Journal
MULTIMEDIA TOOLS AND APPLICATIONS
Volume 76, Issue 19, Pages 20081-20098Publisher
SPRINGER
DOI: 10.1007/s11042-017-4354-1
Keywords
Remote sensing; Change detection; Fuzzy c-means algorithm; Local information; Spatial attraction; Global information
Categories
Funding
- Natural Science Foundation of Jiangsu Province [BK20160248]
- China Postdoctoral Science Foundation
- Fundamental Research Funds for the Central Universities [2015XKQY09]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
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This paper presents a novel fuzzy c-means (FCM) clustering simultaneously incorporating local and global information (FLGICM) method to unsupervised change detection (CD) from remotely sensed images. A new factor including three local, global and edge parameters is added into the conventional FCM to enhance the insensitivity to noise and preserve detailed features. The spatial attraction between the central pixel and its neighborhood pixels is incorporated as a local parameter to utilize spatial information. A global parameter designed based on the estimated mean values of changed and unchanged pixels is introduced into the new factor to enhance its robustness and ability of separating changed from unchanged pixels. In addition, an edge parameter is also added to remain accurate edges and change details. Two experiments were carried out on Landsat images to test the performance of FLGICM. Experimental results indicate that FLGICM always achieves high accuracy and overperforms some state-of-the-art CD methods. Therefore, the proposed FLGIC provides an effective unsupervised CD method.
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