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Ieee transactions on geoscience and remote sensing 1 geometric unmixing of large hyperspectral images: a barycentric coordinate approach.
| Content Provider | CiteSeerX |
|---|---|
| Author | Honeine, Paul Richard, Cédric |
| Abstract | Abstract—In hyperspectral imaging, spectral unmixing is one of the most challenging and fundamental problems. It consists of breaking down the spectrum of a mixed pixel into a set of pure spectra, called endmembers, and their contributions, called abundances. Many endmember extraction techniques have been proposed in literature, based on either a statistical or a geometrical formulation. However, most, if not all, of these techniques for estimating abundances use a least-squares solution. In this paper, we show that abundances can be estimated using a geometric formulation. To this end, we express abundances with the barycentric coordinates in the simplex defined by endmembers. We propose to write them in terms of a ratio of volumes or a ratio of distances, which are quantities that are often computed to identify endmembers. This property allows us to easily incorporate abundance estimation within conventional endmember extraction techniques, without incurring additional computational complexity. We use this key property with various endmember extraction techniques, such as N-Findr, vertex component analysis, simplex growing algorithm, and iterated constrained endmembers. The relevance of the method is illustrated with experimental results on real hyperspectral images. Index Terms—Abundance estimation, Cramer’s rule, endmember extraction, hyperspectral image, iterated constrained endmembers algorithm, N-Findr, orthogonal subspace projection, simplex, simplex growing algorithm, unmixing spectral data, vertex component analysis. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Ieee Transaction Geoscience Remote Sensing Large Hyperspectral Image Geometric Unmixing Barycentric Coordinate Approach Component Analysis Abundance Estimation Index Term Abundance Estimation Additional Computational Complexity Geometric Formulation Hyperspectral Image Many Endmember Extraction Technique Least-squares Solution Real Hyperspectral Image Key Property Hyperspectral Imaging Barycentric Coordinate Various Endmember Extraction Technique Conventional Endmember Extraction Technique Pure Spectrum Cramer Rule Fundamental Problem Orthogonal Subspace Projection Mixed Pixel Spectral Data Constrained Endmembers Geometrical Formulation Experimental Result Spectral Unmixing |
| Content Type | Text |
