Enhanced RSVQA Insight Through Synergistic Visual-Linguistic Attention Models

The interpretation of remote sensing images remains a significant challenge due to their complex, information-rich nature. Current Remote Sensing Visual Question Answering (RSVQA) techniques have been a step forward towards building intelligent analysis systems for remote sensing images. However most existing RSVQA models rely on already existing deep learning models for their representation (visual and language feature extraction) and fusion (combining the extracted features) modules, which poses a limitation to their performance. To address these limitations, this paper introduces a novel Remote Sensing Visual Question Answering (RSVQA) approach that leverages state-of-the-art components with an innovative architecture to advance interactive remote sensing analysis. The proposed model features a novel dual-layer visual attention mechanism in the Representation module to process intricate features and capture regional relationships alongside processing the overall features. The Fusion module employs a unique attention-based design, combining both self-attention and mutual attention, to integrate these features into a unified vector representation. Finally, the Answering module utilizes a refined Multi-Layer Perceptron classifier for precise response generation. Evaluations on RSVQA benchmarks demonstrate the system’s superiority over existing methods, marking a significant step forward in remote sensing analytics.