Spectral-Spatial Classification of Hyperspectral Image Using Improved Functional Principal Component Analysis

Abstract

The functional principal component analysis (FPCA) method can effectively solve the problems of the high dimensionality of data, large information redundancy, and noise interference in hyperspectral image (HSI) classification. However, this unsupervised FPCA cannot make full use of the label information of training samples or spatial information, so that it is impossible to obtain satisfactory classification results. In this letter, a set of improved FPCA methods for HSI classification are proposed. First, the B-spline basis system is used to establish the functional data fitting model, which can convert discrete spectral information into continuous spectral curves and lay the foundation for functional feature extraction. Second, a supervised FPCA (SFPCA) method is built for extracting more effective functional features by making full use of the label information of training samples. Furthermore, to overcome the lack of training samples, two semisupervised FPCA (SSFPCA) methods are proposed for extracting more discriminative functional features and improving the classification accuracies. Finally, we perform the local mean filtering method on the HSI in order to extract the spatial information for each pixel, and then design spectral-spatial classification frameworks based on improved FPCA. Experiments on the commonly used HSI dataset show that improved FPCA can achieve higher classification accuracies than FPCA, and the proposed functional spectral-spatial classification frameworks can greatly improve classification accuracies.