SCIENTIA SINICA Informationis, Volume 47 , Issue 12 : 1662-1673(2017) https://doi.org/10.1360/N112016-00265

Semi-supervised classification algorithm of hyperspectral image based on DL1 graph and KNN superposition graph

• AcceptedMay 8, 2017
• PublishedAug 30, 2017
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References

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• Figure 1

(a) Pseudo color image and (b) four kinds of vegetation of the sub scene of AVIRIS Indiana Pines

• Figure 2

The overall classification accuracy comparison chart

• Figure 3

Classification accuracies of sub scene of Indiana Pines, the percentage of labeled samples is 5%, $k$=5. (a) L1 graph; (b) DL1 graph; (c) L1KNN graph; (d) DL1KNN graph

• Figure 4

Classification accuracies of sub scene of Indiana Pines, the percentage of labeled samples is 25%, $k$=5. (a) L1 graph; (b) DL1 graph; (c) L1KNN graph; (d) DL1KNN graph

• Figure 5

Confusion matrix of sub scene of Indiana Pines, the percentage of labeled samples is 25%, $k$=5. class1: Corn-notill, class2: Grass-trees, class3: Soybean-notill, class4: Soybean-mintill. (a) L1 graph; (b) DL1 graph; (c) L1KNN graph; (d) DL1KNN graph

• Figure 6

(Color online) Overall accuracy and Kappa factor of Indiana Pines sub scene when percentage of labeled samples is 25%, $k=5$

• Figure 7

(Color online) Accuracy per class of Indiana Pines sub scene when percentage of labeled samples is 25%, $k=5$

• Figure 8

(Color online) Omission of Indiana Pines sub scene when percentage of labeled samples is 25%, $k=5$

• Figure 9

(Color online) The curve graph of classification accuracy (%) with varying scale coefficient of graph under different percentages of labeled samples

• Table 1   Classification accuracy (%) of various graphs combined with the GHF label propagation method under different percentages of labeled samples
 Indiana Pines (%) L1图 DL1图 L1KNN图 DL1KNN图 $k=5$ $k=8$ $k=10$ Average $k=5$ $k=8$ $k=10$ Average 5 0.556 0.744 0.792 0.891 0.883 0.855 0.818 0.876 0.906 0.867 10 0.771 0.873 0.887 0.909 0.873 0.890 0.881 0.910 0.880 0.890 15 0.715 0.868 0.932 0.904 0.898 0.911 0.913 0.909 0.918 0.913 20 0.769 0.871 0.931 0.924 0.918 0.924 0.931 0.923 0.928 0.927 25 0.794 0.880 0.939 0.916 0.923 0.926 0.948 0.920 0.923 0.930
•

Algorithm 1 DL1KNN图构造算法

输入高光谱图像, 其中$l$个标记样本$X_l=[x_1,x_2,\ldots,x_l]$, $u$个无标记样本$X_u=[x_{l+1},x_{l+2},\ldots,x_{l+u}]$, 初始的标记矩阵$Y_l\in~\mathbb{R}^{l~\times~c}$;

预处理样本: 归一化样本$x_i=x_i/\Vert~{x_i}~\Vert_2$, 去掉样本$x_i$得到预处理样本$X=[x_1,x_2,\ldots,x_{i-1},x_{i+1},\ldots,x_n]$;

通过式(2)得到L1范数图权值矩阵$W_{{\rm~L}1}=\{W_{ij}\}_{n\times~n}$;

通过式(4)和(5)得到类概率矩阵$\{P_{ij}\}_{n\times~n}$;

根据式(6)获得DL1图的权值矩阵$W_{(\rm~DL1)}$;

通过式(7)得到K近邻矩阵$K=\{K_{ij}\}_{n~\times~n}$;

通过式(8)将DL1图和KNN图叠加得到叠加矩阵$W_3$, 根据实验设置$\beta$的值为0.2;

输出叠加图$G_3=(X,W_3~)$.

• Table 2   The influence of varying $\beta$ on the classification accuracy (%) under different percentages of labeled samples
 $\beta$ (%) 0 0.1 0.2 0.25 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 10 100 5 74.4 84.4 86.7 85.5 84.9 85.3 85.2 83.2 82.4 82.8 83.2 84.4 83 80.5 10 87.3 86.3 89 88.4 87 86.6 84.9 83 82.7 82.6 82.4 82.5 84.9 84.2 15 86.8 88.8 91.3 88.8 88 88.9 86.8 85.1 84.1 84.2 83.5 83.8 81.9 83.3 20 87.1 90.6 92.7 90.6 90.9 89.7 89.5 88.1 87.2 87.9 87.6 87.9 90.6 91.7 25 88 89 93 93.4 90 88 89 88.5 88.7 87 87.5 86.7 92.3 89.3

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