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

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All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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