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| 投影寻踪学习网络的遥感影像分类 | |
| 引用本文： | 严勇,李清泉,孙久运.投影寻踪学习网络的遥感影像分类[J].武汉大学学报(信息科学版),2007,32(10):876-879. |
| 作者姓名： | 严勇 李清泉 孙久运 |
| 作者单位： | 1. 武汉大学测绘遥感信息工程国家重点实验室,武汉市珞喻路129号,430079;苏州科技学院城市与环境学系,苏州市新区滨河路1701号,215011 2. 武汉大学测绘遥感信息工程国家重点实验室,武汉市珞喻路129号,430079 3. 中国矿业大学环境与测绘学院,徐州市三环南路,221008 |
| 摘 要： | 采用投影寻踪(projection pursuit,PP)学习网络方法建立了一种新的遥感影像分类模型。该方法结合了统计学中投影寻踪算法节点函数灵活的非参数估计特点和人工神经网络的自学习功能,具有简捷的网络结构和良好的鲁棒性能。利用苏州市TM影像进行了分类实验,将分类结果与BP神经网络和最大似然法的分类结果相比较,投影寻踪学习网络的分类精度较高,具有一定的实用性。 |
| 关 键 词： | 投影寻踪学习网络 人工神经网络 遥感图像分类 |
| 文章编号： | 1671-8860（2007）10-0876-04 |
| 修稿时间： | 2007-08-15 |
Classification of RS Image Using Projection Pursuit Learning Network |
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| YAN Yong,LI Qingquan,SUN Jiuyun.Classification of RS Image Using Projection Pursuit Learning Network[J].Geomatics and Information Science of Wuhan University,2007,32(10):876-879. | |
| Authors: | YAN Yong LI Qingquan SUN Jiuyun |
| Institution: | 1. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China; 2.Department of Urban and Environment, University of Science and Technology of Suzhou, 1701 Binhe Road, Suzhou 215011, China;3.College of Environment and Spatial Informatics, China University of Mining and Technology, South Sanhuan Road, Xuzhou 221008, China |
| Abstract: | Using projection pursuit learning network (PPLN), a new classification for remote sensing image is proposed. The PPLN algorithm integrates the advantage of artificial neural network (ANN) with nonparametric statistical technique, projection pursuit algorithm (PP), which is capable of providing less network neurons and good robustness. In this study, the structure and improved learning algorithm of PPLN is introduced in detail. Using the TM image of Suzhou, an experiment of classification is done and the classification precision is superior to that of BP neural network and conventional maximum-likelihood. |
| Keywords: | projection pursuit learning network artificial neural network classification of remote sensing image |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |