Remote sensing imagery offers rich spectral data across extensive areas for Earth observation. Many attempts have been made to leverage these data with transfer learning to develop scalable alternatives for estimating socio-economic conditions, reducing reliance on expensive survey-collected data. However, much of this research has primarily focused on daytime satellite imagery due to the limitation that most pre-trained models are trained on 3-band RGB images. Consequently, modeling techniques for spectral bands beyond the visible spectrum have not been thoroughly investigated. Additionally, quantifying uncertainty in remote sensing regression has been less explored, yet it is essential for more informed targeting and iterative collection of ground truth survey data. In this paper, we introduce a novel framework that leverages generic foundational vision models to process remote sensing imagery using combinations of three spectral bands to exploit multi-spectral data. We also employ methods such as heteroscedastic regression and Bayesian modeling to generate uncertainty estimates for the predictions. Experimental results demonstrate that our method outperforms existing models that use RGB or multi-spectral models with unstructured band usage. Moreover, our framework helps identify uncertain predictions, guiding future ground truth data acquisition.

翻译：遥感影像为地球观测提供了覆盖广阔区域的丰富光谱数据。已有诸多研究尝试利用这些数据，通过迁移学习开发可扩展的替代方案来估算社会经济状况，从而减少对昂贵调查收集数据的依赖。然而，由于大多数预训练模型基于三波段RGB图像训练的限制，现有研究主要集中在日间卫星影像的应用。因此，可见光谱之外波段的光谱建模技术尚未得到充分研究。此外，遥感回归中的不确定性量化研究相对不足，而这对于更精准的目标定位和地面实况调查数据的迭代收集至关重要。本文提出一种新颖框架，利用通用基础视觉模型处理遥感影像，通过三波段组合方式充分利用多光谱数据。我们还采用异方差回归和贝叶斯建模等方法为预测生成不确定性估计。实验结果表明，我们的方法优于现有使用RGB或非结构化波段组合的多光谱模型。此外，该框架有助于识别不确定预测，为未来地面实据采集提供指导。