A Multi-Feature Fusion-Based Change Detection Method for Remote Sensing Images |
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| Authors: | Liping Cai Wenzhong Shi Ming Hao Hua Zhang Lipeng Gao |
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| Institution: | 1.School of Geography and Tourism,Qufu Normal University,Rizhao,China;2.Key Laboratory of Coastal Zone Exploitation and Protection,Ministry of Land and Resource,Nanjing,China;3.Department of Land Surveying and Geo-Informatics,The Hong Kong Polytechnic University,Hong Kong,China;4.School of Environment Science and Spatial Informatics,China University of Mining and Technology,Xuzhou,China;5.School of Remote Sensing and Information Engineering,Wuhan University,Wuhan,China |
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| Abstract: | An object-oriented change detection method for remote sensing images based on multiple features using a novel weighted fuzzy c-means (WFCM) method is presented. First, Gabor and Markov random field textures are extracted and added to the original images. Second, objects are obtained by using a watershed segmentation algorithm to segment the images. Third, simple threshold technology is applied to produce the initial change detection results. Finally, refining is conducted using WFCM with different feature weights identified by the Relief algorithm. Two satellite images are used to validate the proposed method. Experimental results show that the proposed method can reduce uncertainties involved in using a single feature or using equally weighted features, resulting in higher accuracy. |
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