This study concerns with classification techniques in high dimensional space such as that of Hyperspectral Imaging (HSI) data sets, with objectives of understanding the strength and weakness of various classifiers and at the same time to study how their performances can be assessed particularly when there is an absence of ground truth target map in the data set. The thesis summaries the work that carried out during the course of this study and it encompasses a brief survey of machine learning and classification theories, an outline of the HSI instrumentations, data sets that collected in the study and classification analysis. It is found that the supervised classifiers such as the Maximum Likelihood (QD) and the Mahalanobis Distance (FD) classifiers, especially when they are coupled with techniques like Regularised Discriminant Analysis (RDA) or leave-one-out covariance estimations (LOOC), have demonstrated excellent performances comparable to that of the more complicated and computational costly classifiers like the Support Vector Machine (SVM). This work has also revealed that separability measures such as the Total Transformed Divergence (TTD) and Total Jeffries-Matusita Distance (TJM) can be an invaluable method for assessing the goodness of classification in principle. However, the present methods for the evaluation of the separability measures are insufficient for achieving this goal and further work in this area is needed. This study has also confirmed the effectiveness for using RDA and LOOC techniques for a better estimation of the covariance when the sample size is small, ie when the sample size per class to band ratio is less than 100. Through team work this study has contributed partially a number of publications in the area of hyperspectral imaging and machine visions
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