Retrieval of land cover information under thin fog in Landsat TM image

被引:0
|
作者
Wei Yuchun [1 ]
机构
[1] Nanjing Normal Univ, Key Lab Virtual Geog Environm, MOE, Nanjing 210046, Jiangsu, Peoples R China
来源
关键词
image retrieval; thin fog; TM image; regression analysis;
D O I
10.1117/12.780929
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Thin fog, which often appears in remote sensing image of subtropical climate region, has resulted in the low image quantity and bad image mapping. Therefore, it is necessary to develop the image processing method to retrieve land cover information under thin fog. In this paper, the Landsat TM image near the Taihu Lake that is in the subtropical climate zone of China was used as all example, and the workflow and method used to retrieve the land cover infort-nation under thin fog have been built based oil ENVI software and a single TM image. The basic step covers three parts: 1) isolating the thin fog area in image according to the spectral difference of different bands; 2) retrieving the visible band information of different land cover types under thin fog from the near-infrared bands according to the relationships between near-infrared bands and visible bands of different land cover types in the area without fog; 3) image post-process. The result showed that the method in the paper is easy and suitable, and can be used to improve the quantity of TM image mapping more effectively.
引用
收藏
页数:8
相关论文
共 50 条
  • [31] Land cover target mapping at subpixel scale for Landsat 8 OLI image by using multiscale-infrared information
    Wang, Peng
    Yao, Hongyu
    Zhang, Gong
    Kong, Yingying
    Lu, Shifang
    Peng, Xiangyang
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2021, 42 (03) : 1054 - 1076
  • [32] Sensitivity of hyperclustering and labelling land cover classes to Landsat image acquisition date
    Wulder, MA
    Franklin, SE
    White, JC
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2004, 25 (23) : 5337 - 5344
  • [33] IMPROVED METHOD OF LAND SURFACE EMISSIVITY RETRIEVAL FROM LANDSAT TM/ETM plus DATA
    Huang, QingNi
    Guo, HuaDong
    Xi, XiaoHuan
    Li, XinWu
    Du, XiaoPing
    Yang, HuaiNing
    2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 4206 - 4208
  • [34] Accuracy assessment of land cover dynamic in hill land on integration of DEM data and TM image
    Li Yunmei
    Wang Xin
    Wang Qiao
    Wu Chuanqing
    Huang Jiazhu
    ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY XVI, 2010, 7695
  • [35] EVALUATION OF SPECTRAL ANGLE INDEX FROM LANDSAT TM IMAGE FOR CROP RESIDUE COVER ESTIMATION
    Zhang, Miao
    Wu, Bingfang
    Meng, Jihua
    Li, Qiangzi
    Dong, Taifeng
    2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2012, : 5073 - 5076
  • [36] Characterization of changes in land cover and carbon storage in Northeastern China: An analysis based on Landsat TM data
    王绍强
    田汉勤
    刘纪远
    庄大方
    张树文
    胡文言
    Science China Life Sciences, 2002, (S1) : 40 - 47
  • [37] A method for object-oriented land cover classification combining Landsat TM data and aerial photographs
    Geneletti, D
    Gorte, BGH
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2003, 24 (06) : 1273 - 1286
  • [38] Pasture land cover in eastern Australia from NOAA-AVHRR NDVI and classified Landsat TM
    Hill, MJ
    Vickery, PJ
    Furnival, EP
    Donald, GE
    REMOTE SENSING OF ENVIRONMENT, 1999, 67 (01) : 32 - 50
  • [39] Enhancing land cover maps derived from Landsat TM with multi-temporal SAR data
    Solbo, S
    Johansen, B
    Malnes, E
    Solheim, I
    IGARSS 2005: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8, PROCEEDINGS, 2005, : 1287 - 1290
  • [40] Incorporating the Downscaled Landsat TM Thermal Band in Land-cover Classification using Random Forest
    Rodriguez-Galiano, V. F.
    Ghimire, B.
    Pardo-Iguzquiza, E.
    Chica-Olmo, M.
    Congalton, R. G.
    PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 2012, 78 (02): : 129 - 137