A Multimodal Hyperspectral Unmixing Method Under Spectral Variability

Yanyuan Huang, Yanfeng Gu, Tianzhu Liu

Abstract: Variation in illumination conditions can give rise to divergences in the reflectance profiles corresponding to identical feature categories. This phenomenon has always been a very important challenge that cannot be avoided in spectral unmixing (SU) algorithms. Current methods primarily focus on modeling spectral errors or using spectral libraries for optimization. This paper presents a new multimodal hyperspectral unmixing method under spectral variability that incorporates DSM data to model external shading variations in complicated illumination conditions. The proposed model aims to reduce spectral variability caused by external imaging changes by fitting the shading information with the digital surface model (DSM) data and the illumination information. The reflectance information, which represents the properties of the features themselves, is used for spectral unmixing after introducing the intrinsic decomposition. The method proposed in this paper can effectively attenuate the effect of spectral variability, as demonstrated by experimental validation on real MUFFL multimodal datasets.