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遥感技术与应用  2020, Vol. 35 Issue (1): 185-193    DOI: 10.11873/j.issn.1004-0323.2020.1.0185
数据与图像处理     
ESTARFM相似像元选取方法的改进研究
董世元1,2(),张文娟2(),许君一1,马建行2
1. 山东科技大学测绘科学与工程学院,山东 青岛 710054
2. 中国科学院遥感与数字地球研究所,北京 100094
Study of the Improved Similar Pixel Selection Method on ESTARFM
Shiyuan Dong1,2(),Wenjuan Zhang2(),Junyi Xu1,Jianhang Ma2
1. College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
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摘要:

ESTARFM(Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model)是一种经典的基于权重滤波的时空融合算法,它在众多领域得到广泛应用。相似像元选取是其一个重要步骤,ESTARFM模型中相似像元选取过程受搜索框大小和分类数影响,当前的研究中搜索框大小的设定较为统一,而分类数大小设定缺乏统一性。为降低ESTARFM算法中分类数对算法性能的影响,将STNLFFM(A Spatial and Temporal Nonlocal Filter-Based Data Fusion Method)中相似像元选取方法与ESTARFM模型相结合,提出改进的ESTARFM_NL模型。研究设计了两组不同时相变化条件下的数据进行对比分析。结果表明:ESTARFM_NL与ESTARFM融合结果相对误差直方图总体分布趋近一致,同时利用平均相对误差和相关系数对融合结果进行评价,发现两种算法之间精度差异较小,表明两种算法融合精度相当;对比两种算法运算效率,发现ESTARFM_NL运行时间能够得到大幅缩减。因此,ESTARFM_NL为大区域或长时间序列遥感数据的时空融合提供了一种可选择的融合方案。

关键词: 时空融合ESTARFM相似像元选取阈值法运行效率    
Abstract:

The ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) is a classic spatiotemporal filter-based algorithm, which is used in the many fields. The similar pixel selection process in the ESTARFM model is affected by the size of the size of search window and the number of classifications. In the current study, the size of the search windows is more uniform, and the number of classifications lacks uniformity. In order to reduce the influence of the number of classifications in the ESTARFM algorithm on the performance of the algorithm. The similar pixel selection method in the STNLFFM (A Spatial and Temporal Nonlocal Filter-Based Data Fusion Method) combined with the ESTARFM model to propose the ESTARFM_NL model. The study designed two sets of data under different conditions of phase change for comparative analysis. The results show that the overall distribution of the relative error histogram of ESTARFM_NL and ESTARFM is tight and consistent. When the fusion results are evaluated by the average relative error and correlation coefficient, the difference between the two algorithms is considerable, indicating that the fusion accuracy of the two algorithms is equivalent. Comparing the efficiency of the two algorithms, we found that the ESTARFM_NL running time can be greatly reduced. Therefore, ESTARFM_NL provides an alternative fusion scheme for large-area or long-term sequence remote sensing data with large data volume.

Key words: Spatiotemporal fusion    ESTARFM    Similar pixel selection    Threshold value method    Running efficiency
收稿日期: 2018-10-10 出版日期: 2020-04-01
ZTFLH:  TP79  
基金资助: 中国科学院遥感数字所所长青年基金项目(Y5ZZ11101B);国家重点研发计划项目“静止轨道全谱段高光谱探测技术”、“高精度定标与反演技术”(2016YFB0500304)
通讯作者: 张文娟     E-mail: dongsyRS@163.com;zhangwj@radi.ac.cn
作者简介: 董世元(1994-),男,河北邢台人,硕士研究生,主要从事遥感数据融合研究。E?mail:dongsyRS@163.com
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引用本文:

董世元,张文娟,许君一,马建行. ESTARFM相似像元选取方法的改进研究[J]. 遥感技术与应用, 2020, 35(1): 185-193.

Shiyuan Dong,Wenjuan Zhang,Junyi Xu,Jianhang Ma. Study of the Improved Similar Pixel Selection Method on ESTARFM. Remote Sensing Technology and Application, 2020, 35(1): 185-193.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.1.0185        http://www.rsta.ac.cn/CN/Y2020/V35/I1/185

图1  不同时刻MODIS与Landsat图像展示(标准假彩色),黑框内为裁剪图像区域
Landsat7波段 波段范围/nm MOIDS波段 波段范围/nm
1 450~520 3 459~479
2 530~610 4 545~565
3 630~690 1 620~670
4 780~900 2 841~876
5 1 550~1 750 6 1 628~1 652
7 2 090~2 350 7 2 105~2 155
表 1  Landsat 7与MODIS波段展示
图2  真实图像和融合结果对比 ((a)~(c)表示实验一数据组T3时刻观测图像,ESTARFM、ESTARFM_NL的融合结果;(d)~(f)表示实验二数据组T2时刻观测图像,ESTARFM、ESTARFM_NL的融合结果)
图3  融合结果与观测图像相对误差直方图
实验 整景影像 裁剪后影像 相对差异(%)
ESTARFM ESTARFM_NL ESTARFM ESTARFM_NL 整景 裁剪后
实验一 0.134 0.139 1 0.181 9 0.189 6 3.806 4.233
实验二 0.155 5 0.162 7 0.233 8 0.246 0 4.63 5.218
表2  融合结果全谱段平均相对误差REm 及算法之间的精度相对差异
实验 波段 整景影像 裁剪后影像 相对差异/%
ESTARFM ESTARFM_NL ESTARFM ESTARFM_NL 整景 裁剪后
实验一 Band2 0.820 9 0.815 2 0.847 0 0.829 0 0.694 4 2.125 1
Band3 0.874 7 0.866 3 0.868 4 0.851 5 0.960 3 1.946 1
Band4 0.924 8 0.912 7 0.932 1 0.919 3 1.308 4 1.373 2
实验二 Band2 0.806 1 0.786 0 0.762 7 0.736 4 2.493 5 3.448 3
Band3 0.865 6 0.845 7 0.812 8 0.792 4 2.299 0 2.509 8
Band4 0.908 3 0.891 2 0.909 6 0.889 9 1.882 6 2.165 8
表3  融合结果相关系数R及算法之间的精度相对差异
实验 整景图像 裁剪后图像 整景 裁剪后
ESTARFM ESTARFM_NL ESTARFM ESTARFM_NL
实验一 2 h5 m15 s 1 h14 m58 s 19 m28 s 11 m22 s 40.8 41.6
实验二 2 h1 m27 s 1 h7 m31 s 18 m36 s 10 m9 s 44.6 45
表4  实验运行时间
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