Fast Detection of Oil Spills and Ships Using SAR Images

Lupidi, Alberto; Stagliano, Daniele; Martorella, Marco; Berizzi, F.

doi:10.3390/rs9030230

Cited by 31 publications

(19 citation statements)

References 14 publications

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“…Their frequent transits over the polar regions make these satellites particularly useful for Artic surveillance and for monitoring icebergs, sea-ice coverage [2,3], ships [4][5][6][7][8][9][10][11][12][13][14][15][16][17], oil spills [18][19][20], crop and forestation [21,22].…”

Section: Introductionmentioning

confidence: 99%

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Heiselberg¹,

Heiselberg

2017

Remote Sensing

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Abstract:The European Space Agency Sentinel-2 satellites provide multispectral images with pixel sizes down to 10 m. This high resolution allows for fast and frequent detection, classification and discrimination of various objects in the sea, which is relevant in general and specifically for the vast Arctic environment. We analyze several sets of multispectral image data from Denmark and Greenland fall and winter, and describe a supervised search and classification algorithm based on physical parameters that successfully finds and classifies all objects in the sea with reflectance above a threshold. It discriminates between objects like ships, islands, wakes, and icebergs, ice floes, and clouds with accuracy better than 90%. Pan-sharpening the infrared bands leads to classification and discrimination of ice floes and clouds better than 95%. For complex images with abundant ice floes or clouds, however, the false alarm rate dominates for small non-sailing boats.

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Section: Introductionmentioning

confidence: 99%

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Heiselberg¹,

Heiselberg

2017

Remote Sensing

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“…As the situation on the ground is relatively complex, and some buildings and number of buildings are shaded, the segmentation of the collected images poses a great challenge. The fast detection of oil spill method (FD) [79] can detect offshore oil spills and identify vessels that have dumped oil. On the surface of the sea, the background is single and the oil target is obvious.…”

Section: Discussionmentioning

confidence: 99%

Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

2019

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This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background.

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“…In terms of radar tools, satellite-borne and airborne synthetic aperture radars (SAR) have been widely used for offshore oil spill monitoring. The use of satellite-borne radar detection technology has become widespread [9][10][11][12], as it can detect offshore oil films over large areas [13,14]. The shortcoming of satellite-borne remote sensing is that it cannot monitor spills in real-time.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Cui

et al. 2019

Remote Sensing

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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.

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Fast Detection of Oil Spills and Ships Using SAR Images

Cited by 31 publications

References 14 publications

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

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

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