Remote sensing image change detection based on improved interval type-2 fuzzy clustering

doi:10.13474/j.cnki.11-2246.2021.0207

Abstract

Abstract: The complex fuzziness of remote sensing image can interfere with the result of image change detection so that the interval binary fuzzy C-means clustering algorithm is introduced to solve the problem. But the randomness of algorithm parameters will affect the accuracy of change detection. In this paper, the candidate solution of firefly algorithm is optimized by using local optimal solution, and the variable step size factor is introduced, so as to find the fuzzy factors of interval type-2 fuzzy C-means clustering algorithm adaptively. The interval type-2 fuzzy C-means clustering is carried out combining with the fuzzy factors obtained by optimization and the image change information is extracted by iteratively updating the membership degree. Finally, the weighted Karnik-mendel algorithm based on compound trapezoid rule is used to reduce type and resolve fuzzy to optimize clustering centre. And the change types are determined according to the principle of maximum membership. Through experimental verification, the method in this paper obtains the better fuzzy factors and more accurate clustering centers, has better robustness, and improves the change detection accuracy, and the detected change area is more elaborate.

Key words: remote sensing image change detection, firefly algorithm, interval type-2 fuzzy C-means clustering, fuzzy factors, type-reduction algorithm

CLC Number:

P237

SU Yifan, DANG Jianwu, WANG Yangping, YANG Jingyu. Remote sensing image change detection based on improved interval type-2 fuzzy clustering[J]. Bulletin of Surveying and Mapping, 2021, 0(7): 44-51,58.

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