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Deep learning-based object detectors in remote sensing (RS) scenarios typically follow the paradigm of pretraining and fine-tuning to alleviate the limitation of insufficient downstream data. Despite the improved performance, existing pretraining paradigms are suboptimal due to three deficiencies: 1) inconsistent domains, i.e., pretraining on natural scenes and fine-tuning for RS scenes; 2) mismatched task objectives, i.e., classification-oriented pretraining while detection-oriented fine-tuning; and 3) misaligned architectures, i.e., pretraining only one bare backbone yet neglecting other vital detection components. Against these issues, this article proposes a novel pretraining paradigm specifically for the task of RS object detection, namely, RS strong-classification weak-localization (SCWL) pretraining. Unlike conventional classification pretraining, such as the widely used ImageNet pretraining, our pretraining strategy …