对比四种小目标检测方法在卫星图像中的表现，验证其适用性与挑战。
An Empirical Study of Methods for Small Object Detection from Satellite Imagery
- 基于城市卫星图和农田卫星图，评估四类先进检测方法。
- 在高分辨率公开数据集上测试，揭示小目标识别的性能瓶颈。
- 适合遥感、农业监测等需要精准小目标检测的应用场景。
本文回顾了从遥感图像中检测小目标的方法，并对四种最先进的方法进行了实证评估，以深入理解其性能表现与技术挑战。具体应用包括城市卫星图像中的汽车检测以及农业用地卫星图像中的蜂箱检测。基于现有综述与文献，我们选取了几种表现优异的方法进行实验。所有实验均使用公开的高分辨率卫星图像数据集，旨在为小目标检测在遥感领域的实际应用提供参考。
原文摘要 · Abstract (English)
This paper reviews object detection methods for finding small objects from remote sensing imagery and provides an empirical evaluation of four state-of-the-art methods to gain insights into method performance and technical challenges. In particular, we use car detection from urban satellite images and bee box detection from satellite images of agricultural lands as application scenarios. Drawing from the existing surveys and literature, we identify several top-performing methods for the empirical study. Public, high-resolution satellite image datasets are used in our experiments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。