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国土资源遥感  2020, Vol. 32 Issue (1): 216-223    DOI: 10.6046/gtzyyg.2020.01.29
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基于无人机影像的喀斯特农耕区地物识别——以桂林市为例
娄佩卿1,陈晓雨2,王疏桐3,付波霖1(),黄永怡1,唐廷元1,凌铭1
1. 桂林理工大学测绘地理信息学院,桂林 541006
2. 桂林理工大学土木与建筑学院,桂林 541006
3. 桂林理工大学信息科学与工程学院,桂林 541006
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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摘要 

为了探究低空无人机遥感技术对喀斯特地貌条件下不同形态农耕区地物类型的识别精度,以桂林市3个200 m×200 m样方的农耕区为研究区,在无人机航拍影像和地面调查数据的支持下,分别将基于像元和面向对象的影像分析技术与支持向量机(support vector machine,SVM)算法相结合,构建不同地貌条件下农耕区地物遥感识别模型,并进行精度对比分析。结果表明,面向对象的SVM分类结果保留了原始地物的大致轮廓,且地块较完整,更为适用于喀斯特地貌条件下的农耕区地物识别,较基于像元的SVM分类方法总体精度高6.54%,Kappa系数高0.135; 基于像元的SVM分类方法适用于地物分布规则的农耕区地物识别,相比面向对象的SVM分类方法总体精度高2.92%,Kappa系数高0.026。

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娄佩卿
陈晓雨
王疏桐
付波霖
黄永怡
唐廷元
凌铭
关键词 无人机影像农耕区多尺度影像分割SVM算法    
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 .

Key wordsUAV image    agricultural areas    Multi-resolution image segmentation    SVM algorithm
收稿日期: 2019-02-01      出版日期: 2020-03-14
ZTFLH:  TP79  
基金资助:国家自然科学青年基金项目“基于主被动遥感的沼泽植被群丛时空分布与水文情势耦合研究”(编号: 41801071);“广西八桂学者”专项经费、广西省自然科学青年基金项目“基于主被动遥感的北部湾红树林群丛时空分布与水文情势耦合研究”(编号: 2018GXNSFBA281015);桂林理工大学科研启动基金项目(编号: GUTQDJJ2017096)
通讯作者: 付波霖     E-mail: fbl2012@126.com
作者简介: 娄佩卿(1995-),男,硕士,主要从事遥感图像智能处理研究。Email: gislou@126.com。
引用本文:   
娄佩卿,陈晓雨,王疏桐,付波霖,黄永怡,唐廷元,凌铭. 基于无人机影像的喀斯特农耕区地物识别——以桂林市为例[J]. 国土资源遥感, 2020, 32(1): 216-223.
Peiqing LOU,Xiaoyu CHEN,Shutong WANG,Bolin FU,Yongyi HUANG,Tingyuan TANG,Ming LING. Object recognition of karst farming area based on UAV image: A case study of Guilin. Remote Sensing for Land & Resources, 2020, 32(1): 216-223.
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http://www.gtzyyg.com/CN/10.6046/gtzyyg.2020.01.29      或      http://www.gtzyyg.com/CN/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  不同农耕区及其典型地物类型
Fig.1  航摄耕地+居民地复合区的部分原始影像
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  无人机航摄影像部分参数
Fig.2  3个试验区镶嵌完整的DOM数据
Fig.3  研究技术路线
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  影像分割参数
Fig.4  梯田+林地复合区及梯田区影像多尺度分割结果
Fig.5  梯田+林地区分类结果对比
Fig.6  梯田区分类结果对比
Fig.7  耕地+居民地复合区分类结果对比
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  梯田+林地复合区混淆矩阵
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  梯田区混淆矩阵
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  耕地+居民地复合区混淆矩阵
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