Field road recognition method based on high-resolution remote sensing image

doi:10.13474/j.cnki.11-2246.2023.0024

Bulletin of Surveying and Mapping ›› 2023, Vol. 0 ›› Issue (1): 145-148.doi: 10.13474/j.cnki.11-2246.2023.0024

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Field road recognition method based on high-resolution remote sensing image

YUAN Cuixia¹, REN Yanmin^2,3, HAN Zhiqiang⁴, LIU Yu^2,3, LI Shuhua^2,3

1. School of Land Engineering, Chang'an University, Xi'an 710054, China;
2. Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;
3. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;
4. Zhaodong Agricultural Technology Extension Center of Heilongjiang Province, Zhaodong 151100, China

Received:2022-01-13 Published:2023-02-08

Abstract

Abstract: Field road is an important project for farmland construction and management,and its direction,width and accessibility directly affect the convenience of farmland cultivation.Therefore,accurate identification of field road is of great significance for evaluation of farmland construction effect.With the help of high-resolution images,this paper adopts object-oriented method to identify the road based on the idea of hierarchical classification.Then,tensor voting method,eight-neighborhood breakpoint detection and morphological algorithm are introduced to repair the breakpoints.Take Dingzhou city,Hebei province as an example,the result of field road recognition is obtained,and the integrity rate,accuracy rate and quality of field road identification are 91%,74% and 69%,respectively.

Key words: high-resolution image, field road, recognition method, object-orientation, breakpoint detect

CLC Number:

P237

YUAN Cuixia, REN Yanmin, HAN Zhiqiang, LIU Yu, LI Shuhua. Field road recognition method based on high-resolution remote sensing image[J]. Bulletin of Surveying and Mapping, 2023, 0(1): 145-148.

References

[1] 叶英聪,匡丽花,赵小敏,等. 基于时间可达性的土地整治道路布局研究[J]. 自然资源学报,2017,32(6):1064-1073.
[2] 国家质量监督检验检疫总局,中国国家标准化管理委员会. 高标准农田建设通则:GB/T 30600-2014[S]. 北京:中国标准出版社,2014.
[3] M H L,Z Y,L H B,et al. Road extraction base on Zernike algorithm on SAR image[C]//Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).Beijing:IEEE,2016.
[4] 黄巍,黄辉先,徐建闽,等. 基于Canny边缘检测思想的改进遥感影像道路提取方法[J]. 国土资源遥感,2019,31(1):65-70.
[5] 孙瑶,孙国强,赵慧敏. 面向对象的高分辨率影像的道路提取[J]. 北京测绘,2021,35(5):578-582.
[6] 蒋星详,肖莉. 一种多特征融合的高分辨率遥感影像道路中心线提取算法[J]. 测绘地理信息,2019,44(4):98-101.
[7] 朱恩泽,宋伟东,戴激光. 改进支持向量机的高分遥感影像道路提取[J].测绘科学,2016,41(12):224-228.
[8] 雷惠敏,张和生. 最优特征选择下多层次分割的城市道路提取[J].中国空间科学技术,2022,42(2):99-107.
[9] 王睿,漆泰岳,胡燊,等. 隧道衬砌裂缝检测中的背景处理和断点连接算法[J].应用基础与工程科学学报,2017,25(4):742-750.
[10] 刘金丽,陈钊. Landsat 8多光谱数据辅助下的高分影像多尺度分割[J]. 测绘通报,2019(9):38-43.
[11] 何惠馨,范俊甫,陈文贺,等. 基于亮度补偿的遥感影像阴影遮挡道路提取方法[J]. 地球信息科学学报,2020,22(2):258-267.
[12] 韩洁. 融合非监督分类和几何-纹理-光谱特征的高分辨率遥感影像道路提取[D]. 北京:中国科学院大学(中国科学院遥感与数字地球研究所),2018.
[13] SOBEL I. Neighborhood coding of binary images for fast contour following and general binary array processing[J]. Computer Graphics and Image Processing,1978,8(1):127-135.
[14] 王斌,陈占龙,吴亮,等. 兼顾连通性的U-Net网络高分辨率遥感影像道路提取[J]. 遥感学报,2020,24(12):1488-1499.
[15] HEIPKE C,MAYER H,WIEDEMANN C. Evaluation of automatic road extraction[J]. International Archives of Photogrammetry and Remote Sensing,1997,32(3):151-160.

Field road recognition method based on high-resolution remote sensing image

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