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概率神经网络的水稻种植面积遥感信息提取研究
引用本文:杨晓华,黄敬峰.概率神经网络的水稻种植面积遥感信息提取研究[J].浙江大学学报(农业与生命科学版),2007,33(6):691-698.
作者姓名:杨晓华  黄敬峰
作者单位:浙江大学,环境与资源学院,农业遥感与信息技术应用研究所,浙江,杭州,310029
基金项目:国家高技术研究发展计划(863计划);国家自然科学基金;国家科技支撑计划
摘    要:为了提高水稻种植面积遥感信息提取精度,将根据水稻生长期所选择的多时相遥感影像经过大气校正和几何校正后,实施单波段统计、主成份变换和比值变换,选出最佳组合波段,通过分析概率神经网络(probabilistic neural network,PNN)的学习算法和基本结构,对最佳组合波段影像实现PNN模型分类,并将其分类结果与反向传播(back propagation,BP)神经网络模型和最小距离法的分类结果进行比较.结果表明:PNN模型比最小距离法的分类精度高出近6个百分点;PNN模型比BP模型的分类精度高出近13个百分点;对于水稻种植面积提取精度,PNN模型比最小距离法的结果高出15个百分点.从本次试验可知,PNN模型是一种有效的遥感影像分类方法,在作物种植面积提取方面将具有独到的功效.

关 键 词:概率神经网络  遥感  影像分类
文章编号:1008-9209(2007)06-0691-08
收稿时间:2007-01-21
修稿时间:2007年1月21日

Study on probabilistic neural network for extracting remote sensing information of rice planting area
YANG Xiao-hua,HUANG Jing-feng.Study on probabilistic neural network for extracting remote sensing information of rice planting area[J].Journal of Zhejiang University(Agriculture & Life Sciences),2007,33(6):691-698.
Authors:YANG Xiao-hua  HUANG Jing-feng
Abstract:In order to improve the extraction precision of rice planting area,multitemporary remote sensing images chosen based on the growth stages of rice were performed atmospheric correction and geometric rectification.The fusion algorithms which are used to select the optimal bands combination include single band statistic,principal component transformation and ratio transformation.The basic algorithm and theory of the PNN(probabilistic neural network) were analyzed,and it was applied to classify the image of the optimal bands combination.The classified result was compared with those of BP(back propagation) neural network and minimum-distance method.Results show that the classification precision of PNN is higher than that of minimum-distance by 6 percentage points,and BP by 13 percentage points.As for the precision of rice planting area extraction,the PNN's extraction precision is higher than that of minimum-distance by 15 percent points.Therefore,PNN is an effective method for classification of remote sensing images,and it plays a unique role in extracting the crops planting area.
Keywords:probabilistic neural network  remote sensing  image classification
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