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
PublicationPublication
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
Spectral unmixing cluster validity index for multiple sets of endmembers
Abstract: A hyperspectral pixel is generally composed of a relatively small number of endmembers. Several unmixing methods have been developed to enforce this concept through sparsity promotion or piece-wise convex mixing models. Piece-wise convex unmixing methods often require as parameters… 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
Directly measuring material proportions using hyperspectral compressive sensing
Abstract: A compressive sensing framework is described for hyperspectral imaging. It is based on the widely used linear mixing model, LMM, which represents hyperspectral pixels as convex combinations of small numbers of endmember (material) spectra. The coefficients of the endmembers… Read More
Multi-modal change detection, application to the detection of flooded areas: outcome of the 2009-2010 data fusion contest
Abstract: The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and… 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