Change detection for high-resolution remote sensing images is more and more widespread in the application of monitoring the Earth’s surface. However, on the one hand, the ground truth could facilitate the distinction between changed and unchanged areas, but it is hard to acquire them. On the other hand, due to the complexity of remote sensing images, it is difficult to extract features of difference, let alone the construction of the classification model that performs change detection based on the features of difference in each pixel pair. Aiming at these challenges, this paper proposes a weakly supervised change detection method based on edge mapping and Stacked Denoising Auto-Encoders (SDAE) network called EM-SDAE. We analyze the difference in edge maps of bi-temporal remote sensing images to acquire part of the ground truth at a relatively low cost. Moreover, we design a neural network based on SDAE with a deep structure, which extracts the features of difference so as to efficiently classify changed and unchanged regions after being trained with the ground truth. In our experiments, three real sets of high-resolution remote sensing images are employed to validate the high efficiency of our proposed method. The results show that accuracy can even reach up to 91.18% with our method. In particular, compared with the state-of-the-art work (e.g., IR-MAD, PCA-k-means, CaffeNet, USFA, and DSFA), it improves the Kappa coefficient by 27.19% on average.
Weakly Supervised Change Detection Based on Edge Mapping and SDAE Network in High-Resolution Remote Sensing Images
