Advanced Search
中文
Volume 43 Issue 6
Jun.  2021
Turn off MathJax
Article Contents
Abstract
References
WANG Guangyuan, DENG Zhengdong, LU Zhao, WANG Daqing, SHI Yue, XU Haoli, ZHAO Xiaoning. Snow Information Recognition based on GF-6 PMS Images[J]. Infrared Technology , 2021, 43(6): 543-556.
Citation: WANG Guangyuan, DENG Zhengdong, LU Zhao, WANG Daqing, SHI Yue, XU Haoli, ZHAO Xiaoning. Snow Information Recognition based on GF-6 PMS Images[J]. Infrared Technology , 2021, 43(6): 543-556.
shu

Snow Information Recognition based on GF-6 PMS Images

  • College of National Defense Engineering, The Army Engineering University of PLA, Nanjing 210007, China

More Information
  • Received Date: November 04, 2020
  • Revised Date: December 19, 2020
    Graphical Abstract
    • Abstract
  • From the perspective of limited research on snow recognition in high spatial resolution optical remote sensing images, considering Daowai District of Harbin City as the research area, this paper systematically discusses and analyzes the application of different methods in snow information recognition. First, ground feature types and snow distribution characteristics were mastered through visual interpretation of GF-6 PMS images of two phases in the study area. Second, based on the results of visual interpretation, eight typical surface feature types were selected, and the spectral characteristics of "snow"and "non-snow"pixels were obtained. Third, the application of the six methods in snow recognition was discussed and analyzed. Accuracy was evaluated using three indexes: positive predictive value, recall rate, and F-score. Finally, a final recognition result judgment method based on voting results was developed, and the final recognition result of snow information in the study area was obtained. The results showed that, owing to the influence of the underlying surface and shadow, the phenomenon of "same spectrum foreign matter"and "same object different spectrum"was common in the study area, which interfered with the snow recognition process to a large extent. The recognition effect of the deep learning algorithm was the best, while that of the decision tree method was relatively poor; the recognition accuracy was higher for the farmland area than that for the pond area, and the phenomenon of false and missing recognitions was relatively less. The final recognition result judgment method based on voting results can effectively improve the phenomenon of false and missing recognitions in a single recognition method. This paper has important guiding significance for snow recognition based on high-spatial-resolution optical remote sensing images.
    • FullText(HTML)
    • References (34)
  • [1]
    HE C, Liou K N, Takano Y, et al. Impact of grain shape and multiple black carbon internal mixing on snow albedo: Parameterization and radiative effect analysis[J]. Journal of Geophysical Research: Atmospheres, 2018, 123(2): 1253-1268. DOI: 10.1002/2017JD027752
    [2]
    Butt M J, Bilal M. Application of snowmelt runoff model for water resource management[J]. Hydrological Processes, 2011, 25(24): 3735-3747. DOI: 10.1002/hyp.8099
    [3]
    LIU M, XIONG C, PAN J, et al. High-resolution reconstruction of the maximum snow water equivalent based on remote sensing data in a mountainous area[J]. Remote Sensing, 2020, 12(3): 460-479. DOI: 10.3390/rs12030460
    [4]
    XU L, Dirmeyer P. Snow–atmosphere coupling strength. Part Ⅱ: Albedo effect versus hydrological effect[J]. Journal of Hydrometeorology, 2013, 14(2): 404-418. DOI: 10.1175/JHM-D-11-0103.1
    [5]
    汪左. 新疆玛纳斯河流域典型区雪水当量的SAR反演研究[D]. 南京: 南京大学, 2014.

    WANG Zuo. Retrieval of Snow Water Equivalence Using SAR Data for Typical Area of Manas River Basin in Xinjiang, China[D]. Nanjing: Nanjing University, 2014.
    [6]
    贺广均. 联合SAR与光学遥感数据的山区积雪识别研究[D]. 南京: 南京大学, 2015.

    HE Guangjun. Snow Recognition in Mountainous Areas Based on SAR and Optical Remote Sensing Data[D]. Nanjing: Nanjing University, 2015.
    [7]
    Barnes J C, Bowley C J. Snow cover distribution as mapped from satellite photography[J]. Water Resources Research, 1968, 4(2): 257-272. DOI: 10.1029/WR004i002p00257
    [8]
    冯学智. 卫星雪盖制图及其应用研究概况[J]. 遥感技术动态, 1989(1): 25-29. https://www.cnki.com.cn/Article/CJFDTOTAL-YGJS198901004.htm

    FENG Xuezhi. Satellite snow cover mapping and its application[J]. Development of Remote Sensing Technology, 1989(1): 25-29. https://www.cnki.com.cn/Article/CJFDTOTAL-YGJS198901004.htm
    [9]
    冯学智. 卫星雪盖制图中的一些技术问题[J]. 遥感技术与应用, 1991(4): 10-15. https://www.cnki.com.cn/Article/CJFDTOTAL-YGJS199104001.htm

    FENG Xuezhi. Some technical problems in satellite snowcover mapping[J]. Remote Sensing Technology and Application, 1991(4): 10-15. https://www.cnki.com.cn/Article/CJFDTOTAL-YGJS199104001.htm
    [10]
    白磊, 郭玲鹏, 马杰, 等. 基于数字相机拍摄影像的山区积雪消融动态观测研究——以天山积雪站为例[J]. 资源科学, 2012, 34(4): 620-628. https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZY201204006.htm

    BAI Lei, GUO Lingpeng, MA Jie, et al. Observation and analysis of the process of snow melting at Tianshan station using the images by digital camera[J]. Resouces Science, 2012, 34(4): 620-628. https://www.cnki.com.cn/Article/CJFDTOTAL-ZRZY201204006.htm
    [11]
    Kim S-H, Hong C-H. Antarctic land-cover classification using IKONOS and Hyperion data at Terra Nova Bay[J]. International Journal of Remote Sensing, 2012, 33(22): 7151-7164. DOI: 10.1080/01431161.2012.700136
    [12]
    ZHU L, XIAO P, FENG X, et al. Support vector machine-based decision tree for snow cover extraction in mountain areas using high spatial resolution remote sensing image[J]. Journal of Applied Remote Sensing, 2014, 8(1): 084698. DOI: 10.1117/1.JRS.8.084698
    [13]
    Dozier J. Spectral signature of alpine snow cover from the Landsat Thematic Mapper[J]. Remote Sensing of Environment, 1989, 28(1): 9-22. http://static1.1.sqspcdn.com/static/f/891472/15152129/1321451632750/Dozier_J._1989.pdf?token=sMvVhVmXaLvHsiAGq%2FlmCvgkjlA%3D
    [14]
    Cea C, Cristóbal J, Pons X. An improved methodology to map snow cover by means of Landsat and MODIS imagery[C]//2007 IEEE International Geoscience and Remote Sensing Symposium, 2007: 4217-4220.
    [15]
    Khosla D, Sharma J, Mishra V. Snow cover monitoring using different algorithm on AWiFS sensor data[J]. International Journal of Advanced Engineering Sciences and Technologies, 2011, 7(1): 42-47.
    [16]
    延昊. 利用MODIS和AMSR-E进行积雪制图的比较分析[J]. 冰川冻土, 2005(4): 515-519. DOI: 10.3969/j.issn.1000-0240.2005.04.008

    YAN Hao. A comparison of MODIS and passive microwave snow mapping[J]. Journal of Glaciology and Geocryology, 2005(4): 515-519. DOI: 10.3969/j.issn.1000-0240.2005.04.008
    [17]
    郝晓华, 王建, 李弘毅. MODIS雪盖制图中NDSI阈值的检验——以祁连山中部山区为例[J]. 冰川冻土, 2008(1): 132-138. https://www.cnki.com.cn/Article/CJFDTOTAL-BCDT200801020.htm

    HAO Xiaohua, WANG Jian, LI Hongyi. Evaluation of the NDSI threshold value in mapping snow cover of MODIS[J]. Journal of Glaciology and Geocryology, 2008(1): 132-138. https://www.cnki.com.cn/Article/CJFDTOTAL-BCDT200801020.htm
    [18]
    汪凌霄. 玛纳斯河流域山区积雪遥感识别研究[D]. 南京: 南京大学, 2012.

    WANG Lingxiao. Retrieval of Snow Water Equivalence Using SAR Data for Typical Area of Manas River Basin in Xinjiang, China[D]. Nanjing: Nanjing University, 2012.
    [19]
    Baghdadi N, Gauthier Y, Bernier M. Capability of multitemporal ERS-1 SAR data for wet-snow mapping[J]. Remote Sensing of Environment, 1997, 60(2): 174-186. DOI: 10.1016/S0034-4257(96)00180-0
    [20]
    Rott H, Nagler T. Capabilities of ERS-1 SAR for snow and glacier monitoring in alpine areas[J]. European Space Agency-Publications-ESA SP, 1994, 361: 965-965. http://www.researchgate.net/publication/245605630_Capabilities_of_ERS-1SAR_for_snow_and_glacier_monitoring_in_Alpine_areas
    [21]
    Koskinen J T, Pulliainen J T, Hallikainen M T. The use of ERS-1 SAR data in snow melt monitoring[J]. IEEE Transactions on Geoscience Remote Sensing, 1997, 35(3): 601-610. DOI: 10.1109/36.581975
    [22]
    Luojus K P, Pulliainen J T, Cutrona A B, et al. Comparison of SAR-based snow-covered area estimation methods for the boreal forest zone[J]. IEEE Geoscience Remote Sensing Letters, 2009, 6(3): 403-407. DOI: 10.1109/LGRS.2009.2014786
    [23]
    Caves R, Hodson A, Turpin O, et al. Field verification of SAR wet snow mapping in a non-Alpine environment[J]. European Space Agency- Publications- ESA SP, 1998, 441: 519-526. http://www.researchgate.net/publication/228650728_Field_verification_of_SAR_wet_snow_mapping_in_a_non-Alpine_environment/download
    [24]
    Malnes E, Guneriussen T. Mapping of snow covered area with Radarsat in Norway[C]//IEEE International Geoscience and Remote Sensing Symposium, 2002: 683-685.
    [25]
    Kelly R. The AMSR-E snow depth algorithm: description and initial results[J]. Journal of the Remote Sensing Society of Japan, 2009, 29(1): 307-317. https://www.jstage.jst.go.jp/article/rssj/29/1/29_1_307/_pdf/-char/en
    [26]
    PAN J, JIANG L, ZHANG L. Wet snow detection in the south of China by passive microwave remote sensing[C]//IEEE International Geoscience and Remote Sensing Symposium, 2012: 4863-4866.
    [27]
    Singh P R, Gan T Y. Retrieval of snow water equivalent using passive microwave brightness temperature data[J]. Remote Sensing of Environment, 2000, 74(2): 275-286. DOI: 10.1016/S0034-4257(00)00121-8
    [28]
    LIU X, JIANG L, WU S, et al. Assessment of methods for passive microwave snow cover mapping using FY-3C/MWRI data in China[J]. Remote Sensing, 2018, 10(4): 524-545. DOI: 10.3390/rs10040524
    [29]
    Hinkler J, Pedersen S B, Rasch M, et al. Automatic snow cover monitoring at high temporal and spatial resolution, using images taken by a standard digital camera[J]. International Journal of Remote Sensing, 2002, 23(21): 4669-4682. DOI: 10.1080/01431160110113881
    [30]
    Keshri A, Shukla A, Gupta R. ASTER ratio indices for supraglacial terrain mapping[J]. International Journal of Remote Sensing, 2009, 30(2): 519-524. DOI: 10.1080/01431160802385459
    [31]
    Hinkler J, Ørbæk J B, Hansen B. Detection of spatial, temporal, and spectral surface changes in the Ny-Ålesund area 79 N, Svalbard, using a low cost multispectral camera in combination with spectroradiometer measurements[J]. Physics Chemistry of the Earth, Parts A/B/C, 2003, 28(28-32): 1229-1239. DOI: 10.1016/j.pce.2003.08.059
    [32]
    XIAO X M, SHEN Z X, QIN X G. Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: a normalized difference snow and ice index[J]. International Journal of Remote Sensing, 2001, 22(13): 2479-2487. DOI: 10.1080/01431160119766
    [33]
    XIAO X, Moore B, QIN X, et al. Large-scale observations of alpine snow and ice cover in Asia: using multi-temporal vgetation sensor data[J]. International Journal of Remote Sensing, 2002, 23(11): 2213-2228. DOI: 10.1080/01431160110076180
    [34]
    Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431-3440.
    • Related Articles

Catalog

    Get Citation
    PDF
    XML
    Article views (366) PDF downloads (42) Cited by()
    Related

    Copyright © 《Infrared Technology》Editorial Office滇ICP备12000354号-3

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return