Regularization in Hyperspectral Unmixing PDF

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.

Authors: Bhatt, Jignesh