Towards a Large Language-Vision Question Answering Model for MSTAR Automatic Target Recognition

• URL: http://arxiv.org/abs/2605.10772v1
• Date: Mon, 11 May 2026 16:05:58 GMT
• Title: Towards a Large Language-Vision Question Answering Model for MSTAR Automatic Target Recognition
• Abstract summary: Large language-vision models (LLVM) have gained prominence as powerful tools for analyzing text and imagery.<n>Recent transformer-based LLVM research has shown substantial improvements for geospatial perception tasks.<n>This study examines the application of LLVM to remote sensing image captioning and visual question-answering.
• Score: 3.3274747298291203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language-vision models (LLVM), such as OpenAI's ChatGPT and GPT-4, have gained prominence as powerful tools for analyzing text and imagery. The merging of these data domains represents a significant paradigm shift with far-reaching implications for automatic target recognition (ATR). Recent transformer-based LLVM research has shown substantial improvements for geospatial perception tasks. Our study examines the application of LLVM to remote sensing image captioning and visual question-answering (VQA), with a specific focus on synthetic aperture radar (SAR) imagery. We examine newly published LLVM methods, including CLIP and LLaVA neural network transformer architectures. We have developed a work-in-progress SAR training and evaluation benchmark derived from the MSTAR Public Dataset. This has been extended to include descriptive text captions and question-answer pairs for VQA tasks. This challenge dataset is designed to push the boundaries of an LLVM in identifying nuanced ATR details in SAR imagery. Utilizing parameter-efficient fine-tuning, we train an LLVM method to identify fine-grained target qualities at 98% accuracy. We detail our data setup and experiments, addressing potential pitfalls that could lead to misleading conclusions. Accurately identifying and differentiating military vehicle types in SAR data poses a critical challenge, especially under complex environmental conditions. Mastering this target recognition skill may require a human analyst months of training and years of practice. This research represents a unique effort to apply LLVM to SAR applications, advancing machine-assisted remote sensing ATR for military and intelligence contexts.

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