S <sup>3</sup> Net: Superpixel-Guided Self-Supervised Learning Network for Multitemporal Image Change Detection
Tao Zhan, Maoguo Gong, Xiangming Jiang, Erlei Zhang
Abstract
Deep learning (DL) have recently achieved outstanding performance in change detection of multitemporal images. However, most existing DL-based change detection methods still suffer from the problem of insufficient labeled training samples. To overcome this limitation, an unsupervised superpixel-guided self-supervised learning network (S3Net) is proposed for detecting changes occurred on the land surface. By performing principal component analysis on two input images, a triple-channel pseudo-color image containing the main information of both images is first generated, which is used for superpixel segmentation to produce homogeneous image objects. Then, a siamese network composing of two identical subnetworks with shared weight based on transfer learning is trained for pretext task in a self-supervised learning way, aiming to obtain multiscale object-level spatial feature difference images. On this basis, a high-quality difference image is generated by incorporating the pixel-level and object-level difference information using a simple weighted fusion strategy, which can be analyzed by thresholding to produce the final binary change map. The experimental results on four real-world datasets from different sensors show that the proposed approach can obtain superior performance in comparison with several state-of-the-art change detection methods, which further demonstrates its effectiveness and practicability. We make our data and code publicly available (https://github.com/OMEGA-RS/S3Net_CD).