Combining iterative slow feature analysis and deep feature learning for change detection in high-resolution remote sensing images

Junfeng Xv; Baoming Zhang; Haitao Guo; Jun Lu; Yuzhun Lin

doi:10.1117/1.JRS.13.024506

23 April 2019 Combining iterative slow feature analysis and deep feature learning for change detection in high-resolution remote sensing images

Junfeng Xv, Baoming Zhang, Haitao Guo, Jun Lu, Yuzhun Lin

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 13, Issue 2, 024506 (April 2019). https://doi.org/10.1117/1.JRS.13.024506

Abstract

In order to make full use of local neighborhood information for high-resolution remote sensing images, this study combined iterative slow feature analysis (ISFA) and stacked denoising autoencoder (SDAE) to improve the change detection precision. First, this approach introduced ISFA for initial change detection in an unsupervised way, which enlarged the separability of changed and unchanged areas. Then, by setting different membership degrees, the changed and unchanged samples were obtained through fuzzy-means clustering. Finally, the change model was built by SDAE to represent the local neighborhood features deeply, and the change detection result can be obtained after all the samples were fed into the model. Experiments were performed on three real datasets, and the results validated the effectiveness and superiority of the proposed approach.

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Junfeng Xv, Baoming Zhang, Haitao Guo, Jun Lu, and Yuzhun Lin "Combining iterative slow feature analysis and deep feature learning for change detection in high-resolution remote sensing images," Journal of Applied Remote Sensing 13(2), 024506 (23 April 2019). https://doi.org/10.1117/1.JRS.13.024506

Received: 7 October 2018; Accepted: 9 April 2019; Published: 23 April 2019

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