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Superpixel-based multiobjective change detection based on self-adaptive neighborhood-based binary differential evolution
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DOI：10.1016/j.eswa.2022.118811.png)
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Abstract
En 中文
With strong penetrability and high resolution, synthetic aperture radar (SAR) images have been widely used in remote sensing image change detection. With the essence of heuristics, metaheuristics are suitable for the needs of most real-life optimization problems according to the expected solution quality and allowable calculation time. To improve the accuracy of change detection, this paper proposes a novel efficient metaheuristic change detection procedure. More specifically, we develop a superpixel-based multiobjective change detection method based on superpixel-wise feature representation and self-adaptive neighborhood-based binary difference evolution. During the clustering for feature analysis, a multiobjective optimization problem (MOP) is modeled which take the likelihood and Bhattacharyya distance between changed and unchanged classes as two objective functions. To solve the MOP, this paper firstly applies the superpixel binary representation technique into the encoding process of multiobjective evolutionary algorithm so that the dimension of pixel coding space can be substantially reduced. Then a self-adaptive neighborhood-based binary differential evolution strategy is proposed to explore the optimal change detection strategy, where a novel mutation operator integrating neighborhood information is designed for the improvement of change detection performance. Experimental results on three real SAR datasets confirm that the proposed method can make a better performance on change detection and the metaheuristic algorithm has good convergence and stability.
Keywords:
Change detection
Evolutionary multiobjective optimization
Difference evolution
Superpixel segmentation
Heuristic search
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W