Relevance-Guided Adaptive Learning for Remote Sensing Image–Text Retrieval

The remote sensing image–text retrieval (RSITR) aims to establish semantic alignment between images and texts to enable accurate cross-modal retrieval. Existing methods usually extract features from images and texts independently, aligning them in a shared embedding space to achieve cross-modal retrieval. However, these methods often assume complete alignment between image and text pairs, overlooking the inherent disparities between the rich visual details in remote sensing (RS) images and the abstract nature of textual descriptions. These disparities result in image–text pairs only sharing partial semantic correlations, rather than one-to-one complete alignment. Such incomplete alignment adversely affects model training and retrieval accuracy. To address this problem, a relevance-guided adaptive learning (RGAL) method is proposed, which quantifies and leverages the relevance of image–text pairs to refine the training process while enhancing retrieval performance. First, the proposed method introduces an image–text relevance measurement mechanism that integrates global and local feature distances to accurately evaluate the degree of semantic relevance between images and texts. Second, a relevance-based sample division (RBSD) strategy is proposed, utilizing a Gaussian mixture model to dynamically redivide samples into positive and negative pairs according to the measured image–text relevance. This strategy refines the training dataset, reduces noise, and enhances the effectiveness of model learning. Finally, a relevance-weighted triplet loss (RWTL) is designed to adaptively adjust the contribution of sample pairs to the loss function based on their relevance, further optimizing model training and enhancing retrieval accuracy. Experimental results on multiple RSITR datasets demonstrate that the proposed method significantly improves retrieval accuracy and performance.