| 周扬帆 陈佑启 邹金秋 何英彬.基于BP神经网络的马铃薯遥感识别图像数据分析研究[J].中国科技资源导刊,2017,(5):104~110 |
| 基于BP神经网络的马铃薯遥感识别图像数据分析研究 |
| Research on Image Recognition of Potato Remote Sensing Based on BP Neural Network for potatoes |
| 投稿时间：2017-07-14 |
| DOI： |
| 中文关键词: BP神经网络；马铃薯；遥感影像；遥感数据；遥感影像识别；最优参数；误差调节 |
| 英文关键词: BP neural network, potato, remote sensing image, remote sensing data, remote sensing image
identification, optimal parameter, error adjustment |
| 基金项目:科技基础性工作专项项目“科技基础性工作数据资料集成与规范化整编”（2013FY110900）；国家国际科技合作专项项
目“天空地一体化精准农业物联网平台联合研发”（2014DFE10220）。 |
| 作者 | 单位 | | 周扬帆 陈佑启 邹金秋 何英彬 | 中国农业科学院农业资源与农业区划研究所，北京 100081 |
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| 中文摘要: |
| 运用遥感手段精确监测马铃薯种植面积是稳固马铃薯主粮化政策、维护国家粮食安全的必要保障。本文以
吉林省长春市九台区纪家镇、兴隆镇为研究区，选用landsat8 OLI遥感数据，借助ENVI平台构建了基于BP神经网络的
土地覆盖分类模型，应用于研究区的马铃薯等作物分类研究。以landsat8 OLI7个彩色波段作为输入，不断调节分类参
数，最终确定了最优分类网络结构。结果显示，BP神经网络法马铃薯的分类生产者精度为94.22%。研究表明，BP神经
网络分类方法是一种手段灵活、结果较准确的马铃薯遥感识别方法。 |
| 英文摘要: |
| The efficient and accurate remote sensing means of potato acreage monitoring is vital to stabilize
potato production as a staple food and maintain the necessary guarantee for national food security. In this
paper, a land cover classification model based on BP neural network was constructed to detect potato and other
crops in the area of Jijia Town and Xinglong Town, locating in Jiutai District, Changchun City, Jilin Province.
Taking the seven color bands of landsat8 OLI as input, the classification parameters are adjusted continuously,
and the optimal classification network structure is finally determined. The results showed that the producer
accuracy of potato was 94.22%. The research shows that BP neural network classification method is a kind of
potato remote sensing identification method with flexible measure and accurate classification result. |
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