Multiple support vector machines for land cover change detection: An application for mapping urban extensions
Multiple support vector machines for land cover change detection: An application for mapping urban extensions
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DOI：
10.1016/j.isprsjprs.2006.09.004
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发表时间：
2006-11
影响因子：
12.7
通讯作者：
H. Nemmour;Y. Chibani
中科院分区：
文献类型：
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作者：
H. Nemmour;Y. Chibani
支持向量机用于遥感高光谱图像分类的可靠性已在各种研究中得到证明。在本文中，我们研究了它们在土地覆盖变化检测中的适用性。首先，提出并执行基于支持向量机的变化检测，以绘制阿尔及利亚首都的城市增长图。不同的性能指标以及与人工神经网络的比较用于支持我们的实验分析。第二步，提出了一个组合框架来提高变化检测的准确性。两个组合规则，即模糊积分和吸引子动力学，是针对各个 SVM 来实现和评估的。与神经网络相比，单个支持向量机实现的识别率证实了它们在土地覆盖变化检测方面的效率。此外，还强调了 SVM 组合的相关性。
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.