Unmixing of Hyperspectral Data Using Spectral Libraries

Year 2020, Volume: 7 Issue: 1, 93 - 101, 26.04.2020

Sefa Küçük , Seniha Esen Yüksel

https://doi.org/10.30897/ijegeo.649394

Abstract

In hyperspectral images,
pixels are found as a mixture of the spectral signatures of several materials,
especially when there is an insufficient spatial resolution. In recent years,
spectral libraries have provided spectral information of hundreds of materials
that allow the development of techniques to solve the problem of hyperspectral
unmixing in a semi-supervised fashion. These methods which are also known as
sparse regression techniques assume that mixed pixels are a sparse linear
combination of spectral signatures of materials in already available spectral
libraries. In this paper, the spectral mixing problem has been solved via
sparse separation methods. The United States Geological Survey (USGS) spectral
library is used to generate simulated hyperspectral data. A comparative
analysis is performed to determine which material signatures in the library are
mixed in the pixels by using the convex-relaxation-based sparse regression
methods. Root Mean Square Error (RMSE), Signal to Reconstruction Error (SRE)
and processing time of the algorithms are used as comparing criterions.
Moreover, Hinton diagrams are used to visualize which material signatures are
found in the library and the proportions of these found material signatures.

Keywords

: Hyperspectral Imaging, Hyperspectral Unmixing, Sparse Regression, Sparse Unmixing, Spectral Library

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