Proceedings Fifth IEEE Southwest Symposium on Image Analysis and Interpretation
DOI: 10.1109/iai.2002.999900
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Cross-power spectrum phase for automated registration of multi/hyperspectral data-cubes for efficient information retrieval

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Cited by 3 publications
(2 citation statements)
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“…Researchers usually consider the dimensionality reduction problem and the pairwise registration problem separately. Traditional rules, such as band selection, averaging, or principal component analysis (PCA) [1], have been applied to multi-spectral or hyper-spectral data sets to reduce data dimensionality. For example, the PCA method produces the images that contain most information of the data sets, but these do not necessarily lead to higher registration accuracy.…”
Section: Introductionmentioning
confidence: 99%
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“…Researchers usually consider the dimensionality reduction problem and the pairwise registration problem separately. Traditional rules, such as band selection, averaging, or principal component analysis (PCA) [1], have been applied to multi-spectral or hyper-spectral data sets to reduce data dimensionality. For example, the PCA method produces the images that contain most information of the data sets, but these do not necessarily lead to higher registration accuracy.…”
Section: Introductionmentioning
confidence: 99%
“…In recent years, intensity based image registration has been widely investigated for remote sensing applications [1] [2]. Performing intensity based registration directly on two highdimensional data sets is extremely computationally expensive.…”
Section: Introductionmentioning
confidence: 99%