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Proceedings Paper

Identification of weak faint point sources by using principal component analysis
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Paper Abstract

The Principal Component Analysis (PCA) has been successfully applied to the characterization of noise in a sequence of frames and to the identification of bad pixels in an imaging array. Remote sensing scenery includes visualization through atmospheric turbulence and sea surfaces. These conditions produce spatial-temporal patterns that can be properly treated with the PCA method. A faint or weak source may be masked by the spatial features of the scene, or even by a fluctuating structure embedded on it. The PCA method is able to filter out these contributions related with global correlations of the set of data. In this sense the identification of the sources with the PCA is not based on the values of the Signal-to-Noise Ratio (SNR). It pays more attention to the spatio-temporal structure of the signals. Therefore, it is possible to identify sources below the classical SNR threshold. Another advantage is that the method corresponds with linear transformations, therefore it is easily implemented requiring a low computational effort. The approach used in this contribution is based on the same reasoning applied to the identification and classification of bad pixels. When the source is a point source, its image will fall on a small cluster of pixels (in the limit it will be only one pixel). This cluster is identified because the spatial-temporal evolution is different from the rest of the array. The method is applied to simulated sceneries as those found in images through atmospheric turbulence and detection of targets in sea images.

Paper Details

Date Published: 5 February 2004
PDF: 11 pages
Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.509655
Show Author Affiliations
Jose Manuel Lopez-Alonso, Univ. Complutense de Madrid (Spain)
Javier Alda, Univ. Complutense de Madrid (Spain)


Published in SPIE Proceedings Vol. 5238:
Image and Signal Processing for Remote Sensing IX
Lorenzo Bruzzone, Editor(s)

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