A Lightweight Dual-Branch Network for Building Change Detection in Remote Sensing Images Integrating Cross-Scale Coupling and Boundary Constraint

Capturing spatial details and contextual semantics is crucial for building change detection (BCD) in remote sensing images. However, achieving both aspects within traditional single-branch feature extraction networks faces challenges in computation costs and model sizes. To tackle these challenges, this article introduces a lightweight neural model tailored for BCD in remote sensing images. Its primary contribution lies in the design of a lightweight dual branch for efficient feature extraction, a cross-scale coupling module (CSCM) for effective multiscale feature enhancement, and a boundary constraint module (BCM) for edge details compensation. Specifically, the lightweight dual branch can efficiently extract spatial details and contextual semantics in two independent branches, thereby generating changed detail-semantics feature maps. The CSCM further enriches the semantic and scale representation ability of changed feature maps, by adapting to the multiscale characteristics of changed buildings. In addition, the BCM can improve the model's sensitivity to the boundaries of changed buildings, mitigating the loss of edge information caused by convolution and pooling operations. As a result, the fine-grained and high-level semantic feature maps for BCD are obtained. We comprehensively evaluate the effectiveness of our proposed method against numerous state-of-the-art lightweight and non lightweight change detection models on the LEVIR and WHU datasets. The results demonstrate that our method not only achieves remarkable accuracy but also stands out in efficiency.