Log Transformed Coherency Matrix for Differentiating Scattering Behaviour of Oil Spill Emulsions Using SAR Images
Abstract
:1. Introduction
1.1. Motivation
1.2. Novelty and Scientific Contribution
1.3. Organization
2. State-of-the-Art
3. Proposed Approach
3.1. Input Dataset
3.2. Significance of Log Transformed T3 Matrix
- (i)
- It is observed from the computed using conventional T3 in Figure 7a and using log-transformed T3 in Figure 7b that oil slicks are clearly visible and easily distinguishable from surrounding water in log-transformed version as compared to the conventional version. In Figure 7a, all the features of the water and oil are suppressed in the dark region due to low backscatter, and the area of water and oil are not discriminated due to minor variation in their ranges. The log-transformed version gives superior results due to the enhancement of lower pixel values which enhances the ranges of oil and water in the image resulting in the proper visible distinction between oil and water in the image as shown in Figure 7a.
- (ii)
- Comparison of using conventional T3 and log-transformed T3 in Figure 7c,d has led to a very interesting and important observation: Image of of log-transformed T3 also highlights slicks of oil along with mudflats and man-made houses present on along sides of canal structure seen at the top of the image. As the oil slicks present in these patches are mixed with sediments (refer Section 3.1) and thus exhibit double-bounce scattering along with surface scattering. of conventional T3 fails to capture this signature.
- (iii)
- Image of Figure 7f calculated using log-transformed T3 reveals no particular structure as there may not be any object present that exhibits volume scattering dominantly. Thus, a clear distinction of features is possible due to eigenvalues of log scaled T3, resulting in better discrimination among different emulsified slicks based on the calculated Entropy, Anisotropy, and Alpha angle.
3.3. H/A/ Decomposition
4. Experiments and Results Analysis
4.1. Experimental Setup and Parameters
- Michelson Contrast (MC)MC is one of the general criteria for evaluating target separability. It has thus been used to quantitatively define and evaluate contrasts between oil slicks and seawater under various polarimetric feature spaces [19]. MC is calculated as Equation (7).Here and indicate the maximum and minimum mean polarimetric feature values between the two target samples being tested, respectively, and the value range of MC is [0, 1].
- M-Statistic (MS)The MS assesses the degree of discrimination between the two-pixel groups. It operates by evaluating the separation between the histograms produced by plotting the frequency of all the pixel values within the two classes [49]. The M-statistic can be calculated using the mean and standard deviation of two targets to be tested, respectively, as shown in Equation (8)A value of M < 1 denotes that the histograms significantly overlap and the ability to separate (or discriminate) the two regions is poor. A value of M > 1 denotes that the histogram means are well separated and that the two regions are relatively easy to discriminate.
4.2. H/A/ Decomposition Result Analysis
- Entropy calculated using log-transformed T3 Figure 11d captures subtle contrast changes in oil-contaminated patches resulting due to different stages of emulsification which is not the case with entropy calculated using classical T3 Figure 11a. As shown in the histogram in Figure 11d, the oil-water emulsion has a range of 0.74 to 0.76 in the log-transformed approach. It can also differentiate between oil-water emulsion, thick oil, and heavy sedimented oil with an extended upper bound of the range. Further, the entropy values increase gradually from moderate weathering stage oil to high emulsified oil. However, the entropy range calculated using classical T3 Figure 11a for oil-water emulsion is 0.2 to 0.4, roughly which is the same as clean water and surface oil1. This indicates that it does not differentiate between fresh and weakly weathered oil. Further, it also fails to capture minor changes in physical and electrical properties of thick oil and heavy sedimented oil as both have the same range. The blue line in the histogram is for a sample taken of mudflat/shrub/building present near Barataria Bay (BB), Louisiana. It can be clearly seen that the log-transformed T3 gives a different entropy range for highly mixed sedimented oil and mudflat/building regions even though both exhibit a similar scattering mechanism - moderate entropy double bounce. The separation between values of entropy for highly mixed sedimented oil and mudflat/building region is not that clear in the case of classical T3.
- Anisotropy values in Figure 11e calculated using log-transformed T3 show opposite behaviour than classical T3 in Figure 11b. Anisotropy calculated using log-transformed T3 has higher values for clean water and surface oil, which reduce from weakly emulsified oil to highly emulsified oil. However, the anisotropy values calculated using classical T3 cannot differentiate between clean water and any oil sample; it does not show a separate range for building/mudflat samples. On the other hand, anisotropy calculated using log-transformed T3 differentiates between clean water/surface water (Bragg scattering) from different emulsified oils (non-Bragg scattering). However, it fails to differentiate between building/mudflat and oil mixed with partial sediments.
- Alpha values Figure 11f calculated using log-transformed T3 do not show any favorable result in capturing differences between the type of scattering mechanism exhibited by water and different emulsified oils. It shows that clean water and all kinds of oil samples were taken to follow the double bounce scattering. On the other hand, though, Alpha values Figure 11c calculated using classical T3 show surface scattering for clean water and surface oil; double-bounce scattering for oil mixed with sediments. However, it fails to differentiate between thick oil from surface oil/clean water and mudflat/building from oil mixed with sediments.
4.3. Statistical Analysis and Accuracy Assessment
4.4. SVM Classification
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Ref. | Dataset | Approach | Objective and Outcome |
---|---|---|---|
[8] | Envisat ASAR, ERS-1 ERS-2, AVHRR | Oil spill Detection and Lookalike Discrimination |
|
[30] | RadarSat-2 | Oil Spill Detection and Classification |
|
[7] | Full Pol UAVSAR | Oil Spill Thickness discrimination |
|
[18] | UAVSAR, RADARSAT-2, Worldview-2 | Oil Spill Thickness Classification |
|
[36] | Dual-Pol TerraSAR-X | Oil spill detection and Lookalike Discrimination |
|
[37] | C Band Sentinel-1 | Oil Spill detection and Segmentation using Deep Learning |
|
[28] | Radarsat-2, UAVSAR | Oil spill detection and Lookalike Discrimination |
|
[19] | RADARSAT 2 | Oil Spill Classification based on thickness |
|
[38] | RADARSAT-2 | Impact of seasons on oil spill detection |
|
[39] | Deep SAR Oil Dataset | Oil Spill Segmentation using CBD-Net |
|
[40] | ERS SAR, ENVISAT 2 SAR | Feature Selection for efficient Oil spill Detection |
|
[41] | RADARSAT-2, SIR-C/X SAR | Oil spill Detection |
|
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Prajapati, K.; Ramakrishnan, R.; Bhavsar, M.; Mahajan, A.; Narmawala, Z.; Bhavsar, A.; Raboaca, M.S.; Tanwar, S. Log Transformed Coherency Matrix for Differentiating Scattering Behaviour of Oil Spill Emulsions Using SAR Images. Mathematics 2022, 10, 1697. https://doi.org/10.3390/math10101697
Prajapati K, Ramakrishnan R, Bhavsar M, Mahajan A, Narmawala Z, Bhavsar A, Raboaca MS, Tanwar S. Log Transformed Coherency Matrix for Differentiating Scattering Behaviour of Oil Spill Emulsions Using SAR Images. Mathematics. 2022; 10(10):1697. https://doi.org/10.3390/math10101697
Chicago/Turabian StylePrajapati, Kinjal, Ratheesh Ramakrishnan, Madhuri Bhavsar, Alka Mahajan, Zunnun Narmawala, Archana Bhavsar, Maria Simona Raboaca, and Sudeep Tanwar. 2022. "Log Transformed Coherency Matrix for Differentiating Scattering Behaviour of Oil Spill Emulsions Using SAR Images" Mathematics 10, no. 10: 1697. https://doi.org/10.3390/math10101697