Supervised Feature Extraction of Hyperspectral Images Using Partitioned Maximum Margin Criterion

Dimensionality reduction is an important task where the aim is to reduce the number of features and make the system less time consuming for classification. Here, the drawbacks of Fisher's linear-discriminant-analysis-based feature extraction (FE) methods are addressed and a proposal is made to overcome it as well as to reduce the Hughes phenomenon and computational complexity of the system. The proposed FE technique initially partitions the complete set of features into several highly correlated subgroups. Then a linear transformation is performed using a maximal margin criterion over each subgroup. The proposed method is supervised in nature, because prior information about the class label of data is required to calculate the maximum margin criterion based on interclass and intraclass scatter matrices. Experiments are conducted with the PaviaU and Indian pine data sets, and the results are compared with five state-of-the-art techniques, both qualitatively and quantitatively, to demonstrate the effectiveness of the proposed method.