Ship Lock Extraction from High-Resolution Remote Sensing Images Based on Fuzzy Theory and Prior Knowledge

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 17; p. 3181
• Main Authors: Chen, Bingsun, Bao, Yi, Song, Yanjiao, Li, Ziyang, Wang, Zhe, Wang, Xi, Ma, Runsheng, Meng, Lingkui, Zhang, Wen, Li, Linyi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2024
• Subjects: Algorithms; Bridges; Classification; Clustering; Cost allocation; Economic development; Flood control; Flood management; fuzzy classification; Fuzzy control; Fuzzy sets; Gaofen-1; High resolution; Hydraulics; Image resolution; Knowledge; Locks; Object recognition; prior knowledge; Regional development; Remote control; Remote sensing; Resource allocation; Rivers; ship lock extraction; Transportation systems; Water conservation; Water management; Water resources; Water transport; Water transportation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16173181

As crucial water conservancy projects, ship locks play a key role in flood control, shipping, water resource allocation, and promoting regional economic development, making them an indispensable part of the modern water transportation system. Utilizing satellite remote sensing for lock extraction can significantly reduce manual workload and costs, assist in the daily dynamic maintenance of lock hubs, and provide more comprehensive data support for the construction and management of water transport infrastructure. In this context, this paper proposes a new method for ship lock object extraction. Leveraging fuzzy theory and prior knowledge of locks, the extraction of lock objects is achieved from Gaofen-1 (GF-1) high-resolution remote sensing images. The experimental results demonstrate that the proposed algorithm can effectively extract small lock objects in remote sensing images, achieving an average extraction accuracy of 80.9% in the study area.