Automatic classification for noise of infrared images into processes by means of the principal component analysis

Jose Manuel Lopez-Alonso; Javier Alda

doi:10.1117/12.477455

29 July 2002 Automatic classification for noise of infrared images into processes by means of the principal component analysis

Jose Manuel Lopez-Alonso, Javier Alda

Author Affiliations +

Proceedings Volume 4719, Infrared and Passive Millimeter-wave Imaging Systems: Design, Analysis, Modeling, and Testing; (2002) https://doi.org/10.1117/12.477455
Event: AeroSense 2002, 2002, Orlando, FL, United States

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.

Citation Download Citation

Jose Manuel Lopez-Alonso and Javier Alda "Automatic classification for noise of infrared images into processes by means of the principal component analysis", Proc. SPIE 4719, Infrared and Passive Millimeter-wave Imaging Systems: Design, Analysis, Modeling, and Testing, (29 July 2002); https://doi.org/10.1117/12.477455

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