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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (1) : 216-223     DOI: 10.6046/gtzyyg.2020.01.29
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Object recognition of karst farming area based on UAV image: A case study of Guilin
Peiqing LOU1, Xiaoyu CHEN2, Shutong WANG3, Bolin FU1(), Yongyi HUANG1, Tingyuan TANG1, Ming LING1
1. Institute of Surveying and Mapping, Guilin University of Technology, Guilin 541006, China
2. Institute of Civil and Architectural Engineering, Guilin University of Technology, Guilin 541006, China
3. Institute of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China
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Abstract  

In order to explore the recognition accuracy of remote sensing technology of low-altitude UAV for surface features in agricultural areas with different forms under karst landform conditions, the authors chose three agricultural areas (each having an area size of 200 m×200 m) in Guilin City as the research object. Supported by UAV aerial images and ground survey data, the image analysis technology based on pixel and object-oriented was combined with support vector machine (SVM) algorithm, respectively, to build the remote sensing recognition model of agricultural areas under different geomorphological conditions, and the precision was comparatively studied and analyzed. The results show that the object-oriented SVM classification results retain the rough outline of the original ground features, and the plot is relatively complete, and hence this means is more suitable for the recognition of ground features in agricultural areas under karst landform conditions. Compared with the pixel based SVM classification method, the overall accuracy is higher by 6.54% , and the Kappa coefficient is higher by 0.135 . The SVM classification method based on pixel is suitable for feature recognition in agricultural areas with regular feature distribution. Compared with the object-oriented SVM classification method, the overall accuracy is higher by 2.92% and the Kappa coefficient is higher by 0.026 .
Keywords UAV image      agricultural areas      Multi-resolution image segmentation      SVM algorithm     
:  TP79  
Corresponding Authors: Bolin FU     E-mail: fbl2012@126.com
Issue Date: 14 March 2020
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Peiqing LOU
Xiaoyu CHEN
Shutong WANG
Bolin FU
Yongyi HUANG
Tingyuan TANG
Ming LING
Cite this article:   
Peiqing LOU,Xiaoyu CHEN,Shutong WANG, et al. Object recognition of karst farming area based on UAV image: A case study of Guilin[J]. Remote Sensing for Land & Resources, 2020, 32(1): 216-223.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.01.29     OR     https://www.gtzyyg.com/EN/Y2020/V32/I1/216
农耕区类型 典型地物类型
梯田+林地复合区 水体、道路、建筑物、林地、秧田和水田
梯田区 草地、经济作物、道路、建筑物、林地和果园
耕地+居民地复合区 经济作物、林地、菜地、果园、草地、水田、秧田、水体、道路和建筑物
Tab.1  Different farming areas and their typical feature types
Fig.1  Some original images of cultivated land and residential land compound area
农耕区类型 图幅大小/像元 空间分辨率/m 飞行高度/m
梯田+林地复合区 5 222×5 063 0.069 100
梯田区 8 252×5 801 0.042 85
耕地+居民地复合区 9 111×7 454 0.044 85
Tab.2  Part parameters of UAV aerial photography
Fig.2  DOM of three test areas
Fig.3  Research technical route
影像名称 分割尺度 形状因子 紧实度 波段权重
(R,G,B)
梯田+林地复合区 30 0.4 0.5 1,1,1
梯田区 200 0.3 0.5 1,1,1
耕地+居民地复合区 300 0.3 0.5 1,1,1
Tab.3  Image segmentation parameters
Fig.4  Multi-scale segmentation results of images of terraced +forest area and terraced area
Fig.5  Comparison between the classification results of terrace + forest area and the original image
Fig.6  Comparison between the classification results of terrace area and the original image
Fig.7  Comparison between the classification results of cultivated land and residential compound area and the original image
分类方法 类别 水田 秧田 林地 道路 建筑物 水体 总量 误分率/%
基于像元 水田 1 042 0 0 317 26 0 1 385 24.77
秧田 0 930 75 3 0 0 1 008 7.74
林地 40 439 10 032 82 4 77 10 674 6.01
道路 108 0 0 763 29 0 900 15.22
建筑物 0 0 0 74 993 3 1 070 7.20
水体 0 0 0 0 0 73 73 0
面向对象 水田 41 0 0 2 0 0 43 4.65
秧田 5 30 1 0 0 0 36 16.67
林地 0 2 21 0 0 2 25 16.00
道路 0 0 0 26 1 0 27 3.70
建筑物 0 0 0 1 27 0 28 3.57
水体 0 0 2 0 0 11 13 15.38
Tab.4  Confusion matrix of the terrace and forest area
分类方法 类别 果园 林地 建筑物 道路 经济作物 草地 总量 误分率/%
基于像元 果园 3 034 3 18 243 718 343 4 359 30.40
林地 1 218 8 307 3 15 10 427 9 980 16.76
建筑物 0 0 1 024 56 107 0 1 187 13.73
道路 8 0 3 1 045 22 0 1 078 3.06
经济作物 98 0 5 3 878 13 997 11.94
草地 0 0 0 0 0 134 134 0.00
面向对象 果园 26 2 0 0 1 0 29 10.34
林地 7 19 0 0 1 3 30 36.67
建筑物 0 0 26 0 0 0 26 0
道路 0 0 0 24 2 0 26 7.69
经济作物 1 0 0 0 15 0 16 6.25
草地 0 0 0 0 0 13 13 0
Tab.5  Confusion matrix of the terrace area
分类方法 类别 建筑物 道路 水体 秧田 水田 草地 果园 菜地 林地 经济作物 总量 误分率/%
基于像元 建筑物 518 127 23 0 47 0 0 1 0 1 652 27.75
道路 23 497 0 0 34 0 0 0 0 213 689 35.20
水体 0 0 136 0 0 0 0 0 0 0 217 0
秧田 6 0 21 670 1 154 170 121 0 63 823 44.44
水田 83 62 4 88 604 0 1 0 0 17 686 29.69
草地 0 0 2 9 0 76 11 0 0 0 674 22.45
果园 1 1 21 56 0 177 492 35 26 0 762 39.18
菜地 5 0 0 0 0 3 0 98 0 0 301 7.55
林地 15 2 10 0 0 264 88 46 1535 0 1 561 21.68
经济作物 1 0 0 0 0 0 0 0 0 102 396 0.97
面向对象 建筑物 51 1 8 5 10 0 0 0 0 2 652 33.77
道路 0 30 0 0 0 0 0 0 0 2 39 6.25
水体 4 0 20 0 0 0 0 1 0 0 28 20.00
秧田 0 0 0 13 0 0 6 1 0 1 24 38.10
水田 1 4 0 0 16 0 0 0 0 4 36 36.00
草地 0 0 0 2 0 16 5 7 0 0 19 46.67
果园 0 0 0 3 0 0 17 0 0 0 30 15.00
菜地 0 0 0 0 0 3 2 8 0 1 18 42.86
林地 1 0 0 0 0 0 0 1 21 0 21 8.70
经济作物 6 4 0 1 10 0 0 0 0 3 13 87.50
Tab.6  Confusion matrix of cultivated land and residential compound area
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