FSG-Net: Frequency-Spatial Synergistic Gated Network for High-Resolution
  Remote Sensing Change Detection
Authors: name: Zhongxiang Xie, name: Shuangxi Miao, name: Yuhan Jiang, name: Zhewei Zhang, name: Jing Yao, name: Xuecao Li, name: Jianxi Huang, name: Pedram Ghamisi

Change detection from high-resolution remote sensing images lies as a
cornerstone of Earth observation applications, yet its efficacy is often
compromised by two critical challenges. First, false alarms are prevalent as
models misinterpret radiometric variations from temporal shifts (e.g.,
illumination, season) as genuine changes. Second, a non-negligible semantic gap
between deep abstract features and shallow detail-rich features tends to
obstruct their effective fusion, culminating in poorly delineated boundaries.
To step further in addressing these issues, we propose the Frequency-Spatial
Synergistic Gated Network (FSG-Net), a novel paradigm that aims to
systematically disentangle semantic changes from nuisance variations.
Specifically, FSG-Net first operates in the frequency domain, where a
Discrepancy-Aware Wavelet Interaction Module (DAWIM) adaptively mitigates
pseudo-changes by discerningly processing different frequency components.
Subsequently, the refined features are enhanced in the spatial domain by a
Synergistic Temporal-Spatial Attention Module (STSAM), which amplifies the
saliency of genuine change regions. To finally bridge the semantic gap, a
Lightweight Gated Fusion Unit (LGFU) leverages high-level semantics to
selectively gate and integrate crucial details from shallow layers.
Comprehensive experiments on the CDD, GZ-CD, and LEVIR-CD benchmarks validate
the superiority of FSG-Net, establishing a new state-of-the-art with F1-scores
of 94.16%, 89.51%, and 91.27%, respectively. The code will be made available at
https://github.com/zxXie-Air/FSG-Net after a possible publication.