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This paper introduces ASFR-Net, an end-to-end adversarial spatio-frequency refinement network designed to improve heterogeneous change detection in remote sensing imagery by effectively separating genuine land-cover changes from modal disparities. The methodology includes a modality-invariant representation learner to extract consistent features across different imaging modalities, followed by a spatio-frequency synergistic enhancement module that mitigates sensor-specific noise. Extensive experiments on the newly released VisNIR-HCD dataset and other public datasets demonstrate that ASFR-Net achieves state-of-the-art performance, significantly surpassing existing methods in accuracy.
ASFR-Net not only bridges the gap between different imaging modalities but also sets a new benchmark for heterogeneous change detection with its innovative spatio-frequency processing.
The core challenge of heterogeneous change detection in remote sensing imagery lies in effectively decoupling genuine land-cover changes from significant modal disparities caused by distinct imaging mechanisms. These intrinsic inconsistencies are prone to introducing pseudo-changes, thereby constraining detection accuracy. To address this, we propose a novel, end-to-end adversarial spatio-frequency refinement network (ASFR-Net). Initially, a modality-invariant representation learner (MIR-Learner) guides the backbone to extract modality-invariant features, effectively bridging the primary domain gap. Subsequently, to address persistent residual modal differences, we design an innovative spatio-frequency synergistic enhancement module (SFEM), which identifies and suppresses sensor-specific noise and artifacts that are difficult to discern in the spatial domain by leveraging frequency-domain processing. Multi-level difference features are then computed from these refined representations and fed into a decoder equipped with cascaded hierarchical guided fusion module (HGFM) blocks to generate precise change maps. To alleviate the data scarcity in heterogeneous tasks, we construct and release a new high-resolution benchmark specifically focused on building changes: the visible-near-infrared heterogeneous change detection (VisNIR-HCD) dataset. It presents unique scientific challenges arising from deceptive visual similarity and non-linear spectral inversions, providing a robust platform for evaluating model generalization. Extensive experiments on VisNIR-HCD and public datasets demonstrate that ASFR-Net achieves state-of-the-art (SOTA) performance, significantly outperforming existing methods. The source code and the VisNIR-HCD dataset are publicly available at https://github.com/LuoYang2024/ASFR-Net.
