Observation of the Earth using satellites mounted with optical sensors is an important application of remote sensing. Owing to physical constraints, multispectral (MS) sensors acquire images of lower spatial resolution than a single-band panchromatic (PAN) sensor that acquires images of the same scene. Pansharpening fuses the MS and PAN images to obtain an MS image with the same spatial resolution as the PAN image. In this letter, we propose to expand a method, initially developed for Sentinel-2 single-sensor sharpening, for pansharpening. The expanded method is based on solving a non-convex MS acquisition model using optimization methods based on cyclic decent and manifold optimization. The tuning parameters of the method are chosen using Bayesian optimization with reduced-scale evaluation. The proposed method is compared with a number of established pansharpening methods and is validated using both synthetic and real data sets.

中文翻译：

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基于模型的降阶全色锐化

使用安装有光学传感器的卫星对地球进行观测是遥感的重要应用。由于物理限制，多光谱 (MS) 传感器获取的图像空间分辨率低于获取相同场景图像的单波段全色 (PAN) 传感器。Pansharpening 融合 MS 和 PAN 图像以获得与 PAN 图像具有相同空间分辨率的 MS 图像。在这封信中，我们建议扩展一种最初为 Sentinel-2 单传感器锐化开发的方法，用于全色锐化。扩展方法基于使用基于循环下降和流形优化的优化方法求解非凸 MS 采集模型。该方法的调整参数是使用贝叶斯优化和缩减规模评估来选择的。