Hyperspectral imaging is a new technique in remote sensing that collects hundreds of images, at different wavelength values, for the same area in the surface of the Earth. For instance, the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) instrument operated by NASAs Jet Propulsion Laboratory collects 224 spectral channels in the wavelength range from 40 to 250 nanometers using narrow s...

Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent limitations of endmember extraction algorithms in many applications. This strategy often leads to ill-posed inverse problems, which can benefit from spatial regularization strategies. While existing spatial regularization methods improve the problem conditioning and promote piecewise smooth solutions, ...

Given a set of mixed spectral vectors, spectral mixture analysis (or spectral unmixing) aims at estimating the number of reference materials, also called endmembers, their spectral signatures, and their fractional abundances. A semi-supervised approach to deal with the linear spectral unmixing problem consists in assuming that the observed spectral vectors are linear combinations of a small num...

We introduce a method for hyperspectral unmixing that incorporates wavelength dependence in addition to spatial dependence. Spatial dependence is incorporated into the model using class labels on the pixels that is assigned using spectral clustering. Wavelength dependence is introduced by correlating the errors in the unmixing regression models. We propose a non-standard alternating direction m...

In hyperspectral images, once the pure spectra of the materials are known, hyperspectral unmixing seeks to find their relative abundances throughout the scene. We present a novel variational model for hyperspectral unmixing from incomplete noisy data, which combines a spatial regularity prior with the knowledge of the pure spectra. The material abundances are found by minimizing the resulting c...

Abundance estimation is an important step of quantitative analysis of hyperspectral remote sensing data. Due to physical interpretation, sum-to-one and non-negativity constraints are generally imposed on the abundances of materials. This paper presents a geometric approach to fully constrained linear spectral unmixing using variable endmember sets for the pixels. First, an improved method for s...

This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are post-nonlinear functions of unknown pure spectral components contaminated by an additive white Gaussian noise. The nonlinear effects are approximated by a polynomial leading to a polynomial post-nonlinear mixing model. A Bayesian algorithm is proposed to esti...

Accurate mapping is prepared using Linear unmixing of satellite images. Endmember extraction contributes the unmixing accuracy. In this paper, Endmembers are extracted using different Geometrical algorithms like Pixel Purity Index (PPI), Nearest Finder (N-FINDR) and Sequential Maximum Angle Convex Cone (SMACC) algorithms. Extracted Endmembers are given as input for unmixing and it is attempted ...

Multispectral optoacoustic (photoacoustic) tomography (MSOT) is a hybrid modality that can image through several millimeters to centimeters of diffuse tissues, attaining resolutions typical of ultrasound imaging. The method can further identify tissue biomarkers by decomposing the spectral contributions of different photo-absorbing molecules of interest. In this work we investigate the performa...