Comparative study of feature space projection methods for hyperspectral image classification

Feature space projection, or feature projection is an active research topic in machine learning. Some projection methods have been used in remote sensing for dimension reduction, especially for hyperspectral data due to high dimensionality. Projection methods can improve the performance of classifiers susceptible to the Hughes phenomenon. However, the effect of feature projection for more advanced classifiers has not been well-studied, and there are few studies comparing projection methods for hyperspectral image classification. A comprehensive study has been performed on the effect of feature projection for classification using both reduced and full dimensions. The performance of six feature projection methods (PCA, LLE, LDA, LFDA, LMNN, and SPCA) using three classifiers has been explored on three hyperspectral data sets. Results show that the performance of feature projection methods on different classifiers are mainly consistent for different data sets. LFDA achieves the best overall performance considering all data sets and all classifiers.