计算机工程与应用2026，Vol.62Issue(16)：21-41,21.DOI:10.3778/j.issn.1002-8331.2510-0057
遥感小样本目标检测研究进展与展望
Research Progress and Prospects of Few-Shot Object Detection in Remote Sensing Images
摘要
Abstract
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.关键词
遥感图像/小样本目标检测/深度学习/多尺度特征融合/自监督学习Key words
remote sensing images/few-shot object detection/deep learning/multi-scale feature fusion/self-supervised learning分类
信息技术与安全科学引用本文复制引用
高广帅,张芝琳,董燕..遥感小样本目标检测研究进展与展望[J].计算机工程与应用,2026,62(16):21-41,21.基金项目
国家自然科学基金(62301623) （62301623）
河南省重点研发专项(241111220700). （241111220700）
