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遥感技术与应用  2020, Vol. 35 Issue (6): 1394-1403    DOI: 10.11873/j.issn.1004-0323.2020.6.1394
数据与图像处理     
热带地区Landsat干季数据的无效像元修复方法研究
徐艳豪1,3(),刘晓龙1,2(),史正涛1,高书鹏1
1.云南师范大学 旅游与地理科学学院,云南 昆明 650500
2.云南师范大学高原地理过程与环境变化云南省重点实验室,云南 昆明 650500
3.西南大学 地理科学学院,重庆 400715
Study on Invalid Pixel Repair Methods for Landsat Dry Season Images in Tropical Areas
Yanhao Xu1,3(),Xiaolong Liu1,2(),Zhengtao Shi1,Shupeng Gao1
1.College of Tourism & Geography Science,Yunnan Normal University,Kunming 650500,China
2.Provincial Key Laboratory of Plateau Geographical Processes & Environmental Change,Kunming 650500,China
3.School of Geographical Sciences,Southwest University,Chongqing 400715,China
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摘要:

热带地区完整中分辨率时间序列遥感数据对于地表过程及地表扰动观测具有重要应用价值。针对热带地区Landsat数据存在云、雾污染及传感器本身缺陷导致的大量数据缺失问题,在现有的条带数据缺失填充算法(GNSPI)基础上,提出三次修复法。该方法能自动识别当前待修复像元前后48 d内有效参考像元,继而经三次修复法实现当前缺失数据的修复。实验结果表明:三次修复法整体平均修复精度(R2)可达0.88,该方法弥补了GNSPI填充算法需要整幅参考影像不存在无效像元方可执行的苛刻要求,提高了有效观测像元的利用率,对于获取热带地区长时间序列有效观测数据具有重要意义。

关键词: Landsat数据缺失数据修复热带地区    
Abstract:

Time series remote sensing data with moderate resolution is playing as an important role for surface process and surface disturbance observation. For Landsat data in tropical areas, there are invalid land observation data and data loss caused by cloud, fog or sensor defects. Based on the existing GNSPI algorithm, a triple repairing method is proposed. This method automatically identifies the effective reference pixels within 48 days that before and after the current invalid pixels, and then obtain effective reference pixels for the current data to be repaired, and then fill the current missing data using the three triple repair which was based on the GNSPI method. The overall average filling accuracy is up to 0.88 in our study area. This method makes up for the GNSPI filling algorithm's harsh requirement that the reference image needs the whole image without invalid pixels, and improves the utilization rate of observed high quality pixels. The method proposed in this paper has a great significance for the establishment of long time series data and the related research.

Key words: Landsat    Data missing    Data repair    Tropical region
收稿日期: 2019-08-25 出版日期: 2021-01-26
ZTFLH:  TP75  
基金资助: 云南省青年基金“基于多源遥感数据的植被类型精细分类方法研究”(2016FD021);云南省水利厅水利科技项目“云南主要人工经济林对区域水资源安全的影响调查研究”(2014003)
通讯作者: 刘晓龙     E-mail: 1913830603@qq.com;liuxl@mail.bnu.edu.cn
作者简介: 徐艳豪(1997—),女,云南保山人,硕士研究生,主要从事热带植被遥感分类研究。E?mail:1913830603@qq.com
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引用本文:

徐艳豪,刘晓龙,史正涛,高书鹏. 热带地区Landsat干季数据的无效像元修复方法研究[J]. 遥感技术与应用, 2020, 35(6): 1394-1403.

Yanhao Xu,Xiaolong Liu,Zhengtao Shi,Shupeng Gao. Study on Invalid Pixel Repair Methods for Landsat Dry Season Images in Tropical Areas. Remote Sensing Technology and Application, 2020, 35(6): 1394-1403.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2020.6.1394        http://www.rsta.ac.cn/CN/Y2020/V35/I6/1394

图1  研究区地理位置 审图号:云S(2017)055号
数据获取年份待修复数据(景)GNSPI参考影像数(景)三次修复法参考影像数 (景)修复数据(景)

Landsat-5

1989~1990

1994~1995

2000~2001

2006~2007

10

10

12

13

182610
182610
223212
243513
Landsat-72002~200311202911

Landsat-8

2013~201412223212
2014~201512223212
2015~201612223212
表 1  不同方法获取到的参考影像数量
图2  有效观测数据自动识别方法流程
图3  三次修复法示意
图4  影像修复结果
图5  影像修复结果在各个波段上的修复精度
图6  云覆盖对三次修复法修复精度的影响
图7  不同修复方法修复结果
图8  GNSPI方法在各个波段的修复精度
图9  三次修复法在各个波段的修复精度
图10  数据源对Landsat各波段修复精度的影响
1 Man D C, Luu, V H, Hoang V T, et al. Cloud Detection Algorithm for Landsat 8 Image Using Multispectral Rules and Spatial Variability[C]// Proceedings of International Conference on Knowledge and Systems Engineering, 2014.
2 Hughes M J, Hayes D J. Automated Detection of Cloud and Cloud Shadow in Single-date Landsat Imagery Using Neural Networks and Spatial Post-Processing[J]. Remote Sensing, 2014,6(6):4907-4926.doi:10.3390/rs6064907
doi: 10.3390/rs6064907
3 Ju J, Roy D P. The Availability of Cloud-free Landsat ETM+ Data over the Conterminous United States and Globally[J]. Remote Sensing,2008,112(3):1196-1211. doi:10.1016/j.rse.2007.08.011.
doi: 10.1016/j.rse.2007.08.011
4 Zhu Xifang, Wu Feng, Zhuang Yanbin. An Improved Approach to Remove Cloud and Mist from Remote Sensing Digital Images based on Mallat Algorithm[J]. Journal of Remote Sensing, 2007,11(2): 241-246.
4 朱锡芳, 吴峰, 庄燕滨. 基于Mallat算法遥感图像去云雾处理的改进方法[J]. 遥感学报, 2007,11(2):241-246.
5 Martinuzzi S, Gould W A, González O M R. Creating Cloud-Free Landsat ETM+ Data Sets in Tropical Landscapes: Cloud and Cloud-Shadow Removal[J]. General Technical Report IITF-GTR-32. International Institute of Tropical Forestry (IITF), U.S. Department of Agriculture (USDA), Forest Service.Puerto Rico:IITF.2007. doi:10.2737/IITF-GTR-32.
doi: 10.2737/IITF-GTR-32
6 Jin S, Homer C, Yang L, et al. Automated Cloud and Shadow Detetion and Filling Using Two-date Landsat Imagery in the USA[J]. International Journal of Remote Sensing, 2013,34(5):1540-1560. doi:10.1080/01431161.2012.720045.
doi: 10.1080/01431161.2012.720045
7 Shou Jingwen. Research on Recovering and Application of the Landsat 7 ETM+ SLC-off Image[D]. Harbin:Harbin Engineering University,2006.寿敬文.陆地卫星7号ETM+图像数据缺行的修复与应用研究[D].哈尔滨: 哈尔滨工程大学, 2006.
8 Zeng C, Shen H, Zhang L. Recovering Missing Pixels for Landsat ETM+ SLC-off Imagery Using Multi-temporal Regression Analysis and a Regularization Method[J]. Remote Sensing of Environment, 2013,131(4):182-194. doi:10.1016/j.rse.2012.12.012.
doi: 10.1016/j.rse.2012.12.012
9 Pringle M J, Schmidt M, Muir J S. Geostatistical Interpolation of SLC-off Landsat ETM+ Images[J]. ISPRS Journal of Photogrammetry & Remote Sensing,2009,64(6):654-664. doi:10.1016/j.isprsjprs.2009.06.001.
doi: 10.1016/j.isprsjprs.2009.06.001
10 Chen J, Zhu X, Vogelmann J E, et al. A Simple and Effective Method for Filling Gaps in Landsat ETM+ SLC-off Images[J]. Remote Sensing of Environment, 2011,115(4):1053-1064.
11 Guo Huancheng, Zhou Feng, Dao Xu. State-of-art on Geostatistical Methodology[J]. Geographical Research, 2008,27(5):1191-1202.
11 郭怀成, 周丰,刀谞.地统计方法学研究进展[J]. 地理研究, 2008,27(5):1191-1202.
12 Zhu Xiaolin, Liu Desheng, Chen Jin. A New Geostatistical Approach for Filling Gaps in Landsat ETM plus SLC-off Images[J]. Remote Sensing of Environment, 2012,124(9):49-60.
13 Shi Yulin. On the Utilization and Protection of Natural Resources in Xishuangbanna[J]. Resources Science, 1980(2):84-89.
13 石玉林. 关于西双版纳自然资源利用与保护问题[J]. 资源科学, 1980(2):84-89.
14 Yu Yan. The Rich Climate Resources of Xishuangbanna[J]. Meteorological Knowledge, 2004(1):27-28.
14 喻彦. 西双版纳丰富的气候资源[J]. 气象知识, 2004(1):27-28.
15 Yu Yan, Meng Guiyun, Zhang Licai. Characteristics of Climate Change in Recent 45 Years in Xishuanbanna[J]. Meteorological Science and Techology, 2008,36(4):410-413.
15 喻彦, 蒙桂云, 张利才. 西双版纳地区近45年来气候变化特征[J]. 气象科技, 2008,36(4):410-413.
16 Gong Shixian,Ling Shenghai. Fog Decreasing in Xishuangbanna Region[J].Meteorological,1996,22(11):10-14.
16 宫世贤,凌升海.西双版纳雾在减少[J].气象,1996,22(11):10-14.
17 Liu Xiaolong, Xu Rui, Fu Zhuo, et al. Monitoring Land Use for Human Activities in Nabanhe National Nature Reserve based on Multi-source Remote Sensing Data[J]. Transactions of the Chinese Society of Agricultural Engineering,2018,34(19):266-275.
17 刘晓龙, 徐瑞,付卓,等.基于多源遥感数据的纳板河国家级自然保护区人类活动用地监测[J]. 农业工程学报,2018,34(19):266-275.
18 Gao Shupeng, Shi Zhengtao, Liu Xiaolong, et al. Identification of Rubber Plantations in Tropical Mountainous Region based on High Spatio-temporal Resolution Visible Remote Sensing Data[J]. Remote Sensing Technology and Application, 2018, 33(6):142-151.
18 高书鹏, 史正涛, 刘晓龙,等. 基于高时空分辨率可见光遥感数据的热带山地橡胶林识别[J]. 遥感技术与应用, 2018, 33(6):142-151.
19 Zhu Z, Woodcock C E. Object-based Cloud and Cloud Shadow Detection in Landsat Imagery[J]. Remote Sensing of Environment, 2012,118(6):83-94.
20 Liu Xiaolong. Study of Multi-source Remote Sensing Data Integrated Classification Method of Vegetation[D].Beijing: Beijing Normal University,2015.
20 刘晓龙. 集成多源遥感数据的植被类型分类方法研究[D].北京:北京师范大学,2015.
21 Sun Yue, Zhang Hua. Landsat 7 SLC-off Image Restoration based on a New Object-oriented Interpolation Method[J].Remote Sensing Information,2018,33(5):27-34.
21 孙悦, 张华. 一种新的面向对象Landsat 7 SLC-off影像插值修复算法[J]. 遥感信息, 2018,33(5):27-34.
22 Forkuo A, Thiel F. Evaluation of Gap-filling Methods for Landsat 7 ETM+ SLC-off Image for LULC Classification in a Heterogeneous Landscape of West Africa[J]. International Journal of Remote Sensing,2020,41(7). doi:10.1080/01431161. 2019.1693076.
doi: 10.1080/01431161. 2019.1693076
23 Jia Chen,Sun Lin,Chen Yunfang,et al. Inversion of Aerosol Optical Depth for Landsat 8 OLI Data Using Deep Belief Network[J]. Journal of Remote Sensing, 2020,24(10):1180-1192.
23 贾臣,孙林,陈允芳,等.深度置信网络算法反演Landsat 8 OLI气溶胶光学厚度[J].遥感学报,2020,24(10):1180-1192.
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