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Proceedings Paper

Data processing method applying principal component analysis and spectral angle mapper for imaging spectroscopic sensors
Author(s): P. B. García-Allende; O. M. Conde; J. Mirapeix; A. M. Cubillas; J. M. López-Higuera
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Paper Abstract

A data processing method for hyperspectral images is presented. Each image contains the whole diffuse reflectance spectra of the analyzed material for all the spatial positions along a specific line of vision. This data processing method is composed of two blocks: data compression and classification unit. Data compression is performed by means of Principal Component Analysis (PCA) and the spectral interpretation algorithm for classification is the Spectral Angle Mapper (SAM). This strategy of classification applying PCA and SAM has been successfully tested on the raw material on-line characterization in the tobacco industry. In this application case the desired raw material (tobacco leaves) should be discriminated from other unwanted spurious materials, such as plastic, cardboard, leather, candy paper, etc. Hyperspectral images are recorded by a spectroscopic sensor consisting of a monochromatic camera and a passive Prism- Grating-Prism device. Performance results are compared with a spectral interpretation algorithm based on Artificial Neural Networks (ANN).

Paper Details

Date Published: 2 July 2007
PDF: 4 pages
Proc. SPIE 6619, Third European Workshop on Optical Fibre Sensors, 66193Q (2 July 2007); doi: 10.1117/12.738768
Show Author Affiliations
P. B. García-Allende, Univ. of Cantabria (Spain)
O. M. Conde, Univ. of Cantabria (Spain)
J. Mirapeix, Univ. of Cantabria (Spain)
A. M. Cubillas, Univ. of Cantabria (Spain)
J. M. López-Higuera, Univ. of Cantabria (Spain)

Published in SPIE Proceedings Vol. 6619:
Third European Workshop on Optical Fibre Sensors
Antonello Cutolo; Brian Culshaw; José Miguel López-Higuera, Editor(s)

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