Adaptive MAP sub-pixel mapping model based on regularization curve for multiple shifted hyperspectral imagery |
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| Institution: | 1. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, PR China;2. College of Surveying and Geo-Informatics, Tongji University, PR China;1. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, PR China;2. Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong;3. Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, PR China;1. Jiangsu Key Laboratory of Resources and Environment Information Engineering, China University of Mining and Technology, Xuzhou, China;2. Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-Sen University, Guangzhou, China;3. Key Laboratory for Satellite Mapping Technology and Applications of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing, China;1. Center for Brain-like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2. Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing 100101, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China;4. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;1. State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, PR China;2. School of Computer Engineering, Nanyang Technological University, Singapore;3. College of Computer Science and Software Engineering, Shenzhen University, PR China;1. State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China;2. University of Chinese Academy of Sciences, Beijing, 100049, China;3. Soil Geography and Landscape Group, Wageningen University, PO Box 47, 6700 AA, Wageningen, The Netherlands;4. ISRIC ? World Soil Information, PO Box 353, 6700 AJ, Wageningen, The Netherlands |
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| Abstract: | Sub-pixel mapping is a promising technique for producing a spatial distribution map of different categories at the sub-pixel scale by using the fractional abundance image as the input. The traditional sub-pixel mapping algorithms based on single images often have uncertainty due to insufficient constraint of the sub-pixel land-cover patterns within the low-resolution pixels. To improve the sub-pixel mapping accuracy, sub-pixel mapping algorithms based on auxiliary datasets, e.g., multiple shifted images, have been designed, and the maximum a posteriori (MAP) model has been successfully applied to solve the ill-posed sub-pixel mapping problem. However, the regularization parameter is difficult to set properly. In this paper, to avoid a manually defined regularization parameter, and to utilize the complementary information, a novel adaptive MAP sub-pixel mapping model based on regularization curve, namely AMMSSM, is proposed for hyperspectral remote sensing imagery. In AMMSSM, a regularization curve which includes an L-curve or U-curve method is utilized to adaptively select the regularization parameter. In addition, to take the influence of the sub-pixel spatial information into account, three class determination strategies based on a spatial attraction model, a class determination strategy, and a winner-takes-all method are utilized to obtain the final sub-pixel mapping result. The proposed method was applied to three synthetic images and one real hyperspectral image. The experimental results confirm that the AMMSSM algorithm is an effective option for sub-pixel mapping, compared with the traditional sub-pixel mapping method based on a single image and the latest sub-pixel mapping methods based on multiple shifted images. |
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| Keywords: | Hyperspectral image Sub-pixel mapping Multiple shifted images Maximum a posteriori (MAP) L-curve U-curve |
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