Revolutionizing Remote Sensing Image Analysis With BESSL-Net: A Boundary-Enhanced Semi-Supervised Learning Network

Deep learning (DL) has become increasingly popular in remote sensing (RS) change detection (CD), leading to the development of massive networks that surpass traditional methods in accuracy and automation. However, the need for enormous amounts of annotated data remains a major concern, and accurate boundary segmentation in RS images is challenging due to their complexity and heterogeneity. Moreover, properly aggregating the bitemporal feature pairs and creating highly discriminative change features are crucial for detection performance. This article proposes a boundary-enhanced semi-supervised network (BESSL-Net) to tackle these issues for CD tasks. The network adopts dual encoders and one decoder architecture for segmentation and incorporates pseudolabeling, contrastive learning (CL), along with a teacher–student scheme to leverage unlabeled data. A boundary extraction module (BEM) is used to conduct boundary segmentation, while a change segmentation feature learning module (SFLM) is used to create discriminative change features in both channel and spatial domains by integrating multilevel features. Three publicly available CD datasets are used to validate the proposed BESSL-Net. Compared with the current state-of-the-art networks, the semi-supervised network demonstrates advanced performance metrics, especially regarding IoU metric of change-class (IoUc), showing improvements ranging from 1% to 9%.