A Hierarchical Local-Sparse Model for Semantic Change Detection in Remote Sensing Imagery

Abstract

In response to the existing challenges in semantic change detection (SCD) for remote sensing images, such as weak spatiotemporal correlation and insufficient utilization of local neighborhood information, this article proposes a SCD network based on hierarchical local-sparse attention (HLSNet). The network combines a fully convolutional network with a deep transformer structure to leverage the advantages of local feature extraction and long-range information connection. Next, a hierarchical local-sparse attention is proposed to exploit the neighborhood characteristics of target pixels using a dual-window attention mechanism, the aim is to increase the receptive field while minimizing the interference of redundant information. By focusing on all tokens within a smaller window and dynamically selecting key tokens within a larger window for attention calculation, this two-tiered attention approach allows the model to handle details while capturing broader contextual information. The small window provides tightly related local information, while the larger window offers relevant but potentially more distant information, achieving a hierarchical processing of information from local to long-range. In order to facilitate more comprehensive interaction between the features of pre- and postchange images, each transformer block in the network employs a strategy of concatenating self-attention and cross attention. This approach better captures the spatiotemporal correlations and feature integration, thus achieving efficient and precise change detection. HLSNet achieves the highest accuracy on the two commonly used SCD datasets, SECOND, and Landsat-SCD, with ${{F}_{\text {scd}}}$ values reaching 62.53% and 91.67%, respectively.