Morphological independence and hyperspectral image indexing

Manuel Grana; Orlando Maldonado; David Vicente

doi:10.1117/12.616013

30 August 2005 Morphological independence and hyperspectral image indexing

Manuel Grana, Orlando Maldonado, David Vicente

Proceedings Volume 5916, Mathematical Methods in Pattern and Image Analysis; 59160L (2005) https://doi.org/10.1117/12.616013
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

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.

Citation Download Citation

Manuel Grana, Orlando Maldonado, and David Vicente "Morphological independence and hyperspectral image indexing", Proc. SPIE 5916, Mathematical Methods in Pattern and Image Analysis, 59160L (30 August 2005); https://doi.org/10.1117/12.616013

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