NDLI: Improved Spectral Unmixing of Hyperspectral Images Using Spatially Homogeneous Endmembers

Content Provider	IEEE Xplore Digital Library
Author	Zortea, M. Plaza, A.
Copyright Year	2008
Description	Author affiliation: Dept. of Technol. of Comput. & Commun., Univ. of Extremadura, Caceres (Zortea, M.; Plaza, A.)
Abstract	Hyperspectral imaging is a new technique in remote sensing which provides image data at hundreds of spectral wave-lengths, thus allowing a very detailed characterization of the surface of the Earth (from an airborne or satellite platform). One of the most important challenges in hyperspectral imaging is to find an adequate pool of pure signature spectra of the materials present in the scene. These pure signatures are then used to decompose the scene into a set of so-called abundance fractions by means of a spectral unmixing algorithm, thus allowing a detailed analysis of the scene with sub-pixel precision. Most techniques available in endmember extraction literature rely on exploiting the spectral properties of the data alone. As a result, the search for endmembers in a scene is often conducted by treating the data as a collection of spectral measurements with no spatial arrangement. In this paper, we propose a novel strategy to incorporate spatial information into the traditional spectral-based endmember search process. Specifically, we propose to estimate, for each pixel vector in the scene, a scalar value which is used to weight the importance of the spectral information associated to each pixel in terms of its spatial context. The proposed methodology, which favours the selection of highly representative endmembers located in spatially homogeneous areas, is shown in this work to significantly improve several spectral-based endmember extraction algorithms available in the literature.
Starting Page	258
Ending Page	263
File Size	4219243
Page Count	6
File Format	PDF
ISBN	9781424435548
DOI	10.1109/ISSPIT.2008.4775716
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-16
Publisher Place	Serbia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral imaging Hyperspectral sensors Layout Data mining Signal processing algorithms Spatial resolution Surface waves Earth Algorithm design and analysis Spectroscopy spectral unmixing Hyperspectral signal processing spatial-spectral analysis endmember extraction fractional abundance estimation
Content Type	Text
Resource Type	Article

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