Noise estimation in remote sensing imagery using data masking

B. R. Corner, R. M. Narayanan, S. E. Reichenbach

Research output: Contribution to journalArticle

122 Scopus citations

Abstract

Estimation of noise contained within a remote sensing image is essential in order to counter the effects of noise contamination. The application of convolution data-masking techniques can effectively portray the influence of noise. In this paper, we describe the performance of a developed noise-estimation technique using data masking in the presence of simulated additive and multiplicative noise. The estimation method employs Laplacian and gradient data masks, and takes advantage of the correlation properties typical of remote sensing imagery. The technique is applied to typical textural images that serve to demonstrate its effectiveness. The algorithm is tested using Landsat Thematic Mapper (TM) and Shuttle Imaging Radar (SIR-C) imagery. The algorithm compares favourably with existing noise-estimation techniques under low to moderate noise conditions.

Original languageEnglish (US)
Pages (from-to)689-702
Number of pages14
JournalInternational Journal of Remote Sensing
Volume24
Issue number4
DOIs
StatePublished - Feb 20 2003

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ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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