Deep Hash Remote-Sensing Image Retrieval Assisted by Semantic Cues

With the significant and rapid growth in the number of remote-sensing images, deep hash methods have become a research topic. The main work of deep hash method is to build a discriminate embedding space through the similarity relation between sample pairs and then map the feature vector into Hamming space for hashing retrieval. We demonstrate that adding a binary classification label as a kind of semantic cue could further improve the retrieval performance. In this work, we propose a new method, which we called deep hashing, based on classification label (DHCL). First, we propose a network architecture, which can classify and retrieve remote-sensing images under a unified framework, and the classification labels are further utilized as the semantic cues to assist in network training. Second, we propose a hash code structure, which can integrate the classification results into the hash-retrieval process to improve accuracy. Finally, we validate the performance of the proposed method on several remote-sensing image datasets and show the superiority of our method.