Remote sensing change detection (RSCD) is crucial for our understanding of the dynamic pattern of the Earth’s surface and human influence. Recently, transformer-based methodologies have advanced from their powerful global modeling capabilities in RSCD tasks. Nevertheless, they remain under excessive parameterization, which continues to be severely constrained by time and computation resources. Here, we present a transformer-based RSCD model called the Segmentation Multi-Branch Change Detection Network (SMBCNet). Our proposed approach combines a hierarchically structured transformer encoder with a cross-scale enhancement module (CEM) to extract global information with lower complexity. To account for the diverse nature of changes, we introduce a plug-and-play multi-branch change fusion module (MCFM) that integrates temporal features. Within this module, we transform the change detection task into a semantic segmentation problem. Moreover, we identify the Temporal Feature Aggregation Module (TFAM) to facilitate integrating features from diverse spatial scales. These results demonstrate that semantic segmentation is an effective solution to change detection (CD) problems in remote sensing images.

Building change detection (BCD) is a widely used method for monitoring human activities. Despite advancements in deep learning (DL) in computer vision, recent DL-based BCD methods still face challenges in extracting discriminative features due to irrelevant noisy changes and the lack of consideration for specific semantic information related to changes. To address this limitation, we propose the Semantic-Guided Network (SGNet), a multi-scale pure Transformer-based building change detection method that delves into more discriminative class-specific feature representations for changed buildings. Specifically, we first breakdown the change detection process into multiple stages: changed semantics extraction, enhancement, fusion, and decoding. Then, we analyze the nature of building change events in remote sensing images (RSIs) and categorize them into three main groups. To detect positive changes and reduce noise, we developed the Various Changes Extraction Module (VCEM). Additionally, we introduce the semantic-guided Changed-Semantics Augmentation Module (CSAM) to enhance the semantic information of target changes while suppressing irrelevant regions or objects. We also propose the Various Changes Fusion Module (VCFM) to model semantic latency in bi-temporal RSIs. Extensive experiments on two building change datasets, i.e., LEVIR-CD and WHU-CD demonstrate that our SGNet(MiT-b0) outperforms the state-of-the-art baselines and achieves F1 score of 90.99% and 92.47%, respectively, with improvements of 1.9% and 7.89%, respectively, highlighting the exceptional sample-efficient learning capability. The source code will be released at https://github.com/Anderien/official-SGNet