Feedforward neural networks with multilevel hidden neurons for remotely sensed image classification
Feedforward neural networks with multilevel hidden neurons for remotely sensed image classification
复制标题
DOI：
10.1109/icip.1997.638580
复制
发表时间：
1997-10
期刊：
影响因子：
--
通讯作者：
Zhong-yu Chen;M. Desai;Xiao-Ping Zhang
中科院分区：
文献类型：
--
作者：
Zhong-yu Chen;M. Desai;Xiao-Ping Zhang
人工神经网络已成为模式分类的有力工具。然而，当数据表现出非稀疏或重叠的模式类时，这是很难训练的，这在实际应用中经常发生。本文介绍了一种隐层由多层神经元组成的前向神经网络。证明了多层神经元单层神经网络的收敛性。新的前馈模型本质上能够对非稀疏或重叠的模式类进行模糊模式分类。作为一个应用，我们将该网络应用于LANDSAT TM数据的分类。结果表明，该方法产生更好的结果相比，传统的神经网络。
Artificial neural network has been, used as a powerful tool for pattern classification. However, it is difficult to train when the data exhibit non-sparse or overlapping pattern classes which is often the case in practical applications. In this paper, we introduce the feedforward neural network with the hidden layer consisting of multilevel neurons. The convergence property of one-layer neural network with multilevel neurons is proved. The new feedforward model is inherently capable of fuzzy pattern classification of non-sparse or overlapping pattern classes. As an application, we apply the network for the classification of LANDSAT TM data. The results show that this approach produces better results compared with conventional neural networks.