A change detection method for multisource heterogeneous images using a refined hierarchical clustering approach

For change detection in multi-source heterogeneous images, this paper proposes a change detection method using a refined hierarchical clustering approach. For multi-source heterogeneous multi-temporal images, Stacked Denoising Autoencoders (SDAE) is used to extract deep features from multi-source heterogeneous images. On this basis, the comparability of heterogeneous data in deep feature space is guaranteed by iterative transformation of features. Finally, the correlation between deep features of heterogeneous data is described by introducing a variety of distance measures, and the hierarchical clustering method is improved. The classification of change types is gradually realized through multiple clustering, while improving the accuracy of change detection.