Cross-power spectrum phase for automated registration of multi/hyperspectral data-cubes for efficient information retrieval

Mandujano, C.E.; Mitra, Sunanda

doi:10.1109/iai.2002.999900

Cited by 3 publications

(2 citation statements)

References 9 publications

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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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Registration of high-dimensional remote sensing data based on a new dimensionality reduction rule

Chen

Varshney

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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Registration of remote sensing data often involves dimensionality reduction of high-dimensional data to yield an image from each data set followed by pairwise image registration. We develop a new rule for dimensionality reduction such that the the Cramér-Rao lower bound (CRLB) for the estimation of the transformation parameters is minimized. A hyperspectral data set and a multispectral data set are used to evaluate our proposed rule. The experimental results using Mutual Information (MI) based pairwise registration technique demonstrate that our proposed rule can select the image pair with more texture, resulting in improved image registration results.

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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%

Registration of high-dimensional remote sensing data based on a new dimensionality reduction rule

Chen

Varshney

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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Dimensionality Reduction for Registration of High-Dimensional Data Sets

Chen

Varshney

2013

IEEE Trans. on Image Process.

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Registration of two high-dimensional data sets often involves dimensionality reduction to yield a single-band image from each data set followed by pairwise image registration. We develop a new application-specific algorithm for dimensionality reduction of high-dimensional data sets such that the weighted harmonic mean of Cramér-Rao lower bounds for the estimation of the transformation parameters for registration is minimized. The performance of the proposed dimensionality reduction algorithm is evaluated using three remotes sensing data sets. The experimental results using mutual information-based pairwise registration technique demonstrate that our proposed dimensionality reduction algorithm combines the original data sets to obtain the image pair with more texture, resulting in improved image registration.

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Automated registration of hyperspectral images

Erives¹,

Fitzgerald

2004

SPIE Proceedings

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Cross-power spectrum phase for automated registration of multi/hyperspectral data-cubes for efficient information retrieval

Cited by 3 publications

References 9 publications

Registration of high-dimensional remote sensing data based on a new dimensionality reduction rule

Registration of high-dimensional remote sensing data based on a new dimensionality reduction rule

Dimensionality Reduction for Registration of High-Dimensional Data Sets

Automated registration of hyperspectral images

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