Multiscale Context Deep Hashing for Remote Sensing Image Retrieval

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2023, Vol.16, p.7163-7172
• Main Authors: Zhao, Dongjie, Chen, Yaxiong, Xiong, Shengwu
• Format: Article
• Language: English
• Subjects: Attention mechanism; Context; Datasets; deep hash; Feature extraction; Image retrieval; multiscale context information; Receptive field; Remote sensing; Remote sensors; Semantics; Sensors; Task analysis; Visualization
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2023.3298990

With the advancement of remote sensing satellites and sensor technology, the quantity and diversity of remote sensing imagery have exhibited a sustained trend of growth. Remote sensing image retrieval has gained significant attention in the realm of remote sensing. Hashing methods have been widely applied in remote sensing image retrieval due to their high computational efficiency, low storage cost, and effective performance. However, existing remote sensing image retrieval methods often struggle to accurately capture the intricate information of remote sensing images. They often lack high attention to key features. The neglect of multiscale and saliency information in remote sensing images can result in feature loss and difficulties in maintaining the balance of hash codes. In response to the issues, we introduce a multiscale context deep hashing network (MSCDH). First, we can obtain finer-grained multi-scale features and achieve a larger receptive field by incorporating the proposed multiscale residual blocks. Then, the proposed multicontext attention modules can increase the perceptual field and suppress the interference from irrelevant information by aggregating contextual information along channels and spatial dimensions. The experimental results on the UCMerced dataset and WHU-RS dataset demonstrate that the proposed method achieves state-of-the-art retrieval performance.