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Proceedings Paper

Hyperspectral image analysis by scale-orientation morphological profiles
Author(s): Antonio J. Plaza; Pablo Martinez; Rosa M. Perez; Javier Plaza
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Paper Abstract

Mathematical morphology is a classic nonlinear image processing technique that has been successfully applied to analysis and classification of remotely sensed grayscale image data. The extension of basic morphological operations (i.e. erosion and dilation) to multi/hyperspectral imagery is not straightforward. In our approach, we treat the data at each pixel as a vector and impose a partial ordering of vectors in the selected vector space, based on their spectral purity. As a result, basic morphological operations are defined by extension, allowing joint spatial/spectral analysis of remotely sensed multispectral data. In this paper, we introduce the concept of scale-orientation morphological profile, and explore its application to mixed-pixel analysis and classification of hyperspectral data. The effectiveness of the proposed approach is assessed by using both simulated and real hyperspectral datasets collected by the NASA/Jet Propulsion Laboratory Airborne Visible-Infrared Imaging Spectrometer (AVIRIS). The proposed method is successfully applied for the purpose of land-cover classification and delineation of agricultural fields located at the Salinas Valley in California.

Paper Details

Date Published: 5 February 2004
PDF: 8 pages
Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.511110
Show Author Affiliations
Antonio J. Plaza, Univ. de Extremadura (Spain)
Pablo Martinez, Univ. de Extremadura (Spain)
Rosa M. Perez, Univ. de Extremadura (Spain)
Javier Plaza, Univ. de Extremadura (Spain)

Published in SPIE Proceedings Vol. 5238:
Image and Signal Processing for Remote Sensing IX
Lorenzo Bruzzone, Editor(s)

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