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

Abstract

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.

Curve extraction and matching algorithm for building images with viewpoint differences

“…Line features include straight lines and curves, and curves are more adaptable for irregular building contour description. However, most existing curve extraction algorithms focus on road detection [2] , coastline identification [3] , industrial part detection [4] , medical image analysis [5] and so on, which only need to extract specific curves, making them difficult to apply directly to matching and stereo scene recovery. In this regard, many scholars follow certain rules to connect discrete contour points to obtain all edges in the image [6] , but such methods are prone to connection errors due to the background information of the image and noise interference.…”

Geospatial analysis of shoreline changes in the Oman coastal region (2000-2022) using GIS and remote sensing techniques

“…Given the dynamic nature of this boundary, a temporal and spatial evaluation of the chosen shoreline definition is imperative (Zheng et al, 2023). The instantaneous shoreline refers to the immediate location of the land-water interface (Mullick et al, 2020;Hui et al, 2022). It is important to acknowledge that the cost is a time-dependent phenomenon with short-term variability.…”

Coastal resilience and shoreline dynamics: assessing the impact of a hybrid beach restoration strategy in Puducherry, India

“…Manual digitization is time-consuming and requires intensive user effort and visual perception that varies from person to person (Matin and Jahid Hasan, 2021). Meanwhile, thresholding-based automated shoreline extraction techniques are a faster approach to detecting and extracting shorelines (Toure et al, 2019;Hui et al, 2022). This study uses a semi-automated model to extract shorelines that use an image segmentation technique based on land and water reflectance properties.…”