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Proceedings Paper

Feature extraction and band selection methods for hyperspectral imagery applied for identifying defects
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Paper Abstract

An important task in hyperspectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details that are needed for the subsequent detection, discrimination and classification processes. Band selection and combination not only serves as the first step of hyperspectral data processing that leads to a significant decrease in computational complexity in the successive procedures, but also a research tool for determining optimal spectra requirements for different online applications. In order to uniquely characterize the materials of interest, band selection criteria for optimal band was defined. An integrated PCA and Fisher linear discriminant (FLD) method has been developed based on the criteria that used for hyperspectral feature band selection and combination. This method has been compared with other feature extraction and selection methods when applied to detect apple defects, and the performance of each method was evaluated and compared based on the detection results.

Paper Details

Date Published: 9 November 2005
PDF: 10 pages
Proc. SPIE 5996, Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality, 59960U (9 November 2005); doi: 10.1117/12.631068
Show Author Affiliations
Xuemei Cheng, Univ. of Maryland, College Park (United States)
Tao Yang, Univ. of Maryland, College Park (United States)
Yud-Ren Chen, USDA Instrumentation and Sensing Lab. (United States)
Xin Chen, Univ. of Maryland, College Park (United States)


Published in SPIE Proceedings Vol. 5996:
Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality
Yud-Ren Chen; George E. Meyer; Shu-I Tu, Editor(s)

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