Abstract—In this work, we exploit two assumed properties of the abundances of the observed signatures (endmembers) in order to reconstruct the abundances from hyperspectral data. Joint-sparsity is the first property of the abundances, which assumes the adjacent pixels can be expressed as different linear combinations of same materials. The second property is rank-deficiency where the number of ...

Hyperspectral Endmember extraction of a set of accurateendmembers is critical for the proper unmixing of Hyperspectral image. Several preprocessing algorithms such as spatial preprocessing (SPP), region based spatial preprocessing (RBSPP), and spatial spectral preprocessing (SSPP) have been developed for the extraction of endmembers. These algorithms require complex operations and huge computat...

We present a new algorithm for feature extraction in hyperspectral images based on source separation and parallel computing. In source separation, given a linear mixture of sources, the goal is to recover the components by producing an unmixing matrix. In hyperspectral imagery, the mixing transform and the separated components can be associated with endmembers and their abundances. Source separ...

In this paper, we introduce a set of taxonomies that hierarchically organize and specify algorithms associated with hyperspectral unmixing. Our motivation is to collectively organize and relate algorithms in order to assess the current state-of-the-art in the field and to facilitate objective comparisons between methods. The hyperspectral sensing community is populated by investigators with dis...

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...

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...

The hyperspectral subpixel detection and classification problem has been intensely studied in the downward-looking case, typically satellite imagery of agricultural and urban areas. In contrast, the hyperspectral imaging case when "looking up" at small or distant satellites creates new and unforeseen problems. Usually one pixel or one fraction of a pixel contains the imaging target, and spectra...