Du, Q. Jenny
Fowler, E. James
Younan, H. Nicholas
Durbha, S. Surya
Date of Degree
Dissertation - Open Access
Doctor of Philosophy
James Worth Bagley College of Engineering
Department of Electrical and Computer Engineering
The objective of this dissertation is to investigate all the necessary components in spectral mixture analysis (SMA) for hyperspectral imagery under an unsupervised circumstance. When SMA is linear, referred to as linear spectral mixture analysis (LSMA), these components include estimation of the number of endmembers, extraction of endmember signatures, and calculation of endmember abundances that can automatically satisfy the sum-to-one and non-negativity constraints. A simple approach for nonlinear spectral mixture analysis (NLSMA) is also investigated. After SMA is completed, a color display is generated to present endmember distribution in the image scene. It is expected that this research will result in an analytic system that can yield optimal or suboptimal SMA output without prior information. Specifically, the uniqueness in each component is described as follow. 1)A new signal subspace-based approach is developed to determine the number of endmembers with relatively robust performance and the least parameter requirement. 2)The best implementation strategy is determined for endmember extraction algorithms using simplex volume maximization and pixel spectral similarity; and algorithm with the special consideration for anomalous pixels is developed to improve the quality of extracted endmembers. 3)A new linear mixture model (LMM) is deployed where the number of endmembers and their types can be changed from pixel to pixel such that the resulting endmember abundances are sum-to-one and nonnegative as required; and fast algorithms are developed to search for a sub-optimal endmember set for each pixel. 4)A simple approach for NLSMA based on LMM is investigated and an automated approach is developed to determine either linear or nonlinear mixing is actually experienced. 5)A color display strategy is developed to present SMA results with high class/endmember separability.
Raksuntorn, Nareenart, "Unsupervised spectral mixture analysis for hyperspectral imagery" (2009). Theses and Dissertations MSU. 4851.