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遥感技术与应用  2015, Vol. 30 Issue (6): 1176-1181    DOI: 10.11873/j.issn.1004-0323.2015.6.1176
图像与数据处理     
基于局部空间自相关分析的时空数据融合
康峻1,2,王力1,牛铮1,高帅1,邬明权1
(1.中国科学院遥感与数字地球研究所,遥感科学国家重点实验室,北京100101
2.中国科学院大学资源与环境学院,北京100049)
A Spatial and Temporal Fusion Model Using Local Spatial Association Analysis Method
Kang Jun1,2,Wang Li1,Niu Zheng1,Gao Shuai1,Wu Mingquan1
(1.The State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing and Digital Earth,
Chinese Academy of Sciences,Beijing 100101,China;
2.College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China)
 全文: PDF(5081 KB)  
摘要:

由于受到16 d重访周期与云等对数据质量的影响,具有时间与空间连续性的Landsat 8 OLI观测数据难以直接获取。考虑地物分布的空间自相关性,提出一种基于STARFM模型改进的局部自相关时空数据融合模型(LASTARFM),以新疆维吾尔族自治区喀什地区叶城县为研究区,利用Landsat 8 OLI数据和MODIS数据的红光波段和近红外波段进行融合方法测试。结果表明:利用LASTARFM模型得到的融合影像,与真实影像NDVI相关系数达到0.92;在局部空间自相关性低的区域比STARFM模型影像反映出更多地物细节,具有更高的融合精度;在土地利用类型发生显著变化的区域与真实影像具有一定差异。

关键词: 局部空间自相关分析LandsatMODIS时空数据融合STARFM    
Abstract:

Due to limitations of 16-day revisit cycle and frequent cloud contamination,the images of Landsat8 satellite OLI sensor in fine spatial and temporal resolution is urgently needed.This paper took local spatial association of ground features into consideration,the application of fusing Moderate Resolution Imaging Spectroradiometer (MODIS) images with Landsat8 OLI images is studied,using a new algorithm model called Local Associated Spatial and Temporal Adaptive Reflectance Fusion Model (LASTARFM).The algorithm is tested in Yecheng County,Kashi Prefecture of Xinjiang Uygur Autonomous Region.The result showed that,the correlation r-square of LASTARFM fused NDVI against true Landsat NDVI reached 0.92;LASTARFM showed more ground feature details than STARFM,and a higher fusion accuracy in low local associated areas;while in the ground features significantly changed areas,the LASTARFM images and true Landsat images had distinctions in a certain extent.

Key words: Local spatial association analysis    Landsat    MODIS    Spatial and temporal fusion    STARFM
出版日期: 2016-01-25
:  P 237  
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引用本文:

康峻,王力,牛铮,高帅,邬明权 . 基于局部空间自相关分析的时空数据融合[J]. 遥感技术与应用, 2015, 30(6): 1176-1181.

Kang Jun,Wang Li,Niu Zheng,Gao Shuai,Wu Mingquan . A Spatial and Temporal Fusion Model Using Local Spatial Association Analysis Method. Remote Sensing Technology and Application, 2015, 30(6): 1176-1181.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2015.6.1176        http://www.rsta.ac.cn/CN/Y2015/V30/I6/1176

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