The fast data acquisition rate due to the shorter revisit periods and wider observation
coverage of satellites results in large amounts of remote sensing images every day. This …

We introduce an information-theoretic loss function, RankMI, and an associated training
algorithm for deep representation learning for image retrieval. Our proposed framework …

Current remote sensing platforms generate a vast amount of imagery but the best current
methods to index and retrieve that data require expensive and difficult to procure labels. In …

With the continuous maturity of remote sensing technology, the obtained remote sensing
images' quality and quantity have surpassed any previous period. In this context, the content …

This paper presents a novel graph-theoretic deep representation learning method in the
framework of multi-label remote sensing (RS) image retrieval problems. The proposed …

Deeply learned representations have achieved superior image retrieval performance in a
retrieve-then-rerank manner. Recent state-of-the-art single stage model, which heuristically …

With the rapid development of deep learning methods, researchers have gradually shifted
the research focus from hand-crafted features to deep features in the field of the content …

Cross-modal text-image retrieval in remote sensing (RS) provides a flexible retrieval
experience for mining useful information from RS repositories. However, existing methods …

With the increasing amount of high-resolution remote sensing images, it becomes more and
more urgent to retrieve remote sensing images from large archives efficiently. The existing …

In this paper, we address the semantic gap problem in high-spatial resolution remote
sensing images retrieval. We propose a useful semantic image representation that improves …