A robust classification procedure based on mixture classifiers and nonparametric weighted feature extraction

This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by sending a blank email message to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Abstract There are many factors to consider in carrying out a hyperspectral data classification, perhaps chief among them are class training sample size, dimensionality, and distribution separability. The intent of this study is to design a classification procedure which is robust and maximally effective, but which provides the analyst with significant assists, thus simplifying the analyst's task. The result is a quadratic mixture classifier based on Mixed-LOOC2 regularized discriminant analysis and Nonparametric Weighted Feature Extraction. This procedure has the advantage of providing improved classification accuracy compared to typical previous methods but requires minimal need to consider the factors mentioned above. Experimental results demonstrating these properties are presented.