Sub-pixel land-cover mapping with improved fraction images upon multiple-point simulation |
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| Institution: | 1. Department of Geoinformatics, Central University of Jharkhand, Ranchi 835205, India;2. Department of Environmental Sciences, Central University of Jharkhand, Ranchi 835205, India;3. Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi 221005, India;4. IUCN Commission of Ecosystem Management, South Asia;1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China;2. Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Fudan University, China;3. Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA;4. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, China;5. Program in Public Health, Susan and Henry Samueli College of Health Sciences, University of California, Irvine, CA 92697, United States;1. School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China;2. Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation, Wuhan University, 129 Luoyu Road, Wuhan 430079, China;3. Reed Elsevier Information Technology (Beijing) Co., Ltd., Beijing, China;4. State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China;5. Sustainable Soils and Grassland Systems, Rothamsted Research, North Wyke, Okehampton, UK |
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| Abstract: | Outputs of soft classification inherently contain uncertainty. As an input for the sub-pixel mapping (SPM) method, the uncertainty is propagated to SPM result especially the boundary region between classes. Therefore, reducing the uncertainty within the outputs of soft classification is worth exploring. This paper firstly utilizes multiple-point simulation (MPS) through training images for characterizing the spatial structural properties of a surface object/class. Consequently, MPS results are used to increase the accuracy of the fraction image of the surface object/class. The improved fraction image then inputs to the SPM method for producing the land cover map with finer spatial resolution. In order to validate the proposed method, a remotely sensed image from Landsat TM 30 m over the Qianyanzhou red earth hill region in China is used. This experimental study not only compares the results from SPM with improved fraction images with MPS and results from SPM with original fraction images, but also investigates the performances of different soft classifiers. It has been demonstrated that this proposed method is an effective way to reduce the uncertainty in outputs of different soft classification, increase the recognition accuracies of boundary regions and thus increase the accuracies of SPM simulated images. |
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| Keywords: | Soft classification Uncertainty Sub-pixel mapping Multiple-point simulation |
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