Real-time constrained linear discriminant analysis to target detection and classification in hyperspectral imagery

Abstract

In this paper, we present a constrained linear discriminant analysis (CLDA) approach to hyperspectral image detection and classification as well as its real-time implementation. The basic idea of CLDA 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 pre-assigned 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 the immediate data assessment is critical. The experiments using HYDICE data demonstrate the strength of CLDA approach in discriminating the targets with subtle spectral difference.