Content based retrieval of hyperspectral images using AMM induced endmembers

Authors:
Orlando Maldonado;David Vicente;Manuel Graña;Alicia d'Anjou
Affiliations:
Dept. CCIA, UPV/EHU, San Sebastian, Spain;Dept. CCIA, UPV/EHU, San Sebastian, Spain;Dept. CCIA, UPV/EHU, San Sebastian, Spain;Dept. CCIA, UPV/EHU, San Sebastian, Spain
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Year:
2005

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Content-Based Image Retrieval at the End of the Early Years

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

Indexing hyperspectral images is a special case of content based image retrieval (CBIR) systems, 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. For this induction we use Associative Morphological Memories (AMM) and the notion of Morphological Independence.