摘要
以内蒙古地区Spot/vegetation归一化植被指数(NDVI)影像为基本信息源,综合应用地理信息系统(G IS)技术进行了大尺度神经网络分类实验研究.建立多年份高分辨影像数据库,通过G IS软件集成与遥感影像目视解译方法,在全区范围选取了“纯净”样本数据,并辅助应用DTM数据和影像化多年气像观测数据,完成土地覆盖类型的BP人工神经网络分类.结果表明,G IS技术支持下,大面积区域尺度上spot/vegetation NDVI影像的BP神经网络分类可达到较高的分类精度.
An experiment research of large scale neural networks classification was carried out by applying GIS technique synthetically, which took the Spot/vegetation NDVI image in Inner Mongolia as basic information source. A high-resolution image database of many years time series was build by integrating GIS software with remote sensing visual interpretation and selecting “pure” sample data in the range of the whole municipality. Land cover＇s BP artificial neural networks classification of the area was accomplished by combining DTM data and grid climate data with the database~ It turns out that with the support of GIS technique, BP artificial neural networks classification of spot/vegetation NDVI image at large scale can reach a higher classification precision.
出处
《红外与毫米波学报》
SCIE
EI
CAS
CSCD
北大核心
2005年第6期427-431,共5页
Journal of Infrared and Millimeter Waves
基金
中国科学院知识创新工程重大项目(KZCX-Y-02-01-04)
国家留学基金资助项目(20815038)
日本千叶大学国际合作项目
内蒙古自治区高校重大研究项目(NJ03046)
