SDMNet: A Deep-Supervised Dual Discriminative Metric Network for Change Detection in High-Resolution Remote Sensing Images
Xi Li, Li Yan, Yi Zhang, Nan Mo
Abstract
High-resolution remote sensing image change detection (CD) is one of the main methods to analyze land surface changes. How to effectively distinguish interesting changes and pseudochanges in high-resolution remote sensing images and form accurate and robust CD results is crucial. To deal with these problems, in this letter, we propose a deep-supervised dual discriminative metric network (SDMNet) trained end-to-end for CD in high-resolution bitemporal remote sensing images. A discriminative decoder network is designed in SDMNet to aggregate global and multiscale contextual information, utilizing high stage features to guide the selection of low stage features stage-by-stage to obtain more consistent and robust features. A discriminative implicit metric module is designed in SDMNet to measure the distance between features to detect changes and utilize batch-balanced contrastive loss (BCL) to enlarge the distance difference between unchanged pairs and changed pairs, while alleviate the problem of sample imbalance, and multiple change graph losses are introduced in the intermediate layer of the network for deep supervision. Quantitative evaluation of our method on the CD datasets SYSU, DSIFN, and CLCD demonstrates that the proposed method can provide superior performance than the other state-of-the-art methods.