摘要
针对传统基于像元的农田遥感技术,存在着对于农作物的识别精度较差、处理效率较低的问题。文中提出了一种遥感数据压缩分级传输系统的设计方案,对遥感数据自适应扫描,码流的比特分配,最终实现了遥感数据的可视化自定义传输。通过多组基于机器学习分类的农作物分类实验,确定了遥感数据传输的最优参数。测试与实验结果表明,在不损失传输信息的情况下,使用机器学习算法的面向对象分类设计总体精度高于95%,Kappa系数大于0.91。
Aiming at the traditional pixel based remote sensing technology for farmland,there are some problems,such as low recognition accuracy and low processing efficiency for crops.In this paper,a design scheme of remote sensing data compression and hierarchical transmission system is proposed,which adaptively scans remote sensing data and allocates bit streams,and finally realizes the visual and customized transmission of remote sensing data.The optimal parameters of remote sensing data transmission are determined through a series of experiments of crop classification based on machine learning classification.The test and experimental results show that the overall accuracy of object oriented classification design using machine learning algorithm is higher than 95%and the Kappa coefficient is greater than 0.91 without losing the transmission information.
作者
叶满珠
廖世芳
田正华
YE Man Zhu;LIAO Shi fang;TIAN Zheng hua(Shaanxi Railway Institute,Weinan 714000,China;Xianyang Normal University,Xianyang 712000,China)
出处
《电子设计工程》
2020年第9期167-170,175,共5页
Electronic Design Engineering
基金
陕西省职业教育研究课题(SZJYB19-215)
陕西铁路工程职业技术学院首批自然科学基金项目(KY2019-16)。
关键词
机器学习
遥感数据
农业资源
压缩传输
Machine learning
remote sensing data
agricultural resources
compressed transmission