processing algorithms and results verification. With all this in mind we present here some results from a study of different spectral unmixing techniques over rock-soil-vegetation objects in relation to mixtures decomposition, objects type and proportions determination and biophysical properties retrieval

This document presents the sparse Bayesian unmixing algorithms recently developed in the framework of the “HSI-MARS ” research project. The un-mixing process is formulated as a linear regression problem, where the abun-dance’s physical constraints are taken into account. Based on this formu

This Dissertation is brought to you for free and open access by ThinkIR: The University of Louisville's Institutional Repository. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of ThinkIR: The University of Louisville's Institutional Repository. This title appears here courtesy of the author, who has retained all other copyrights. For more information, please contact rihowa01@louisville.edu.

as the proportions in which they appear, is important to numerous tactical scenarios in which subpixel detail is valuable. With this goal in mind, spectral unmixing algorithms have proliferated in a variety of disciplines that exploit hyperspectral data, often duplicating and renaming previous techniques

Abstract. In this paper, we present a statistical approach to spectral unmixing with unknown endmember spectra and unknown illuminant power spectrum. The method presented here is quite general in nature, being applicable to settings in which sub-pixel information is required. The method

This paper is concerned with a joint Bayesian formulation for de-termining the endmembers and abundances of hyperspectral images along with sparse outliers which can lead to estimation errors unless accounted for. We present an inference method that generalizes pre-vious work and provides a MCMC estimate of the posterior distribu-tion. The proposed method is compared empirically to state-of-the-art algorithms, showing lower reconstruction and detection errors. 1.

Abstract Spectral unmixing is an important task for remotely sensed hyper-spectral data exploitation. The spectral signatures collected in natural environ-ments are invariably a mixture of the pure signatures of the various materials found within the spatial extent of the ground instantaneous field

The light spectrum that is recorded by a pixel on an imaging sensor is often a mixture of several distinct source spectra. Such a mixture may be modeled as a linear combination of some basis spectra, and if the basis for the scene is known, each pixel may be decomposed into its original components using linear inversion. Since the maximum number of separable basis spectra is equal to the number of image bands, multispectral images offer advantages compared to RGB-images. It is shown how multispectral images may be used to separate various re ection components, including second-order scattering, based on the Dichromatic Reflection Model.

Accurate spectral unmixing of fluorescent images is clini-cally important because it is one of the key steps in multiplex histopathology image analysis. The narrow-band reference spectra for quantum dot biomarkers are often precisely known apriori, while the broad-band DAPI (nuclear biomarker

, with high sensitivity and temporal resolution are therefore required. Here, we investigate to what extent spectral unmixing addresses these issues. Methodology/Principal Findings: Using fluorescence resonance energy transfer (FRET)-based reporters, and two-photon laser scanning microscopy with synchronous