Hyperspectral image unmixing with LiDAR data-aided spatial regularization

TL;DR

This paper introduces a hyperspectral unmixing method that leverages LiDAR data to improve spatial regularization, especially in shadowed regions, outperforming traditional hyperspectral-only approaches.

Contribution

It proposes a novel framework integrating LiDAR data into spectral unmixing, enhancing edge preservation and abundance estimation accuracy.

Findings

Improved abundance estimates in shadowed areas.

Better edge preservation compared to hyperspectral-only methods.

Validated on simulated and real datasets.

Abstract

Spectral unmixing methods incorporating spatial regularizations have demonstrated increasing interest. Although spatial regularizers which promote smoothness of the abundance maps have been widely used, they may overly smooth these maps and, in particular, may not preserve edges present in the hyperspectral image. Existing unmixing methods usually ignore these edge structures or use edge information derived from the hyperspectral image itself. However, this information may be affected by large amounts of noise or variations in illumination, leading to erroneous spatial information incorporated into the unmixing procedure. This paper proposes a simple, yet powerful, spectral unmixing framework which incorporates external data (i.e. LiDAR data). The LiDAR measurements can be easily exploited to adjust standard spatial regularizations applied to the unmixing process. The proposed framework is rigorously evaluated using two simulated datasets and a real hyperspectral image. It is compared with competing methods that rely on spatial information derived from a hyperspectral image. The results show that the proposed framework can provide better abundance estimates and, more specifically, can significantly improve the abundance estimates for pixels affected by shadows.