Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR
Abstract
:1. Introduction
2. Dataset
2.1. Landsat-8
2.2. GF-3 SAR Images
3. SAR Parameters
3.1. Quad-Pol SAR Parameters
3.2. Hybrid-Pol SAR Parameters
4. Methods
4.1. Separability Index (SI)
4.2. Parameters Selection
- Co-pol intensity parameters (, T11),
- Total power parameters (, SEi, Span),
- Parameters that indicate surface scattering (Fs, Ys, Ks).
- 4.
- Circle-polarized intensity parameters (g3, , , ) whose dominant scattering is surface scattering,
- 5.
- Total power parameters (g0, CPSEi), which are similar to the corresponding quad-pol,
- 6.
- Depolarization parameters () due to multi-scattering
- 7.
- Parameters representing the orientation and polarization angle (, , , ).
4.3. Random Forest Classifier
5. Results
5.1. Classification Results
5.2. Verification
6. Discussion
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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SAR Mode | Sub-Class | Parameters | Description | Reference |
---|---|---|---|---|
Quad-pol | Based on backscatter intensity and polarization features | , , | backscatter intensity(dB) | [45] |
co-pol phase difference | ||||
co-pol correlation coefficients | ||||
, , | backscattering ratio | |||
depolarization ratio | ||||
Span | total power | |||
Based on polarization target decomposition | T11, T22, T33 | Huynen decomposition | [48] | |
Fd, Fv, Fs | three-component freeman decomposition | [52] | ||
Yd, Yv, Ys, Yh | four-component Yamaguchi decomposition | [55] | ||
, , | vanZyl decomposition | [60] | ||
H, A, , | decomposition | [51] | ||
Ks, Kd, Kh | Krogager decomposition | [54] | ||
Other parameters | SEi, SEP | Shannon entropy | [67] | |
PR | target randomness | [68] | ||
PA | polarimetric asymmetry | [69] | ||
PF | polarization fraction | |||
PH | pedestal height | [70] | ||
PK | relative kurtosis | [71] | ||
Hybrid-pol | Stokes parameters | g0, g1, g2, g3 | Stokes vector elements | [63] |
Based on hybrid-pol and circle-pol backscatter intensity | , , , , | hybrid-pol backscatter intensity | [22] | |
, | correlation coefficients | [22,65] | ||
hybrid-pol backscattering ratio | ||||
Hybrid-pol decomposition | m, , , , | decomposition | [17] | |
m, , , , | decomposition | |||
m, , , , | decomposition | |||
m, , , , | decomposition | |||
Other parameters | , | circular (right) polarization ratio and ellipticity. | [65] | |
CPSEi, CPSEP | hybrid-pol Shannon entropy | [67] |
Type Pair | Quad-Pol Parameters | Hybrid-Pol Parameters | Total |
---|---|---|---|
OW, NI | , Ks, Fs, Ys, SEi, SEP, PF, PH, PR, Span | , CPSEi | 36 SI average is 1.10 |
OW, YI | , Ks, Kd, Fd, Fv, Fs, Ys, Yv, SEi, SEP, PF, PH, PR, PK, Span | ), CPSEi | 52 SI average is 2.04 |
OW, FYI | , Ks, Fs, Ys, SEi, SEP, PF, PH, PR, PK, Span | , ), CPSEi, CPSEp | 45 SI average is 1.70 |
NI, YI | , Ks, Kd, Fd, Fv, Fs, Yv, Ys, SEi, SEP, PF, PH, PR, PK, Span | ), CPSEi | 46 SI average is 1.48 |
NI, FYI | , Ks, Fs, Ys, SEi, SEP, PR, Span | , CPSEi | 25 SI average is 0.91 |
(YI, FYI) | , Kd, Ks, Fd, Fv, Fs, Yv, Ys, SEi, Span | ), CPSEi | 34 SI average is 1.08 |
(OW, NI, YI, FYI) | , Fs, Ys, Ks, SEi, Span | , CPSEi | 19 SI average is 1.53 |
The Classifier | Training Accuracy | Test Accuracy |
---|---|---|
Compared Logistic Regression | 0.83 | 0.82 |
Naive Bayes | 0.78 | 0.77 |
Random Forest Classifier | 0.99 | 0.92 |
Gradient Boosting | 0.83 | 0.82 |
Support Vector Machine | 0.84 | 0.83 |
SAR Images | OW | NI | YI | FYI | Total |
---|---|---|---|---|---|
S1 | 3198 | 9914 | 44,688 | 0 | 57,800 |
S2 | 518 | 7545 | 62,437 | 0 | 70,500 |
S3 | 7946 | 19,180 | 83,208 | 0 | 110,334 |
S4 | 3410 | 8160 | 6976 | 28,842 | 47,388 |
S6 | 0 | 0 | 32,086 | 113,514 | 145,600 |
Total | 15,072 | 44,799 | 229,395 | 142,356 | 431,622 |
Parameter Set | OW | NI | YI | FYI | Total |
---|---|---|---|---|---|
Quad_pol | 95.29% | 81.65% | 98.76% | 77.61% | 88.36% |
Hybrid_pol | 95.58% | 84.29% | 98.39% | 85.95% | 92.30% |
Quad + Hybrid_pol | 91.35% | 96.88% | 98.89% | 83.35% | 91.53% |
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Yang, K.; Li, H.; Perrie, W.; Scharien, R.K.; Wu, J.; Zhang, M.; Xu, F. Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR. Remote Sens. 2023, 15, 2399. https://doi.org/10.3390/rs15092399
Yang K, Li H, Perrie W, Scharien RK, Wu J, Zhang M, Xu F. Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR. Remote Sensing. 2023; 15(9):2399. https://doi.org/10.3390/rs15092399
Chicago/Turabian StyleYang, Kun, Haiyan Li, William Perrie, Randall Kenneth Scharien, Jin Wu, Menghao Zhang, and Fan Xu. 2023. "Fine Resolution Classification of New Ice, Young Ice, and First-Year Ice Based on Feature Selection from Gaofen-3 Quad-Polarization SAR" Remote Sensing 15, no. 9: 2399. https://doi.org/10.3390/rs15092399