Remote sensing image retrieval based on deep multi-similarity hashing method

Abstract

Hashing methods are widely used in remote sensing image retrieval owing to their low storage and high efficiency. Unsupervised hashing methods for remote sensing image retrieval tasks are often associated with unreliable pseudo-labeling, the same training weights of image pairs, and the low accuracy of image retrieval. Hence, a remote sensing image retrieval method based on Deep Multi-Similarity Hashing (DMSH) is proposed herein. The Adaptive Pseudo-Labeling Module (APLM) and Paired Structure Information Module (PSIM) are established to achieve optimal pseudo-labeling and training attention, respectively. The APLM uses the K-Nearest Neighbor (KNN) and kernel similarity to evaluate the similarity relationship between images for the initial generation and online correction of pseudo-labeling. The PSIM maps the multi-scale structural similarity of image pairs to training concerns and assigns different training weights to optimize deep hash learning. The DMSH uses the Swin Transformer backbone network to extract the high-dimensional features of images as well as a pseudo-labeling based on a semantic similarity matrix as supervised information to train the deep network. Furthermore, the network is alternately optimized on two modules designed based on different similarity degrees to fully exploit the multiple similarity information among images and then generate highly discriminative hash codes to retrieve remote sensing images with high precision. Experimental results show that the mean Average Precision (mAP) of the DMSH improved by 0. 8%-3. 0% and 9. 8%-12. 5% on the EuroSAT and PatternNet datasets, respectively, compared with the optimal results yielded by other methods analyzed. Hence, the proposed method can effectively improve the retrieval accuracy of remote sensing images.