Hyperspectral Image Classification using Support Vector Machine with Guided Image Filter

Abstract

Hyperspectral images are used to identify and detect the objects on the earth's surface. Classifying of these hyperspectral images is becoming a difficult task, due to more number of spectral bands. These high dimensionality problems are addressed using feature reduction and extraction techniques. However, there are many challenges involved in the classification of data with accuracy and computational time. Hence in this paper, a method has been proposed for hyperspectral image classification based on support vector machine (SVM) along with guided image filter and principal component analysis (PCA). In this work, PCA is used for the extraction and reduction of spectral features in hyperspectral data. These extracted spectral features are classified using SVM like vegetation fields, building, etc., with different kernels. The experimental results show that SVM with Radial Basis Functions (RBF) kernel will give better classification accuracy compared to other kernels. Moreover, classification accuracy is further improved with a guided image filter by incorporating spatial features.

A Novel Band Selection Approach for Hyperspectral Image Classification using the Kolmogorov Variational Distance

“…The selected bands are fed to classifiers in order de show their classification performances. The used classifier is SVM through the LIBSVM library with RBF as kernel function and the grid search technique to find the C and γ parameters [40].…”