CTPMSN: Enhancing Multimodal Remote Sensing Classification With Composite Text Prompts

Remote sensing multimodal data integrates information from various sensors, providing detailed characterization and reliable monitoring capabilities for Earth observation tasks. This data is crucial in environmental surveillance, disaster assessment, and related domains. However, current state-of-the-art methods predominantly rely on category names within prompts. These methods demonstrate reduced effectiveness when processing ambiguous or poorly defined category designations, thus compromising classification accuracy. To overcome these limitations, we propose the Composite Text Prompt Multimodal Synergy Network (CTPMSN). This architecture integrates a text encoder with a remote sensing visual joint encoder and implements dual-level composite text supervision at both categorical and content strata. Our approach enhances inter-class discriminability while effectively capturing intra-class variations. Additionally, we introduce the Text-Visual Prompt Alignment Module (TVPAM) to optimize the association between text prompts and visual features, thereby strengthening their correspondence. Extensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed method, achieving overall accuracies of 93.99 % on MUUFL Gulfport, 93.74 % on Houston 2013, and 91.56 % on Augsburg.