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基于记忆关联学习的小样本高光谱图像分类方法

王聪 张锦阳 张磊 魏巍 张艳宁

王聪, 张锦阳, 张磊, 等 . 基于记忆关联学习的小样本高光谱图像分类方法[J]. 北京航空航天大学学报, 2021, 47(3): 549-557. doi: 10.13700/j.bh.1001-5965.2020.0498
引用本文: 王聪, 张锦阳, 张磊, 等 . 基于记忆关联学习的小样本高光谱图像分类方法[J]. 北京航空航天大学学报, 2021, 47(3): 549-557. doi: 10.13700/j.bh.1001-5965.2020.0498
WANG Cong, ZHAGN Jinyang, ZHANG Lei, et al. Small sample hyperspectral image classification method based on memory association learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 549-557. doi: 10.13700/j.bh.1001-5965.2020.0498(in Chinese)
Citation: WANG Cong, ZHAGN Jinyang, ZHANG Lei, et al. Small sample hyperspectral image classification method based on memory association learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 549-557. doi: 10.13700/j.bh.1001-5965.2020.0498(in Chinese)

基于记忆关联学习的小样本高光谱图像分类方法

doi: 10.13700/j.bh.1001-5965.2020.0498
基金项目: 

深圳市科技创新委员会基金 JCYJ20190806160210899

国家自然科学基金 61671385

国家自然科学基金 U19B2037

详细信息
    作者简介:

    王聪  女,博士研究生。主要研究方向:高光谱图像分类

    张锦阳  男,硕士。主要研究方向:高光谱图像分类

    张磊  男,博士,教授。主要研究方向:图像处理及机器学习

    魏巍  男,博士,副教授,博士生导师。主要研究方向:计算机视觉、图像处理

    张艳宁  女,博士,教授,博士生导师。主要研究方向:图像处理与计算机视觉、模式识别与人工智能、机器学习等

    通讯作者:

    魏巍, E-mail: weiweinwpu@nwpu.edu.cn

  • 中图分类号: V221+.3;TB553

Small sample hyperspectral image classification method based on memory association learning

Funds: 

Foundation of Science, Technology and Innovation Commission of Shenzhen Manicipality JCYJ20190806160210899

National Natural Science Foundation of China 61671385

National Natural Science Foundation of China U19B2037

More Information
  • 摘要:

    高光谱图像(HSI)分类是遥感领域的基础应用之一。该任务旨在根据部分带类别标签的像素样本训练分类器,预测图像中剩余像素对应的类别标签。在实际应用中,由于人工标记样本成本过高,只能获得少量带标签的样本。针对少量样本无法准确描述数据分布从而导致训练过程过拟合的问题,提出一种基于记忆关联学习的小样本高光谱图像分类方法。考虑到无标签样本中包含大量与数据分布相关的信息,构建基于有标签样本记忆模块,并根据样本间的特征关联,利用不断更新的记忆模块学习无标签样本的潜在类别分布,构建无监督分类模型,并与传统的有监督分类模型进行联合学习。在多个高光谱图像分类数据集上的实验结果表明,所提方法能有效提升小样本高光谱图像分类的准确性。

     

  • 图 1  基于记忆关联学习的小样本高光谱图像分类方法流程

    Figure 1.  Flowchart of small sample hyperspectral image classification method based on memory association learning

    图 2  三个高光谱图像分类数据集的代表性波段图像和真实分类结果

    Figure 2.  Representative band image and ground truth classification map of three HSI classification datasets

    图 3  Indian Pines数据集上的分类结果

    Figure 3.  Classification results of different methods on Indian Pines datasets

    图 4  Pavia University数据集上的分类结果

    Figure 4.  Classification results of different methods on Pavia University datasets

    图 5  Salinas数据集上的分类结果

    Figure 5.  Classification results of different methods on Salinas datasets

    表  1  Indian Pines数据集中样本类别和样本数

    Table  1.   Name and pixel numbers of per adopted class on Indian Pines datasets

    序号 样本类别名称 样本数
    1 Alfalfa 46
    2 Corn-notill 1 428
    3 Corn-mintill 830
    4 Corn 237
    5 Grass-pasture 483
    6 Grass-trees 730
    7 Grass-pasture-mowed 28
    8 Hay-windrowed 478
    9 Oats 20
    10 Soybean-notill 972
    11 Soybean-mintill 2 455
    12 Soybean-clean 593
    13 Wheat 205
    14 Woods 1 265
    15 Buildings-Grass-Trees-Drives 386
    16 Stone-Steel-Towers 93
    总计 10 249
    下载: 导出CSV

    表  2  Pavia University数据集中样本类别和样本数

    Table  2.   Name and pixel numbers of per adopted class on Pavia University datasets

    序号 样本类别名称 样本数
    1 Asphalt 6 631
    2 Meadows 18 649
    3 Gravel 2 099
    4 Trees 3 064
    5 Painted metal sheets 1 345
    6 Bare Soil 5 029
    7 Bitumen 1 330
    8 Self-Blocking Bricks 3 682
    9 Shadows 947
    总计 42 776
    下载: 导出CSV

    表  3  Salinas数据集中样本类别和样本数

    Table  3.   Name and pixel numbers of per adopted class on Salinas datasets

    序号 样本类别名称 样本数
    1 Brocoli_green_weeds_1 2 009
    2 Brocoli_green_weeds_2 3 726
    3 Fallow 1 976
    4 Fallow_rough_plow 1 394
    5 Fallow_smooth 2 678
    6 Stubble 3 959
    7 Celery 3 579
    8 Grapes_untrained 11 271
    9 Soil_vinyard_develop 6 203
    10 Corn_senesced_green_weeds 3 278
    11 Lettuce_romaine_4wk 1 068
    12 Lettuce_romaine_5wk 1 927
    13 Lettuce_romaine_6wk 916
    14 Lettuce_romaine_7wk 1 070
    15 Vinyard_untrained 7 268
    16 Vinyard_vertical_trellis 1 807
    总计 54 129
    下载: 导出CSV

    表  4  卷积网络

    Table  4.   Convolution network

    卷积层 卷积核 卷积步长 特征数 补齐
    conv1 (3, 1, 8) (1, 1, 3) 64 valid
    conv2 (1, 1, 3) (1, 1, 2) 64 valid
    conv3 (1, 3, 3) (1, 1, 2) 128 valid
    conv4 (1, 1, 3) (1, 1, 2) 128 valid
    conv5 (1, 1, 3) (1, 1, 2) 256 valid
    conv6 (1, 1, s) (1, 1, 1) 128 valid
    下载: 导出CSV

    表  5  分类器层

    Table  5.   Classifier layer

    名称 神经元节点 激活函数
    fc 128 ReLU
    logits C Softmax
    下载: 导出CSV

    表  6  不同方法在Indian Pines数据集上的分类结果

    Table  6.   Classification results of different methods on Indian Pines datasets %

    序号 KNN SVM 1D-CNN 3D-CNN SS-CNN ISSR-HIC 本文方法
    1 86.11 82.50 79.44 79.44 76.09 90.76 63.89
    2 44.63 41.70 31.71 29.28 18.00 55.25 10.65
    3 36.05 47.74 47.63 44.15 6.86 52.56 11.59
    4 48.02 57.31 49.16 42.29 70.04 64.98 46.26
    5 69.98 76.77 74.97 77.59 17.18 76.09 64.38
    6 64.39 81.19 89.44 87.72 80.41 85.82 92.36
    7 92.22 92.22 83.33 98.89 92.86 92.86 91.67
    8 66.03 82.46 76.03 77.82 78.87 78.09 97.44
    9 68.00 98.00 94.00 100 100 95.00 90.00
    10 39.33 49.95 49.29 54.51 55.33 55.14 17.05
    11 29.87 39.75 38.45 46.74 68.73 47.82 89.65
    12 26.83 42.04 38.46 36.50 31.20 48.06 23.50
    13 90.15 93.23 92.00 96.62 93.17 95.61 92.56
    14 61.96 66.22 70.17 70.80 96.05 74.35 84.22
    15 14.31 36.91 46.65 38.24 38.95 41.97 28.99
    16 86.02 91.57 88.43 98.07 100 93.55 96.99
    AA 57.74 67.47 65.57 67.42 63.98 71.74 62.57
    OA 44.99 53.80 52.60 54.26 55.44 60.63 56.94
    Kappa 38.69 48.46 47.16 48.71 48.96 55.87 49.28
    下载: 导出CSV

    表  7  不同方法在Pavia University数据集上的分类结果

    Table  7.   Classification results of different methods on Pavia University datasets %

    序号 KNN SVM 1D-CNN 3D-CNN SS-CNN ISSR-HIC 本文方法
    1 58.07 68.75 68.15 68.84 75.19 69.44 96.55
    2 66.60 68.17 62.95 74.63 86.05 65.22 91.94
    3 48.22 66.40 66.18 68.43 61.99 66.33 62.83
    4 85.38 82.27 86.64 90.22 93.81 85.13 83.72
    5 92.27 99.28 99.43 98.97 99.18 99.57 99.91
    6 29.25 67.02 71.30 50.37 32.85 67.21 39.74
    7 89.97 87.83 86.50 87.88 74.06 91.24 90.71
    8 56.00 81.55 71.14 73.40 72.41 80.75 92.46
    9 99.89 99.91 99.96 99.59 97.99 98.76 100
    AA 70.07 80.13 79.14 79.15 77.06 80.41 84.21
    OA 62.83 72.47 69.98 73.30 76.47 71.56 84.94
    Kappa 53.02 65.14 70.32 65.79 68.95 64.38 79.65
    下载: 导出CSV

    表  8  不同方法在Salinas数据集上的分类结果

    Table  8.   Classification results of different methods on Salinas datasets %

    序号 KNN SVM 1D-CNN 3D-CNN SS-CNN ISSR-HIC 本文方法
    1 98.27 94.92 95.55 91.23 99.29 97.55 99.28
    2 81.15 97.47 99.77 97.34 98.09 97.82 99.60
    3 74.38 87.60 92.33 89.70 69.38 86.29 98.19
    4 94.45 99.29 98.71 95.55 97.99 98.05 99.47
    5 88.54 92.83 90.41 93.64 99.18 95.44 94.61
    6 96.58 98.34 99.21 96.28 99.09 99.25 99.26
    7 98.55 98.83 99.00 96.02 99.80 99.46 99.46
    8 62.39 53.00 68.02 58.28 25.30 58.08 79.12
    9 91.36 96.03 95.60 92.80 99.00 96.02 99.03
    10 58.92 83.71 83.48 76.08 81.96 82.82 74.96
    11 78.11 86.60 93.72 87.75 86.48 95.13 96.96
    12 91.46 99.74 99.45 99.46 99.90 98.62 99.96
    13 98.72 98.10 95.74 95.81 99.56 96.97 99.58
    14 88.17 90.91 94.11 92.87 94.53 95.35 94.83
    15 52.84 70.70 54.26 69.76 93.09 67.23 56.91
    16 43.17 90.04 79.67 90.16 77.30 92.17 98.25
    AA 81.32 89.88 89.94 88.92 88.75 91.02 92.74
    OA 76.20 82.61 83.57 82.31 79.73 83.72 87.31
    Kappa 73.56 80.76 81.74 80.41 77.69 81.97 85.84
    下载: 导出CSV

    表  9  Indian Pines数据集上不同数量训练样本下的分类结果

    Table  9.   Classification results of different methods with changed numbers of training samples on Indian Pines datasets %

    方法 评价指标 训练样本数
    1 5 10 50 100 200
    3D-CNN AA 32.96 54.23 67.42 87.42 91.09 92.64
    OA 22.70 43.42 54.26 77.01 85.54 87.71
    Kappa 15.33 36.90 48.71 72.32 83.47 85.68
    ISSR-HIC AA 40.93 66.35 71.74 84.31 88.73 91.01
    OA 30.55 55.05 60.63 79.09 81.14 84.22
    Kappa 23.31 49.64 55.87 75.79 78.80 82.22
    本文方法 AA 33.77 48.18 62.57 89.45 92.49 95.80
    OA 39.60 49.94 56.94 83.92 86.27 92.46
    Kappa 29.67 41.64 49.28 81.54 84.16 91.14
    下载: 导出CSV

    表  10  Pavia University数据集上不同数量训练样本下的分类结果

    Table  10.   Classification results of different methods with changed numbers of training samples on Pavia University datasets %

    方法 评价指标 样本数
    1 5 10 50 100 200
    3D-CNN AA 55.03 68.27 79.15 92.70 95.74 96.04
    OA 49.97 63.81 73.30 92.15 94.84 96.43
    Kappa 37.48 53.36 65.79 89.65 93.18 95.22
    ISSR-HIC AA 65.94 76.94 80.41 86.10 89.89 92.51
    OA 53.46 62.72 71.56 81.55 86.84 90.52
    Kappa 43.11 54.70 64.38 76.26 82.93 87.63
    本文方法 AA 55.80 72.21 84.21 95.83 97.87 98.80
    OA 55.81 73.89 84.94 97.32 98.42 99.31
    Kappa 41.88 65.14 79.65 96.41 97.89 99.07
    下载: 导出CSV

    表  11  Salinas数据集上不同数量训练样本下的分类效果

    Table  11.   Classification results of different methods with changed numbers of training samples on Salinas datasets %

    方法 评价指标 样本数
    1 5 10 50 100 200
    3D-CNN AA 62.02 85.24 88.92 94.08 94.76 95.82
    OA 57.24 76.97 82.31 87.25 88.13 89.86
    Kappa 53.04 74.61 80.41 85.83 86.79 88.68
    ISSR-HIC AA 77.67 87.39 91.02 94.47 96.01 96.63
    OA 70.65 79.93 83.72 90.21 91.52 91.97
    Kappa 67.44 77.67 81.97 89.13 90.56 91.08
    本文方法 AA 78.30 90.99 92.74 94.56 96.76 97.27
    OA 78.30 85.49 87.31 89.01 92.78 93.65
    Kappa 74.39 83.83 85.84 87.73 91.94 92.89
    下载: 导出CSV

    表  12  不同损失函数在不同数据集上的分类结果

    Table  12.   Classification results of the proposed methods with different loss function on three datasets %

    数据集 评价指标 损失函数
    Indian Pines AA 62.57 55.58 61.20
    OA 56.94 52.26 48.33
    Kappa 49.28 45.28 42.28
    Pavia University AA 84.21 83.08 77.42
    OA 84.94 81.64 68.68
    Kappa 79.65 75.38 60.88
    Salinas AA 92.74 90.78 90.08
    OA 87.31 85.49 83.29
    Kappa 85.84 83.78 81.46
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-09-07
  • 录用日期:  2020-09-11
  • 刊出日期:  2021-03-20

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