Change detection in remote sensing images combined with intuitionistic fuzzy clustering and change vector analysis

Aiming at the problems of multi-temporal remote sensing images change detection with data uncertainty and low detection accuracy, a multi-temporal remote sensing images change detection method combined with change vector analysis (CVA) and intuitionistic fuzzy C-means clustering algorithm (IFCM) is proposed. Firstly, the difference image of bi-temporal remote sensing images is obtained by change vector analysis method. Then the difference image is clustered by the intuitionistic fuzzy C-means clustering algorithm to obtain the change areas and the non-change areas. Finally, the change detection results are binarized and the accuracy is evaluated. The bi-temporal Gaofeng-1 remote sensing images and Szada image data sets were selected as experimental data. The experimental results show that the proposed method can effectively solve the data uncertainty problem existing in the traditional method, it is a feasible remote sensing images change detection method. The overall accuracy of change detection achieved 95.92% and 92.70%. The research results can be used for forest dynamic change monitoring, land reclamation utilization planning change analysis and damage assessment.