Large margin distribution machine for hyperspectral image classification

Kun Zhan; Haibo Wang; He Huang; Yuange Xie

doi:10.1117/1.JEI.25.6.063024

22 December 2016 Large margin distribution machine for hyperspectral image classification

Kun Zhan, Haibo Wang, He Huang, Yuange Xie

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China, National Science Foundation of China, Natural Science Foundation of China, Doctoral Program of Higher Education, Specialized Research Fund for the Doctoral Program of Higher Education, Fundamental Research Funds for the Central Universities

Journal of Electronic Imaging, Vol. 25, Issue 6, 063024 (December 2016). https://doi.org/10.1117/1.JEI.25.6.063024

Abstract

Support vector machine (SVM) classifiers are widely applied to hyperspectral image (HSI) classification and provide significant advantages in terms of accuracy, simplicity, and robustness. SVM is a well-known learning algorithm that maximizes the minimum margin. However, recent theoretical results pointed out that maximizing the minimum margin leads to a lower generalization performance than optimizing the margin distribution, and proved that the margin distribution is more important. In this paper, a large margin distribution machine (LDM) is applied to HSI classification, and optimizing the margin distribution achieves a better generalization performance than SVM. Since the raw HSI feature space is not the most effective space for representing HSI, we adopt factor analysis to learn an effective HSI feature and the learned features are further filtered by a structure-preserved filter to fully exploit the spatial structure information of HSI. The spatial structure information is integrated in the feature learning process to obtain a better HSI feature. Then we propose a multiclass LDM to classify the filtered HSI feature. Experimental results show that the proposed LDM with feature learning method achieves the classification performance of the state-of-the-art methods in terms of visual quality and three quantitative evaluations and indicates that LDM has a high generalization performance.

Citation Download Citation

Kun Zhan, Haibo Wang, He Huang, and Yuange Xie "Large margin distribution machine for hyperspectral image classification," Journal of Electronic Imaging 25(6), 063024 (22 December 2016). https://doi.org/10.1117/1.JEI.25.6.063024

Received: 30 July 2016; Accepted: 5 December 2016; Published: 22 December 2016

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