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
Conference PapersConference Papers
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
Pattern recognition using functions of multiple instances
Abstract: The Functions of Multiple Instances (FUMI) method for learning a target prototype from data points that are functions of target and non-target prototypes is introduced. In this paper, a specific case is considered where, given data points which are… Read More
An investigation of likelihoods and priors for bayesian endmember estimation
Abstract: A Gibbs sampler for piece-wise convex hyperspectral unmixing and endmember detection is presented. The standard linear mixing model used for hyperspectral unmixing assumes that hyperspectral data reside in a single convex region. However, hyperspectral data is often non-convex. Furthermore,… Read More
Robust endmember detection using L1 norm factorization
Abstract: The results from L1-Endmembers display the algorithm’s stability and accuracy with increasing levels of noise. The algorithm was extremely stable in the number of endmembers when compared to the SPICE algorithm and the Virtual Dimensionality methods for estimating the… Read More
Multiple model endmember detection based on spectral and spatial information
Abstract: We introduce a new spectral mixture analysis approach. Unlike most available approaches that only use the spectral information, this approach uses the spectral and spatial information available in the hyperspectral data. Moreover, it does not assume a global convex… Read More
A comparison of deterministic and probabilistic approaches to endmember representation
Abstract: The piece-wise convex multiple model endmember detection algorithm (P-COMMEND) and the Piece-wise Convex End-member detection (PCE) algorithm autonomously estimate many sets of endmembers to represent a hyperspectral image. A piece-wise convex model with several sets of endmembers is more… Read More
Spatially-smooth piece-wise convex endmember detection
Abstract: An endmember detection and spectral unmixing algorithm that uses both spatial and spectral information is presented. This method, Spatial Piece-wise Convex Multiple Model Endmember Detection (Spatial P-COMMEND), autonomously estimates multiple sets of endmembers and performs spectral unmixing for input… Read More
L1-endmembers: a robust endmember detection and spectral unmixing algorithm
Abstract: A hyperspectral endmember detection and spectral unmixing algorithm based on an l1 norm factorization of the input hyperspectral data is developed and compared to a method based on l2 norm factorization. Both algorithms, the L1-Endmembers algorithm based on the… Read More
Quantifying the benefit of airborne and ground sensor fusion for target detection
Abstract: In this paper, a study involving the detection of buried objects by fusing airborne Multi-Spectral Imagery (MSI) and ground-based Ground Penetrating Radar (GPR) data is investigated. The benefit of using the airborne sensor to cue the GPR, which will… Read More