Automated identification of endmembers from hyperspectral data using mathematical morphology

Antonio Plaza; Pablo Martinez; J. Anthony Gualtieri; Rosa Perez

doi:10.1117/12.454162

28 January 2002 Automated identification of endmembers from hyperspectral data using mathematical morphology

Antonio Plaza, Pablo Martinez, J. Anthony Gualtieri, Rosa Perez

Proceedings Volume 4541, Image and Signal Processing for Remote Sensing VII; (2002) https://doi.org/10.1117/12.454162
Event: International Symposium on Remote Sensing, 2001, Toulouse, France

Abstract

One of the most widely used approaches to analyze hyperspectral data is pixel unmixing, which relies on the identification of the purest spectra from the data cube. Once these elements, known as 'endmembers', are extracted, several methods can be used to map their spatial distributions, associations and abundances. A large variety of methodologies have been recently proposed with the purpose of extracting endmembers from hyperspectral data. Nevertheless, most of them only rely on the spectral response; spatial information has not been fully exploited yet, specially in unsupervised classification. The integration of both spatial and spectral information is becoming more relevant as the sensors tend to increase their spatial/spectral resolution. Mathematical morphology is a non-linear image analysis and pattern recognition technique that has proved to be especially well suited to segment images with irregular and complex shapes, but has rarely been applied to the classification/segmentation of multivariate remote sensing data. In this paper we propose a completely automated method, based on mathematical morphology, which allows us to integrate spectral and spatial information in the analysis of hyperspectral images. The accuracy of the proposed algorithm is tested by its application to real hyperspectral data, and the results provided are compared to those found using other existing endmember extraction algorithms.

Citation Download Citation

Antonio Plaza, Pablo Martinez, J. Anthony Gualtieri, and Rosa Perez "Automated identification of endmembers from hyperspectral data using mathematical morphology", Proc. SPIE 4541, Image and Signal Processing for Remote Sensing VII, (28 January 2002); https://doi.org/10.1117/12.454162

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