基于全极化SAR数据散射机理的农作物分类  被引量:8

Crop classification based on scattering model using full-polarization SAR data

在线阅读下载全文

作  者:化国强[1,2,3] 王晶晶[1] 黄晓军[1] 陈尔学[3] 李秉柏[1] 

机构地区:[1]江苏省农业科学院农业经济与信息研究所,江苏南京210014 [2]南京信息工程大学应用气象学院,江苏南京210044 [3]中国林业科学研究院资源信息研究所,北京100091

出  处:《江苏农业学报》2011年第5期978-982,共5页Jiangsu Journal of Agricultural Sciences

基  金:国家"863"高技术研究发展专项(2006AA120108;2006AA120101;2009AA12Z1462);公益性行业气象科研专项(GYHY201106027);江苏省农业科技自主创新基金项目[CX(10)430]

摘  要:提出了利用极化相干矩阵对雷达遥感影像进行特征值分解,通过分析分解特征图达到农作物识别、分类的目的。该方法不仅能识别不同农作物,而且对水稻、玉米、大豆较好的识别精度。以江苏省睢宁地区农作物识别为例,利用2009年8月23日Radarsat-2全极化SAR影像数据,结合极化相干矩阵的方法提取不同农作物散射特征的3个参量,分析不同农作物散射机理;结合地面GPS数据进行Wishart监督分类和非监督分类。分类结果表明:城市及水体散射特征特点明显、识别清晰;水稻由于植株底部具有含水层,易与其它农作物区别,水稻分类精度达到97.92%;根据平均散射角的差异性对大豆和玉米进行了区分,最终总分类精度达到78.1%。研究结果表明,全极化雷达数据能提供更为丰富的地物散射信息,是农作物遥感监测的重要数据来源。The results of eigenvalue decomposition based on the polarimetric coherency matrix using synthetic aperture radar(SAR) data were analyzed for the purposes of identification and classification of crops.This method can identify different crops and have better classification accuracy of rice,corn,and soybean.Taking Suining experimental area in Jiangsu Province as an example,using Radarsat-2 polarimetric SAR image data of August 23,2009,three parameters of scattering characteristics of different crops were extracted and the scattering mechanisms of these crops were analyzed.Using ground GPS data,the different crops were classified with Wishart supervised and unsupervised method.The results showed that the scattering characteristics of city and water were clear.Rice was easily distinguished from other crops for having water layer in plant bottom with the classification accuracy being 97.92%.Corn and soybean were respectively identified according to the difference of average scattering angle.The total accuracy of paddy identification was 78.1%.Overall,full-polarization SAR data can provide richer land scattering information which is an important data source of remote sensing monitoring of crops.

关 键 词:农作物 全极化 散射特征 Wishart分类 

分 类 号:TP79[自动化与计算机技术—检测技术与自动化装置]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象