# Fast Detection of Oil Spills and Ships Using SAR Images

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## Abstract

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## 1. Introduction

## 2. Sea Surface Model

## 3. Oil Spill and Ship Detection Algorithms

#### 3.1. Image Enhancement

#### 3.2. Oil Spill Detection

#### 3.2.1. Image Segmentation

- Step 1: search the smaller among all of the maxima of the PDF of the data in the block under observation (${M}_{min}$);
- Step 2: estimate the derivative of the PDF to find possible saddle points;
- Step 3: sort in ascending order the value of saddle points and keep as a candidate threshold $Th$ the smaller one;
- Step 4: if no saddle points are found, select as a $Th$ candidate the point that is distant by one standard deviation toward the right from the maximum of the PDF;
- Step 5: memorize the $Th$ candidate and repeat for every block;
- Step 6: sort all of the $Th$ candidates in ascending order;
- Step 7: choose as a threshold $Th$ the first candidate in ascending order whose amplitude is greater than ${M}_{min}$.

#### 3.2.2. Classification

#### 3.2.3. Oil Spill Direction Estimation

#### 3.3. Ship Detection

#### 3.3.1. SAR Image Preprocessing (Wavelet Correlator)

#### 3.3.2. S-Detector

#### 3.3.3. W-CFAR

#### 3.4. Ship’s Direction Estimation

- wake extraction (performed by means of segmentation algorithm with different parameters);
- DRT over four quarters of the window.

#### 3.5. Correlation between Oil Spill and Ship

- the ship must belong to the angular sector;
- the ship’s position and direction must be consistent with the oil spill position and direction.

## 4. Ship-Spill Correlation Results

^{TM}Core i7 CPU 860 @ 2.80 GHz with eight cores using parallel processing.

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Abbreviations

AIS | Automatic Identification System |

CFAR | Constant False Alarm Rate |

CSK | COSMO-SkyMed |

DOAJ | Directory of Open Access Journals |

DRT | Discrete Radon Transform |

EMSA | European Maritime Safety Agency |

FEXP | Fractional EXPonential |

GEV | Generalized Extreme Value |

HH | Horizontal-Horizontal |

KDE | Kernel Density Estimation |

LRD | Long-Range Dependence |

MDPI | Multidisciplinary Digital Publishing Institute |

Probability Density Function | |

PSD | Power Spectral Density |

ROI | Region Of Interest |

RS | Radon Space |

SAR | Synthetic Aperture Radar |

SLC | Single Look Complex |

SRD | Short Range Dependence |

WCOR | Wavelet CORrelator |

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**Figure 3.**CSK StripMap HIMAGE of the southern Adriatic Sea before morphological processing (image not rotated into the Lat-Lon grid).

**Figure 4.**CSK StripMap HIMAGE of the southern Adriatic Sea after morphological processing (image not rotated into the Lat-Lon grid).

**Figure 8.**Segmented binary image. See Figure 4 for the location.

**Figure 9.**CSK StripMap HIMAGE of the Ionian Sea image after morphological processing (image not rotated into the Lat-Lon grid).

**Figure 10.**Segmented binary image with look-alikes. See Figure 9 for the location.

**Figure 15.**Clutter Gaussian trend with pixel values in dB. Logistic and t-location scale distribution for comparison.

**Figure 17.**Example of a ship and its wakes with the grid for the resolution in ambiguity direction. The ship results displaced from its wake due to the relative motion between ship and platform. Roman numerals refer to the quadrant number.

**Figure 19.**Example of an oil spill and three ships with their directions (blue arrows). The red cross means that the corresponding ship cannot be a candidate and vice versa for the green V.

$\widehat{\mathit{d}}$ | ${\overline{\mathit{A}}}_{\mathbf{SRD}}$ | |
---|---|---|

Clean sea | 0.1206 | 216.2745 |

Oil spill | 0.1580 | 100.4706 |

$\widehat{\mathit{d}}$ | ${\overline{\mathit{A}}}_{\mathbf{SRD}}$ | |
---|---|---|

Clean sea | 0.3737 | 49.1312 |

Oil spill | 0.5666 | 12.8820 |

Wind fall | 1.2004 | 1.4208 |

Area | 8870 m${}^{2}$ |

Length | 228 m |

Width | 49.5 m |

Distance from the oil spill extreme | 11.23 km |

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Lupidi, A.; Staglianò, D.; Martorella, M.; Berizzi, F.
Fast Detection of Oil Spills and Ships Using SAR Images. *Remote Sens.* **2017**, *9*, 230.
https://doi.org/10.3390/rs9030230

**AMA Style**

Lupidi A, Staglianò D, Martorella M, Berizzi F.
Fast Detection of Oil Spills and Ships Using SAR Images. *Remote Sensing*. 2017; 9(3):230.
https://doi.org/10.3390/rs9030230

**Chicago/Turabian Style**

Lupidi, Alberto, Daniele Staglianò, Marco Martorella, and Fabrizio Berizzi.
2017. "Fast Detection of Oil Spills and Ships Using SAR Images" *Remote Sensing* 9, no. 3: 230.
https://doi.org/10.3390/rs9030230