INCLUDING INVARIANCES IN SVM REMOTE SENSING IMAGE CLASSIFICATION

被引:3
作者
Izquierdo-Verdiguier, Emma [1 ]
Laparra, Valero [1 ]
Gomez-Chova, Luis [1 ]
Camps-Valls, Gustavo [1 ]
机构
[1] Univ Valencia, Image Proc Lab IPL, E-46003 Valencia, Spain
来源
2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2012年
关键词
Image classification; invariance; support vector machines (SVM);
D O I
10.1109/IGARSS.2012.6351931
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
This paper introduces a simple method to include invariances in support vector machine (SVM) for remote sensing image classification. We rely on the concept of virtual support vectors, by which the SVM is trained with both the selected support vectors and synthetic examples encoding the invariance of interest. The algorithm is very simple and effective, as demonstrated in two particularly interesting examples: invariance to the presence of shadows and to rotations in patch-based image segmentation. The improved accuracy (around +6% both in OA and Cohen's. statistic), along with the simplicity of the approach encourage its use and extension to encode other invariances and other remote sensing data analysis applications.
引用
收藏
页码:7353 / 7356
页数:4
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