Multiple support vector machines for land cover change detection: An application for mapping urban extensions

被引：359

作者：

Nemmour, Hassiba ^{[1
]}

Chibani, Youcef ^{[1
]}

机构：

[1] Univ Sci & Technol, Fac Elect & Comp Sci, Signal Proc Lab, HOUARI BOUMEDIENE,USTHB, Algiers 16111, Algeria

来源：

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING | 2006年 / 61卷 / 02期

关键词：

change detection; fuzzy integral; combination; support vector machines; attractor dynamics;

D O I：

10.1016/j.isprsjprs.2006.09.004

中图分类号：

P9 [自然地理学];

学科分类号：

0705 [地理学]; 070501 [自然地理学];

摘要：

The reliability of support vector machines for classifying hyper-spectral images of remote sensing has been proven in various studies. In this paper, we investigate their applicability for land cover change detection. First, SVM-based change detection is presented and performed for mapping urban growth in the Algerian capital. Different performance indicators, as well as a comparison with artificial neural networks, are used to support our experimental analysis. In a second step, a combination framework is proposed to improve change detection accuracy. Two combination rules, namely, Fuzzy Integral and Attractor Dynamics, are implemented and evaluated with respect to individual SVMs. Recognition rates achieved by individual SVMs, compared to neural networks, confirm their efficiency for land cover change detection. Furthermore, the relevance of SVM combination is highlighted. (C) 2006 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

引用

页码：125 / 133

页数：9

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