Intrinsic Hyperspectral Image Decomposition With DSM Cues

Intrinsic hyperspectral image decomposition (IHID) aims to recover physical scene properties such as reflectance and illumination from a given hyperspectral image (HSI), which directly respects the physical imaging process and can benefit many HSI processing tasks. It is a severely ill-posed problem and is challenging to solve using HSI alone. Additional geometric information provided by digital surface models (DSMs) can otherwise help immensely. While intrinsic image decomposition for RGB images and RGB-D images has been studied extensively during the past few decades and has seen significant progress, studies of the problem for other types of data, such as HSIs and DSMs, are still needed. It is much more challenging to handle an HSI with hundreds of channels than an RGB image with only three channels. Moreover, compared with RGB-D data, HSIs and DSM data usually have much lower spatial resolutions and more complicated land covers, making it difficult to extend the RGB-D intrinsic image method directly. In this article, we present a novel IHID framework for HSIs with DSM cues. Utilizing spherical-harmonic illumination, we first propose a convenient HSI rendering model with DSM, which describes the interplay of material reflectance, geometric distribution, and environment illumination. Then, we introduce local and nonlocal priors on reflectance that ensure the local smooth and global consistency of recovered reflectance. Experiments on synthetic and real data demonstrate that the proposed method outperforms the state-of-the-art methods and is robust to illumination changes.