Dynamic analysis of the uptake and metabolism of polycyclic aromatic hydrocarbons (PAHs) and their metabolites within live cells in real time has the potential to provide novel insights into genotoxic and non-genotoxic mechanisms of cellular injury caused by PAHs. The present work, combining the use of metabolite spectra generated from metabolite standards using multiphoton spectral analysis an...

Because hyperspectral imagery is generally low resolution, it is possible for one pixel in the image to contain several materials. The process of determining the abundance of representative materials in a single pixel is called spectral unmixing. We discuss the L1 unmixing model and fast computational approaches based on Bregman iteration. We then use the unmixing information and Total Variatio...

As a supplement or an alternative to classification of hyperspectral image data the linear mixture model is considered in order to obtain estimates of abundance of each class or endmember in pixels with mixed membership. Full unmixing and the partial unmixing methods orthogonal subspace projection (OSP), constrained energy minimization (CEM) and an eigenvalue formulation alternative are dealt w...

Spectral unmixing is a popular technique for hyperspectral data interpretation. It focuses on estimating the abundance of pure spectral signature (called as endmembers) in each observed image signature. However, the identification of the endmembers in the original hyperspectral data becomes a challenge due to the lack of pure pixels in the scenes and the difficulty in estimating the number of e...

An intensively used agricultural test site in Switzerland is covered by the DAIS 7915 imaging spectrometer in summer 1997. Three different methods of collecting endmembers for spectral unmixing are selected and compared against each other. The methods include a soil-vegetation-atmosphere-transfer approach (SVAT) based on a leaf optical properties model (PROSPECT) and a canopy model (SAIL), imag...

Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which w...

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. The spectral signatures collected in natural environments are invariably a mixture of the pure signatures of the various materials found within the spatial extent of the ground instantaneous field view of the imaging instrument. Spectral unmixing aims at inferring such pure spectral signatures, called en...

Spectral mixture analysis (SMA) is a very important task for hyper-spectral image analysis, in general, and subpixel data extraction, in particular. In this paper we present a new methodology for spectral unmixing, where a vector of fractions, corresponding to a set of endmembers (EMs), is estimated for each pixel in the image. The process first provides an initial estimate of the fraction vect...