Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques

The classification of remote sensing data based on the exploitation of spatial features extracted with morphological and attribute profiles has been recently gaining importance. With the development of efficient algorithms to construct the profiles for large datasets, such methods are becoming even more relevant. When dealing with hyperspectral imagery, the profiles are traditionally built on the first few principal components computed from the data. However, it needs to be determined if other feature reduction approaches are better suited to create base images for the profiles. In this article, we explore the use of profiles based on features derived from three supervised feature extraction techniques (i.e. Discriminant Analysis Feature Extraction, Decision Boundary Feature Extraction and Non-parametric Weighted Feature Extraction) and two unsupervised feature-extraction techniques (i.e. Principal Component Analysis (PCA) and Kernel PCA) in classification and compare the classification accuracies obtained by using different techniques for two different classification methods. The obtained results indicate significant improvements in the accuracies using the supervised feature extraction methods. However, the choice of the method affects the quality of the results for different datasets depending on the availability of the training samples.