A multidimensional scaling optimization and fusion approach for the unsupervised change detection problem in remote sensing images

2016 ◽  
Author(s):  
Redha Touati ◽  
Max Mignotte
Keyword(s):  
Remote Sensing ◽  
Multidimensional Scaling ◽  
Change Detection ◽  
Remote Sensing Images ◽  
Detection Problem ◽  
Fusion Approach

scholarly journals Unsupervised Change Detection Using Fuzzy Topology-Based Majority Voting

2021 ◽  
Vol 13 (16) ◽  
pp. 3171
Author(s):  
Pan Shao ◽  
Wenzhong Shi ◽  
Zhewei Liu ◽  
Ting Dong
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Majority Voting ◽  
Fuzzy Topology ◽  
Remote Sensing Images ◽  
Fuzzy Topological Space ◽  
Multispectral Remote Sensing ◽  
Effectiveness And Efficiency ◽  
Difference Images ◽  
Fusion Approach

Remote sensing change detection (CD) plays an important role in Earth observation. In this paper, we propose a novel fusion approach for unsupervised CD of multispectral remote sensing images, by introducing majority voting (MV) into fuzzy topological space (FTMV). The proposed FTMV approach consists of three principal stages: (1) the CD results of different difference images produced by the fuzzy C-means algorithm are combined using a modified MV, and an initial fusion CD map is obtained; (2) by using fuzzy topology theory, the initial fusion CD map is automatically partitioned into two parts: a weakly conflicting part and strongly conflicting part; (3) the weakly conflicting pixels that possess little or no conflict are assigned to the current class, while the pixel patterns with strong conflicts often misclassified are relabeled using the supported connectivity of fuzzy topology. FTMV can integrate the merits of different CD results and largely solve the conflicting problem during fusion. Experimental results on three real remote sensing images confirm the effectiveness and efficiency of the proposed method.

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