Deep Hashing Network Based on Split Channels for Hybrid-Source Remote Sensing Image Retrieval

Abstract

Traditional remote sensing image retrieval (RSIR) methods are generally based on images from a specific single source. As different sources and huge volumes of remote sensing images have been easily available nowadays, RSIR is facing the challenge of retrieving remote sensing images with different spectral and spatial information from different sources. Benefited from compelling image feature extraction ability of deep neural networks and efficient computing power and effective retrieval ability of hashing, deep hashing networks has become prevalent for image retrieval researches. In this paper, a deep hashing network based on split channels is proposed for hybrid source RSIR called split-channels triplet deep hashing networks(SCTDHNs). It takes skillfully splitting channels as input, and is mainly composed of a hybrid source deep hashing subnetwork for cross source images retrieval and single-source deep hashing sub-network for a multi-spectral image retrieval, and each of them achieves high retrieval performance. Furthermore, a novel trick for loss function is proposed, called increased intervals between dissimilar pairs during training stage that dramatically improves the retrieval performance. Extensive experiments implement on dual-source remote sensing data set demonstrate that proposed method yields better performance than existing state-of-art hybrid source retrieval methods as far as is known.