STMINet: Spatio-Temporal Multigranularity Intermingling Network for Remote Sensing Change Detection

Abstract

Change detection (CD) in optical remote sensing images has advanced significantly with the adoption of deep learning. However, CD inherently faces two challenges: 1) varying sizes and shapes of change regions arising from spatial complexity; and 2) pseudo-changes caused by temporal migrations, such as seasonal variations and lighting differences. Existing methods typically focus on single-grained information during the feature interaction stage, which limits their ability to perceive changes in features of varying sizes. In addition, these methods inadequately address ambiguous regions during the difference capture stage, making them highly susceptible to pseudo-change interference. To address these challenges, we propose a novel network called the spatio-temporal multigranularity intermingling network (STMINet). First, we introduce the spatio-temporal multigranularity interleaving module to capture multigranularity information across both time and space, greatly enhancing the detection of changes in features of varying sizes and shapes. Second, we propose the multibranch differential acquisition, which incorporates information from inconspicuous regions and mitigates pseudo-change interference through a three-branch design. Experimental results on four publicly available datasets (learning, vision, and remote sensing-CD, Guangzhou dataset-CD, Wuhan university-CD, and Sun Yat-Sen university dataset-CD) demonstrate that STMINet significantly outperforms state-of-the-art methods in performance metrics and visualization, achieving F1 score improvements of 0.22–3.42% over existing approaches while employing a simple ResNet-18 backbone.