遥感小样本目标检测研究进展与展望OA

This paper systematically summarizes recent advances of few-shot object detection in optical remote sensing images,and analyzes key challenges and corresponding solutions in the field.Few-shot object detection in remote sensing images aims to accurately detect novel object categories using only a few annotated samples,making it suitable for data-scarce scenarios in remote sensing images.The paper outlines the fundamental definition and framework of few-shot object detection in remote sensing images,and addresses six major challenges:scale variation,shape diversity,background interference,class imbalance,incomplete annotation,and orientation uncertainty.Various deep learning-based methods is reviewed,including multi-scale feature fusion,intra/inter-class variation handling,background suppression,and Pseudo-label generation strategy.Commonly used datasets and evaluation metrics are introduced,and representative algorithms are compared to identify core techniques affecting performance.Finally,future research directions,such as self-supervised learning,multimodal fusion,and incremental learning,are discussed,providing valuable references for research on few-shot object detection in remote sensing images.