FSG-Net: Frequency-Spatial Synergistic Gated Network for High-Resolution
  Remote Sensing Change Detection
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.
68cbb3b45a7803ff3be42ec2
Zhongxiang Xie
claimed_verified
2025-09-18T13:25:51.321Z
68cbaffdbba5bcf511763e56
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/FWcmzd2m3h4NXZp0VYenS.png
Zhongxiang Xie
user
zx-Xie
68cbb3b45a7803ff3be42ec3
Shuangxi Miao
68cbb3b45a7803ff3be42ec4
Yuhan Jiang
68cbb3b45a7803ff3be42ec5
Zhewei Zhang
68cbb3b45a7803ff3be42ec6
Jing Yao
68cbb3b45a7803ff3be42ec7
Xuecao Li
68cbb3b45a7803ff3be42ec8
Jianxi Huang
68cbb3b45a7803ff3be42ec9
Pedram Ghamisi
admin_assigned
2025-09-19T10:27:01.051Z
65783559dea7e2122d05a773
https://cdn-avatars.huggingface.co/v1/production/uploads/65783559dea7e2122d05a773/etMIinzsmbxT9EqO6WpkS.png
Pedram Ghamisi
user
pedramghamisi
FSG-Net: Frequency-Spatial Synergistic Gated Network for High-Resolution
  Remote Sensing Change Detection
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.
FSG-Net addresses false alarms and semantic gaps in change detection by using a frequency-spatial synergistic approach with wavelet interaction, attention mechanisms, and gated fusion.
Frequency-Spatial Synergistic Gated Network FSG-Net Discrepancy-Aware Wavelet Interaction Module DAWIM Synergistic Temporal-Spatial Attention Module STSAM Lightweight Gated Fusion Unit LGFU change detection high-resolution remote sensing images Earth observation false alarms semantic gap high-level semantics shallow layers CDD GZ-CD LEVIR-CD F1-scores