Hyperspectral image analysis with piece-wise convex endmember estimation and spectral unmixing

October 11, 2012 in Conference Papers

Abstract:

A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. This algorithm, the Piece-wise Convex Multiple Model Endmember Detection (P-COMMEND) algorithm, models a hyperspectral image using a piece-wise convex representation. By using a piece-wise convex representation, non-convex hyperspectral data are more accurately characterized. For example, the well-known Indian Pines hyperspectral image is used as an example of a piece-wise convex collection of pixels. The convex regions, weights, endmembers and abundances are found using an iterative fuzzy clustering method. Results indicate that the piece-wise convex representation provides endmembers that better represent hyperspectral data sets over methods that use a single convex region.

Links:

IEEE LinkPDF

Citation:

A. Zare, O. Bchir, H. Frigui, and P. Gader, “Hyperspectral image analysis with piece-wise convex endmember estimation and spectral unmixing,” in 19th IEEE Int. Conf. Image Proc. (ICIP), 2012, pp. 2681-2684.
@InProceedings{zare2012hyperspectral,
author = {Alina Zare and Ouiem Bchir and Hichem Frigui and Paul Gader},
title = {Hyperspectral image analysis with piece-wise convex endmember estimation and spectral unmixing},
booktitle = {19th IEEE Int. Conf. Image Proc. (ICIP)},
year = {2012},
pages = {2681-2684},
month = {Oct.},
doi = {10.1109/ICIP.2012.6467451},
}