Modified wavelet kernel methods for hyperspectral image classification

Pai-Hui Hsu; Xiu-Man Huang

doi:10.1117/12.2194890

15 October 2015 Modified wavelet kernel methods for hyperspectral image classification

Pai-Hui Hsu, Xiu-Man Huang

Proceedings Volume 9643, Image and Signal Processing for Remote Sensing XXI; 96432C (2015) https://doi.org/10.1117/12.2194890
Event: SPIE Remote Sensing, 2015, Toulouse, France

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.

Citation Download Citation

Pai-Hui Hsu and Xiu-Man Huang "Modified wavelet kernel methods for hyperspectral image classification", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96432C (15 October 2015); https://doi.org/10.1117/12.2194890

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Wavelets

Hyperspectral imaging

Image classification

Discrete wavelet transforms

Remote sensing

Feature extraction

Continuous wavelet transforms

Show All Keywords

Keywords/Phrases

Search In:

Publication Years