Oil Spill Segmentation in Ship-Borne Radar Images with an Improved Active Contour Model

Abstract

Oil spills cause serious damage to marine ecosystems and environments. The application of ship-borne radars to monitor oil spill emergencies and rescue operations has shown promise, but has not been well-studied. This paper presents an improved Active Contour Model (ACM) for oil film detection in ship-borne radar images using pixel area threshold parameters. After applying a pre-processing scheme with a Laplace operator, an Otsu threshold, and mean and median filtering, the shape and area of the oil film can be calculated rapidly. Compared with other ACMs, the improved Local Binary Fitting (LBF) model is robust and has a fast calculation speed for uniform ship-borne radar sea clutter images. The proposed method achieves better results and higher operation efficiency than other automatic and semi-automatic methods for oil film detection in ship-borne radar images. Furthermore, it provides a scientific basis to assess pollution scope and estimate the necessary cleaning materials during oil spills.

Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image

“…Active contour model (ACM) extracts the edge of the target by setting an initial contour and inverting it continuously. Xu et al [22] proposed an improved LBF model [45] for oil film recognition of shipborne radar images (called Method 5 here). LBF model is a region-based ACM with a variable level set formulation.…”

“…However, due to the confidentiality policy of commercial companies, the core technologies have not yet been published. Since 2010, based on the shipboard radar images collected in the oil spill accident of Dalian on July 16, 2010, the researchers of Dalian Maritime University have successively published the achievements of oil spill monitoring by using adaptive threshold methods, active contour models, and machine learning methods [16][17][18][19][20][21][22][23].…”

Automatic detection of thin oil films on water surfaces in ultraviolet imagery

“…Microwave remote sensing technologies mostly detect oil spills on water surfaces because they suppress capillary waves and reduce sea-surface roughness. According to this theoretical basis, oil spills could be detected in marine radar images (Liu et al, 2017(Liu et al, , 2019Xu et al, 2018Xu et al, , 2019Zhu et al, 2015) or synthetic aperture radar (SAR) images (Espeseth et al, 2017;Fiscella et al, 2000;Liu et al, 2010;Skrunes et al, 2014;Solberg et al, 2007). Alternatively, optical remote sensing technologies distinguish the oil polluted seawater from clean seawater by their different optical properties (Hu et al, 2021;Otremba, 2000), for example, reflectance, absorption and scattering.…”

Multi-Feature Driven Active Contour Segmentation Model for Infrared Image With Intensity Inhomogeneity

“…With the development of IR imaging and computer vision [1], image segmentation which aims to extract object of interest from image plays an essential Minjie Wan E-mail: minjiewan1992@njust.edu.cn Guohua Gu E-mail: gghjust@mail.njust.edu.cn 1 School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China 2 Shanghai Institute of Spaceflight Control Technology, Shanghai 201109, China role in many areas of both civil and military applications, such as geology exploration [2], aerospace engineering [3], security monitoring [4] and so on. Among various image segmentation methods [5][6][7][8], ACM has gained popularity because of its excellent ability to obtain closed contours with sub-pixel accuracy [9].…”

“…Among various image segmentation methods [5,6,7,8], ACM has gained popularity because of its excellent ability to obtain closed contours with sub-pixel accuracy [9]. Although, a number of ACMs [10,11,12,13] have achieved satisfactory performances in clear visible images, they only use single image feature information to construct the energy function. Therefore, the results may suffer from false segmentations when the traditional ACMs are directly applied to handle IR images with intensity inhomogeneity, blurred boundary, and low contrast.…”