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Proceedings Paper

Noise reduction in remote sensing imagery using data masking and principal component analysis
Author(s): Brian R. Corner; Ram Mohan Narayanan; Stephen E. Reichenbach
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Paper Abstract

Noise contamination of remote sensing data is an inherent problem and various techniques have been developed to counter its effects. In multiband imagery, principal component analysis (PCA) can be an effective method of noise reduction. For single images, convolution masking is more suitable. The application of data masking techniques, in association with PCA, can effectively portray the influence of noise. A description is presented of the performance of a developed masking technique in combination with PCA in the presence of simulated additive noise. The technique is applied to Landsat Thematic Mapper (TM) imagery in addition to a test image. Comparisons of the estimated and applied noise standard deviations from the techniques are presented.

Paper Details

Date Published: 28 December 2000
PDF: 11 pages
Proc. SPIE 4115, Applications of Digital Image Processing XXIII, (28 December 2000); doi: 10.1117/12.411533
Show Author Affiliations
Brian R. Corner, Univ. of Nebraska/Lincoln (United States)
Ram Mohan Narayanan, Univ. of Nebraska/Lincoln (United States)
Stephen E. Reichenbach, Univ. of Nebraska/Lincoln (United States)


Published in SPIE Proceedings Vol. 4115:
Applications of Digital Image Processing XXIII
Andrew G. Tescher, Editor(s)

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