A Semantically Guided Deep Supervised Hashing Model for Multi-Label Remote Sensing Image Retrieval

With the rapid growth of remote sensing data, efficiently managing and retrieving large-scale remote sensing images has become a significant challenge. Specifically, for multi-label image retrieval, single-scale feature extraction methods often fail to capture the rich and complex information inherent in these images. Additionally, the sheer volume of data creates challenges in retrieval efficiency. Furthermore, leveraging semantic information for more accurate retrieval remains an open issue. In this paper, we propose a multi-label remote sensing image retrieval method based on an improved Swin Transformer, called Semantically Guided Deep Supervised Hashing (SGDSH). The method aims to enhance feature extraction capabilities and improve retrieval precision. By utilizing multi-scale information through an end-to-end learning approach with a multi-scale feature fusion module, SGDSH effectively integrates both shallow and deep features. A classification layer is introduced to assist in training the hash codes, incorporating RS image category information to improve retrieval accuracy. The model is optimized for multi-label retrieval through a novel loss function that combines classification loss, pairwise similarity loss, and hash code quantization loss. Experimental results on three publicly available remote sensing datasets, with varying sizes and label distributions, demonstrate that SGDSH outperforms state-of-the-art multi-label hashing methods in terms of average accuracy and weighted average precision. Moreover, SGDSH returns more relevant images with higher label similarity to query images. These findings confirm the effectiveness of SGDSH for large-scale remote sensing image retrieval tasks and provide new insights for future research on multi-label remote sensing image retrieval.