the U.S. Army Research Office under Grant Number DAAH04-96-1-0444.- ii-- iii-TABLE OF CONTENTS ABSTRACT................................................................................................................................................... V CHAPTER 1: INTRODUCTION................................................................................................................ 1 1.1 STATEMENT OF PROBLEM...................................................................................................................... 1
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2322
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Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images
– Geman, Geman
- 1984
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615
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Spatial interaction and the statistical analysis of lattice systems (with discussion
– Besag
- 1974
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565
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Monte Carlo sampling methods using Markov chains and their applications
– Hastings
- 1970
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552
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On the statistical analysis of dirty pictures
– Besag
- 1986
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374
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Mixture densities, maximum likelihood and the em algorithm
– Redner, Walker
- 1984
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207
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Markov Random Field Modeling in Computer Vision
– Li
- 1995
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145
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Regularized discriminant analysis
– Friedman
- 1989
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145
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A multiscale random field model for Bayesian image segmentation
– Bouman, Shapiro
- 1994
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97
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Multiple resolution segmentation of textured images
– Bouman, Liu
- 1991
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68
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Markov Random Fields and their Applications
– Kindermann, Snell
- 1980
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67
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The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon
– Shahshahani, Landgrebe
- 1994
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66
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On the mean accuracy of statistical pattern recognizers
– Hughes
- 1968
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50
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A renormalization group approach to image processing problems
– Gidas
- 1989
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34
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Feature extraction based on decision boundaries
– Lee, Landgrebe
- 1993
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32
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Parameter Estimation: Principles and Problems
– Sorenson
- 1980
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29
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Remote Sensing: The Quantitative Approach
– Swain
- 1978
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24
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Supervised classification in highdimensional space: geometrical, statistical, and asymptotical properties of multivariate data
– Jimenez, Landgrebe
- 1998
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23
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Covariance Matrix Estimation and Classification with Limited Training Data
– Hoffbeck, Landgrebe
- 1996
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19
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Classification of Multispectral Image Data by Extraction and Classification of Homogeneous Objects
– Kettig, Landgrebe
- 1976
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15
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Covariance pooling and stabilization for classification
– Greene, Rayens
- 1991
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13
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The development of a spectral-spatial classifier for earth observational data
– Landgrebe
- 1980
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11
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Maximum likelihood discriminant analysis on the plane using a markovian model of spatial context
– Haslett
- 1985
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11
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Bayesian contextual classification based on modified M-estimates and markov random fields
– Jhung, Swain
- 1996
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10
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Classification of high-dimensional multispectral data
– Hoffbeck, Landgrebe
- 1995
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9
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Covariance Estimation With Limited Training Samples
– Tadjudin, Landgrebe
- 1999
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8
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et al, “Textural features for image classification
– Haralick
- 1973
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7
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A Monte Carlo comparison of four estimators of a covariance matrix," Multivariate analysis--VI
– Lin, Perlman
- 1985
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6
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An Introduction to MultiSpec
– Landgrebe, Biehl
- 1994
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6
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Contextual classification of multispectral pixel data
– Kittler, Foglein
- 1984
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6
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Spatial-temporal autocorrelated model for contextual classification
– Khazenie, Crawford
- 1990
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6
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Multiscale Markov random fields and constrained relaxation in low level image analysis
– Perez, Heitz
- 1992
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5
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Discriminant Functions when Covariances are Equal and Sample Sizes are Moderate
– Wahl, Kronmall
- 1977
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5
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Discriminant Functions when the Covariance Matrices are unequal
– Marks, Dunn
- 1974
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5
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Contextual classification of multispectral image data
– Swain, Vardeman, et al.
- 1981
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4
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Toward robust analysis of satellite images using map information: Application to urban area detection
– Yu, Berthod, et al.
- 1999
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2
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Information and Mixtures of two normal
– Hosmer
- 1997
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1
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Recursive contextual classification using a spatial stochastic model
– Yu, Fu
- 1983
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1
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Spatio-temperoal contextual classification of remotely sensed multispectral data
– Jeon, Landgrebe
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1
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et al., “The development of improved algorithms for image processing and classification,” Final Report of NERC
– Drake
- 1987
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