Spectral Unmixing with Hyperspectral Datasets

Vidhi Joshi; Ritika Lohiya; Shailendra Shrivastava; Debjyoti Dhar

Spectral Unmixing with Hyperspectral Datasets

Authors: Vidhi Joshi, Ritika Lohiya, Shailendra Shrivastava, Debjyoti Dhar

Unique Paper ID: 144197
Volume: 3
Issue: 8
PageNo: 116-121

Keywords: Hyperspectral data spectrum mixture Linear-Nonlinear spectral mixture Linear-Nonlinear Model PCA

Abstract:
Hyperspectral imaging is a continuously growing area of remote sensing application. Hyperspectral data is often used to determine what materials are present in a scene. Materials of interest could include roadways, vegetation, and specific targets (i.e. minerals, manmade materials etc.). The AVIRIS-NG (Airborne Visible InfraRed Imaging Spectrometer - Next Generation) data set can be seen as a cube in which each pixel is given by the spectral signature of the underlying materials in that area of the image. The motivation of unmixing is to find a collection of pure spectral constituents. Some of the unmixing techniques are used as Linear Mixing Model (LMM) and Nonlinear Mixing Model. LMM assumes that endmember substances are sitting side-byside within the FOV. Nonlinear Mixture Model assumes that endmembers components are randomly distributed throughout the FOV with Multiple scattering effects. Hyperspectral unmixing is used in many practical applications, such as precision agriculture, monitoring and management of natural disaster, issues related to security, and defense. This paper is related to the basic concepts of hyperspectral unmixing and techniques respectively.

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Cite This Article

ISSN: 2349-6002
Volume: 3
Issue: 8
PageNo: 116-121

Spectral Unmixing with Hyperspectral Datasets

Available:https://ijirt.org/Article?manuscript=144197

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