Abstract: We present a computational framework for modeling geospatial dynamics of crowd emotions as part of anticipatory analysis. The framework is based on agent based modeling (ABM) and evaluation-activation space for emotion representation. ABM is a simulation technique that uses… Read More
Conference PapersConference Papers
Endmember extraction using the physics-based multi-mixture pixel model
Abstract: A method of incorporating the multi-mixture pixel model into hyperspectral endmember extraction is presented and discussed. A vast majority of hyperspectral endmember extraction methods rely on the linear mixture model to describe pixel spectra resulting from mixtures of endmembers.… Read More
Hyperspectral image analysis with piece-wise convex endmember estimation and spectral unmixing
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
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