A Self-Supervised Approach to Pixel-Level Change Detection in Bi-Temporal RS Images

Deep-learning techniques have achieved great success in remote-sensing image change detection. Most of them are supervised techniques, which usually require large amounts of training data and are limited to a particular application. Self-supervised methods solve these problems and are widely used in unsupervised binary change detection tasks. However, the existing self-supervised methods in change detection are suboptimal for pixel-wise change detection tasks. In this work, a pixel-wise contrastive approach is proposed to overcome this limitation. This is achieved by using contrastive loss in superpixel-level features on an unlabeled multiview setting. In this approach, a pseudo-Siamese network is trained to obtain pixel-wise representations and to align features from shifted image pairs. The final binary change map is obtained by using thresholding methods on learned temporal features. To overcome the season-related noise in binary change maps, we also used an uncertainty method to enhance the temporal robustness of the proposed approach. Two homogeneous (OSCD and MUDS) datasets and one heterogeneous (California Flood) dataset are used to evaluate the performance of the proposed approach. Results demonstrate improvements in both efficiency and accuracy over the patch-wise multiview contrastive method.