Tag: unmixing

Abstract: A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. Hyperspectral data are often nonconvex. The Piecewise Convex Multiple-Model Endmember Detection algorithm accounts for this using a piecewise convex model. Multiple sets of endmembers and abundances are found using an iterative fuzzy clustering and spectral unmixing method. The […]

Abstract: A Metropolis-within-Gibbs sampler for piecewise convex hyperspectral unmixing and endmember extraction is presented. The standard linear mixing model used for hyperspectral unmixing assumes that hyperspectral data reside in a single convex region. However, hyperspectral data are often nonconvex. Furthermore, in standard endmember extraction and unmixing methods, endmembers are generally represented as a single point […]

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. Methods exist to unmix hyperspectral pixels using nonlinear models, but rely on severely limiting assumptions […]

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 piece-wise convex representation, non-convex hyperspectral data are more accurately characterized. For example, the well-known Indian […]

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 the number of endmembers and the number of sets of endmembers needed. However, these values […]

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 and BISPICE are similar. However, the endmember updates, one novel aspect of the work, are […]

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 as endmember distributions. The proposed method combines the Piece-wise Convex Multiple Model Endmember Detection (PCOMMEND) […]

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. Methods exist to unmix hyperspectral pixels using nonlinear models, but rely on severely limiting assumptions […]

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 for each pixel are called proportions. The endmembers and proportions are often the sought-after quantities; […]

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 in a hyperspectral data cube. In this work, a spectral unmixing algorithm, the Local Information […]