Multispectral image change detection based on spatial context and slow feature analysis

doi:10.13474/j.cnki.11-2246.2022.0322

Abstract

Abstract: To improve the accuracy of multispectral change detection, a method combining spatial context and slow feature analysis is proposed. First, an adaptive spatial context extraction algorithm is used to construct an adaptive region around the pixel to explore the context information around the pixel. Then through the iterative slow feature analysis, the change intensity between paired pixels is quantitatively calculated from the paired adaptive region around the corresponding pixel. The separability of the changed area and the unchanged area is enhanced. Finally, the change intensity image is generated, and the Otsu threshold method is used for binary classification, and the change intensity map is divided into binary change detection maps. Experiments use images from the Landsat 7 satellite TM sensor to compare with four algebra-based and transformation-based methods. The results demonstrate that the method in this paper performs better in terms of reducing omission errors and improving recall rate.

Key words: multispectral image, change detection, adaptive spatial context, iterative slow feature analysis

CLC Number:

P237

WANG Xiaowen, DAI Chenguang, ZHANG Zhenchao, JI Hongliang. Multispectral image change detection based on spatial context and slow feature analysis[J]. Bulletin of Surveying and Mapping, 2022, 0(11): 39-43.

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