Feature-Aligned and Frequency-Aware Network for High-Resolution Remote Sensing Change Detection
Abstract
Current spatiotemporal modeling approaches in remote sensing change detection (RSCD) often struggle to distinguish phenological pseudo-changes from semantic changes, leading to false alarms and blurred boundaries. To address this, a feature-aligned and frequency-aware network (FACDNet) is proposed based on a physics-inspired decomposition strategy. It consists of three main modules: feature distribution alignment module (FDAM), frequency-domain adaptive filtering module (FAFM), and change attention guidance module (CAGM). Specifically, FDAM is first integrated within the Siamese encoder and employs a statistical feature alignment mechanism to reduce style-sensitive feature distribution discrepancies. Subsequently, FAFM leverages the wavelet transform to adaptively suppress low-frequency environmental noise while amplifying high-frequency boundary signals, thereby enhancing boundary-aware change cues under background interference. Finally, CAGM adaptively fuses spatial and frequency-domain evidence through an attention-guided gating scheme to support reliable change detection. Experiments on three benchmarks show that FACDNet leads on LEVIR-CD and WHU-CD and remains competitive on SYSU-CD, demonstrating robustness to radiometric inconsistencies.