Spatially Aware Stable Learning for Single-Domain Generalizable Remote Sensing Object Detection

Abstract

Single-domain generalization object detection (S-DGOD) in remote sensing trains a detector using labeled data from a single source domain and deploys it to unseen domains, where performance often drops under cross-region distribution shift. Existing methods mainly enforce domain-invariant constraints or enlarge source diversity, but they rarely model object-background co-occurrences that become shortcuts when regional context changes. This letter proposes a stability-enhanced S-DGOD framework that integrates spatially aware stable learning with a perturbation-and-averaging training mechanism. The spatially aware stable learning (SASL) module constructs paired object and surrounding-background features for each RoI and learns instance weights to reduce dependencies within object features and between object and background features, thereby suppressing region-specific shortcut correlations. To stabilize the high-variance reweighting process, the QAT-driven perturbation and ensemble (QPE) mechanism uses quantization-aware training as a structured training-time perturbation to explore neighboring decorrelated solutions, and then applies stochastic weight averaging (SWA) to aggregate them into a single inference-time detector. Experiments on the real-world distribution shift (RWDS) dataset show consistent gains over representative S-DGOD baselines; the proposed method improves the average harmonic mean by 2.3 mAP50 over the best competing method on RWDS-FR. Codes are available at https://github.com/JunhongLu0704/SASL-DGOD