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10 items by relevance
1 Article

Kernel principal component and maximum autocorrelation factor analyses for change detection

Nielsen, Allan A.; Canty, Morton J.

Proceedings of Spie--the International Society for Optical Engineering — 2009, Volume 7477, Issue 1, pp. 74770T-6

Kernel versions of the principal components (PCA) and maximum autocorrelation factor (MAF) transformations are used to postprocess change images obtained with the iteratively re-weighted multivariate alteration detection (MAD) algorithm. It is found that substantial improvements in the ratio

Year: 2009

Language: Undetermined

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2 Conference paper

Kernel principal component and maximum autocorrelation factor analyses for change detection

Nielsen, Allan Aasbjerg; Canty, Morton John

Spie Europe Remote Sensing Conference — 2009

covering the same geographical region acquired at two different time points. In this paper kernel versions of the principal component and maximum autocorrelation factor (MAF) transformations are used to carry out the analysis. An example is based on bi-temporal Landsat-5 TM imagery over irrigation fields

Year: 2009

Language: English

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