The Robust Classification of Hyperspectral Images Using Adaptive Wavelet Kernel Support Vector Data Description

Abstract

Detection of targets in hyperspectral images is a specific case of one-class classification. It is particularly relevant in the area of remote sensing and has received considerable interest in the past few years. The thesis proposes the use of wavelet functions as kernels with Support Vector Data Description for target detection in hyperspectral images. Specifically, it proposes the Adaptive Wavelet Kernel Support Vector Data Description (AWK-SVDD) that learns the optimal wavelet function to be used given the target signature. The performance and computational requirements of AWK-SVDD is compared with that of existing methods and other wavelet functions.

An introduction to target detection and target detection in the context of hyperspectral images is given. This thesis also includes an overview of the thesis and lists the contributions of the thesis. A brief mathematical background into one-class classification in reference to target detection is included. Also described are the existing methods and introduces essential concepts relevant to the proposed approach. The use of wavelet functions as kernels with Support Vector Data Description, the conditions for use of wavelet functions and the use of two functions in order to form the kernel are checked and analyzed. The proposed approach, AWKSVDD, is mathematically described. The details of the implementation and the results when applied to the Urban dataset of hyperspectral images with a random target signature are given. The results confirm the better performance of AWK-SVDD compared to conventional kernels, wavelet kernels and the two-function Morlet-Radial Basis Function kernel. The problems faced with convergence during the Support Vector Data Description optimization are discussed. The thesis concludes with the suggestions for future work.