ASFR-Net: Adversarial Alignment and Spatio-Frequency Refinement Network for Heterogeneous Remote Sensing Image Change Detection

• URL: http://arxiv.org/abs/2607.07161v1
• Date: Wed, 08 Jul 2026 08:52:59 GMT
• Title: ASFR-Net: Adversarial Alignment and Spatio-Frequency Refinement Network for Heterogeneous Remote Sensing Image Change Detection
• Abstract summary: We propose a novel, end-to-end adversarial refinement-frequency network (ASFR-Net) for heterogeneous change detection in remote sensing imagery.<n>A learner (MIRLearn-er) guides the backbone to extract intrinsic modality-invariant features, effectively bridging the primary domain gap.<n>SFEM identifies and suppresses sensor-specific noise and artifacts that are difficult to discern in the spatial domain.<n>VisNIR-HCD dataset presents unique scientific challenges arising from deceptive visual similarity and non-linear spectral inversions.
• Score: 16.766607258562992
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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