Explainable Remote Sensing Image Captioning with Uncertainty-Aware Vision-Language Feature Fusion for SMB Decision Support

The democratization of remote sensing data presents a transformative opportunity for Small and Medium Businesses (SMBs), yet the adoption of automated interpretation tools is hindered by the "black box" nature of current Vision-Language Models (VLMs). Standard models frequently exhibit overconfidence in ambiguous scenarios, posing financial risks for applications in precision agriculture and logistics. This paper introduces SentiMap, an uncertainty-aware image captioning framework that disentangles aleatoric and epistemic uncertainty through a dual-stream Bayesian architecture. We propose a novel Adaptive Fusion Mechanism that dynamically re-weights visual representations based on spatial variance maps, prioritizing semantic priors when image quality degrades. Extensive experiments on the RSICD dataset and a curated "SMB-Risk" benchmark demonstrate that SentiMap achieves state-of-the-art calibration (ECE: 0.05) without compromising captioning accuracy. User studies confirm that providing interpretable "Trust Scores" and uncertainty heatmaps significantly enhances human decision confidence, bridging the gap between raw pixel data and actionable business intelligence.