Real-time constrained linear discriminant analysis for hyperspectral imagery

Abstract

A constrained linear discriminant analysis (CLDA) approach is presented for hyperspectral image detection and classification. Its basic idea is to design an optimal transformation matrix which can maximize the ratio of inter- class distance to intra-class distance while imposing the constraint that different class centers after transformation are along different directions such that different classes can be better separated. The solution turns out to be a constrained version of orthogonal subspace projection (OSP) implemented with a data whitening process. The CLDA approach can be applied to solve both detection and classification problems. In particular, by introducing color for display the classification is achieved with a single classified image where a predetermined color is used to display a specified class. The real-time implementation is also developed to meet the requirement of on-line image analysis when immediate data assessment is critical. The experiments using HYDICE data demonstrate the strength of CLDA approach in discriminating the small targets with subtle spectral difference.