多样种植区冬小麦RS影像精确提取方法研究  

Accurate Extraction Method of RS Image of Winter Wheat in Various Planting Areas

作　　者：李宾 荆华[2] 张殷钦 王利书[1] LI Bin;JING Hua;ZHANG Yin-qin;WANG Li-shu(Hebei Key Laboratory of Intelligent Water Conservancy,Hebei University of Engineering,Handan 056038,China;Handan Zhangfu River Irrigation Water Supply Administration Office,Handan 056001,China)

机构地区：[1]河北工程大学河北省智慧水利重点实验室,河北邯郸056038 [2]邯郸市漳滏河灌溉供水管理处,河北邯郸056001

出　　处：《海河水利》2023年第7期65-69,128,共6页Haihe Water Resources

基　　金：国家自然科学基金青年基金项目(52209049)。

摘　　要：以河北省邯郸市永年区为示例,基于ENVI平台,采用最大似然分类法对Sentinel-2冬小麦RS(Remote Sensing)影像数据进行精确解析,并通过混淆矩阵评价其分类精度。研究结果表明,使用最大似然分类法可快速提取冬小麦影像数据,精准获取粮食种植面积及其分布情况,有助于提升农业区土地利用的监测评估效果,并提升精细化灌溉水资源管理水平。Taking Yongnian District of Handan City,Hebei Province,as an example,the maximum likelihood supervised classification method is used on the ENVI platform to accurately analyze the Sentinel-2 winter wheat RS(Remote Sensing)image data.The classification accuracy is evaluated using a confusion matrix.The research results show that the maximum likelihood classification method can quickly extract winter wheat image data,accurately obtain the planting area and distribution of grain crops,and help improve the monitoring and assessment of land use in agricultural areas.It also enhances the level of precision irrigation water resource management.

关 键 词：最大似然分类法 ENVI 冬小麦 Sentinel-2影像提取 混淆矩阵 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]