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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (2) : 26-32     DOI: 10.6046/gtzyyg.2020.02.04
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Extraction of mechanical damage surface using GF-2 remote sensing data
Jisheng XIA, Mengying MA, Zhongren FU
School of Earth Science, Yunnan University, Kunming 650500, China
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Abstract  

Mechanical damaged surface tends to cause soil erosion, secondary geological hazards and other ecological environment problems, but there is still a lack of effective extraction methods based on remote sensing images. Based on the GF-2 remote sensing image, the authors studied the object-oriented extraction method based on texture features in Tanglangchuan watershed with densely distributed mechanical damage surface. According to the seven types of features, the classification rules were established. On the basis of the optimal scale segmentation, the decision tree A based on spectral features and the decision tree B based on "spectral + texture" features are classified in object-oriented way. Precision evaluation and analysis show that, compared with the traditional supervised classification method and the spectral-based object-oriented classification method, the classification method improves the Kappa coefficient and the total accuracy to 0.82 and 86.25%, respectively, and also effectively improves the extraction accuracy of mechanical damage surface.
Keywords GF-2      mechanical damage surface      object-oriented classification      decision tree     
:  TP79  
Issue Date: 18 June 2020
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Jisheng XIA
Mengying MA
Zhongren FU
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Jisheng XIA,Mengying MA,Zhongren FU. Extraction of mechanical damage surface using GF-2 remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(2): 26-32.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.04     OR     https://www.gtzyyg.com/EN/Y2020/V32/I2/26
Fig.1  Remote sensing image of research area
Fig.2  Flowchart of research methods
Fig.3  Image segmentation
指数 统计值 林地 水体 居民地 耕地(有植被) 耕地(无植被) 裸地 机械性破损面
最小值 0.08 -0.87 -1.00 0.05 -0.38 -0.06 -0.15

NDVI		 最大值 1.00 -0.02 1.00 0.94 0.42 0.60 0.45
平均值 0.45 -0.69 0.10 0.32 0.15 0.15 0.05
标准差 0.14 0.07 0.09 0.24 0.07 0.09 0.03
最小值 -1.00 0.90 -0.64 -0.74 -0.45 -0.62 -0.57

NDWI		 最大值 -0.09 0.06 -0.54 -0.52 -0.17 0.00 0.00
平均值 -0.50 0.77 -0.20 -0.67 -0.28 -0.28 -0.22
标准差 -0.08 0.07 -0.09 -0.04 -0.05 -0.08 -0.04
Stddev of length of 最小值 1.30 8.16 2.60 4.35 9.03 2.03 1.89
edges (polygon) 最大值 4.13 9.35 4.26 16.49 15.62 7.56 4.25

length/width		 最小值 1.30 1.24 2.52 1.43 1.33 1.28 1.11
最大值 2.25 2.59 37.50 2.65 3.81 2.84 2.48
Tab.1  Characteristic indices of various types of land features
Fig.4  GLCM texture characteristic diagram
纹理特征值 均值 方差 对比度 相异性 信息熵 同质度
JM距离 1.29 1.28 1.86 0.65 0.67 1.82
转换分离度 1.71 1.98 1.98 0.97 0.75 1.94
Tab.2  Separable parameter table of texture features
类别 分类规则
水体 (0.35<NDWI<0.8) and (110<mean NIR<220)
林地 (0.2<NDVI<0.7) and (Stddev of length of edges (polygon)<4)
有作物的耕地 (0.2<NDVI<0.7) and (Stddev of length of edges (polygon)≥4)
居民地 (NDVI≥0.7 or NDVI≤0.2) and (length/width<2.8 or length/width>35) and (600<mean NIR<1 500)
无作物的耕地 (meanNIR≥1 500 or meanNIR≤600) and (2.8≤length/width≤35)and (8<Stddev of length of edges (polygon)<52)
裸地 (NDVI≥0.07 and NDVI≤0.01)
机械性破损面 (Stddev of length of edges (polygon)≤8 or Stddev of length of edges (polygon)≥52) and (0.01<NDVI<0.07)
and (contrast>1 000) and (Homogeneity<0.07)
Tab.3  Classification Rules of Terrain Objects
Fig.5  Object-oriented classification results based on decision tree A and decision tree B
土地利用
类型		 决策树A 决策树B
用户精
度/%		 制图精
度/%		 用户精
度/%		 制图精
度/%
裸地 83.56 83.56 87.33 84.42
机械性破损面 86.43 86.53 89.63 86.83
总体精度/% 78.34 86.25
Kappa系数 0.73 0.82
Tab.4  Comparison of extraction accuracy of mechanical damage surface between decision tree A and decision tree B
Fig.6  Comparison of the results of different supervised classification methods
精度 基于像元的监督分类 面向对象分类
最小距
离法		 马氏距
离法		 最大似
然法		 基于光谱
的决策树A		 基于“光谱+
纹理”的
决策树B
总体精度/% 70.21 72.67 74.53 78.34 86.25
Kappa系数 0.60 0.62 0.65 0.73 0.82
Tab.5  Accuracy comparison between object-oriented classification and supervised classification
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