The evolution of Remote Sensing Vision-Language Models(RS-VLMs) emphasizes the importance of transitioning from perception-centric recognition toward high-level deductive reasoning to enhance cognitive reliability in complex spatial tasks. However, current models often suffer from logical hallucinations, where correct answers are derived from flawed reasoning chains or rely on positional shortcuts rather than spatial logic. This decoupling undermines reliability in strategic spatial decision-making. To address this, we present GeoReason, a framework designed to synchronize internal thinking with final decisions. We first construct GeoReason-Bench, a logic-driven dataset containing 4,000 reasoning trajectories synthesized from geometric primitives and expert knowledge. We then formulate a two-stage training strategy: (1) Supervised Knowledge Initialization to equip the model with reasoning syntax and domain expertise, and (2) Consistency-Aware Reinforcement Learning to refine deductive reliability. This second stage integrates a novel Logical Consistency Reward, which penalizes logical drift via an option permutation strategy to anchor decisions in verifiable reasoning traces. Experimental results demonstrate that our framework significantly enhances the cognitive reliability and interpretability of RS-VLMs, achieving state-of-the-art performance compared to other advanced methods.

翻译：遥感视觉语言模型的发展强调从以感知为中心的识别向高级演绎推理转变的重要性，以提升复杂空间任务中的认知可靠性。然而，现有模型常受逻辑幻觉困扰，即正确答案源自有缺陷的推理链或依赖位置捷径而非空间逻辑。这种思维与决策的脱钩削弱了战略性空间决策的可靠性。为解决此问题，我们提出了GeoReason框架，旨在同步内部思维与最终决策。我们首先构建了GeoReason-Bench，这是一个包含4,000条从几何基元与专家知识合成的推理轨迹的逻辑驱动数据集。随后，我们设计了一个两阶段训练策略：（1）监督知识初始化，为模型赋予推理语法与领域专业知识；（2）一致性感知强化学习，以优化演绎可靠性。第二阶段整合了一种新颖的逻辑一致性奖励，该奖励通过选项排列策略惩罚逻辑漂移，从而将决策锚定于可验证的推理轨迹中。实验结果表明，我们的框架显著提升了遥感视觉语言模型的认知可靠性与可解释性，相较于其他先进方法实现了最先进的性能。