Qiong Jackson

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In this paper an Adaptive Bayesian Contextual classification procedure that utilizes both spectral and spatial interpixel dependency contexts in estimation of statistics and classification is proposed. Essentially, this classifier is the constructive coupling of an adaptive classification procedure and a Bayesian contextual classification procedure. In this(More)
In this paper, we propose a self-learning and self-improving adaptive classifier to mitigate the problem of small training sample size that can severely affect the recognition accuracy of classifiers when the dimensionality of the multispectral data is high. This proposed adaptive classifier utilizes classified samples (referred as semilabeled samples) in(More)
In this paper a family of adaptive covariance estimators is proposed to mitigate the problem of limited training samples for application to hyperspectral data analysis in quadratic maximum likelihood classification. These estimators are the combination of adaptive classification procedures and regularized covariance estimators. In these proposed estimators,(More)
.......................................................................................................................................................... V CHAPTER 1: INTRODUCTION ..................................................................................................................... 1 CHAPTER 2: EFFECT OF SEMI-LABELED SAMPLES IN REDUCING THE(More)
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