Noise reduction in remote sensing imagery using data masking and principal component analysis

Brian R. Corner, Ram Mohan Narayanan, Stephen E. Reichenbach

Research output: Contribution to journalConference article

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.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4115
DOIs
StatePublished - Dec 1 2000
EventApplications of digital Image Procedding XXIII - San Diego, CA, USA
Duration: Jul 31 2000Aug 3 2000

Fingerprint

Noise Reduction
Masking
masking
principal components analysis
Noise abatement
noise reduction
imagery
Remote Sensing
Principal component analysis
Principal Component Analysis
remote sensing
Remote sensing
Additive noise
thematic mappers (LANDSAT)
Convolution
Contamination
convolution integrals
Landsat
Additive Noise
standard deviation

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

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Noise reduction in remote sensing imagery using data masking and principal component analysis. / Corner, Brian R.; Narayanan, Ram Mohan; Reichenbach, Stephen E.

In: Proceedings of SPIE - The International Society for Optical Engineering, Vol. 4115, 01.12.2000, p. 1-11.

Research output: Contribution to journalConference article

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