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Proceedings Paper

Hyperspectral endmember detection based on strong lattice independence
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Paper Abstract

The advances in image spectroscopy have been applied for Earth observation at different wavelengths of the electromagnetic spectrum using aircrafts or satellite systems. This new technology, known as hyperspectral remote sensing, has found many applications in agriculture, mineral exploration and environmental monitoring since images acquired by these devices register the constituent materials in hundred of spectral bands. Each pixel in the image contains the spectral information of the zone. However, processing these images can be a difficult task because the spatial resolution of each pixel is in the order of meters, an area of such size that can be composed of different materials. The following research presents an alternative methodology to detect pixels in the image that best represent the spectrum of one material with as little contamination of any other as possible. The detection of these pixels, also called endmembers, represents the first step for image segmentation and is based on morphological autoassociative memories and the property of strong lattice independence between patterns. Morphological associative memories and strong lattice independence are concepts based on lattice algebra. Our procedure subdivides a hyperspectral image into regions looking for sets of strong lattice independent pixels. These patterns will be identified as endmembers and will be used for the construction of abundance maps.

Paper Details

Date Published: 24 September 2007
PDF: 12 pages
Proc. SPIE 6696, Applications of Digital Image Processing XXX, 669625 (24 September 2007); doi: 10.1117/12.735262
Show Author Affiliations
Juan-Carlos Valdiviezo-Navarro, National Institute of Astrophysics, Optics and Electronics (Mexico)
Gonzalo Urcid-Serrano, National Institute of Astrophysics, Optics and Electronics (Mexico)


Published in SPIE Proceedings Vol. 6696:
Applications of Digital Image Processing XXX
Andrew G. Tescher, Editor(s)

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