Regularization in Hyperspectral Unmixing

Regularization in Hyperspectral Unmixing
Author(s):    Jignesh S. Bhatt; Manjunath V. Joshi
Published:   2016
DOI:             10.1117/3.2264037
eISBN: 9781510607590
Description:

Spectral unmixing is a challenging mixed-pixel decomposition problem that can be addressed by regularization This Spotlight presents methods to obtain better estimates of underlying abundances. It discusses least-squares, total-least squares, and Markov random-field-based frameworks to unmix hyperspectral data. Particular attention is paid to spectral-space-based regularization methods. Detailed theoretical analysis is performed to illustrate the advantages of this approach. The performance of the proposed methods is tested using a simulated database as well as by conducting experiments on real AVIRIS data. Other topics include parameter estimation, noise sensitivity, and time-complexity-related issues. Finally, the primary results of parallel computations are provided for real-time applications.

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Remote sensing involves acquiring data from the Earth’s surface without physical contact with the area being sensed. Classically, the satellite-based sensors capture the data in four to six different regions in the electromagnetic spectrum covering the visible, infrared, and thermal infrared wavelength bands and are known as multispectral (MS) sensors. Recently, hyperspectral imaging has emerged as a powerful, passive remote sensing technology. The hyperspectral imagers (HySI, also known as imaging spectrometers) acquire a set of coregistered images of a scene with a relatively large instantaneous field of view (IFOV) (about 4m×4m to 20m×20m) and much finer spectral resolution (10nm within more than 200 contiguous wavelength bands). This has enabled quantitative analysis of an area within an IFOV of the sensor. The unprecedented capability of the hyperspectral sensors enables the remote acquisition of images where each pixel is a vector with a high spectral resolution that enables better analysis of the contents in an area.1–3

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