Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers. The presence percentages of endmembers in mixed pixels are called abundance fractions. Spectral unmixing problem refers to decomposing these pixels into a set of endmembers and abundance fractions. Due to nonnegat...

Field test results are presented for a prototype long-wave adaptive imager that provides both hyperspectral imagery and contrast imagery based on the direct application of hyperspectral detection algorithms in hardware. Programmable spatial light modulators are used to provide both spectral and spatial resolution using a single element detector. Programmable spectral and spatial detection filte...

The Brazilian savanna, locally known as "cerrado", is the most intensely stressed biome with both naturaland human-induced pressures. In this study, we aimed to improve discrimination and characterization of the Brazilian cerrado physiognomies using hyperspectral Hyperion imagery, the first space-borne imaging spectrometer onboard the NASA's Earth Observing-1 (EO-1) platform. A Hyperion image w...

A Gibbs sampler for piece-wise convex hyperspectral unmixing and endmember detection is presented. The standard linear mixing model used for hyperspectral unmixing assumes that hyperspectral data reside in a single convex region. However, hyperspectral data is often nonconvex. Furthermore, in standard unmixing methods, endmembers are generally represented as a single point in the high dimension...

Although many endmember extraction algorithms have been proposed for hyperspectral images in recent years, there are still some problems in endmember extraction which would lead to inaccurate endmember extraction. One important problem is the variation in endmember spectral signatures due to spatial and temporal variability in the condition of scene components and differential illumination cond...

Change detection by unmixing has been shown to provide enhanced change detection performance for hyperspectral images with respect to more traditional approaches, especially when the temporal images contain sub-pixel level changes. In a recent paper, change detection by spectral unmixing was investigated in detail and the advantages that can be gained by using such an approach were systematical...

Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions...

This article presents a comparative study of three different types of estimators used for supervised linear unmixing of a MEx/OMEGA hyperspectral cube. The algorithms take into account the constraints of the abundance fractions, so as to get physically interpretable results. Abundance and spatial reconstruction error maps show that using a Bayesian MAP estimator, a satisfying compromise between...