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Proceedings Paper

Morphological independence and hyperspectral image indexing
Author(s): Manuel Grana; Orlando Maldonado; David Vicente
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Paper Abstract

Content based image retrieval (CBIR) systems are database management systems that emply features extracted from the image as the indices used in the search of the database. Images are retrieved on the basis of the similarity with the query image. Indexing hyperspectral images is a special case of CBIR, with the added complexity of the high dimensionality of the pixels. We propose the use of endmembers as the hyperspectral image characterization. We thus define a similarity measure between hyperspectral images based on these image endmembers. The endmembers must be induced from the image data in order to automate the process. Enmembers can be assumed to be morphologically independent, a notion originally introduced to study the noise robustnes of Morphological Networks. For this induction we use Associative Morphological Memories (AMM) as detectors of Morphological Independence conditions.

Paper Details

Date Published: 30 August 2005
PDF: 10 pages
Proc. SPIE 5916, Mathematical Methods in Pattern and Image Analysis, 59160L (30 August 2005); doi: 10.1117/12.616013
Show Author Affiliations
Manuel Grana, Univ. Pais Vasco (Spain)
Orlando Maldonado, Univ. Pais Vasco (Spain)
David Vicente, Univ. Pais Vasco (Spain)


Published in SPIE Proceedings Vol. 5916:
Mathematical Methods in Pattern and Image Analysis
Jaakko T. Astola; Ioan Tabus; Junior Barrera, Editor(s)

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