SIMPLIFYING SUPPORT VECTOR MACHINES FOR REGRESSION ANALYSIS OF HYPERSPECTRAL IMAGERY

被引：0
|
作者：
Rabe, Andreas [1 ]
van der Linden, Sebastian [1 ]
Hostert, Patrick [1 ]
机构：
[1] Humboldt Univ, Geomat Lab, D-10099 Berlin, Germany
来源：
2009 FIRST WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING | 2009年
关键词：
Support Vector Machines; SVM; approximation; regression; hyperspectral; EnMAP;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Support Vector Machines for Regression (SVR) proved to perform well. However, they are not preferred in image analysis due to a high number of needed support vectors (SV) and consequently long processing times. We present a method for simplifying the original SVR regression function up to a user-specified degree of accepted performance decrease. We show results for two regression problems: modelling leaf area index and dry vegetation mixing fraction using simulated hyperspectral EnMAP data. In both cases, SVR demonstrate high potential for modelling complex dependencies between hyperspectral reflectance and quantitative targets. By simplifying the original SVR, we observed reduction rates in number of SV in the 86% to 95% range for acceptable degrees of approximation quality. This enables a fast mapping of complete EnMAP scenes.
引用
收藏
页码：376 / 379
页数：4
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