遥感影像小目标检测方法与应用

doi:10.3969/j.issn.0255-8297.2026.04.006

摘要/Abstract

摘要： 遥感影像小目标检测是遥感与计算机视觉领域的核心问题之一，对遥感场景智能理解、遥感影像智能解译等具有关键作用。受目标像素占比低、场景复杂多变等因素影响，遥感影像小目标检测面临目标特征提取困难、易受噪声与遮挡干扰、边界框回归精度要求严苛、专用数据集稀缺、模型设计适配性不足等多重挑战。本文系统梳理了遥感影像小目标检测的技术难点及瓶颈，分析了传统遥感图像处理检测方法、基于数据集增强的检测方法、基于分辨率增强的检测方法、基于特征融合与注意力机制的检测方法、基于Transformer的检测方法、基于无锚框技术的检测方法等6类主流检测方法，详细分析了各类方法的技术原理、核心创新点与适用场景，总结了遥感小目标数据集的特性、标注规格与应用价值，分析了遥感小目标检测技术在舰船及航标检测、车辆检测、军事目标检测等领域的应用。最后展望了遥感小目标检测的多模态融合、轻量化架构、小样本学习等未来发展方向。

关键词: 小目标检测, 深度学习, 数据增强, Transformer, 目标检测数据集

Abstract: Small object detection in remote sensing imagery is one of the core issues in the fields of remote sensing and computer vision, playing a crucial role in the intelligent understanding of remote sensing scenes and intelligent interpretation of remote sensing images. Due to factors such as the low pixel ratio of targets and complex scenes, small object detection in remote sensing imagery faces multiple challenges, including difficulties in target feature extraction, susceptibility to noise and occlusion interference, stringent requirements for bounding box regression accuracy, scarcity of dedicated datasets, and insufficient model design adaptability. The technical difficulties and bottlenecks of small object detection in remote sensing imagery were systematically reviewed. Six mainstream detection methods, including traditional remote sensing image processing detection methods, dataset augmentation-based detection methods, resolution enhancement-based detection methods,feature fusion and attention mechanism-based detection methods, Transformer-based detection methods, and anchor-free technology-based detection methods, were analyzed, and the technical principles, core innovations, and applicable scenarios of each method were detailed. The characteristics, annotation specifications, and application value of remote sensing small object datasets were summarized, and the applications of remote sensing small object detection technology in ship and navigation mark detection, vehicle detection,and military target detection were analyzed. Finally, future development directions for remote sensing small object detection, including multimodal fusion, lightweight architecture,and few-shot learning, were prospected.

Key words: small object detection, deep learning, data augmentation, Transformer, object detection dataset

中图分类号:

TP391

王宇帆, 邵子龙, 蒲媛雪, 章昊文, 张静怡, 秦昆. 遥感影像小目标检测方法与应用[J]. 应用科学学报, 2026, 44(4): 598-614.

WANG Yufan, SHAO Zilong, PU Yuanxue, ZHANG Haowen, ZHANG Jingyi, QIN Kun. Methods and Applications of Small Object Detection in Remote Sensing Images[J]. Journal of Applied Sciences, 2026, 44(4): 598-614.

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