Frequency-Spatial Dual-Level Selective Alignment for Domain-Adaptive Object Detection in Remote Sensing

Remote sensing object detection suffers from severe performance degradation under cross-domain transfer, where domain gaps arise from differences in spectral response, spatial resolution, and viewing geometry. Most existing unsupervised domain-adaptive object detection (DAOD) methods pursue cross-domain invariance through feature distribution alignment but encounter two limitations specific to remote sensing: cross-domain appearance variation, where differences in imaging conditions produce divergent visual appearances, and foreground-background imbalance, where targets are sparsely distributed across vast backgrounds and alignment is dominated by background statistics. To address these two limitations, frequency-spatial dual-level selective alignment (FS²A), a framework with two complementary modules, is proposed. At the frequency level, scale-class-conditioned frequency modulation (SCFM) decomposes multiscale features via FFT and selectively modulates the low-frequency amplitude conditioned on scale level and categorical composition, restricting adversarial alignment to domain-variant spectral components while preserving the domain-invariant phase spectrum. At the spatial level, kernel relational distillation (KRD) distills pairwise relational structure from a frozen satellite-pretrained vision foundation model (VFM) in polynomial kernel space, where polynomial kernel functions preferentially concentrate alignment on foreground feature pairs over weakly correlated background pairs. Both modules are decoupled from the detection forward pass, introducing no additional inference cost. Experiments on two cross-domain remote sensing benchmarks demonstrate that FS²A achieves 67.6% mAP50 on xView → DOTA, surpassing the state-of-the-art by 2.7%, and competitive results on satellite-to-UAV benchmarks.