# Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data

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## Abstract

**:**

## 1. Introduction

## 2. Theory and Methods

#### 2.1. Fundamentals of Polarimetric SAR

#### Quad-Polarimetric SAR Mode

**S**is scattered matrix to link the incidence and scattered Jones vectors E

^{i}and E

^{s}. Based on Jones vectors of the received signal, the Stokes vector can be received.

_{h}and E

_{v}stands for electric field signal received in horizontal and vertical polarization channels respectively.

**S**is a 2 × 2 matrix, which can be described by:

**T**can be derived from the scattering matrix or the covariance matrix by:

**T**of the target, the eigenvalues and eigenvector can be derived:

**U**can be parameterized by:

_{3}#### 2.2. Polarimetric Filters

#### 2.2.1. Boxcar Filter

**C**or

**T**matrix of polarimetric SAR data. Image and real parts in

**C**and

**T**matrix are filtered separately:

#### 2.2.2. Refined Lee

- (a)
- Selecting a nonsquare window to match the direction of edges in the span image;
- (b)
- Applying local statistics filter to the span image based on the multiplicative noise model;
- (c)
- Using the window directions and weight derived in (a) and (b) to filter the whole covariance matrix.

#### 2.2.3. Lopez Filter

**C**are processed by using a multi-look filtering approach. The off-diagonal elements are filtered based on the polarimetric model, which consider the nature of the speckle for every off-diagonal element as a combination of a multiplicative noise and a complex additive component. Both of them are functions of the complex coefficient among different polarimetric channels. The complex correlation coefficient is also estimated by using a multi-look. Then the complex additive speckle noise and multiplicative component can be filtered out. Lopez’ filter could keep the spatial resolution (especially of point scatterers), while suppressing the speckle noise, which boosts the estimation of polarimetric information.

#### 2.3. Polarimetric Features for Marine Oil Spills Detection

#### 2.4. Deep Learning Classification Algorithm

#### 2.5. The Architecture for Marine Oil Spill Detection Based on Polarimetric SAR

#### 2.5.1. Preprocessing

#### 2.5.2. Polarimetric Filtering

#### 2.5.3. Features Extraction

#### 2.5.4. Classification

#### 2.5.5. Post Processing

_{SDT}is the output of special density thresholding, and I

_{Gauss}is the result of Gauss kernel convolution.

- Conduct eroding to the classification map to eliminate separate false targets to obtain I
_{Erosion} - Conduct dilating to the resulting map to fix holes and link nearby oil slick pieces to obtain I
_{Dilation} - Multiply the processed classification map with the output of SDT to take the best advantage of the classification result and precise boundary details and shape information of oil slicks.

## 3. Experiment and Results

#### 3.1. Study Site and SAR Image

^{3}and 30 m

^{3}, respectively. The slicks were released 5–9 h before the SAR acquisition when the local wind speed is 1.6–3.3 m/s. The slicks covered thousands of square meters of sea surface. More detailed information about the experiment can be found in [24].

#### 3.2. Oil Spill Detection Experiments

#### 3.2.1. Comparison of Polarimetric SAR Filters

^{2}image is shown in Figure 3. The features introduced in Section 2.3 are extracted from the filtered SAR data. The SAE introduced in Section 2.4 are trained by using the training samples. The SAE applied in the experiment has four layers, numbers of the neural for each layer are [2,6,8,10]. Taking Sigmoid function as the activation function, the SAE was trained for 10 epochs with the batch size of 100. Then the SAE was fine-tuned for 100 epochs with the batch size of 100 and the learning rate of 3. Then based on the testing data set, the oil spill classification result corresponding to different polarimetric filters are analyzed. Among them, Lopez filter achieved the highest classification accuracy (99.34%) on testing data samples, and the classification result is shown in Figure 4. The confusion matrix of classification results based on different filters is shown in Table 3, Table 4 and Table 5.

#### 3.2.2. Optimization of Post-Processing Procedures

^{2}power image and accurate oil type distinguishing capability of classification based on polarimetric SAR features. The procedure of post-processing can be divided into the following steps:

#### Spacial Density Thresholding (SDT) on VV^{2} Power Image

^{2}power image is smoothed by Gauss kernel with the size of 5 × 5 (chosen based on experiments), and the result is shown in Figure 5. Then a threshold from −20 to −24 dB is applied to derive the oil slick covered area respectively (from Figure 6a–e). From the analysis, it was found the most proper thresholding is −22 dB, which could best eliminate the effect of speckle while keeping the integrity of the oil boundary. The result proved that through kernel based spacial density thresholding, the detailed boundary information of oil slicks could be effectively retained.

#### Morphological Processing on Classification Result by Polarimetric SAR Features

#### None Crude Oil Spill Masking

## 4. Discussion

## 5. Conclusions

- (1)
- By using model-based polarimetric filter, the speckle noise can be effectively suppressed;
- (2)
- By using SAE, the deep neural network is efficiently established given limited data samples;
- (3)
- By using a post-processing step, the intact oil slick piece with high confidence level can be obtained.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**(

**a**) Pauli RGB image of the RADARSAT-2 data used for the study. (

**b**) ROI selected from the intensity image of the SAR image. Red: crude oil; Green: none- crude oil.

**Figure 3.**VV

^{2}image of polarimetric SAR data filtered by different filters. (

**a**) Boxcar filter; (

**b**) Refined Lee filter; (

**c**) Lopez Filter.

**Figure 4.**Classification result derived from Stacked-autoencoder (SAE) based on polarimetric SAR features.

**Figure 6.**Thresholding result derived from the smoothed VV

^{2}image, with the thresholding of −20 dB, −21 dB, −22 dB, −23 dB, −24 dB (

**a**–

**e**).

**Figure 7.**Erosion (

**a**,

**c**,

**e**) and dilation (

**b**,

**d**,

**f**) result based on structural elements of circle, diamond and square respectively (circle:

**a**,

**b**; diamond:

**c**,

**d**; square:

**e**,

**f**).

**Table 1.**Features extracted from polarimetric Synthetic Aperture Radar (SAR) data [21].

Feature | Definition | For Crude Oil | For Biogenic Slicks | For Clean Sea Surface |
---|---|---|---|---|

VV intensity | ${S}_{VV}^{2}$ | Lower | low | High |

Entropy (H) | $H=-{\displaystyle \sum _{i=1}^{3}{P}_{i}}{\mathrm{log}}_{3}({P}_{i})$, ${P}_{i}=\frac{{\lambda}_{i}}{{\displaystyle \sum _{j=1}^{3}{\lambda}_{j}}}$ | High | Low | Lower |

Alpha (α) | $\alpha ={P}_{1}{\alpha}_{1}+{P}_{2}{\alpha}_{2}+{P}_{3}{\alpha}_{3}$ | High | Low | Lower |

The degree of Polarization (DoP) | $P=\frac{\sqrt{{g}_{i1}^{2}+{g}_{i2}^{2}+{g}_{i3}^{2}}}{{g}_{i0}^{2}}$ | Low | High | High |

Ellipticity ($\chi $) | $\mathrm{sin}(2\chi )=\frac{{g}_{3}}{m{g}_{0}}$ | Positive | Negative | Negative |

Pedestal Height (PH) | $NPH=\frac{\mathrm{min}({\lambda}_{1},{\lambda}_{2},{\lambda}_{3})}{\mathrm{max}({\lambda}_{1},{\lambda}_{2},{\lambda}_{3})}$ | High | Low | Lower |

Standard Deviation of CPD | $Std({\phi}_{c}),$${\phi}_{c}=\mathrm{arg}(\langle {S}_{HH}{S}_{VV}^{*}\rangle )$ | High | Low | Lower |

Conformity Coefficient (Conf. Co.) | $\mu \cong \frac{2(\mathrm{Re}({S}_{HH}{S}_{VV}^{*})-{\left|{S}_{HV}\right|}^{2})}{{\left|{S}_{HH}\right|}^{2}+2{\left|{S}_{HV}\right|}^{2}+{\left|{S}_{VV}\right|}^{2}}$ | Negative | Positive | Positive |

Correlation Coefficient (Corr. Co.) | ${\rho}_{HH/VV}=\left|\frac{\langle {S}_{HH}{S}_{VV}^{*}\rangle}{\langle {S}_{HH}^{2}\rangle \langle {S}_{VV}^{2}\rangle}\right|$ | Low | High | Higher |

Coherence Coefficient (Coh. Co.) | $Coh=\frac{\left|\langle {T}_{12}\rangle \right|}{\sqrt{\langle {T}_{11}\rangle \langle {T}_{22}\rangle}}$ | Low | High | Higher |

Parameters | Configurations |
---|---|

Sensor | RadarSAT-2 |

Acquisition mode | Quad-polarization: HH, HV, VH, VV |

Incidence angle | 34.5°–36.1° |

Special resolution | Range: around 4.7; Azimuth: 4.8 meters |

Acquisition time | 8th June 2011 UTC, 17:27 |

Location | 59°59′ N, 2°27′ E |

Confusion Matrix | Crude Oil (Truth) | Biogenic Slicks and CLEAN Sea Surface (Truth) | Total |
---|---|---|---|

Crude oil (Classification) | 1604 | 9 | 1613 |

Biogenic slicks and Clean sea surface (Classification) | 15 | 1572 | 1587 |

Total | 1619 | 1581 | 3200 |

Confusion Matrix | Crude Oil (Truth) | Biogenic Slicks and Clean Sea Surface (Truth) | Total |
---|---|---|---|

Crude oil (Classification) | 1593 | 21 | 1614 |

Biogenic slicks and Clean sea surface (Classification) | 25 | 1561 | 1586 |

Total | 1618 | 1582 | 3200 |

Confusion Matrix | Crude Oil (Truth) | Biogenic Slicks and Clean Sea Surface (Truth) | Total |
---|---|---|---|

Crude oil (Classification) | 1589 | 5 | 1594 |

Biogenic slicks and Clean sea surface (Classification) | 16 | 1590 | 1606 |

Total | 1605 | 1505 | 3200 |

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**MDPI and ACS Style**

Li, Y.; Zhang, Y.; Yuan, Z.; Guo, H.; Pan, H.; Guo, J.
Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data. *Sustainability* **2018**, *10*, 4408.
https://doi.org/10.3390/su10124408

**AMA Style**

Li Y, Zhang Y, Yuan Z, Guo H, Pan H, Guo J.
Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data. *Sustainability*. 2018; 10(12):4408.
https://doi.org/10.3390/su10124408

**Chicago/Turabian Style**

Li, Yu, Yuanzhi Zhang, Zifeng Yuan, Huaqiu Guo, Hongyuan Pan, and Jingjing Guo.
2018. "Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data" *Sustainability* 10, no. 12: 4408.
https://doi.org/10.3390/su10124408