Hyperspectral Image Analysis--A Robust Algorithm Using Support Vectors and Principal Components

This paper presents a new algorithm for hyperspectral image analysis using spectral-angle based support vector clustering (SVC) and principal component analysis (PCA). In the classical approach to hyper-spectral dimensionality reduction based on principal component analysis (PCA), no meaning or behavior of the spectrum is considered and results are influenced by majority components in the scene. A spectral angle based classification before dimensionality reduction is a possible solution to this problem. Clustering based on support vectors using spectral based kernels is proposed in this work, which is found to generate good results in hyperspectral image classification. The algorithm is tested with two hyperspectral image data sets of 210 bands each, which are taken with hyper-spectral digital imagery collection experiment (HYDICE) air-borne sensors. A comparative study of the proposed algorithm and other two conventional algorithms (PCA alone and PCA with spectral angle mapping (SAM)) is also done