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引用本文:蒋锦刚,李爱农,邓伟,宋孟强,冯文兰,李晓铃.多时相遥感影像提取湖泊边界信息的融合算法.湖泊科学,2009,21(2):264-271. DOI:10.18307/2009.0216
JIANG Jingang,LI Ainong,DENG Wei,SONG Mengqiang,FENG Wenlan,LI Xiaoling.Fusion algorithm for the information of lake boundary integration from multi-temporalremote sensing images. J. Lake Sci.2009,21(2):264-271. DOI:10.18307/2009.0216
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多时相遥感影像提取湖泊边界信息的融合算法
蒋锦刚1,2, 李爱农2,3, 邓伟2, 宋孟强2, 冯文兰1, 李晓铃2,4
1.成都信息工程学院环境工程系, 成都 610225;2.中国科学院、水利部成都山地灾害与环境研究所, 成都 610041;3.Deptment of Geography, University of Maryland, College Park, MD 20742, USA;4.西南交通大学土木工程学院, 成都 610031
摘要:
为了有效改善遥感影像提取湖泊边界信息的可靠性和精度,减少人为误差,提出了一种利用多时相遥感影像提取边界信息的加权平均融合算法以及误差的域法修正处理方法.结果表明,该方法能有效融合各时相影像信息,提高湖泊边界信息提取的可靠性,并且对融合时相变化较大的湖泊边界都有一定的普适性.通过融合算法提取的呼伦湖面积为1928.35km2,修正后的面积为1929.85km2.通过利用地统计学理论对算法的验证及误差分析,得出相对误差空间变异拟合模型的块金方差与基台值之比都小于25%,具有很强的空间相关性,修正后的数据空间相关性要优于融合数据,空间变程也得到了有效地降低,显示修正后的数据对半方差函数理论模型的拟合程度更好.
关键词:  湖泊边界  多时相遥感影像  数据融合  误差修正  空间变异
DOI:10.18307/2009.0216
分类号:
基金项目:中国科学院“西部之光”重点项目“若尔盖高原湿地动态变化遥感监测体系研”究(08R2130130);国家自然科学基金“973”项目“黄淮海地区湿地水生态过程、水环境效应及生态安全调控”(2006CB403301);“中国湖泊水质、水量和生物资源调查”专项(2006FY110600)子项目:云贵高原湖泊卫星遥感调查(08K0010010)联合资助
Fusion algorithm for the information of lake boundary integration from multi-temporalremote sensing images
JIANG Jingang1,2, LI Ainong2,3, DENG Wei2, SONG Mengqiang2, FENG Wenlan1, LI Xiaoling2,4
1.Department of Environmental Engineering, CUIT, Chengdu 610225, P. R. China;2.Institute of Mountain Hazards and Environment, CAS, Chengdu 610041, P. R. China;3.Department of Geography, University of Maryland, College Park, MD 20742, USA;4.School of Civil Eng, Southwest Jiaotong University, Chengdu 610031, P. R. China
Abstract:
For improving the reliability and accuracy of lake boundary integration information from remote sensing images andreducing human error, this paper put forwards a weighted average algorithm for integrating of the border information extracted byusing the multi-temporal remote sensing images, and a processing approach of error interzone correction which can integrate thevarious temporal information effectively and improve the reliability of extracting the lake border information. According to thisfusion algorithm the area of Lake Hulun is 1928.35km2. Using the geostatistics theory to validate the errors, the area is 1929.85km2.The results illuminated that the ratio of nugget to sill is less than 25%, and the spatial correlation of the corrected data is superior tothat of the integrated data. Furthermore the spatial variation range has been reduced effectively, and the extent of fitting thetheoretical model is better in the corrected data than the original data.
Key words:  Lake border  multi-temporal remote sensing images  data integration  error correction  spatial variability  Lake Hulun
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