航空工程进展2011，Vol.2Issue(3)：339-343,5.
基于BP神经网络的机载数字高程模型压缩
Compression of Airborne Digital Elevation Model Based on BP Neural Network
摘要
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.关键词
数字高程模型压缩/BP神经网络/L-M算法/机载Key words
digital elevation model compression/back-propagation neural network/Levenberg-Marquardt/airborne分类
航空航天引用本文复制引用
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):339-343,5.基金项目
航空科学基金 （）
