The use of backpropagating artificial neural networks in land cover classification
The use of backpropagating artificial neural networks in land cover classification
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DOI：
10.1080/0143116031000114851
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发表时间：
2003-12-01
影响因子：
3.4
通讯作者：
Mather, PM
中科院分区：
文献类型：
--
作者：
Kavzoglu, T;Mather, PM
人工神经网络用于利用遥感数据进行土地覆盖分类。神经网络的训练需要用户指定网络结构并设置学习参数。在这项研究中，分类问题的人工神经网络的优化设计进行了研究。启发式提出了一些研究人员，以确定网络参数的最佳值进行了比较，使用两个数据集。使用两个独立的数据集测试产生最高分类精度的那些算法。使用最佳设置设计的人工神经网络之间的比较，人工神经网络的基础上表现最差的神经网络，和最大似然分类。结果表明，使用人工神经网络与本研究中建议的设置可以产生更高的分类精度比任何替代方案。根据本研究的经验，为有效设计和使用人工神经网络对遥感图像数据进行分类，提出了一些准则。
Artificial neural networks (ANNs) are used for land cover classification using remotely sensed data. Training of a neural network requires that the user specifies the network structure and sets the learning parameters. In this study, the optimum design of ANNs for classification problems is investigated. Heuristics proposed by a number of researchers to determine the optimum values of network parameters are compared using two datasets. Those heuristics that produce the highest classification accuracies are tested using two independent datasets. Comparisons are also made among the ANNs designed using optimum settings, the ANNs based on the worst performing heuristics, and the maximum likelihood classifier. Results show that the use of ANNs with the settings recommended in this study can produce higher classification accuracies than either alternative. A number of guidelines are constructed from the experiences of this study for the effective design and use of artificial neural networks in the classification of remotely sensed image data.