HeteCD: Feature Consistency Alignment and difference mining for heterogeneous remote sensing image change detection

Abstract

Optical change detection is limited by imaging conditions, hindering real-time applications. Synthetic Aperture Radar (SAR) overcomes these limitations by penetrating clouds and being unaffected by lighting, enabling all-weather monitoring when combined with optical data. However, existing heterogeneous change detection datasets lack complexity, focusing on single-scene targets. To address this gap, we introduce the XiongAn dataset, a novel urban architectural change dataset designed to advance heterogeneous change detection research. Furthermore, we propose HeteCD, a fully supervised heterogeneous change detection framework. HeteCD employs a Siamese Transformer architecture with non-shared weights to effectively model heterogeneous feature spaces and includes a Feature Consistency Alignment (FCA) loss to harmonize distributions and ensure class consistency across bi-temporal images. Additionally, a 3D Spatio-temporal Attention Difference module is incorporated to extract highly discriminative difference information from bi-temporal features. Extensive experiments on the XiongAn dataset demonstrate that HeteCD achieves a superior IoU of 67.50%, outperforming previous state-of-the-art methods by 1.31%. The code will be available at https://github.com/weiAI1996/HeteCD.