Paper
11 May 2007 Evaluation of PCA dimensionality reduction techniques in imaging spectroscopy for foreign object detection
Olga M. Conde, Marta Amado, Pilar B. García-Allende, Adolfo Cobo, José Miguel López-Higuera
Author Affiliations +
Abstract
Foreign object detection processes are improving thanks to imaging spectroscopy techniques through the employment of hyperspectral systems such as prism-grating-prism spectrographs. These devices offer a valuable but sometimes huge and redundant amount of spectral and spatial information that facilitates and speed up the classification and sorting procedures of materials in industrial production chains. In this work, different algorithms of supervised and non-supervised Principal Components Analysis (PCA) are thoroughly applied on the experimentally acquired hyperspectral images. The evaluated PCA versions implement different statistical mechanisms to maximize the class separability. PCA alternatives (traditional "m-method", "J-measure", SEPCOR and "Supervised PCA") are compared taking into account how the achieved spectral compression affects the classification performance in terms of accuracy and execution time. During the whole process, the classification stage is fixed and performed by an Artificial Neural Network (ANN). The developed techniques have been probed and successfully checked in tobacco industry where detection of plastics, cords, cardboards, papers, textile threads, etc. must be done in order to enter only tobacco leaves in the industrial chain.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Olga M. Conde, Marta Amado, Pilar B. García-Allende, Adolfo Cobo, and José Miguel López-Higuera "Evaluation of PCA dimensionality reduction techniques in imaging spectroscopy for foreign object detection", Proc. SPIE 6565, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 65650M (11 May 2007); https://doi.org/10.1117/12.719221
Lens.org Logo
CITATIONS
Cited by 7 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Principal component analysis

Detection and tracking algorithms

Feature extraction

Hyperspectral imaging

Imaging spectroscopy

Hyperspectral systems

Surface plasmons

Back to Top