ROI-Guided Attention Learning for Remote Sensing Image Retrieval

Abstract

In the burgeoning remote sensing image data era, the swift and precise retrieval of images from extensive databases has emerged as a critical challenge. This need is particularly pronounced in applications, such as environmental and disaster monitoring, resource investigation, and ground target monitoring, all heavily reliant on remote sensing images. The efficacy of image retrieval hinges significantly on advanced feature extraction methods. However, remote sensing images often suffer from disturbances caused by rich and complex backgrounds. How to extract key regions from remote sensing images, reduce background interference, and improve retrieval accuracy has become a hot research topic. Addressing this challenge, in this article, we propose a region of interest (ROI) guided attention network designed to detect key category regions of targets. This network integrates a class activation map (CAM) module into a deep learning framework for image retrieval. First, the CAM identifies multiple categories corresponding to different remote sensing categories. Second, multiple category features are fed into an ROI-attention module to distinguish the importance of the category. The attention module highlights the category to be detected by suppressing interference from the background. Finally, two branches, the globally extracted image features and the locally extracted features of important categories obtained through the attention module, are integrated to form a comprehensive image representation optimized for retrieving the target object. The efficacy of our proposed method is validated through experiments conducted on diverse datasets, demonstrating an improvement in retrieval accuracy.

Keywords

• Image retrieval
• region of interest (ROI)
• remote sensing image
• ROI-attention module