Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image
Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image
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DOI：
10.1080/01431161.2011.568531
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发表时间：
2012-01-01
影响因子：
3.4
通讯作者：
Jiang, Xiaoguang
中科院分区：
文献类型：
--
作者：
Song, Xianfeng;Duan, Zheng;Jiang, Xiaoguang
本文提出了一个充分的比较两种类型的先进的非参数分类器实现在遥感土地覆盖分类。利用SPOT-5高分辨率遥感影像对北京市延庆县进行了研究，延庆县土地利用以农业和林业为主。人工神经网络（ANN），包括自适应反向传播（ABP）算法，Levenberg-Marquardt（LM）算法，拟牛顿（QN）算法和径向基函数（RBF）进行了仔细的测试。LM-ANN和RBF-ANN，这两个优于其他两个，被选为与支持向量机（SVM）进行详细的比较。实验表明，训练好的ANN和SVM在分类精度上没有显著差异，但SVM通常表现得略好。通过对训练集大小影响的分析，突出了SVM分类器对小训练集有很大的容忍度，避免了人工神经网络分类器训练不足的问题。测试还表明，ANN和SVM在训练时间方面可能会有很大差异。LM-ANN可以非常快地收敛，但不是以稳定的方式。相比之下，RBF-ANN和SVM分类器的训练速度快，可重复。
This article presents a sufficient comparison of two types of advanced non-parametric classifiers implemented in remote sensing for land cover classification. A SPOT-5 HRG image of Yanqing County, Beijing, China, was used, in which agriculture and forest dominate land use. Artificial neural networks (ANNs), including the adaptive backpropagation (ABP) algorithm, Levenberg-Marquardt (LM) algorithm, Quasi-Newton (QN) algorithm and radial basis function (RBF) were carefully tested. The LM-ANN and RBF-ANN, which outperform the other two, were selected to make a detailed comparison with support vector machines (SVMs). The experiments show that those well-trained ANNs and SVMs have no significant difference in classification accuracy, but the SVM usually performs slightly better. Analysis of the effect of the training set size highlights that the SVM classifier has great tolerance on a small training set and avoids the problem of insufficient training of ANN classifiers. The testing also illustrates that the ANNs and SVMs can vary greatly with regard to training time. The LM-ANN can converge very quickly but not in a stable manner. By contrast, the training of RBF-ANN and SVM classifiers is fast and can be repeatable.