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基于GF-6 PMS影像的积雪信息识别

王光远 邓正栋 路钊 王大庆 时玥 许颢砾 赵晓宁

王光远, 邓正栋, 路钊, 王大庆, 时玥, 许颢砾, 赵晓宁. 基于GF-6 PMS影像的积雪信息识别[J]. 红外技术, 2021, 43(6): 543-556.
引用本文: 王光远, 邓正栋, 路钊, 王大庆, 时玥, 许颢砾, 赵晓宁. 基于GF-6 PMS影像的积雪信息识别[J]. 红外技术, 2021, 43(6): 543-556.
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.

基于GF-6 PMS影像的积雪信息识别

详细信息
    作者简介:

    王光远(1991-),男,河北邢台人,博士研究生,主要研究方向为基于遥感的积雪信息识别和应用。E-mail:15295518590@163.com

    通讯作者:

    邓正栋(1960-),男,教授,博士,主要研究方向为野战给水保障理论

  • 中图分类号: TP391

Snow Information Recognition based on GF-6 PMS Images

  • 摘要: 以黑龙江省哈尔滨市道外区为研究区,系统探讨分析了基于遥感的不同方法在积雪信息识别中的应用。首先,对研究区两个时相的高分六号(GF-6)多光谱相机(PMS)影像进行目视解译,掌握了研究区内地物类型和积雪分布特点。其次,基于目视解译结果,选取了8种典型地物类型,得到了“积雪”和“非雪”两类像元的光谱特征规律。再次,探讨分析了6种方法在积雪识别中的应用,利用精确率、召回率和F指数3个指标进行了精度评价。最后,提出了基于投票结果的最终识别结果判定方法,得到了研究区积雪信息最终识别结果。研究表明:①受下垫面和阴影的影响,研究区“同谱异物”和“同物异谱”现象普遍;②深度学习算法的识别效果最好,决策树法的识别效果相对较差;农田区的识别精度高于池塘区,误识别和漏识别的现象都相对较少;③基于投票结果的最终识别结果判定,可以有效改善单一识别方法存在的误识别和漏识别现象。
  • 图  1  研究区位置示意图

    Figure  1.  Location diagram of study area

    图  2  研究区GF-6 PMS影像

    Figure  2.  GF-6 PMS image of the study area

    图  3  研究区典型地物波段反射率直方图

    Figure  3.  Histogram of reflectance of typical surface features in the study area

    图  4  三种NDSII组合下典型地物的NDSII值

    Figure  4.  NDSII values of typical ground objects in three NDSII Combinations

    图  5  决策树算法

    Figure  5.  Decision tree algorithm

    图  6  决策树结构

    Figure  6.  Decision tree structure

    图  7  改进的U-net网络结构

    Figure  7.  Improved U-net network structure

    图  8  积雪识别结果

    Figure  8.  Snow recognition results

    图  9  农田和池塘子研究区及目视解译结果

    Figure  9.  Sub-study area of farmland and ponds and corresponding visual interpretation results

    图  10  农田和池塘子研究区积雪识别结果精度评价

    Figure  10.  Accuracy evaluation of snow recognition results in sub-study area of farmland and ponds

    图  11  积雪识别结果叠加图

    Figure  11.  Stacking diagram of snow recognition results

    图  12  积雪识别结果叠加图像元值统计

    Figure  12.  DN statistics of stacking diagram of snow recognition results

    图  13  包含“争议”像元的典型农田区域

    Figure  13.  Typical farmland areas containing controversial pixels

    图  14  包含小“争议”像元的典型农田区域

    Figure  14.  Typical farmland areas containing little controversial pixels

    图  15  研究区积雪识别的最终结果

    Figure  15.  The final result of snow recognition in the study area

    表  1  GF-6 PMS相机参数

    Table  1.   Parameters of Gf-6 PMS camera

    Band Spectral range/μm Spatial resolution/m Swath width/km Revisit cycle/day
    Blue 0.45-0.52 8 ≥90 4
    Green 0.52-0.60 8
    Red 0.63-0.69 8
    NIR 0.76-0.90 8
    Pan 0.45-0.90 2
    下载: 导出CSV

    表  2  积雪识别结果统计

    Table  2.   Statistics of snow recognition results

    Method Number of snow-covered pixels/ten thousand Ratio of snow-covered pixels/% Number of non-snow pixels/ten thousand Ratio of non-snow pixels/%
    Decision tree 2155.65 75.1 713.56 24.9
    Random forest 2200.94 76.7 668.26 23.3
    Maximum likelihood 2301.13 80.2 568.08 19.8
    SVM 2239.20 78.0 630.01 22.0
    Softmax 2524.23 88.0 344.98 12.0
    Deep learning algorithm 2475.66 86.3 393.55 13.7
    下载: 导出CSV

    表  3  “争议”像元积雪识别结果统计

    Table  3.   Statistics of snow recognition results in controversial pixels

    Area Recognition result Decision tree Random forest Maximum likelihood SVM Softmax Deep learning algorithm
    A Snow 0 97 5756 2196 13347 13347
    Non-snow 13347 13250 7591 11151 0 0
    Proportion of snow/% 0 0.7 43.1 16.5 100 100
    B Snow 0 441 1078 18 2162 2162
    Non-snow 2162 1721 1084 2144 0 0
    Proportion of snow/% 0 20.4 49.9 0.8 100 100
    C Snow 108 313 435 0 1898 1898
    Non-snow 1790 1595 1463 1898 0 0
    Proportion of snow/% 5.7 16.5 22.9 0 100 100
    D Snow 129 316 2854 2151 3766 3765
    Non-snow 3637 3450 912 1615 0 1
    Proportion of snow/% 3.4 8.4 75.8 57.1 100 ≈100
    下载: 导出CSV

    表  4  小“争议”像元积雪识别结果统计

    Table  4.   Statistics of snow recognition results in little controversial pixels

    Area Recognition result Decision tree Random forest Maximum likelihood SVM Softmax Deep learning algorithm
    E Snow 0 10115 10115 10115 10115 10115
    Non-snow 10115 0 0 0 0 0
    Proportion of snow/% 0 100 100 100 100 100
    F Snow 0 23746 23746 23746 23746 23746
    Non-snow 23746 0 0 0 0 0
    Proportion of snow/% 0 100 100 100 100 100
    G Snow 0 0 0 0 1169 0
    Non-snow 1169 1169 1169 1169 0 1169
    Proportion of non-snow/% 100 100 100 100 0 100
    H Snow 19 3 0 0 1351 0
    Non-snow 1354 1370 1373 1373 22 1373
    Proportion of non-snow/% 98.6 99.8 100 100 1.6 100
    下载: 导出CSV
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  • 收稿日期:  2020-11-05
  • 修回日期:  2020-12-20
  • 刊出日期:  2021-06-20

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