An Enhanced and Unsupervised Siamese Network With Superpixel-Guided Learning for Change Detection in Heterogeneous Remote Sensing Images

In this article, we consider the issue of change detection (CD) for heterogeneous remote sensing images. Existing deep learning-based methods for CD usually utilize square convolution receptive fields, which do not sufficiently exploit the contextual and boundary information in heterogeneous images. To address the aforementioned issue, we propose an enhanced and unsupervised Siamese superpixel-based network for CD in heterogeneous remote sensing images. Our newly proposed method innovatively combines superpixels with the square receptive fields to generate the boundary adherence receptive fields and better capture the contextual information than existing methods only with the regular square receptive fields. Furthermore, we utilize an adaptive superpixel merging module to prevent the oversegmentation of superpixels and strengthen the robustness of our method in terms of superpixel sizes. Experiments based on four real datasets demonstrate that the proposed method achieves higher accuracy than other commonly used CD methods in heterogeneous remote sensing images.