Robust discriminant analysis for classification of remote sensing data

This paper discusses the classic and robust discriminant analysis algorithm applied to the classification of rice fields, water, buildings, and bare land areas. Discriminant Analysis for multiple groups is often done. This method relies on the sample averages and covariance matrices computed from the training sample. Since sample averages and covariance matrices are not robust, it has been proposed to use robust estimators and covariance instead. In order to obtain a robust procedure with high breakdown point for discriminant analysis, the classical estimators are replaced by Feasible Solution Algorithm (FSA). The input data is a time-series of Landsat 8 Normalize Difference Vegetation Index (NDVI). The classification process is guided over two steps, training and classification. The purpose of the training step is to produce discriminant functions using FSA estimators, and the purpose of the classification step is to classify rice fields, water, buildings and bare land areas. The aim of this paper is to measure the accuracy of Classic and Robust Discriminant Analysis to classify the rice fields, water, buildings and bare land areas from Landsat 8 NDVI time series.