Coastline extraction based on multi-scale segmentation and multi-level inheritance classification

被引:8
作者
Hui, Sheng [1 ]
Mengliang, Guo [1 ]
Yuliang, Gan [2 ]
Mingming, Xu [1 ]
Shanwei, Liu [1 ]
Yasir, Muhammad [1 ]
Jianyong, Cui [1 ]
Jianhua, Wan [1 ]
机构
[1] China Univ Petr, Coll Oceanog & Space Informat, Qingdao, Peoples R China
[2] Qingdao Geotech & Surveying Res Inst, Qingdao, Peoples R China
关键词
GF-2; images; multi-scale segmentation; multi-level inheritance classification; automatic coastline extraction; coastline types; COASTAL EROSION;
D O I
10.3389/fmars.2022.1031417
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Detailed management of the coastline is critical to the development of coastal states. However, the current classification of the coastline is relatively weak. This study proposed an automatic method to detect coastlines with category attributes based on multi-scale segmentation and multi-level inheritance classification. Fully integrating the advantages of multi-scale segmentation and multi-level classification, it solved the problems that traditional methods could not solve, such as extracting coastlines with categorical attributes, cultivation ponds that are easily affected by tidal flats, and complex coastal terrain. The Chinese GF-2 satellite images are used to extract various types of coastlines in Jiaozhou Bay and its surrounding areas such as the harbor-wharf coastline, silt coastline, pond coastline, rocky coastline, and sandy coastline. Compared with the human interpretation, it is found that the coastline extracted by our proposed method is different by 10.104 km in the harbor-wharf coastline, 0.099 km in the silt coastline, 2.677 km in the pond coastline, 8.831 km in the rocky coastline, and 0.218 km in the sandy coastline. Furthermore, compared to the object-based region growing integrating edge detection (OBRGIE) method, it is increased by 13.52%, 2.16%, 14.48%, 52.57%, and 22.97%, respectively. The results show that our proposed method is algorithmically more reasonable, accurate, and powerful. It can provide data support for refined coastline management.
引用
收藏
页数:13
相关论文
共 35 条
[1]  
Bai T., 2020, Research on Object-Oriented Multi-Level Classification Method of GF-2 Remote Sensing Image, DOI [10.27162/d.cnki.gjlin.2020.005177, DOI 10.27162/D.CNKI.GJLIN.2020.005177]
[2]  
Bi J.P., 2019, COAST ENG, V38, P247, DOI [10.3969/j.issn.1002-3682.2019.03.001, DOI 10.3969/J.ISSN.1002-3682.2019.03.001]
[3]   Shoreline definition and detection: A review [J].
Boak, EH ;
Turner, IL .
JOURNAL OF COASTAL RESEARCH, 2005, 21 (04) :688-703
[4]  
Chang Y.J., 2021, GEOSPATIAL INFORM, V19
[5]   Temporal and spatial variation of coastline using remote sensing images for Zhoushan archipelago, China [J].
Chen, Chao ;
Liang, Jintao ;
Xie, Fang ;
Hu, Zijun ;
Sun, Weiwei ;
Yang, Gang ;
Yu, Jie ;
Chen, Li ;
Wang, Lihua ;
Wang, Liyan ;
Chen, Huixin ;
He, Xinyue ;
Zhang, Zili .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2022, 107
[6]   Changes of the spatial and temporal characteristics of land-use landscape patterns using multi-temporal Landsat satellite data: A case study of Zhoushan Island, China [J].
Chen, Huixin ;
Chen, Chao ;
Zhang, Zili ;
Lu, Chang ;
Wang, Liyan ;
He, Xinyue ;
Chu, Yanli ;
Chen, Jianyu .
OCEAN & COASTAL MANAGEMENT, 2021, 213
[7]  
Cheng H.Y., 2022, COMPUT MULTIMEDIA TE, V02, P157
[8]   Automated parameterisation for multi-scale image segmentation on multiple layers [J].
Dragut, L. ;
Csillik, O. ;
Eisank, C. ;
Tiede, D. .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2014, 88 :119-127
[9]   NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space [J].
Gao, BC .
REMOTE SENSING OF ENVIRONMENT, 1996, 58 (03) :257-266
[10]   Object-oriented coastline classification and extraction from remote sensing imagery [J].
Ge, Xizhi ;
Sun, Xiliang ;
Liu, Zhaoqin .
REMOTE SENSING OF THE ENVIRONMENT: 18TH NATIONAL SYMPOSIUM ON REMOTE SENSING OF CHINA, 2014, 9158