Illumination Invariant Hyperspectral Image Unmixing Based on a Digital Surface Model

Although many spectral unmixing models have been developed to address spectral variability caused by variable incident illuminations, the mechanism of the spectral variability is still unclear. This paper proposes an unmixing model, named illumination invariant spectral unmixing (IISU). IISU makes the first attempt to use the radiance hyperspectral data and a LiDAR-derived digital surface model (DSM) in order to physically explain variable illuminations and shadows in the unmixing framework. Incident angles, sky factors, visibility from the sun derived from the LiDAR-derived DSM support the explicit explanation of endmember variability in the unmixing process from radiance perspective. The proposed model was efficiently solved by a straightforward optimization procedure. The unmixing results showed that the other state-of-the-art unmixing models did not work well especially in the shaded pixels. On the other hand, the proposed model estimated more accurate abundances and shadow compensated reflectance than the existing models.

I. Introduction

Hyperspectral imagery measures reflected light (i.e., radiance) at different wavelengths for each pixel of the image. The radiance is converted into a reflectance spectrum in order to remove the effects of atmospheric gases or water absorption [1]. The reflectance spectrum can be modelled by a linear combination of reference spectra representing the surface materials [2]. A linear mixing model (LMM) or a nonlinear mixing model (NLMM) has been developed to model the relationship between reference spectra (endmembers) and observed reflectance spectra [3]. LMM enables spatial distributions of materials to be quantitatively estimated. While many mixing models have been used for a variety of applications reviewed by [3], there are still two major problems.