Spatio-Spectral Remote Sensing Image Classification With Graph Kernels

被引：103

作者：

Camps-Valls, Gustavo ^{[1
]}

Shervashidze, Nino ^{[2
]}

Borgwardt, Karsten M. ^{[2
]}

机构：

[1] Univ Valencia, Image Proc Lab, Valencia 46980, Spain

[2] Max Planck Inst, D-72076 Tubingen, Germany

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2010年 / 7卷 / 04期

关键词：

Graphs; kernel methods; spatio-spectral image classification; support vector machine (SVM);

D O I：

10.1109/LGRS.2010.2046618

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

This letter presents a graph kernel for spatio-spectral remote sensing image classification with support vector machines (SVMs). The method considers higher order relations in the neighborhood (beyond pairwise spatial relations) to iteratively compute a kernel matrix for SVM learning. The proposed kernel is easy to compute and constitutes a powerful alternative to existing approaches. The capabilities of the method are illustrated in several multi- and hyperspectral remote sensing images acquired over both urban and agricultural areas.

引用

页码：741 / 745

页数：5

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