摘要
为获取煤矿积水沉陷区遥感影像数据与沉陷区水深的定量关系,建立了BP神经网络水深反演模型,并对淮南潘一矿积水沉陷区水深进行了反演。首先对Landsat卫星影像数据(TM影像)进行几何校正、大气校正和沉陷区范围提取等,然后输出像元反射率值,并与水深实测控制点坐标匹配,使水深值与反射率值对应。实验结果表明:以水深值2 m为阈值,水深值小于2 m的区域,模型反演水深值与实测水深值的平均绝对误差为0.166 3 m,平均相对误差为13.29%;水深值为2~6 m的区域,模型反演水深值与实测水深值平均绝对误差为0.578 6 m,平均相对误差为15.20%。
To measure the remote sensing of water depth in subsidence area,the model based on BP neural network is proposed.After geometric calibration,atmospheric correction and subsidence area extraction,the pixels reflectivity is exported.In order to find the relation between actual water depth and pixels reflectivity,the pixels reflectivity are matched to control points.The depth of 2 m is the threshold of the model which corresponds to actually measured water depth less than 2 m and water depth from 2 m to 6 m.Th...
出处
《煤田地质与勘探》
CAS
CSCD
北大核心
2007年第2期41-44,共4页
Coal Geology & Exploration
基金
国家创新研究群体基金(50221402)
教育部"长江学者和创新团队发展计划"(IRT0408)
国家杰出青年基金项目(50025413)
关键词
沉陷区
水深
遥感
人工神经网络
subsidence area
water depth
remote sensing
artificial neural networks
