Share Email Print
cover

Proceedings Paper

Automatic classification for noise of infrared images into processes by means of the principal component analysis
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Noise characterization and classification is an important task to evaluate the performance of an infrared imaging system. The focal plane array infrared cameras present several types of noises: fixed pattern noise, 1/f noise, pure temporal noise, etc. The existence of bad pixels showing a singular behavior must be included in the noise description. In this paper we show how the principal component analysis is able to classify the noise of a set of frames into different subsets. The classification method is integrated into a software package that performs the classification of the obtained eigenimages into processes. This method is specially adapted to the analysis of noise in a set of frames because it produces a corresponding set of images characterizing the noise. A result of the analysis provided with this method is the extraction of the fixed pattern noise, the bad pixel identification, the 1/f nosie components and analysis, the pure temporal noise, and some other processes having intermediate time scales.

Paper Details

Date Published: 29 July 2002
PDF: 12 pages
Proc. SPIE 4719, Infrared and Passive Millimeter-wave Imaging Systems: Design, Analysis, Modeling, and Testing, (29 July 2002); doi: 10.1117/12.477455
Show Author Affiliations
Jose Manuel Lopez-Alonso, Univ. Complutense de Madrid (Spain)
Javier Alda, Univ. Complutense de Madrid (Spain)


Published in SPIE Proceedings Vol. 4719:
Infrared and Passive Millimeter-wave Imaging Systems: Design, Analysis, Modeling, and Testing
Roger Appleby; David A. Wikner; Roger Appleby; Gerald C. Holst; David A. Wikner, Editor(s)

© SPIE. Terms of Use
Back to Top