NDLI: Blind unmixing of remote sensing data with some pure pixels: Extension and comparison of spatial methods exploiting sparsity and nonnegativity properties

Content Provider	IEEE Xplore Digital Library
Author	Karoui, M.S. Deville, Y. Hosseini, S. Ouamri, A.
Copyright Year	2013
Description	Author affiliation: IRAP, Univ. de Toulouse, Toulouse, France (Deville, Y.; Hosseini, S.) \|\| Lab. Signaux et Images, Univ. des Sci. et de la Technol., Oran, Algeria (Ouamri, A.) \|\| Div. Obs. de la Terre, Centre des Tech. Spatiales, Arzew, Algeria (Karoui, M.S.)
Abstract	Multispectral and hyperspectral imaging systems are among the most powerful tools in the field of remote sensing. In remote sensing imagery, pixel values are often linear mixtures of contributions from pure materials contained in the observed scene. In this paper, we extend our recently developed spatial methods for blindly unmixing each pixel of remote sensing data with some pure pixels and we compare their performance, both for multispectral and hyperspectral images. These extended methods are related to the blind source separation (BSS) problem, and are based on sparse component analysis (SCA) and nonnegativity constraints. Spatial correlation-based or variance-based SCA algorithms (which detect a few pure-pixel zones) are firstly used to identify the mixing matrix by means of two different approaches for selecting the columns of this matrix. Nonnegative least squares (NLS) or nonnegative matrix factorization (NMF) methods are then used to extract spatial sources. Experiments based on realistic synthetic data are performed to compare the accuracies and the computational costs of these extended methods. We show that the tested methods yield high accuracy with low computational cost for the variance-based methods as compared to those based on correlation.
Starting Page	42
Ending Page	49
File Size	333125
Page Count	8
File Format	PDF
ISBN	9781467355407
DOI	10.1109/WoSSPA.2013.6602334
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-12
Publisher Place	Algeria
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Source separation Materials Vectors Computational efficiency Hyperspectral imaging
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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