FSG-Net：面向高分辨率遙感變化檢測的頻率-空間協同門控網絡

FSG-Net: Frequency-Spatial Synergistic Gated Network for High-Resolution Remote Sensing Change Detection

September 8, 2025

作者: Zhongxiang Xie, Shuangxi Miao, Yuhan Jiang, Zhewei Zhang, Jing Yao, Xuecao Li, Jianxi Huang, Pedram Ghamisi

cs.AI

摘要

高分辨率遙感影像的變化檢測是地球觀測應用的基石，但其效能常受兩大關鍵挑戰所限。首先，模型易將時間變化（如光照、季節）引起的輻射差異誤解為真實變化，導致誤報頻發。其次，深度抽象特徵與淺層細節豐富特徵之間存在不可忽視的語義鴻溝，阻礙了它們的有效融合，最終導致邊界劃分不清。為進一步解決這些問題，我們提出了頻率-空間協同門控網絡（FSG-Net），這是一種旨在系統性區分語義變化與干擾變化的新範式。具體而言，FSG-Net首先在頻域中運作，通過差異感知小波交互模塊（DAWIM）有選擇性地處理不同頻率成分，從而自適應地抑制偽變化。隨後，在空間域中，協同時空注意力模塊（STSAM）增強了真實變化區域的顯著性，進一步精煉特徵。最後，輕量級門控融合單元（LGFU）利用高層語義有選擇性地門控並整合來自淺層的關鍵細節，以彌合語義鴻溝。在CDD、GZ-CD和LEVIR-CD基準上的全面實驗驗證了FSG-Net的優越性，分別以94.16%、89.51%和91.27%的F1分數建立了新的技術標準。代碼將在可能發表後於https://github.com/zxXie-Air/FSG-Net公開。

English

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.