Acta Optica Sinica, Volume. 39, Issue 1, 0128002(2019)
Gaussian Mixture Model and Classification of Polarimetric Features for SAR Images
Figures & Tables(16)
Fig. 1. Polarimetric SAR images from Radarsat-2 in San Francisco. (a) Pauli pseudo color image; (b) corresponding map; (c) ground truth
Fig. 2. Polarimetric SAR images from Radarsat-2 in Flevoland. (a) Pauli pseudo color image; (b) corresponding map; (c) ground truth
Fig. 3. Polarimetric SAR images from Radarsat-2 in Vancouver. (a) Pauli pseudo color image; (b) corresponding map; (c) ground truth
Fig. 4. Fitting results of each feature at different distributions. (a) Water; (b) forest; (c) farmland; (d) urban
Fig. 8. Flow chart of polarimetric SAR image classification algorithm based on GMM model
Fig. 9. Polarimetric SAR classification results in San Francisco. (a) KNN; (b) SVM; (c) RF; (d) WHRT; (e) GMM
Fig. 10. Polarimetric SAR classification results in Flevoland. (a) KNN; (b) SVM; (c) RF; (d) WHRT; (e) GMM
Fig. 11. Polarimetric SAR classification results in Vancouver. (a) KNN; (b) SVM; (c) RF; (d) WHRT; (e) GMM
Table 1. KS distance mean at different distributions
View tableTable 1. KS distance mean at different distributions
Distribution Water Forest Farmland Urban Gamma 1.414 0.691 0.436 1.221 Log-normal 1.277 0.796 0.400 1.115 K 1.764 1.134 0.784 1.515 GMM 0.245 0.257 0.283 0.175
Table 2. Maximum KS distance at different distributions
View tableTable 2. Maximum KS distance at different distributions
Distribution Water Forest Farmland Urban Gamma 2.544 0.941 0.537 2.087 Log-normal 2.233 1.387 0.506 1.890 K 3.135 1.419 0.882 2.551 GMM 0.305 0.322 0.401 0.250
Table 3. Experimental accuracy in classification of polarimetric SAR%
View tableTable 3. Experimental accuracy in classification of polarimetric SAR%
Place Algorithm Water Forest Farmland Urban Overall Kappa San Francisco KNN 99.82 98.28 - 69.17 94.34 91.16 SVM 99.68 96.70 - 78.26 95.03 91.61 RF 99.78 98.24 - 79.73 95.97 92.76 WHRT 99.42 95.44 - 73.01 93.01 89.24 GMM 99.50 98.98 - 95.84 96.90 93.23 Flevoland KNN 86.77 91.10 75.29 74.91 83.85 76.23 SVM 82.47 90.42 72.88 75.29 82.71 75.15 RF 85.98 86.03 73.69 75.88 83.01 76.60 WHRT 85.60 86.96 72.05 72.19 80.89 72.37 GMM 87.69 85.81 92.70 88.19 89.70 82.11 Vancouver KNN 99.28 87.15 73.88 48.19 81.02 66.77 SVM 99.18 84.72 63.20 66.10 80.57 65.45 RF 99.28 86.86 79.56 72.52 84.16 69.35 WHRT 99.21 88.72 66.49 86.21 80.21 66.22 GMM 99.66 86.88 80.08 96.71 89.97 74.22
Table 4. Experimental accuracy in classification of polarimetric SAR under different features in Flevoland%
View tableTable 4. Experimental accuracy in classification of polarimetric SAR under different features in Flevoland%
Feature sets Algorithm Water Forest Farmland Urban Overall Kappa Cloude-Pottier decomposition KNN 82.73 95.54 74.59 15.59 69.87 61.74 SVM 77.40 92.23 79.83 15.67 69.73 61.63 RF 84.07 94.87 83.04 33.84 77.79 68.75 GMM 88.00 76.48 82.67 82.42 79.86 70.58 Freeman decomposition KNN 92.56 90.39 87.51 37.41 78.87 70.70 SVM 70.20 89.51 77.13 34.24 75.38 69.22 RF 93.01 86.06 88.88 51.15 81.00 71.58 GMM 94.53 67.57 89.77 85.03 82.35 72.78 Yamaguchi decomposition KNN 86.61 89.05 69.76 70.43 80.09 70.78 SVM 89.31 88.31 67.78 65.60 75.89 68.65 RF 86.38 87.68 77.03 72.00 80.69 71.31 GMM 87.58 84.50 70.71 92.95 82.23 72.61
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Luoru Li, Xin Xu, Hao Dong, Rong Gui, Xinfang Xie. Gaussian Mixture Model and Classification of Polarimetric Features for SAR Images[J]. Acta Optica Sinica, 2019, 39(1): 0128002
Paper Information
Category: Remote Sensing and Sensors
Received: Jun. 12, 2018
Accepted: Aug. 23, 2018
Published Online: May. 10, 2019
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