A COMPARATIVE STUDY ON MULTIPLE KERNEL LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION

被引：6

作者：

Niazmardi, Saeid ^{[1
]}

Demir, Beguem ^{[2
]}

Bruzzone, Lorenzo ^{[2
]}

Safari, Abdolreza ^{[1
]}

Homayouni, Saeid ^{[3
]}

机构：

[1] Univ Tehran, Coll Engn, Sch Surveying & Geospatial Engn, Tehran 14174, Iran

[2] Univ Trento, Dept Comp Sci & Informat Engn, Trento, Italy

[3] Univ Ottawa, Dept Geog Environm Studies & Geomat, Ottawa, ON K1N 6N5, Canada

来源：

2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2016年

关键词：

multiple kernel learning; remote sensing; image classification;

D O I：

10.1109/IGARSS.2016.7729386

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 analyzes and compares different Multiple Kernel Learning (MKL) algorithms for the classification of remote sensing (RS) images. The main purpose of the comparison is to identify advantages and disadvantages of different MKL algorithms in terms of their computational time and classification accuracy. Furthermore, some guidelines on the proper selection of the MKL algorithms associated with different RS image classification problems are derived.

引用

页码：1512 / 1515

页数：4

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