Share Email Print
cover

Proceedings Paper

Fault tolerance of SVM algorithm for hyperspectral image
Author(s): Yabo Cui; Zhengwu Yuan; Yuanfeng Wu; Lianru Gao; Hao Zhang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

One of the most important tasks in analyzing hyperspectral image data is the classification process[1]. In general, in order to enhance the classification accuracy, a data preprocessing step is usually adopted to remove the noise in the data before classification. But for the time-sensitive applications, we hope that even the data contains noise the classifier can still appear to execute correctly from the user’s perspective, such as risk prevention and response. As the most popular classifier, Support Vector Machine (SVM) has been widely used for hyperspectral image classification and proved to be a very promising technique in supervised classification[2]. In this paper, two experiments are performed to demonstrate that for the hyperspectral data with noise, if the noise of the data is within a certain range, SVM algorithm is still able to execute correctly from the user’s perspective.

Paper Details

Date Published: 20 October 2015
PDF: 6 pages
Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 964610 (20 October 2015); doi: 10.1117/12.2196704
Show Author Affiliations
Yabo Cui, Chongqing Univ. of Posts and Telecommunications (China)
Institute of Remote Sensing and Digital Earth (China)
Zhengwu Yuan, Chongqing Univ. of Posts and Telecommunications (China)
Yuanfeng Wu, Institute of Remote Sensing and Digital Earth (China)
Lianru Gao, Institute of Remote Sensing and Digital Earth (China)
Hao Zhang, Institute of Remote Sensing and Digital Earth (China)


Published in SPIE Proceedings Vol. 9646:
High-Performance Computing in Remote Sensing V
Bormin Huang; Sebastián López; Zhensen Wu; Jose M. Nascimento; Boris A. Alpatov; Jordi Portell de Mora, Editor(s)

© SPIE. Terms of Use
Back to Top