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
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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
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
PCE: piecewise convex endmember detection
Abstract: A new hyperspectral endmember detection method that represents endmembers as distributions, autonomously partitions the input data set into several convex regions, and simultaneously determines endmember distributions (EDs) and proportion values for each convex region is presented. Spectral unmixing methods… Read More