Attention-Driven Cross-Modal Remote Sensing Image Retrieval

In this work, we address a cross-modal retrieval problem in remote sensing (RS) data. A cross-modal retrieval problem is more challenging than the conventional uni-modal data retrieval frameworks as it requires learning of two completely different data representations to map onto a shared feature space. For this purpose, we chose a photo-sketch RS database. We exploit the data modality comprising more spatial information (sketch) to extract the other modality features (photo) with cross-attention networks. This sketch-attended photo features are more robust and yield better retrieval results. We validate our proposal by performing experiments on the benchmarked Earth on Canvas dataset. We show a boost in the overall performance in comparison to the existing literature. Besides, we also display the Grad-CAM visualizations of the trained model's weights to highlight the framework's efficacy.