Abstract: A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. This algorithm, the Piece-wise Convex Multiple Model Endmember Detection (P-COMMEND) algorithm, models a hyperspectral image using a piece-wise convex representation. By using a… Read More
Conference PapersConference Papers
Agent-based rumor spreading models for human geography applications
Abstract: Communication has a large impact on the outcome of a population’s response during disaster scenarios. The ability of a population to access news and disaster relief information as well as the population’s perception of the information they receive effects… Read More
A sparsity promoting bilinear unmixing model
Abstract: An algorithm, Bilinear SPICE (BISPICE), for simultaneously estimating the number of endmembers, the endmembers, and proportions for a bilinear mixing model is derived and evaluated. BISPICE generalizes the SPICE algorithm for linear mixing. The proportion estimation steps of SPICE… Read More
Bootstrapping for piece-wise convex endmember distribution detection
Abstract: A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmember distributions is presented. If endmembers are represented as random vectors, then they can be characterized by a multivariate probability distribution. These distributions are referred to… Read More
Using physics-based macroscopic and microscopic mixture models for hyperspectral pixel unmixing
Abstract: A method of incorporating macroscopic and microscopic reflectance models into hyperspectral pixel unmixing is presented and discussed. A vast majority of hyperspectral unmixing methods rely on the linear mixture model to describe pixel spectra resulting from mixtures of endmembers.… Read More
Spatial-spectral unmixing using fuzzy local information
Abstract: Hyperspectral unmixing estimates the proportions of materials represented within a spectral signature. The over whelming majority of hyperspectral unmixing algorithms are based entirely on the spectral signatures of each individual pixel and do not incorporate the spatial information found… Read More
Piece-wise convex spatial-spectral unmixing of hyperspectral imagery using possibilistic and fuzzy clustering
Abstract: Imaging spectroscopy refers to methods for identifying materials in a scene using cameras that digitize light into hundreds of spectral bands. Each pixel in these images consists of vectors representing the amount of light reflected in the different spectral… Read More
Sub-pixel target spectra estimation and detection using functions of multiple instances
Abstract: The Functions of Multiple Instances (FUMI) method for learning target pattern and non-target patterns is introduced and extended. The FUMI method differs significantly from traditional supervised learning algorithms because only functions of target patterns are available. Moreover, these functions… Read More
Multiclass subpixel target detection using functions of multiple instances
Abstract: The Multi-class Convex-FUMI (Multi-class C-FUMI) method is developed and described. The method is capable of learning prototypes for multiple target classes from hyperspectral imagery. Multi-class C-FUMI is a non-traditional supervised learning method based on the Functions of Multiple Instances… Read More
Rebuilding the injured brain: use of MRS in clinical regenerative medicine
Abstract: Hypoxic-Ischemic Encephalopathy (HIE) is the brain manifestation of systemic asphyxia that occurs in 20 out of 1000 births. HIE triggers an immediate neuronal and glial injury leading to necrosis secondary to cellular edema and lysis. Because of this destructive… Read More