| 基于BP神经网络的机载数字高程模型压缩 |
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| 引用本文： | 冯琦,肖桥,周德云.基于BP神经网络的机载数字高程模型压缩[J].航空工程进展,2011,2(3):339-343. |
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| 作者姓名： | 冯琦 肖桥 周德云 |
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| 作者单位： | 西北工业大学电子信息学院,西安,710129 |
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| 摘 要： | 现有的数字高程模型压缩方法大多从编码方式上进行优化,而很少利用其数据的自相关性。为此,提出了一种采用L-M训练算法的单隐层BP神经网络实现机载数字高程模型压缩的新方法,并给出了实现压缩的详细过程。论述了采用单隐层网络的理由,并根据机载要求的相对误差精度去选择最少的隐层节点数。通过选取ASTERGDEM30米分辨率的高精...
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| 关 键 词： | 数字高程模型压缩 BP神经网络 L-M算法 机载 |
Compression of Airborne Digital Elevation Model Based on BP Neural Network |
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| Feng Qi,Xiao Qiao,Zhou Deyun.Compression of Airborne Digital Elevation Model Based on BP Neural Network[J].Advances in Aeronautical Science and Engineering,2011,2(3):339-343. |
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| Authors: | Feng Qi Xiao Qiao Zhou Deyun |
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| Institution: | Feng Qi,Xiao Qiao,Zhou Deyun(School of Electronics and Information,Northwestern Polytechnical University,Xi'an 710129,China) |
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| Abstract: | The current compression of airborne Digital Elevation Model（DEM） is optimized mostly by coding method,and is seldom optimized by self-correlation of DEM.A new compression method of airborne DEM is presented,which is based on the Single Hidden Layer Back-Propagation（BP） neural network adopting Levenberg-Marquardt（LM） training algorithm,and the compression process is given in detail.The advantage of a single hidden layer network superior to the multi hidden layer network is discussed,and the least hidden nodes are selected to get the maximum compression ratio based on the relative error of the actual onboard accuracy required.The validity and feasibility of this method are verified by simulation. |
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| Keywords: | digital elevation model compression back-propagation neural network Levenberg-Marquardt airborne |
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