Illumination invariant hyperspectral image unmixing based on a digital surface model

TL;DR

This paper introduces IISU, a novel hyperspectral unmixing model that incorporates LiDAR-derived digital surface models to physically account for illumination variability and shadows, improving accuracy especially in shaded areas.

Contribution

The paper presents the first unmixing model that explicitly uses LiDAR data and incident angles to explain spectral variability caused by illumination and shadows.

Findings

IISU outperforms existing models in shaded pixel unmixing.

The model provides more accurate abundance estimates.

Shadow compensation improves unmixing accuracy.

Abstract

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.