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Proceedings Paper

Spectral morphology for feature extraction from multi- and hyper-spectral imagery
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Paper Abstract

For accurate and robust analysis of remotely-sensed imagery it is necessary to combine the information from both spectral and spatial domains in a meaningful manner. The two domains are intimately linked: objects in a scene are defined in terms of both their composition and their spatial arrangement, and cannot accurately be described by information from either of these two domains on their own. To date there have been relatively few methods for combining spectral and spatial information concurrently. Most techniques involve separate processing for extracting spatial and spectral information. In this paper we will describe several extensions to traditional morphological operators that can treat spectral and spatial domains concurrently and can be used to extract relationships between these domains in a meaningful way. This includes the investgation and development of suitable vector-ordering metrics and machine-learning-based techniques for optimizing the various parameters of the morphological operators, such as morphological operator, structuring element and vector ordering metric. We demonstrate their application to a range of multi- and hyper-spectral image analysis problems.

Paper Details

Date Published: 1 June 2005
PDF: 12 pages
Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.602747
Show Author Affiliations
Neal R. Harvey, Los Alamos National Lab. (United States)
Reid B. Porter, Los Alamos National Lab. (United States)


Published in SPIE Proceedings Vol. 5806:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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