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遥感技术与应用  2015, Vol. 30 Issue (2): 312-320    DOI: 10.11873/j.issn.1004-0323.2015.2.0312
图像与数据处理     
基于时空数据融合模型的TM影像云去除方法研究
陈阳1,2,范建容3,文学虎1,2,曹伟超1,2,王蕾1,2
(1.四川省地理国情监测工程技术研究中心,四川 成都610500;
2.四川省第三测绘工程院,四川 成都610500;
3.中国科学院水利部成都山地灾害与环境研究所,四川 成都610041)
Research on Cloud Removal from Landsat TM Image based on Spatial and Temporal Data Fusion Model
Chen Yang1,2,Fan Jianrong3,Wen Xuehu1,2,Cao Weichao1,2,Wang Lei1,2
(1.Geographic National Condition Monitoring Engineering Research Center
of Sichuan Province,Chengdu 610500,China;
2.The Third Surveying and Mapping Engineering Institute of Sichuan,Chengdu 610500 China;
3.Institute of Mountain Hazards and Environment,Chinese Academy of
Sciences & Ministry of Water Conservancy,Chengdu 610041,China)
 全文: PDF(12884 KB)  
摘要:

针对已提出的各类云去除方法在实际应用中存在的局限性,将时空数据融合模型引入到云去除方法中。首先基于MODIS数据提供的时间维变化信息和辅助时相TM数据提供的空间信息,应用增强时空适应反射率融合模型(ESTARFM)得到了目标时相似TM合成数据;然后用TM合成数据替换掉目标时相TM影像中被云及其阴影覆盖区域的数据。在修复后的影像中替换区域与非云区域色调基本一致。通过非云区TM合成数据间接对替换云及其阴影区数据的精度进行定量评价。结果表明:相对于真实TM影像,非云区域合成数据各波段均值差异都在1%以内;各波段的相对误差分别为16.29%、12.92%、13.47%、12.87%、9.71%和11.84%,且各波段的相关系数均大于0.7;非云及其阴影区融合影像数据间接表明填补云及阴影区数据各波段的总体精度优于83%。因此,所提出的方法能够修复TM影像中被云及其阴影覆盖区域的数据,提高MODIS与TM数据的利用率。

关键词: TMMODIS云及其阴影检测ESTARFM云去除    
Abstract:

To solve the limitation of the existing models for cloud removal in practical application,in this paper,a new method was proposed based on spatial and temporal data fusion models.First,the data,like TM image at target time was composed by enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) based on temporal change of MODIS data and spatial information of auxiliary TM data;Then,the pixels in target TM image where were contaminated by clouds and shades which were replaced by the compose data.The result show that the color of the replaced area is consistent with the color of area uncontaminated by clouds and shade.Ultimately,the precision of the replaced data is verified indirectly based on the data of target TM image and composed image without cloud and its shade cover.Compared to actual image,the result showed that the relative difference of individual band of composed data is less than 1%;The mean relative error of each band are 16.29%,12.92%,13.47%,12.87%,9.71%,11.84%,respectively;All correlation coefficients are greater than 0.7;The accuracy of non\|cloud and non\|shade area fusion data indicates indirectly that the accuracy of each band of the data to fill the area,contaminated by cloud and shade,is better than 83%.Therefore,the method proposed in this paper which can repair the data contaminated by clouds and shades from TM image and improve MODIS and TM data utilization level.

Key words: TM    MODIS    Cloud and its shade detection    STARFM    Cloud removal
收稿日期: 2014-01-09 出版日期: 2015-05-08
:  TP 79  
基金资助:

自然科学基金(杰青)项目“高寒草地生态水文学机理与冻土生态水文模拟研究”(40925002),四川省地理国情监测工程技术研究中心开放基金项目“基于中分辨率遥感影像的川东丘陵地区土地覆盖变化监测研究”(GC201413),“地理国情监测中城市发展变化监测方法研究与应用”(GC201413)。

 

通讯作者: 范建容(1969-),女,四川井研人,博士,研究员,主要从事GIS与环境遥感研究用、山地土壤侵蚀研究。Email: fjrong@imde.ac.cn。    
作者简介: 陈阳(1987-),男,四川仪陇人,硕士,助理工程师,主要从事GIS与环境遥感应用研究。Email: sunshine198761@163.com。
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引用本文:

陈阳,范建容,文学虎,曹伟超,王蕾. 基于时空数据融合模型的TM影像云去除方法研究[J]. 遥感技术与应用, 2015, 30(2): 312-320.

Chen Yang,Fan Jianrong,Wen Xuehu,Cao Weichao,Wang Lei. Research on Cloud Removal from Landsat TM Image based on Spatial and Temporal Data Fusion Model. Remote Sensing Technology and Application, 2015, 30(2): 312-320.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.2.0312        http://www.rsta.ac.cn/CN/Y2015/V30/I2/312

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