detection based on superpixel segmentation and CRF for high-resolution remote sensing images

doi:10.13474/j.cnki.11-2246.2022.0038

Abstract

Abstract: High-resolution remote sensing image change detection algorithms are too sensitive to spectral changes, a new algorithm based on super-pixel segmentation and improved conditional random field (CRF) is proposed in this paper to detect changes in remote sensing images. Firstly, the student's-t mixture model (SMM) with spatial constraints super-pixel segmentation model is used to generate homogeneous patches,which has characteristic of boundary adhesion and brightness uniformity. Then the algorithm obtains change amplitude image by calculating the feature difference between the segmented dual phase image patches. Finally, the change amplitude image is clustered by FCM, and clustering result is used as the first-order potential of CRF, and the spectral spatial similarity constrained function is used as the second-order potential of CRF. Experiments results show that the detection accuracy of the method proposed in this paper is improved by 5%, and the false detection rate and missed detection rate are reduced by 3%, this method can better handle the spectral changes of the input image and keep the edge details of the change detection results.

Key words: change detection, super-pixel segmentation, conditional random field, spectral-spatial constraints, edge keeping

CLC Number:

P237

YANG Jingyu, JIA Duoke, WANG Yangping. detection based on superpixel segmentation and CRF for high-resolution remote sensing images[J]. Bulletin of Surveying and Mapping, 2022, 0(2): 25-30,49.

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