Research on Adaptive Sample Type Discrimination for Remote Sensing Image Retrieval

Remote sensing images are complex in content and rich in categories, and there are numerous images difficult in discrimination, resulting in poor performance of remote sensing image retrieval. For this reason, the adaptive sample type discrimination (ASTD) method is proposed, which dynamically categorizes the sample types into simple samples, ordinary samples and difficult samples, and the network performs different degrees of learning based on the types of samples, so as to effectively improve the discriminative ability of features. Firstly, an SHash network is designed, which takes Swin Transformer as the backbone and adds a hash layer at the end of the network. This network captures the semantic information of images globally, improving feature representation and retrieval efficiency. Secondly, in order to make the same category of images more aggregated, and to better distinguish  different categories of images, a hash center is defined for each category. The center corresponding to the input sample’s own category is specified as the positive center of the sample, and the other centers are the negative centers of the sample. Finally, the sample type discriminative loss STDLoss is proposed to adaptively discriminate the type of samples based on distance relationship between samples and positive and negative centers, so as to improve the network’s ability to learn from each type of samples. Comparison with five hashing methods such as DSH, CSQ and SHC on two remote sensing datasets, UC-Merced and AID, experimental results show that the network trained based on the ASTD can better learn the features of the samples and improve the retrieval performance.