three levels of spatial detail; exploratory, reconnaissance, and semi-detailed. To do so, two different image classification approaches are compared: (i) a traditional pixel-wise approach; and (ii) an object–oriented approach. In both cases, the classification task was conducted using the “RandomForest” algorithm. The case study was also intended to identify a set of radar channels, optical bands, and indices that are relevant for classification. The thematic accuracy of the clas

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El-Ashmawy, N., A. Shaker, and W. Yan. "PIXEL VS OBJECT-BASED IMAGE CLASSIFICATION TECHNIQUES FOR LIDAR INTENSITY DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-5/W12 (September 3, 2012): 43–48. http://dx.doi.org/10.5194/isprsarchives-xxxviii-5-w12-43-2011.

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