Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

TL;DR

This paper systematically investigates the use of deep features for remote sensing image retrieval, analyzing core issues and optimizing feature extraction to significantly improve retrieval performance on high-resolution datasets.

Contribution

It provides a comprehensive review of existing methods and focuses on optimizing deep feature extraction, offering insights and systematic evaluation to advance HRRS image retrieval.

Findings

Optimized deep features improve retrieval accuracy

Systematic analysis of factors affecting deep feature performance

Achieved remarkable results on public HRRS datasets

Abstract

Remote sensing (RS) image retrieval is of great significant 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: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative 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 conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval.