Supervised Hyperspectral Image Classification using SVM and Linear Discriminant Analysis

Shambulinga M,G Sadashivappa

doi:10.14569/ijacsa.2020.0111050

Abstract

Hyperspectral images are used to recognize and determine the objects on the earth’s surface. This image contains more number of spectral bands and classifying the image becoming a difficult task. Problems of higher number of spectral dimensions are addressed through feature extraction and reduction. However, accuracy and computational time are the important challenges involved in the classification of hyperspectral images. Hence in this paper, a supervised method has been developed to classify the hyperspectral image using support vector machine (SVM) and linear discriminant analysis (LDA). In this work, spectral features of the images are extracted and reduced using LDA. Spectral features of hyperspectral images are classified using SVM with RBF kernel like buildings, vegetation fields, etc. The simulation results show that the SVM algorithm combined with LDA has good accuracy and less computational time. Furthermore, the accuracy of classification is enhanced by incorporating the spatial features using edge-preserving filters.

Highlights

Hyperspectral image classification helps in identifying the various objects present on the earth’s surface
Spectral bands covered with water absorption region are removed and total bands of the image are reduced to 200 bands
support vector machine (SVM) with RBF kernel was used in the proposed approach for classification

Summary

Introduction

Hyperspectral image classification helps in identifying the various objects present on the earth’s surface. A Hyperspectral image is captured with different wavelengths and contains a number of spectral bands. Classification of hyperspectral image is based on the spectral signature of the various materials. Spectral signature contains the reflected and absorbed light of the material with respect to different wavelength of electromagnetic spectrum. Has different reflection and absorption at various channel wavelength. These spectral signatures of various materials on the earth are measured using spectrometer. The number of spectral signature values of various spectral wavelength bands, increases the spectral dimensions of the hyperspectral image. To overcome the problem with higher dimensions of image, researchers have developed various band reduction techniques to reduce the number of spectral bands in hyperspectral images

Methods

Results

Conclusion