Image fusion combines data from different heterogeneous sources to obtain more precise information about an underlying scene. Hyperspectral-multispectral (HS-MS) image fusion is currently attracting great interest in remote sensing since it allows the generation of high spatial resolution HS images and circumventing the main limitation of this imaging modality. Existing HS-MS fusion algorithms, however, neglect the spectral variability often existing between images acquired at different time instants. This time difference causes variations in spectral signatures of the underlying constituent materials due to the different acquisition and seasonal conditions. This paper introduces a novel HS-MS image fusion strategy that combines an unmixing-based formulation with an explicit parametric model for typical spectral variability between the two images. Simulations with synthetic and real data show that the proposed strategy leads to a significant performance improvement under spectral variability and state-of-the-art performance otherwise.

中文翻译：

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超光谱和多光谱图像融合的超分辨率，可反映季节性光谱变化。

图像融合结合了来自不同异构源的数据，以获得有关基础场景的更精确信息。目前，高光谱-多光谱（HS-MS）图像融合在遥感领域引起了极大的兴趣，因为它可以生成高空间分辨率的HS图像，并规避了这种成像方式的主要局限性。但是，现有的HS-MS融合算法忽略了在不同时刻获取的图像之间通常存在的光谱可变性。由于不同的采集和季节条件，这种时差会导致基础成分的光谱特征发生变化。本文介绍了一种新颖的HS-MS图像融合策略，该策略将基于分解的公式与显式参数模型相结合，以实现两幅图像之间典型的光谱可变性。