Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data
Abstract
:1. Introduction
2. Theory and Methods
2.1. Fundamentals of Polarimetric SAR
Quad-Polarimetric SAR Mode
2.2. Polarimetric Filters
2.2.1. Boxcar Filter
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
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
- Conduct eroding to the classification map to eliminate separate false targets to obtain IErosion
- Conduct dilating to the resulting map to fix holes and link nearby oil slick pieces to obtain IDilation
- 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
3.2. Oil Spill Detection Experiments
3.2.1. Comparison of Polarimetric SAR Filters
3.2.2. Optimization of Post-Processing Procedures
Spacial Density Thresholding (SDT) on VV2 Power Image
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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Feature | Definition | For Crude Oil | For Biogenic Slicks | For Clean Sea Surface |
---|---|---|---|---|
VV intensity | Lower | low | High | |
Entropy (H) | , | High | Low | Lower |
Alpha (α) | High | Low | Lower | |
The degree of Polarization (DoP) | Low | High | High | |
Ellipticity () | Positive | Negative | Negative | |
Pedestal Height (PH) | High | Low | Lower | |
Standard Deviation of CPD | High | Low | Lower | |
Conformity Coefficient (Conf. Co.) | Negative | Positive | Positive | |
Correlation Coefficient (Corr. Co.) | Low | High | Higher | |
Coherence Coefficient (Coh. Co.) | 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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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
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 StyleLi, 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