Approaches for Characterizing Nonlinear Mixtures in Hyperspectral Imagery

Published

June 21, 2017

Author(s)

Robert S. Rand, Ronald G. Resmini, David W. Allen

Abstract

This study considers a physics-based and a kernel-based approach for characterizing pixels in a scene that may be linear (areal mixed) or nonlinear (intimately mixed). The physics-based method is based on earlier studies that indicate nonlinear mixtures in reflectance space are approximately linear in albedo space. The approach converts reflectance to single scattering albedo (SSA) according to Hapke theory assuming bidirectional scattering at nadir look angles and uses a constrained linear model on the computed albedo values. The kernel-based method is motivated by the same idea, but uses a kernel that seeks to capture the linear behavior of albedo in nonlinear mixtures of materials. The behavior of the kernel method is dependent on the value of a parameter, gamma. Validation of the two approaches is performed using laboratory data.

Citation

Excursions in Harmonic Analysis

Publisher Info

Springer International Publishing AG, CH-6330 Cham (ZG), ME

Pub Type

Book Chapters

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DOI Link

Keywords

Nonlinear mixtures, Kernel-based methods, Single scattering albedo, Hapke theory, Hyperspectral

Citation

Rand, R. , Resmini, R. and Allen, D. (2017), Approaches for Characterizing Nonlinear Mixtures in Hyperspectral Imagery, Excursions in Harmonic Analysis, Springer International Publishing AG, CH-6330 Cham (ZG), ME, [online], https://doi.org/10.1007/978-3-319-54711-4_5 (Accessed November 3, 2025)