Remote sensing (RS) image retrieval based on visual content is of great significance for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues of image retrieval: visual feature, similarity metric and relevance feedback. Along with the advance of these issues, the technology of RS image retrieval has been developed comparatively mature. However, due to the complexity and multiformity of high-resolution remote sensing (HRRS) images, there is still room for improvement in the current methods on HRRS data retrieval. In this paper, we analyze the three key aspects of retrieval and provide a comprehensive review on content-based RS image retrieval methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the visual feature aspect and delve how to use powerful deep representations in this task. We conduct systematic investigation on evaluating factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw instructive conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval.

本篇论文综合研究了遥感图像检索中的特征提取、相似度度量和相关反馈三个核心问题，并提出使用深度学习方法解决特征提取问题，对可能影响深度特征性能的相关因素进行了系统调查，并在公共数据集上获得了显著的检索结果。

利用深度特征实现遥感图像检索：系统性研究