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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (2) : 180-186     DOI: 10.6046/gtzyyg.2019.02.25
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Object-oriented classification of unmanned aerial vehicle image for thermal erosion gully boundary extraction
Linlin LIANG1,2, Liming JIANG1,2(), Zhiwei ZHOU1, Yuxing CHEN1,2, Yafei SUN1,2
1.State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China;
2.University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

Global climate warming and human activities have caused large areas of permafrost degradation and thermal erosion gully in the Tibetan Plateau, seriously affecting the engineering construction and the ecological environment in permafrost regions. In this study, high resolution unmanned aerial vehicle (UAV) images and object-oriented classification approaches were applied to extracting the thermal erosion gullies in Eboling Mountain of Heihe River. Five kinds of object-oriented supervised learning algorithms, namely nearest neighbor, K-nearest neighbor, decision tree, support vector machine (SVM), and random forest, were analyzed for the capability and accuracy of the extraction of thermal erosion gullies in detail. The field GPS data were used for evaluating the classification accuracy. The results show that, in the object-oriented image analysis, the segmentation scale parameters have little effect on the extraction of thermal erosion gullies, wheres classification features have a greater impact, so it is important to select the appropriate classification features. The overall accuracies of the five machine learning methods are all over 90%, among which the Kappa coefficient of the SVM is higher than the other four classification methods. This means that SVM is more suitable for the thermal erosion gullies boundary extraction of UAV images in this study. The combination of high resolution UAV images and object-oriented classification methods has broad application prospects in the extraction of the thermal erosion gullies.
Keywords thermal erosion gully of permafrost      unmanned aerial vehicle      Eboling Mountain of Heihe River permafrost region      high spatial resolution images      object-oriented analysis     
:  TP79  
Corresponding Authors: Liming JIANG     E-mail: jlm@whigg.ac.cn
Issue Date: 23 May 2019
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Linlin LIANG
Liming JIANG
Zhiwei ZHOU
Yuxing CHEN
Yafei SUN
Cite this article:   
Linlin LIANG,Liming JIANG,Zhiwei ZHOU, et al. Object-oriented classification of unmanned aerial vehicle image for thermal erosion gully boundary extraction[J]. Remote Sensing for Land & Resources, 2019, 31(2): 180-186.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.02.25     OR     https://www.gtzyyg.com/EN/Y2019/V31/I2/180
Fig.1  Study area image
Fig.2  Thermal erosion gully in the study area
Fig.3  UX5 fixed-wing unmanned aerial vehicle
Fig.4  GPS field measurement
波段权重(R,G,B,
DSM, slope)		 光谱信
息权重		 紧致度
权重		 分割
尺度
1,1,1,1,1 0.7 0.8 150
1,1,1,1,1 0.7 0.8 220
Tab.1  Segmentation parameters
实验
序号		 分割参数(分割尺
度,光谱信息权重,
紧致度权重)		 滑塌、非滑塌分类特征提取
SY1 150,0.7,0.8 所有波段的平均值、标准差和比率
SY2 220,0.7,0.8 所有波段的平均值、标准差和比率
SY3 220,0.7,0.8 绿光、红光和坡度波段的标准差及蓝光、绿光波段的比率
Tab.2  Classification feature parameters
Fig.5  SY1 sample distribution and classification results
分类方法 类型 滑塌 非滑塌 总数 制图精度/% 用户精度/% 总体精度/% Kappa
最邻近 滑塌 2 149 1 651 3 800 83 57 97 0.66
非滑塌 433 78 830 79 263 98 99
KNN 滑塌 2 150 1 600 3 750 83 57 98 0.67
非滑塌 432 78 881 79 313 98 99
决策树 滑塌 2 195 2 577 4 772 85 46 96 0.58
非滑塌 387 77 904 78 291 97 100
SVM 滑塌 2 011 903 2 914 78 69 98 0.72
非滑塌 571 79 578 80 149 99 99
随机森林 滑塌 2 160 1 363 3 523 84 61 98 0.70
非滑塌 422 79 118 79 540 98 99
Tab.3  Confusion matrix of SY1 classification method
分类方法 类型 滑塌 非滑塌 总数 制图精度/% 用户精度/% 总体精度/% Kappa
最邻近 滑塌 2 251 1 681 3 932 87 57 98 0.68
非滑塌 331 78 788 79 119 98 100
KNN 滑塌 2 213 1 680 3 893 86 57 98 0.67
非滑塌 369 78 801 79 170 98 100
决策树 滑塌 1 897 1 899 3 796 73 50 97 0.58
非滑塌 685 78 582 79 267 98 99
SVM 滑塌 2 096 1 298 3 394 81 62 98 0.69
非滑塌 486 79 183 79 669 98 99
随机森林 滑塌 1 984 1 908 3 892 77 51 97 0.60
非滑塌 598 78 573 79 171 98 99
Tab.4  Confusion matrix of SY2 classification method
分类方法 类型 滑塌 非滑塌 总数 制图精度/% 用户精度/% 总体精度/% Kappa
最邻近 滑塌 2 267 2 598 4 865 88 47 96 0.58
非滑塌 315 77 782 78 097 97 100
KNN 滑塌 2 265 3 041 5 306 88 43 96 0.55
非滑塌 316 77 414 77 730 96 100
决策树 滑塌 2 431 7 595 10 026 94 24 91 0.35
非滑塌 151 72 886 73 037 91 100
SVM 滑塌 2 213 2 325 4 538 86 49 97 0.61
非滑塌 369 78 156 78 525 97 100
随机森林 滑塌 2 296 6 461 8 757 89 26 92 0.37
非滑塌 286 74 020 74 306 92 100
Tab.5  Confusion matrix of SY3 classification method
Fig.6  Comparison of Kappa coefficients of five classification methods
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