Modified wavelet kernel methods for hyperspectral image classification
Journal
Proceedings of SPIE - The International Society for Optical Engineering
Journal Volume
9643
Date Issued
2015
Author(s)
Abstract
Hyperspectral images have the capability of acquiring images of earth surface with several hundred of spectral bands. Providing such abundant spectral data should increase the abilities in classifying land use/cover type. However, due to the high dimensionality of hyperspectral data, traditional classification methods are not suitable for hyperspectral data classification. The common method to solve this problem is dimensionality reduction by using feature extraction before classification. Kernel methods such as support vector machine (SVM) and multiple kernel learning (MKL) have been successfully applied to hyperspectral images classification. In kernel methods applications, the selection of kernel function plays an important role. The wavelet kernel with multidimensional wavelet functions can find the optimal approximation of data in feature space for classification. The SVM with wavelet kernels (called WSVM) have been also applied to hyperspectral data and improve classification accuracy. In this study, wavelet kernel method combined multiple kernel learning algorithm and wavelet kernels was proposed for hyperspectral image classification. After the appropriate selection of a linear combination of kernel functions, the hyperspectral data will be transformed to the wavelet feature space, which should have the optimal data distribution for kernel learning and classification. Finally, the proposed methods were compared with the existing methods. A real hyperspectral data set was used to analyze the performance of wavelet kernel method. According to the results the proposed wavelet kernel methods in this study have well performance, and would be an appropriate tool for hyperspectral image classification. © 2015 SPIE.
SDGs
Other Subjects
Classification (of information); Feature extraction; Image acquisition; Image processing; Independent component analysis; Land use; Learning algorithms; Remote sensing; Signal processing; Space optics; Spectroscopy; Support vector machines; Dimensionality reduction; Hyper-spectral images; Hyperspectral data classification; Hyperspectral image classification; Kernel methods; Multidimensional wavelets; Multiple Kernel Learning; Wavelet kernel; Image classification
Type
journal article