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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

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  • ReceivedMar 7, 2017
  • AcceptedMay 8, 2017
  • PublishedAug 30, 2017

Abstract

The classification of hyperspectral images with a paucity of labeled samples is a challenging task. This paper describes the use of a superpose probability matrix and weight matrix of an L1 graph, thereby forming a strong discriminating DL1 graph. Combining the local information of the space with the global information of the spectrum through the superposition of a KNN graph and a DL1 graph, a graph-based framework is built that combines the spatial and spectral information. This framework of a DL1KNN graph can reflect the more sophisticated structure of hyperspectral image data. Experimental results show that the improvement in classification accuracy is significant when the percentage of labeled samples is 5% through the use of the label propagation of the graph to achieve semi-supervised classification for improving the automatic classification accuracy of hyperspectral data with a small number of samples.


Funded by

国家自然科学基金(61461002)

宁夏自然科学基金(NZ15105)

北方民族大学校级科研项目(JSKY06)

北方民族大学研究生创新项目(YCX1657)


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 83.0 80.5
    10 87.3 86.3 89.0 88.4 87.0 86.6 84.9 83.0 82.7 82.6 82.4 82.5 84.9 84.2
    15 86.8 88.8 91.3 88.8 88.0 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 88.0 89.0 93.0 93.4 90.0 88.0 89.0 88.5 88.7 87.0 87.5 86.7 92.3 89.3

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