摘要
基于“简译”图像处理软件,利用北京二号卫星遥感数据,采用“面向对象+深度学习”的方式,在河南省信阳市浉河区约200 km^2试验区对林地、水稻田、茶园、水体、建设用地等进行了智能化信息提取和准确的自动化分类。林地、茶园和水稻田采用比值植被指数,结合边界指数阈值特征值实时筛选等方法进行智能化分类,水体采用绿光波段与近红外波段的归一化比值指数进行智能化提取,建设用地利用第1波段的标准差作为特征值进行提取。试验区各地类信息智能化提取结果经实地调查验证准确率达90%以上,相较传统人工方法工作效率提高了19倍。实验研究表明,“简译”图像处理软件技术方法可靠,解译精度高,效果事半功倍,社会经济效益显著,在自然资源及环境智能化遥感解译中有比较好的推广应用价值。
Based on“Easy Interpretation”image processing software and using BJ-2 satellite remote sensing images,the method of“object-oriented+deep learning”was introduced into the intelligent information extraction and automatic classification of the 200 km^2 test plot in Shihe District,Xinyang City,which included forestland,tea garden,paddy land,water area,construction land and some other land.By the method of ratio vegetation index(RVI)in combination with the real-time selection of the boundary index threshold and eigenvalues,the forestland,tea garden and paddy land information of the test plot was classified intelligently.The water area information was extracted intelligently by the green and near-infrared band normalized difference vegetation index(NDVI).The information of construction land was extracted by using standard deviation of band1 as the eigenvalues.Based on the above methods and field geological survey,the results show that the intelligent information extraction in the test plot has a high accuracy of over 90%.The efficiency is 19 times higher than the traditional method.The study shows that“Easy Interpretation”image processing software is effective and highly accurate and can do half the work with twice the results,which has good value for extension and application in the intelligentized interpretation of natural resources and environment.
作者
王跃峰
武慧智
何姝珺
黄頔
白朝军
WANG Yuefeng;WU Huizhi;HE Shujun;HUANG Di;BAI Chaojun(Henan Institute of Geological Survey, Zhengzhou 450001, China;Geological Remote Sensing Centre of National Engineering Lab for Satellite Remote Sensing Applications, Zhengzhou 450001, China;Henan Institute of Geological Sciences, Zhengzhou 450001, China)
出处
《国土资源遥感》
CSCD
北大核心
2020年第4期244-250,共7页
Remote Sensing for Land & Resources
基金
河南省地质矿产勘查开发局局管地质科研项目(编号:豫地矿科研〔2019〕1号)资助。
关键词
简译软件
自然资源
智能化提取
自动化分类
Easy Interpretation
natural resources
intelligentized extraction
automatically classification