Application of Support Vector Machines to Airborne Hyper-Spectral Image Classification

Abstract

In this paper, an airborne hyper-spectral image, which has 218 bands within a range of spectral resolution from 427.2nm to 945.7nm, is used to classify the vegetation of Mountain Jou-Jou. However, redundant bands could not significantly increase the accuracy of vegetation classification, but increase the computation cost of pattern recognition. Thus, the dimension of the hyper-spectral image is reduced using Principle Component Analysis (PCA) to extract the useful information for vegetation classification. Finally, Support Vector Machines (SVM) is employed to classify the vegetation based on the extracted useful information. In order to illustrate the classification accuracy of the aforementioned procedure, we tested hyper-spectral images of Purdue’s Indian Pines test site with its ground truth data. SVM gives the classification accuracy reach up to 80%.