Coastline extraction based on multi-scale segmentation and multi-level inheritance classification - Hui, Sheng; Mengliang, Guo; Yuliang, Gan; Mingming, Xu; Shanwei, Liu; Yasir, Muhammad; Jianyong, Cui; Jianhua, Wan (Frontiers in Marine Science, 2022)
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 extract coastlines with categorical attributes, cultivation ponds easily affected by tidal flats and complex coastal terrain. The Chinese GF-2 satellite images are used to extract various types of coastline in Jiaozhou Bay and its surrounding areas such as 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 10.104km in the harbor-wharf coastline, 0.099km in silt coastline, 2.677km in pond coastline, 8.831km in rocky coastline, and 0.218km 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.