Oil spill segmentation in marine radar images based on the improved Sauvola algorithm

Remote Sensing Letters 2026

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Authors: Peng Liu, Xuchong Wang, Pengzhe Shao, Xingquan Zhao, Ben Liu, P. Chen, Xueyuan Zhu, Jin Xu, Ying Li
DOI: 10.1080/2150704x.2026.2613052
Cited by: 3
References: 16

Abstract

Marine oil spill incidents pose persistent threats to coastal ecosystems and maritime operations. This study presents an enhanced segmentation methodology for oil spill detection in X-band marine radar images through an improved Sauvola algorithm. The proposed method integrates negative exponential modelling of the background clutter distribution with adaptive threshold optimization. Validated using operational data from the 2010 Dalian Xingang Harbour oil spill incident, the improved Sauvola algorithm demonstrates the segmentation results consistent with visual interpretation. Moreover, compared with six other methods, both the user accuracy and the producer accuracy of the improved Sauvola algorithm are larger than 96%.

Fields of Study & Subjects

Oil Spill Detection and Mitigation Image Enhancement Techniques Infrared Target Detection Methodologies

Publication details

Journal / source
Remote Sensing Letters
Publisher
Informa UK Limited
Volume
17
Issue
2
Pages
182–189
Publication date
10 January 2026
Language
EN