Scattering Feature Set Optimization and Polarimetric SAR Classification Using Object-Oriented RF-SFS Algorithm in Coastal Wetlands
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data
2.2. Polarimetric Scattering Features Extraction
2.2.1. Matrix Element Features
2.2.2. Polarimetric Decomposition Features
2.3. Object-Oriented Method
2.4. Random Forest and Feature Set Optimization
2.5. Experimental Flowchart
- (1)
- Filtering and other preprocessing were applied to the original polarimetric SAR data;
- (2)
- Twenty polarimetric decompositions listed in Table 2 were utilized to decompose the filtered coherent matrix, and 93 polarimetric decomposition features were obtained. Three scattering matrix elements, S11, S12 and S22, were used as matrix features, and thus a total of 96 polarimetric scattering features could be obtained;
- (3)
- The scattering features obtained from the previous step were combined into a multi-band image, to which object-oriented multi-scale segmentation was then performed;
- (4)
- Training samples based on the segmented object as the basic unit were randomly selected;
- (5)
- Formula (7) was used to calculate the importance of all polarimetric scattering features of training samples;
- (6)
- The features were ranked according to the importance value;
- (7)
- Using the sequential forward selection algorithm to optimize the feature set, the feature subset with the least number of features and the highest classification accuracy was obtained;
- (8)
- The selected optimal polarimetric feature subset was classified based on the object-oriented random forest model;
- (9)
- The classification accuracy was calculated by using validation samples.
3. Results and Discussion
3.1. Importance Analysis of Polarimetric Features and Feature Set Optimization
3.2. Classification Results
3.3. Discussion
4. Conclusions
- (1)
- The proposed method calculated the importance of each polarimetric feature in the construction of a random forest model, and the sequence forward selection algorithm was applied to select the optimal polarimetric feature set that is suitable for Jiangsu coastal wetlands classification according to the importance value. This method provided a quantitative reference for the reasonable optimization of feature sets;
- (2)
- The importance values of features from the scattering matrix and the four decomposition algorithms, namely, decomposition, Yamaguchi3 decomposition, VanZyl3 decomposition, and Krogager decomposition, were higher than other features. This indicated that these features were more important and were determined to be very supportive of land cover identification in the Jiangsu coastal wetlands;
- (3)
- Compared with the object-oriented QUEST decision tree algorithm, regardless of whether the latter has been pruned, the proposed object-oriented RF-SFS method can achieve higher classification accuracies without artificial pruning.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Class | Training Samples | Validation Samples | Total |
---|---|---|---|
Fish pond | 5383 | 3027 | 8410 |
Irrigable land | 1909 | 1024 | 2933 |
Reed and Alterniflora | 4654 | 3021 | 7675 |
Suaeda salsa | 2658 | 1486 | 4144 |
Rice paddy | 2542 | 1324 | 3866 |
River | 1599 | 876 | 2475 |
Road | 1049 | 654 | 1703 |
Sand | 1996 | 1022 | 3018 |
Sea | 6864 | 3873 | 10,737 |
Total | 28,654 | 16,307 | 44,961 |
Decompositions 1 | Polarimetric Decomposition Parameters | ||
---|---|---|---|
Pauli | Pauli_a | Pauli_b | Pauli_c |
Krogager | Krogager_Ks | Krogager_Kd | Krogager_Kh |
Huynen | Huynen_T11 | Huynen_T22 | Huynen_T33 |
Barnes1 | Barnes1_T11 | Barnes1_T22 | Barnes1_T33 |
Barnes2 | Barnes2_T11 | Barnes2_T22 | Barnes2_T33 |
Holm1 | Holm1_T11 | Holm1_T22 | Holm1_T33 |
Holm2 | Holm2_T11 | Holm2_T22 | Holm2_T33 |
VanZyl3 | VanZyl3_Vol | VanZyl3_Odd | VanZyl3_Dbl |
Cloude | Cloude_T11 | Cloude_T22 | Cloude_T33 |
H/A/Alpha | H/A/A_T11 | H/A/A_T22 | H/A/A_T33 |
Entropy | Anisotropy | Shannon Entropy | |
DERD | Polarization Asymmetry | Polarization Fraction | |
SERD | Radar Vegetation Index | Anisotropy12 | |
Pedestal Height | Alpha (,α1,α2,α3) | Anisotropy_Lueneburg | |
Pseudo Probabilities (p1, p2, p3) Lambda | |||
Freeman2 | Freeman2_Vol | Freeman2_Ground | |
Freeman3 | Freeman_Vol | Freeman_Odd | Freeman_Dbl |
Yamaguchi3 | Yamaguchi3_Vol | Yamaguchi3_Odd | Yamaguchi3_Dbl |
Yamaguchi4 | Yamaguchi4_Vol | Yamaguchi4_Odd | Yamaguchi4_Dbl |
Yamaguchi4_Hlx | |||
Neumann | Neumann_delta_mod | Neumann_delta_pha | Neumann_tau |
Touzi | TSVM_alpha_s | TSVM_alpha_s1 | TSVM_alpha_s2 |
TSVM_alpha_s3 | TSVM_tau_m | TSVM_tau_m1 | |
TSVM_tau_m2 TSVM_phi_s2 TSVM_psi1 TSVM_psi | TSVM_tau_m3 TSVM_phi_s3 TSVM_psi2 | TSVM_phi_s1 TSVM_phi_s TSVM_psi3 | |
An_Yang3 | An_Yang3_Vol | An_Yang3_Odd | An_Yang3_Dbl |
An_Yang4 | An_Yang4_Vol | An_Yang4_Odd | An_Yang4_Dbl |
An_Yang4_Hlx | |||
Arii3_NNED | Arii3_NNED_Vol | Arii3_NNED_Odd | Arii3_NNED_Dbl |
Arii3_ANNED | Arii3_ANNED_Vol | Arii3_ANNED_Odd | Arii3_ANNED_Odd |
Polarimetric Feature | IM | Polarimetric Feature | IM | Polarimetric Feature | IM | |||
---|---|---|---|---|---|---|---|---|
1 | S22 | 3.65 | 33 | Holm2_T11 | 1.37 | 65 | TSVM_psi | 0.66 |
2 | Barnes2_T22 | 2.87 | 34 | An_Yang4_Dbl | 1.34 | 66 | Alpha2 | 0.66 |
3 | Entropy_shannon | 2.68 | 35 | An_Yang4_Odd | 1.32 | 67 | Barnes1_T11 | 0.66 |
4 | S11 | 2.61 | 36 | Neumann_delta_pha | 1.26 | 68 | Pauli_T33 | 0.62 |
5 | Barnes1_T22 | 2.3 | 37 | Arii3_NNED_Dbl | 1.24 | 69 | Neumann_tau | 0.61 |
6 | Yamaguchi3_Dbl | 2.18 | 38 | Arii3_ANNED_Dbl | 1.24 | 70 | TSVM_phi_s2 | 0.59 |
7 | Entropy | 2.12 | 39 | TSVM_psi2 | 1.23 | 71 | Cloude_T22 | 0.59 |
8 | VanZyl3_Dbl | 2.01 | 40 | An_Yang3_Dbl | 1.22 | 72 | Barnes2_T11 | 0.58 |
9 | Arii3_NNED_Vol | 1.98 | 41 | Holm1_T11 | 1.22 | 73 | Holm2_T22 | 0.56 |
10 | Neumann_delta_mod | 1.98 | 42 | Anisotropy_Lueneburg | 1.21 | 74 | Huynen_T22 | 0.56 |
11 | Lambda | 1.96 | 43 | Anisotropy12 | 1.21 | 75 | TSVM_phi_s1 | 0.5 |
12 | VanZyl3_Vol | 1.96 | 44 | p2 | 1.18 | 76 | Holm1_T22 | 0.49 |
13 | HAA_T11 | 1.94 | 45 | Arii3_ANNED_Odd | 1.16 | 77 | TSVM_phi_s | 0.43 |
14 | Krogager_Kd | 1.88 | 46 | Yamaguchi4_Y40_Vol | 1.12 | 78 | TSVM_tau_m1 | 0.36 |
15 | S12 | 1.87 | 47 | Polarisation_Fraction | 1.12 | 79 | TSVM_tau_m | 0.36 |
16 | Krogager_Ks | 1.68 | 48 | RVI | 1.07 | 80 | TSVM_alpha_s2 | 0.36 |
17 | Yamaguchi3_Odd | 1.67 | 49 | Arii3_NNED_Odd | 1.05 | 81 | Alpha3 | 0.36 |
18 | An_Yang3_Vol | 1.66 | 50 | An_Yang3_Odd | 1.05 | 82 | Pauli_T11 | 0.36 |
19 | Pedestal | 1.66 | 51 | p3 | 1.05 | 83 | HAA_T33 | 0.35 |
20 | Freeman_Vol | 1.61 | 52 | Yamaguchi4_Y40_Odd | 1.02 | 84 | An_Yang4_Hlx | 0.33 |
21 | Alpha | 1.61 | 53 | Freeman2_Ground | 1.02 | 85 | TSVM_psi3 | 0.31 |
22 | Yamaguchi4_Y40_Dbl | 1.54 | 54 | Barnes2_T33 | 1.01 | 86 | TSVM_tau_m2 | 0.3 |
23 | Cloude_T11 | 1.54 | 55 | TSVM_alpha_s | 0.99 | 87 | Yamaguchi4_Y40_Hlx | 0.3 |
24 | p1 | 1.53 | 56 | HAA_T22 | 0.98 | 88 | Holm1_T33 | 0.3 |
25 | Yamaguchi3_Vol | 1.52 | 57 | Derd | 0.96 | 89 | Pauli_T22 | 0.3 |
26 | Freeman_Dbl | 1.51 | 58 | An_Yang4_Vol | 0.88 | 90 | Cloude_T33 | 0.27 |
27 | Huynen_T11 | 1.45 | 59 | Barnes1_T33 | 0.88 | 91 | Holm2_T33 | 0.25 |
28 | Freeman_Odd | 1.44 | 60 | Asymetry | 0.87 | 92 | Huynen_T33 | 0.23 |
29 | VanZyl3_Odd | 1.43 | 61 | Anisotropy | 0.85 | 93 | TSVM_alpha_s3 | 0.21 |
30 | Serd | 1.42 | 62 | Alpha1 | 0.75 | 94 | Krogager_Kh | 0.2 |
31 | Arii3_ANNED_Vol | 1.41 | 63 | TSVM_alpha_s1 | 0.68 | 95 | TSVM_tau_m3 | 0.17 |
32 | Freeman2_Vol | 1.38 | 64 | TSVM_psi1 | 0.66 | 96 | TSVM_phi_s3 | 0.12 |
Class | FP | IL | RA | SS | RP | RI | RO | SA | S | Total | UA(%) |
---|---|---|---|---|---|---|---|---|---|---|---|
FP | 2543 | 0 | 70 | 0 | 45 | 101 | 0 | 0 | 268 | 3027 | 84.01 |
IL | 0 | 900 | 0 | 41 | 83 | 0 | 0 | 0 | 0 | 1024 | 87.89 |
RA | 0 | 98 | 2534 | 182 | 120 | 0 | 0 | 87 | 0 | 3021 | 83.88 |
SS | 0 | 0 | 138 | 1261 | 87 | 0 | 0 | 0 | 0 | 1486 | 84.86 |
RP | 0 | 103 | 47 | 45 | 1129 | 0 | 0 | 0 | 0 | 1324 | 85.27 |
RI | 99 | 0 | 0 | 0 | 0 | 734 | 0 | 0 | 43 | 876 | 83.79 |
RO | 0 | 23 | 0 | 32 | 0 | 0 | 574 | 25 | 0 | 654 | 87.77 |
SA | 0 | 35 | 30 | 0 | 0 | 0 | 54 | 903 | 0 | 1022 | 88.36 |
S | 158 | 0 | 0 | 0 | 0 | 59 | 0 | 0 | 3656 | 3873 | 94.40 |
Total | 2800 | 1159 | 2819 | 1561 | 1464 | 894 | 628 | 1015 | 3967 | 16307 | |
PA(%) | 90.82 | 77.65 | 89.89 | 80.78 | 77.12 | 82.10 | 91.40 | 88.97 | 92.16 | ||
OA(%) | 87.29 | Kappa coefficient | 0.8503 |
Class | FP | IR | RA | SS | RP | RI | RO | SA | S | Total | UA(%) |
---|---|---|---|---|---|---|---|---|---|---|---|
FP | 2076 | 0 | 109 | 0 | 86 | 151 | 0 | 0 | 605 | 3027 | 68.58 |
IR | 0 | 772 | 0 | 63 | 189 | 0 | 0 | 0 | 0 | 1024 | 75.39 |
RA | 0 | 105 | 2204 | 226 | 403 | 0 | 0 | 83 | 0 | 3021 | 72.96 |
SS | 0 | 119 | 198 | 912 | 257 | 0 | 0 | 0 | 0 | 1486 | 61.37 |
PR | 0 | 255 | 58 | 50 | 961 | 0 | 0 | 0 | 0 | 1324 | 72.58 |
RI | 299 | 0 | 0 | 0 | 0 | 494 | 0 | 0 | 83 | 876 | 56.39 |
RO | 0 | 49 | 0 | 0 | 0 | 0 | 516 | 89 | 0 | 654 | 78.90 |
SA | 0 | 53 | 61 | 0 | 0 | 0 | 62 | 846 | 0 | 1022 | 82.78 |
S | 223 | 0 | 0 | 0 | 0 | 138 | 0 | 0 | 3512 | 3873 | 90.68 |
Total | 2598 | 1353 | 2630 | 1251 | 1896 | 783 | 578 | 1018 | 4200 | 16307 | |
PA(%) | 79.91 | 57.06 | 83.80 | 72.90 | 50.69 | 63.09 | 89.27 | 83.10 | 83.62 | ||
OA(%) | 75.38 | Kappa coefficient | 0.7103 |
Class | FP | IR | RA | SS | RP | RI | RO | SA | S | Total | UA(%) |
---|---|---|---|---|---|---|---|---|---|---|---|
FP | 2413 | 0 | 88 | 0 | 50 | 146 | 0 | 0 | 330 | 3027 | 79.72 |
IR | 0 | 809 | 0 | 65 | 150 | 0 | 0 | 0 | 0 | 1024 | 79.00 |
RA | 0 | 96 | 2490 | 239 | 111 | 0 | 0 | 85 | 0 | 3021 | 82.42 |
SS | 0 | 105 | 129 | 1199 | 53 | 0 | 0 | 0 | 0 | 1486 | 80.69 |
RP | 0 | 110 | 46 | 51 | 1117 | 0 | 0 | 0 | 0 | 1324 | 84.37 |
RI | 132 | 0 | 0 | 0 | 0 | 684 | 0 | 0 | 60 | 876 | 78.08 |
RO | 0 | 43 | 0 | 0 | 0 | 0 | 534 | 77 | 0 | 654 | 81.65 |
SA | 0 | 42 | 35 | 0 | 0 | 0 | 32 | 913 | 0 | 1022 | 89.33 |
S | 219 | 0 | 0 | 0 | 0 | 109 | 0 | 0 | 3545 | 3873 | 91.53 |
Total | 2764 | 1205 | 2788 | 1554 | 1481 | 939 | 566 | 1075 | 3935 | 16307 | |
PA(%) | 87.30 | 67.14 | 89.31 | 77.16 | 75.42 | 72.84 | 94.35 | 84.93 | 90.09 | ||
OA(%) | 84.04 | Kappa coefficient | 0.8123 |
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Chen, Y.; He, X.; Xu, J.; Zhang, R.; Lu, Y. Scattering Feature Set Optimization and Polarimetric SAR Classification Using Object-Oriented RF-SFS Algorithm in Coastal Wetlands. Remote Sens. 2020, 12, 407. https://doi.org/10.3390/rs12030407
Chen Y, He X, Xu J, Zhang R, Lu Y. Scattering Feature Set Optimization and Polarimetric SAR Classification Using Object-Oriented RF-SFS Algorithm in Coastal Wetlands. Remote Sensing. 2020; 12(3):407. https://doi.org/10.3390/rs12030407
Chicago/Turabian StyleChen, Yuanyuan, Xiufeng He, Jia Xu, Rongchun Zhang, and Yanyan Lu. 2020. "Scattering Feature Set Optimization and Polarimetric SAR Classification Using Object-Oriented RF-SFS Algorithm in Coastal Wetlands" Remote Sensing 12, no. 3: 407. https://doi.org/10.3390/rs12030407
APA StyleChen, Y., He, X., Xu, J., Zhang, R., & Lu, Y. (2020). Scattering Feature Set Optimization and Polarimetric SAR Classification Using Object-Oriented RF-SFS Algorithm in Coastal Wetlands. Remote Sensing, 12(3), 407. https://doi.org/10.3390/rs12030407