Support Vector Machines for Automatic Multi-class Change Detection in Algerian Capital Using Landsat TM Imagery

被引：12

作者：

Nemmour, Hassiba ^{[1
]}

Chibani, Youcef ^{[1
]}

机构：

[1] Univ Sci & Technol Houari Boumediene, Signal Proc Lab, Dept Telecommun, Fac Elect & Informat, Algiers 16111, Algeria

来源：

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING | 2010年 / 38卷 / 04期

关键词：

Change detection; Multispectral images; Neural networks; SVMs; REMOTELY-SENSED IMAGERY; COVER CHANGE DETECTION; NEURAL-NETWORK; CLASSIFICATION;

D O I：

10.1007/s12524-011-0060-z

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Recently, Support Vector Machines (SVMs) have shown a practical relevance in various image processing applications. This paper investigates their applicability for land cover and land use change detection using multi-sensor images of remote sensing. Then, the most widely used approaches for multi-class SVMs, which are the One-Against-All and the One-Against-One with both Max-Win and DDAG decision rules are implemented to perform multi-class change detection. SVMs are evaluated in comparison with artificial neural networks using different accuracy indicators. The results obtained showed that SVMs are much more efficient than artificial neural networks and highlighted their suitability for land cover change detection.

引用

页码：585 / 591

页数：7

共 19 条

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