独特的拍摄视角和多变的成像高度使得遥感影像中包含大量尺寸极其有限的目标，如何准确有效地检测这些小目标对于构建智能的遥感图像解译系统至关重要。本文聚焦于遥感场景，对基于深度学习的小目标检测进行了全面调研。首先，本文根据小目标的内在特质梳理了遥感影像小目标检测的三个主要挑战，包括特征表示瓶颈、前背景混淆，以及回归分支敏感。其次，通过深入调研相关文献，本文全面回顾了基于深度学习的遥感影像小目标检测算法。具体说来，选取三种代表性的遥感影像小目标检测任务，即光学遥感图像小目标检测、SAR图像小目标检测和红外图像小目标检测，系统性总结了三个领域内的代表性方法，并根据每种算法所使用的技术思路进行分类阐述。再次，总结了遥感影像小目标检测常用的公开数据集，包括光学遥感图像、SAR图像及红外图像三种数据类型，借助于三种领域的代表性数据集SODA-A、AIR-SARShip和NUAA-SIRST，进一步对主流的遥感影像目标检测算法在面对小目标时的性能表现进行横向对比及深入评估。最后，对遥感影像小目标检测的应用现状进行总结，并展望了遥感场景下小目标检测的发展趋势。

Remote sensing images comprise a mass of objects with limited sizes owing to the particular shooting views and various altitudes, and precisely detecting these small objects plays a crucial role in developing an intelligent interpretation system for remote sensing images. Focusing on the remote sensing images, this paper conducts a comprehensive survey for deep learning-based Small Object Detection (SOD). Firstly, based on the intrinsic properties of small objects, three major challenges to small object detection in remote sensing images are concluded: the bottleneck of representation, the confusion between objects and backgrounds and the sensitivity of regression branch. What is more, this paper extensively reviews the literature of small object detection for remote sensing images in the deep-learning era, then a comprehensive survey in this field is provided. Specifically, by systematically reviewing corresponding methods of three SOD tasks, i.e., SOD for optical remote sensing images, SOD for Synthetic Aperture Radar (SAR) images and SOD for infrared images, an understandable taxonomy of the reviewed algorithms for each task is given. In addition, we retrospect several publicly available datasets for three SOD tasks. Meanwhile, on top of three representative datasets in corresponding domains, namely SODA-A, AIR-SARShip and NUAA-SIRST, we undertake an in-depth evaluation and comparison of main-stream SOD methods for remote sensing images. Afterwards, we discuss the status in applications of small object detection for remote sensing images. In the end, promising directions for SOD in remote sensing images are provided to enlighten the future research.