A Domain-Specific Memory Bank-Enhanced Framework for Remote Sensing Image Captioning

Remote sensing image captioning enables automated interpretation of remote sensing data for applications such as environmental monitoring and urban planning. However, general-purpose multimodal models exhibit suboptimal adaptability on remote sensing imagery due to domain discrepancies in data distribution, resolution, and rotation invariance. We propose a domain-specific memory bank-enhanced framework for remote sensing image caption, built upon the lightweight EVCap architecture with three core innovations: (1) a remote sensing-specific visual memory bank providing domain-representative visual–semantic alignment; (2) a staged transfer training strategy that leverages pretrained knowledge while mitigating overfitting from limited remote sensing data; and (3) a K-means clustering-based small-sample memory bank that preserves performance while reducing memory footprint for resource-constrained deployment. Extensive experiments on the NWPU, RSICD, and Sydney datasets demonstrate substantial improvements over baselines. On the test sets, the domain-specific memory bank (R1) improves CIDEr by 1.87% on RSICD (0.3255 to 0.3316), and the clustering-based variant (R4) by 11.7% on Sydney (0.1422 to 0.1589) using only 39% of the full bank, despite decreases on NWPU and RSICD, with about 3.97 million trainable parameters.