SAMChat: Introducing Chain of Thought Reasoning and GRPO to a Multimodal Small Language Model for Small Scale Remote Sensing

Date

2025-01-01

Author

Köksal, Aybora
Alatan, Abdullah Aydın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Remarkable capabilities in understanding and generating text-image content have been demonstrated by recent advancements in multimodal large language models (MLLMs). However, their effectiveness in specialized domains-particularly those requiring resource-efficient and domain-specific adaptations- has remained limited. In this work, a lightweight multimodal language model termed SAMChat is introduced, specifically adapted to analyze remote sensing imagery in secluded areas, including challenging missile launch sites. A new dataset, SAMData, was compiled by verifying hundreds of aerial images through expert review, and subtle military installations were highlighted via detailed captions. Supervised fine-tuning on a 2B-parameter open-source MLLM with chain-of-thought (CoT) reasoning annotations was performed, enabling more accurate and interpretable explanations. Additionally, Group Relative Policy Optimization (GRPO) was leveraged to enhance the model's ability to detect critical domain-specific cues-such as defensive layouts and key military structures-while minimizing false positives on civilian scenes. Through empirical evaluations, it has been shown that SAMChat significantly outperforms both larger, general-purpose multimodal models and existing remote sensing-adapted approaches on open-ended captioning and classification metrics. Over 80% recall and 98% precision were achieved on the newly proposed SAMData benchmark, underscoring the potency of targeted fine-tuning and reinforcement learning in specialized real-world applications. Code and dataset will be available upon acceptance.

Subject Keywords

Aerial image analysis, chain-of-thought reasoning, domain adaptation, group relative policy optimization, multimodal large language models, remote sensing

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105023136351&origin=inward
https://hdl.handle.net/11511/117688

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Department of Electrical and Electronics Engineering, Article