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Incorporating DeepLabv3+and object-based image analysis for semantic segmentation of very high resolution remote sensing images

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International Journal of Digital Earth 2021年第3期14卷 357-378页

作者： Shouji Du Shihong Du Bo Liu Xiuyuan Zhang Institute of Remote Sensing and GIS Peking UniversityBeijingPeople’s Republic of China

Semantic segmentation of remote sensing images is an important but unsolved problem in the remote sensing *** image semantic segmentation models,such as DeepLabv3+,have achieved astonishing performance for semantically labeling very high resolution(VHR)remote sensing ***,it is difficult for these models to capture the precise outlines of ground objects and explore the context information that revealing relationships among image objects for optimizing segmentation ***,this study proposes a semantic segmentation method for VHR images by incorporating deep learning semantic segmentation model(DeepLabv3+)and objectbased image analysis(OBIA),wherein DSM is employed to provide geometric information to enhance the interpretation of VHR *** proposed method first obtains two initial probabilistic labeling predictions using a DeepLabv3+network on spectral image and a random forest(RF)classifier on hand-crafted features,*** two predictions are then integrated by Dempster-Shafer(D-S)evidence theory to be fed into an object-constrained higher-order conditional random field(CRF)framework to estimate the final semantic labeling results with the consideration of the spatial contextual *** proposed method is applied to the ISPRS 2D semantic labeling benchmark,and competitive overall accuracies of 90.6%and 85.0%are achieved for Vaihingen and Potsdam datasets,respectively.

关键词： Semantic segmentation DeepLabv3+ object-based image analysis DempsterShafer evidence theory conditional random field VHR images

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Evaluating pixel-based ***-based image analysis approaches for lithological discrimination using VNIR data of WorldView-3

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Frontiers of Earth Science 2021年第1期15卷 38-53页

作者： Samira SHAYEGANPOUR Majid H.TANGESTANI Saeid HOMAYOUNI Robert K.VINCENT Department of Earth Sciences Faculty of SciencesShiraz UniversityShiraz ***Iran Centre Eau Terre Environnement Insitut National de la Recherche ScientifiqueQuebec QCJ3X1S2Canada Department of Geography Bowling Green State UniversityOhio 43403-0001USA

The object-based against pixel-based image analysis approaches were assessed for lithological mapping in a geologically complex terrain using Visible Near Infrared(VNIR)bands of WorldView-3(WV-3)satellite *** study area is Hormuz Island,southern Iran,a salt dome composed of dominant sedimentary and igneous *** performing the object-based image analysis(OBLA)approach,the textural and spectral characteristics of lithological features were analyzed by the use of support vector machine(SVM)***,in the pixelbased image analysis(PBIA),the spectra of lithological end-members,extracted from imagery,were used through the spectral angle mapper(SAM)*** test samples were used in a confusion matrix to assess the accuracy of classification methods *** showed that OBIA was capable of lithological mapping with an overall accuracy of 86.54%which was 19.33%greater than the accuracy of *** also reduced the salt-and-pepper artifact pixels and produced a more realistic map with sharper lithological *** research showed limitations of pixel-based method due to relying merely on the spectral characteristics of rock types when applied to high-spatial-resolution VNIR bands of WorldView-3 *** is concluded that the application of an object-based image analysis approach obtains a more accurate lithological classification when compared to a pixel-based image analysis algorithm.

关键词： object-based image analysis pixel-based image analysis lithological mapping Worldview-3 Hormuz Island spectral angle mapper support vector machine

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Effects of spatial resolution of remotely sensed data on estimating urban impervious surfaces

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Journal of Environmental Sciences 2011年第8期23卷 1375-1383页

作者： Weifeng Li Zhiyun Ouyang Weiqi Zhou Qiuwen Chen State Key laboratory of Urban and Region Ecology Research Centre for Eco-Environmental Sciences Chinese Academy of Sciences Beijing1000~85 China. Department of Plant Sciences University of California Davis Mail Stop 1 1210 PES One Shields Ave. Davis CA 95616 USA

Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure the impacts of urbanization on surface runoff, water quality, air quality, biodiversity and rnicroclimate. Therefore, accurate estimation of impervious surfaces is critical for urban environmental monitoring, land management, decision-making and urban planning. Many approaches have been developed to estimate surface imperviousness, using remotely sensed data with various spatial resolutions. However, few studies, have investigated the effects of spatial resolution on estimating surface imperviousness. We compare medium-resolution Landsat data with high-resolution SPOT images to quantify the imperviousness in Beijing, China. The results indicated that the overall 91% accuracy of estimates of imperviousness based on TM data was considerably higher than the 81% accuracy of the SPOT data. The higher resolution SPOT data did not always predict the imperviousness of the land better than the TM data. At the whole city level, the TM data better predicts the percentage cover of impervious surfaces. At the sub-city level, however, the ring belts from the central core to the urban-rural peripheral, the SPOT data may better predict the imperviousness. These results highlighted the need to combine multiple resolution data to quantify the percentage of imperviousness, as higher resolution data do not necessarily lead to more accurate estimates. The methodology and results in this study can be utilized to identify the most suitable remote sensing data to quickly and efficiently extract the pattern of the impervious land, which could provide the base for further study on many related urban environmental problems.

关键词： remote sensing impervious surface landscape pattern spatial resolution object-based image analysis urban landscape

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Effects of atmospheric correction and pansharpening on LULC classification accuracy using WorldView-2 imagery

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Information Processing in Agriculture 2015年第1期2卷 25-36页

作者： Chinsu Lin Chao-Cheng Wu Khongor Tsogt Yen-Chieh Ouyang Chein-I Chang Department of Forestry and Natural Resources National Chiayi University300 University Rd.Chiayi 60004Taiwan Department of Electrical Engineering National Taipei University of Technology1 Zhongxiao E.Rd.Sec.3Taipei 10608Taiwan Department of Electrical Engineering National Chung Hsing University250Kuo Kuang Rd.Taichung 402Taiwan Department of Computer Science and Electrical Engineering UMBC1000 Hilltop CircleBaltimoreMD 20250USA

Changes of Land Use and Land Cover(LULC)affect atmospheric,climatic,and biological spheres of the *** LULC map offers detail information for resources management and intergovernmental cooperation to debate global warming and biodiversity *** paper examined effects of pansharpening and atmospheric correction on LULC ***-based Support Vector Machine(OB-SVM)and Pixel-based Maximum Likelihood Classifier(PB-MLC)were applied for LULC *** showed that atmospheric correction is not necessary for LULC classification if it is conducted in the original multispectral ***,pansharpening plays much more important roles on the classification accuracy than the atmospheric *** can help to increase classification accuracy by 12%on average compared to the ones without ***-MLC and OB-SVM achieved similar classification *** study indicated that the LULC classification accuracy using PB-MLC and OB-SVM is 82%and 89%respectively.A combination of atmospheric correction,pansharpening,and OB-SVM could offer promising LULC maps from WorldView-2 multispectral and panchromatic images.

关键词： LULC Remote sensing object-based image analysis Pixel-based image analysis Maximum likelihood classifier(MLC) Support vector machine(SVM)

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