28 December 2000 Noise reduction in remote sensing imagery using data masking and principal component analysis
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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.
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Brian R. Corner, Ram Mohan Narayanan, Stephen E. Reichenbach, "Noise reduction in remote sensing imagery using data masking and principal component analysis", Proc. SPIE 4115, Applications of Digital Image Processing XXIII, (28 December 2000); doi: 10.1117/12.411533; https://doi.org/10.1117/12.411533
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