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Semi-supervised PolSAR Image Classification Based on the Neighborhood Minimum Spanning Tree
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摘要: 该文针对极化SAR图像分类中只有少量标记样本的问题，提出了一种基于邻域最小生成树的半监督极化SAR图像分类方法。该方法针对极化SAR图像以像素为分类对象的特点，结合自训练方法的思想，利用极化SAR图像像素点的空间信息，提出了基于邻域最小生成树辅助学习的样本选择策略，增加自训练过程中被选择无标记样本的可靠性，扩充标记样本数量，训练更好的分类器。最终用训练好的分类器对极化SAR图像进行测试。对3组真实的极化SAR图像进行测试，实验结果表明，该方法在只有少量标记样本的情况下能获得满意的分类结果，且分类正确率明显优于传统的分类算法。Abstract: In this paper, a novel semi-supervised classification method based on the Neighborhood Minimum Spanning Tree (NMST) is proposed to solve the Polarimetric Synthetic Aperture Radar (PolSAR) terrain classification when labeled samples are few. Combining the idea of self-training method and spatial information of the pixels in PolSAR image, a new help-training sample selection strategy based on spatial neighborhood information is proposed, named as NMST, to select the high reliable unlabeled samples to enlarge the training set and improve the base classifier. Finally, the PolSAR image is classified by this improved classifier. The experiments results tested on three PolSAR data sets show that the proposed method achieves a better performance than existing classification methods when the number of labeled samples is few.
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Key words:
- PolSAR /
- Terrain classification /
- Semi-supervised learning /
- Minimum spanning tree
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图 2 极化SAR协方差矩阵中9个元素的灰度值
Figure 2. The gray value of 9 elements in PolSAR covariance matrix
图 4 基于邻域的最小生成树生成过程
Figure 4. The spanning process of neighborhood minimum spanning tree
图 5 基于邻域最小生成树的半监督极化SAR分类方法
Figure 5. Semi-supervised PolSAR classification based on the neighborhood minimum spanning tree
图 6 Flevoland地区AIRSAR L波段数据不同方法的分类结果
Figure 6. Classification results of the Flevoland data acquired by AIRSAR
图 7 Flevoland地区Radarsat-2 C波段数据不同方法的分类结果
Figure 7. Classification result of the Flevoland data acquired by Radarsat-2
图 8 旧金山地区Radarsat-2 C波段数据不同方法的分类结果
Figure 8. Classification result of the San Francisco data acquired by Radarsat-2
表 1 AIRSAR L波段的Felvoland地区不同分类算法的分类精度(%)
Table 1. Classification accuracy of the Flevoland area acquired by AIRSAR L band (%)
区域 方法 Wishart SVM Self-training 本文方法 Stembeans 91.48 70.07 90.82 98.75 Rapeseed 61.83 38.02 67.14 59.58 Bare soil 97.51 86.89 70.97 96.75 Potatoes 79.47 58.38 80.27 81.99 Beet 92.35 85.61 95.05 94.60 Wheat 2 67.43 71.80 67.39 89.86 Peas 93.10 77.70 95.24 97.56 Wheat 3 82.08 82.42 94.33 97.05 Lucerne 84.53 40.77 81.67 95.06 Barley 81.96 98.29 98.62 98.39 Wheat 81.46 68.28 85.34 85.41 Grasses 66.49 65.03 81.75 80.08 Forest 84.21 61.03 77.66 94.77 Water 46.85 65.32 69.39 93.35 Building 81.77 78.91 2.18 85.58 OA 79.40 70.30 77.19 89.92 
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表 2 AIRSAR L波段的Felvoland 地区不同训练样本的分类结果
Table 2. Classification results of the Flevoland area acquired by AIRSAR L band with different number of training samples
方法 训练样本数 4 6 8 10 OA (%) Kappa OA (%) Kappa OA (%) Kappa OA (%) Kappa Wishart 74.62 0.7215 76.19 0.7459 78.78 0.7656 80.26 0.7831 SVM 56.07 0.5423 58.12 0.5611 64.42 0.6102 70.30 0.6682 Self-training 63.36 0.6025 68.42 0.6569 73.89 0.7146 77.23 0.7489 本文方法 79.33 0.7888 83.06 0.8093 86.90 0.8416 89.92 0.8852
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表 3 Radarsat-2 C波段的Felvoland地区不同分类算法的分类精度(%)
Table 3. Classification accuracy of the Flevoland area acquired by Radarsat-2 C band (%)
区域 方法 Wishart SVM Self-training 本文方法 Urban 69.61 54.75 63.93 71.44 Water 98.71 96.83 99.10 98.82 Forest 91.65 65.25 73.83 83.63 Cropland 55.27 78.97 79.23 82.24 OA 78.81 73.95 79.02 84.03
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表 4 Radarsat-2 C波段的Felvoland 地区不同训练样本的分类结果
Table 4. Classification results of the Flevoland area acquired by Radarsat-2 C band with different number of training samples
方法 训练样本数 4 6 8 10 OA (%) Kappa OA (%) Kappa OA (%) Kappa OA (%) Kappa Wishart 69.21 0.5803 73.65 0.6239 76.81 0.6854 78.81 0.7026 SVM 50.79 0.4153 64.79 0.5471 70.05 0.5968 73.95 0.6394 Self-training 65.69 0.5233 70.41 0.5911 74.40 0.6605 79.45 0.7144 本文方法 76.71 0.6768 79.29 0.7235 82.02 0.7644 84.03 0.7882
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表 5 Radarsat-2 C波段的旧金山地区不同分类算法的分类结果(%)
Table 5. Classification accuracy of the San Francisco area acquired by radarsat-2 C Band (%)
区域 方法 Wishart SVM Self-training 本文方法 Water 98.70 90.04 98.04 99.92 Vegetation 91.03 78.51 84.45 91.50 Low-Density Urban 81.30 42.31 70.18 75.05 High-Density Urban 42.58 77.15 33.01 68.27 Developed 55.26 24.00 56.16 58.81 OA 73.77 62.40 68.37 78.71
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表 6 Radarsat-2 C波段的旧金山地区不同训练样本的分类结果
Table 6. Classification results of the San Francisco area acquired by Radarsat-2 C band with different number of training samples
方法 训练样本数 4 6 8 10 OA (%) Kappa OA (%) Kappa OA (%) Kappa OA (%) Kappa Wishart 68.09 0.5181 70.44 0.5439 72.49 0.5867 73.77 0.6011 SVM 50.24 0.2817 51.25 0.2905 56.31 0.3628 62.40 0.4342 Self-training 52.34 0.3126 58.62 0.3669 63.27 0.4357 68.42 0.5308 本文方法 70.87 0.5482 73.15 0.5986 75.23 0.6284 78.71 0.6852
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