Using Physically-Modeled Synthetic Data to Assess Hyperspectral Unmixing Approaches

Abstract

This paper considers an experimental approach for assessing algorithms used to exploit remotely sensed data. The approach employs synthetic images that are generated using physical models to make them more realistic while still providing ground truth data for quantitative evaluation. This approach complements the common approach of using real data and/or simple model-generated data. To demonstrate the value of such an approach, the behavior of the FastICA algorithm as a hyperspectral unmixing technique is evaluated using such data. This exploration leads to a number of useful insights such as: (1) the need to retain more dimensions than indicated by eigenvalue analysis to obtain near-optimal results; (2) conditions in which orthogonalization of unmixing vectors is detrimental to the exploitation results; and (3) a means for improving FastICA unmixing results by recognizing and compensating for materials that have been split into multiple abundance maps.

Hyperspectral Unmixing with Robust Collaborative Sparse Regression

“…Due to insufficient spatial resolution of the imaging sensor and mixing effects of the ground surface, mixed pixels are widespread in hyperspectral images, which leads to difficulties for conventional pixel-level applications [6][7][8]. Therefore, spectral unmixing is an essential step for the deep exploitation of hyperspectral image, which decomposes mixed pixels into a collection of pure spectra signatures, called endmembers, and their corresponding proportions in each pixel, called abundances [9,10].…”

ℓ0-Norm Sparse Hyperspectral Unmixing Using Arctan Smoothing

“…In some environments, such as urban scenes [7], vegetation areas [8] and those containing specific spectral signatures, such as soil, sand and trees [9,10], we have to use the nonlinear mixing model. However, the linear SU methods are being scrutinized by researchers and scientists extensively because of their capabilities in many applications [4,5,[11][12][13], e.g., minerals [4,14]. In this paper, we focus on the linear SU, which is a method of the separation of the mixed pixel spectrum into a set of the spectral signatures of the materials called endmembers, as well as their corresponding contributions in each mixed pixel called abundances in a linear fashion.…”