Feature selection using Kernel based Local Fisher Discriminant Analysis for hyperspectral image classification

Feature extraction is an important research aspect for hyperspectral remote sensing image classification to reduce the complexity and improve the classification accuracy. In this paper, a new feature extraction method, Kernel based Local Fisher Discriminative Analysis (KLFDA), is applied to hyperspectral remote sensing processing. This method integrates the advantages of conventional supervised Fisher Discriminative Analysis and unsupervised Locality Preserving Projection methods. Several experiments using the real images have been conducted, which indicate a high efficiency of this algorithm for hyperspectral image classification.