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

页码：376 / 379

页数：4

共 50 条

[31] Fast training of support vector machines for regression
Anguita, D
Boni, A
Pace, S
IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL V, 2000, : 210 - 214
[32] Support vector machines for hyperspectral remote sensing classification
Gualtieri, J. Anthony
Cromp, R.F.
Proceedings of SPIE - The International Society for Optical Engineering, 3584 : 221 - 232
[33] Identifying saltcedar with hyperspectral data and support vector machines
Fletcher, Reginald S.
Everitt, James H.
Yang, Chenghai
GEOCARTO INTERNATIONAL, 2011, 26 (03) : 195 - 209
[34] Assessment of the effectiveness of support vector machines for hyperspectral data
Pal, M
Mather, PM
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2004, 20 (07): : 1215 - 1225
[35] Hyperspectral Image Analysis Using Cloud-Based Support Vector Machines
Juan M. Haut
Jose M. Franco-Valiente
Mercedes E. Paoletti
Sergio Moreno-Álvarez
Alfonso Pardo-Diaz
SN Computer Science, 5 (6)
[36] Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery
Guo, Xian
Huang, Xin
Zhang, Lefei
Zhang, Liangpei
Plaza, Antonio
Benediktsson, Jon Atli
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (06): : 3248 - 3264
[37] Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression
Axelsson, Christoffer
Skidmore, Andrew K.
Schlerf, Martin
Fauzi, Anas
Verhoef, Wouter
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2013, 34 (05) : 1724 - 1743
[38] Mapping Bugweed (Solanum mauritianum) Infestations in Pinus patula Plantations Using Hyperspectral Imagery and Support Vector Machines
Atkinson, Jonathan Tom
Ismail, Riyad
Robertson, Mark
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (01) : 17 - 28
[39] Analysis of detectors for support vector machines and least square support vector machines
Kuh, A
PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, 2002, : 1075 - 1079
[40] Analysis of support vector machines
Abe, S
NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS, 2002, : 89 - 98

← 1 2 3 4 5 →