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David A. Landgrebe
Researcher at Purdue University
Publications - 178
Citations - 15293
David A. Landgrebe is an academic researcher from Purdue University. The author has contributed to research in topics: Multispectral image & Feature extraction. The author has an hindex of 48, co-authored 177 publications receiving 14075 citations. Previous affiliations of David A. Landgrebe include DuPont & Rochester Institute of Technology.
Papers
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Covariance matrix estimation and classification with limited training data
TL;DR: A new covariance matrix estimator useful for designing classifiers with limited training data is developed, and in experiments, this estimator achieved higher classification accuracy than the sample covariance matrices and common covariance Matrix estimates.
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Hyperspectral data analysis and supervised feature reduction via projection pursuit
L.O. Jimenez,David A. Landgrebe +1 more
TL;DR: The need for reducing the dimensionality via a preprocessing method that takes into consideration high-dimensional feature-space properties should enable the estimation of feature-extraction parameters to be more accurate and bypass the problems of the limitation of small numbers of training samples.
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Analyzing high-dimensional multispectral data
Chulhee Lee,David A. Landgrebe +1 more
TL;DR: Recognizing the importance of second-order statistics on the one hand, but the increased difficulty in perceiving and comprehending information present in statistics derived from high-dimensional data on the other, the authors propose a method to aid visualization of high- dimensional statistics using a color coding scheme.
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Adaptive Bayesian contextual classification based on Markov random fields
Q. Jackson,David A. Landgrebe +1 more
TL;DR: An adaptive Bayesian contextual classification procedure that utilizes both spectral and spatial interpixel dependency contexts in estimation of statistics and classification is proposed, which can reach classification accuracies similar to that obtained by a pixelwise maximum likelihood pixel classifier with a very large training sample set.