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基于密集连接和空谱变换器的双支路高光谱图像分类模型

李鑫伟 杨甜

李鑫伟, 杨甜. 基于密集连接和空谱变换器的双支路高光谱图像分类模型[J]. 红外技术, 2022, 44(11): 1210-1219.
引用本文: 李鑫伟, 杨甜. 基于密集连接和空谱变换器的双支路高光谱图像分类模型[J]. 红外技术, 2022, 44(11): 1210-1219.
LI Xinwei, YANG Tian. Double-Branch DenseNet-Transformer Hyperspectral Image Classification[J]. Infrared Technology , 2022, 44(11): 1210-1219.
Citation: LI Xinwei, YANG Tian. Double-Branch DenseNet-Transformer Hyperspectral Image Classification[J]. Infrared Technology , 2022, 44(11): 1210-1219.

基于密集连接和空谱变换器的双支路高光谱图像分类模型

基金项目: 

中央高校基本科研业务费专项资金资助 BLX201610

详细信息
    作者简介:

    李鑫伟(1989-),男,博士,河南省洛阳人,讲师,主要从事图像处理、人工智能、智慧林业方面的研究。E-mail:xwli_1989@163.com

  • 中图分类号: TP751

Double-Branch DenseNet-Transformer Hyperspectral Image Classification

  • 摘要: 为了减少高光谱图像的训练样本,同时得到更好的分类结果,本文提出了一种基于密集连接网络和空谱变换器的双支路深度网络模型。该模型包含两个支路并行提取图像的空谱特征。首先,两支路分别使用3D和2D卷积对子图像的空间信息和光谱信息进行初步提取,然后经过由批归一化、Mish函数和3D卷积组成的密集连接网络进行深度特征提取。接着两支路分别使用光谱变换器和空间变换器以进一步增强网络提取特征的能力。最后两支路的输出特征图进行融合并得到最终的分类结果。模型在Indian Pines、University of Pavia、Salinas Valley和Kennedy Space Center数据集上进行了测试,并与6种现有方法进行了对比。结果表明,在Indian Pines数据集的训练集比例为3%,其他数据集的训练集比例为0.5%的条件下,算法的总体分类精度分别为95.75%、96.75%、95.63%和98.01%,总体性能优于比较的方法。
  • 图  1  密集连接网络原理

    Figure  1.  Dense block schematic diagram

    图  2  Spatial attention block 原理图

    Figure  2.  Spatial attention block schematic diagram

    图  3  Spatial transformer block 原理

    Figure  3.  Spatial transformer block schematic diagram

    图  4  Spectral association block 原理

    Figure  4.  Spectral association block schematic diagram

    图  5  Spectral transformer block 原理

    Figure  5.  Spectral transformer block schematic diagram

    图  6  本文提出的整体模型架构

    Figure  6.  Overall model architecture diagram proposed in this paper

    图  7  IP数据集的分类结果图(a)标签;(b)SVM;(c)CDCNN;(d)SSRN;(e)FDSSC;(f)DBMA;(g) DBDA;(h)Ours

    Figure  7.  Classification result diagram of the IP dataset (a) label; (b)SV; (c) CDCNN; (d) SSRN; (e) FDSSC; (f) DBMA; (g) DBDA; (h) Ours

    图  8  UP数据集的分类结果图(a)标签;(b)SVM;(c)CDCNN;(d)SSRN;(e)FDSSC;(f) DBMA;(g) DBDA;(h)Ours

    Figure  8.  Classification result diagram of the UP dataset (a) label; (b) SVM; (c) CDCNN; (d) SSRN; (e) FDSSC; (f) DBMA; (g) DBDA; (h) Ours

    图  9  SV数据集的分类结果图(a)标签;(b)SVM;(c)CDCNN;(d)SSRN;(e)FDSSC;(f)DBMA;(g)DBDA;(h)Ours

    Figure  9.  Classification result diagram of the SV dataset. (a) label; (b) SVM; (c) CDCNN; (d) SSRN; (e) FDSSC; (f) DBMA; (g) DBDA; (h) Ours

    图  10  KSC数据集的分类结果图(a)标签;(b)SVM;(c)CDCNN;(d)SSRN;(e)FDSSC;(f)DBMA;(g)DBDA;(h)Ours

    Figure  10.  Classification result diagram of the KSC dataset (a) label; (b) SVM; (c) CDCNN; (d) SSRN; (e) FDSSC; (f) DBMA; (g) DBDA; (h) Ours

    表  1  数据集参数

    Table  1.   Dataset parameters

    Parameters Dataset
    IP UP SV KSC
    Acquisition time 2001 2003 1992 1996
    Location A farm in northwestern Indiana, USA Part of Pavia, Italy Salinas Valley, California, USA Kennedy Space Center, Florida, USA
    Sensor AVIRIS ROSIS AVIRIS AVIRIS
    Resolution/m 20 1.3 3.7 18
    Spectral range/μm [0.4, 2.5] [0.43, 0.86] [0.4, 2.5] [0.4, 2.5]
    Spectral band 200 103 204 176
    Size/(pixel×pixel) 145×145 610×340 512×217 512×614
    Land-cover 16 9 16 13
    Total sample pixel 21025 42776 54129 5211
    下载: 导出CSV

    表  2  数据集的训练集、测试集和验证集

    Table  2.   Training set, test set, verification set

    Dataset Total Number Training set Verification set Test set
    IP 10249 307 307 9635
    UP 42776 210 210 42356
    SV 54129 263 348 53603
    KSC 5467 256 256 4699
    下载: 导出CSV

    表  3  IP数据集的分类结果

    Table  3.   Classification results for the IP dataset

    Classification SVM CDCNN SSRN FDSSC DBMA DBDA Ours
    Alfalfa/% 36.62±14.63 17.67±22.81 55.37±45.82 68.85±45.15 78.70±23.31 94.79±5.20 98.87±1.80
    Corn-notill/% 55.49±0.81 53.60±6.92 84.08±7.39 90.05±10.16 80.39±8.07 88.60±7.10 94.65±2.70
    Corn-mintill/% 62.33±2.87 34.84±29.78 85.47±10.26 89.44±5.07 84.88±11.49 94.13±4.28 94.76±2.99
    Corn /% 42.54±5.55 32.27±27.98 77.39±26.82 93.98±4.57 91.83±5.96 98.80±0.73 94.23±5.52
    Grass/pasture/% 85.05±3.28 73.83±27.72 95.57±5.19 97.54±3.13 95.61±2.77 99.76±0.24 98.90±0.67
    Grass/trees/% 83.32±3.17 72.35±18.59 96.38±2.67 98.08±1.70 96.75±2.74 99.20±0.36 98.08±1.08
    Grass/pasture-mowed/% 59.87±15.97 24.58±31.97 55.57±39.94 77.94±30.90 50.61±13.26 65.32±19.87 61.68±19.51
    Hay-windrowed/% 89.67±1.61 84.51±3.79 94.93±4.09 96.04±3.60 98.04±1.88 1±0 99.96±0.13
    Oats/% 39.28±17.69 33.57±32.81 71.29±39.40 67.06±44.40 41.28±19.51 83.33±16.67 80.83±11.96
    Soybean-notill/% 62.32±4.66 35.07±25.40 81.14±11.56 90.92±5.86 84.77±7.60 89.60±4.06 91.37±3.82
    Soybean-mintill/% 64.73±3.84 58.30±12.34 90.26±4.13 95.54±3.55 84.01±6.92 99.14±0.32 96.71±1.37
    Soybean-clean/% 50.55±3.63 38.30±20.02 83.75±11.82 92.98±6.94 73.85±12.90 96.39±3.05 93.38±5.52
    Wheat/% 86.74±5.59 81.92±11.77 97.92±2.60 99.30±2.09 97.01±4.64 1±0 98.81±1.17
    Woods /% 88.67±1.75 76.33±9.01 93.83±4.37 95.77±2.10 95.67±1.87 96.71±0.01 97.50±1.39
    Buildings-grass-trees-drives /% 61.82±4.34 50.49±26.87 92.30±3.94 94.99±3.61 82.76±6.79 95.11±0.36 93.37±1.76
    Stone-steel-towers /% 98.66±2.14 67.94±44.52 85.10±29.02 98.67±1.94 94.09±6.65 95.41±1.10 95.94±3.32
    OA/% 68.76±1.28 60.23±7.53 88.52±5.18 93.49±2.65 85.53±5.50 95.23±1.00 95.57±0.78
    AA/% 66.73±1.28 52.22±16.40 83.77±10.60 90.45±6.44 83.14±3.61 93.52±0.33 93.07±0.67
    100*Kappa coefficient 63.98±1.59 53.20±9.86 86.90±5.86 92.57±3.04 83.45±6.28 94.57±1.13 94.95±0.89
    Training time/s 178.2 75.5 1520.3 1614.4 2021.6 252.9 1354.6
    Testing time/s 9.9 14 145.7 48.5 125.5 16.9 106.7
    下载: 导出CSV

    表  4  UP数据集识别结果

    Table  4.   Identification results for the UP dataset

    Classification SVM CDCNN SSRN FDSSC DBMA DBDA Ours
    Asphalt /% 81.26±5.08 78.83±4.45 94.06±4.41 87.78±7.54 89.18±6.15 94.34±0.62 95.05±2.56
    Meadows /% 84.53±3.81 88.42±5.78 97.87±1.24 97.72±2.19 96.49±3.14 98.62±0.79 98.90±0.58
    Gravel /% 56.56±16.17 30.44±19.13 79.34±29.35 82.73±28.16 83.58±17.81 98.69±1.31 95.22±4.09
    Trees /% 94.34±3.50 91.91±12.60 94.09±13.95 94.35±12.21 96.14±2.20 98.26±0.66 97.96±1.00
    Painted metal sheets /% 95.38±3.40 93.77±6.32 99.61±0.35 99.10±1.05 98.78±2.06 99.66±0.11 99.30±0.87
    Bare soil /% 80.66±7.54 74.89±6.81 93.37±5.44 95.23±4.01 96.09±2.90 97.95±0.96 97.60±1.30
    Bitumen /% 49.13±31.04 55.84±34.84 87.90±10.12 57.67±47.14 87.93±29.45 98.56±0.79 97.80±2.33
    Self-blocking bricks/% 71.16±6.24 66.52±7.98 79.81±8.98 72.95±12.52 77.49±5.91 87.37±5.71 87.51±4.66
    Shadows /% 99.94±0.07 90.77±10.74 99.37±0.72 97.82±2.66 93.58±8.04 98.56±0.90 98.61±1.16
    OA /% 82.06±2.78 81.15±3.38 93.36±2.97 91.83±5.16 92.44±2.79 96.71±1.18 96.75±0.84
    AA /% 79.22±5.87 74.60±7.40 91.71±4.63 87.26±10.30 91.03±4.25 96.89±0.80 96.44±0.75
    100*Kappa coefficient 75.44±4.26 74.46±5.16 91.23±3.79 89.15±6.73 89.88±3.81 95.63±1.58 95.69±1.12
    Training time /s 30.3 57.4 187.9 403.7 639.1 55.7 426.9
    Testing time /s 26.5 43.4 106.0 129.0 576.8 39.7 277.7
    下载: 导出CSV

    表  5  SV数据集的测试结果

    Table  5.   Test results for the SV dataset

    Classification SVM CDCNN SSRN FDSSC DBMA DBDA Ours
    Brocoli-green-weeds-1 /% 99.42±0.75 8.69±14.24 99.98±0.06 98.13±5.60 99.99±0.02 1±0 99.99±0.02
    Brocoli-green-weeds-2 /% 98.79±0.38 61.15±3.86 99.67±0.57 99.88±0.14 99.78±0.37 99.99±0.01 99.96±0.06
    Fallow /% 87.98±3.77 70.6±29.27 93.93±4.29 97.01±1.87 94.27±6.57 98.66±1.34 96.47±2.21
    Fallow-rough-plow /% 97.54±0.59 92.48±3.44 97.52±1.46 97.67±1.73 92.26±4.67 94.17±1.46 95.53±2.93
    Fallow-smooth /% 95.10±3.15 87.47±7.32 98.76±1.20 98.54±1.79 94.86±6.43 87.61±10.41 98.81±0.92
    Stubble /% 99.90±0.08 95.37±5.60 99.99±0.01 99.98±0.04 99.28±0.91 1±0 99.99±0.03
    Celery /% 95.59±2.67 90.84±4.79 99.04±1.32 98.73±2.02 97.09±2.80 95.35±4.09 99.42±0.84
    Grapes-untrained /% 71.66±2.54 66.41±10.81 90.48±5.01 90.03±4.85 82.78±11.87 93.25±3.10 90.56±4.28
    Soil-vinyard-develop /% 98.08±1.17 89.13±8.10 99.65±0.24 99.48±0.36 98.91±1.58 98.15±0.24 99.45±0.23
    Corn-senesced-green-weeds /% 85.39±3.46 70.82±12.11 95.35±3.25 94.69±8.28 95.68±3.06 93.21±1.63 97.76±1.60
    Lettuce-romaine-4wk /% 86.98±6.82 46.96±35.75 93.22±9.57 85.68±28.60 95.00±3.12 94.58±4.76 95.56±1.94
    Lettuce-romaine-5wk /% 94.20±4.01 73.85±9.58 98.24±1.80 98.06±1.89 98.15±2.20 99.97±0.02 98.86±1.87
    Lettuce-romaine-6wk /% 93.43±3.32 71.78±38.53 98.13±2.44 99.83±0.23 97.39±3.30 99.50±0.39 99.41±0.60
    Lettuce-romaine-7wk /% 92.03±5.44 64.54±42.82 97.31±2.36 96.41±1.96 97.27±3.94 96.71±1.91 95.86±2.51
    Vinyard-untrained /% 71.02±5.60 54.51±25.93 75.02±16.00 89.52±5.56 86.96±8.10 74.82±10.63 88.54±4.19
    Vinyard-vertical-trellis /% 97.82±1.19 68.57±44.91 99.68±0.58 99.25±1.10 99.23±0.96 1±0 99.99±0.02
    OA /% 86.98±0.87 75.32±6.63 91.82±4.32 95.22±1.65 91.97±4.13 91.99±2.78 95.63±0.96
    AA /% 91.56±0.63 69.57±14.19 96.00±1.44 96.43±2.92 95.56±1.44 95.37±1.08 97.26±0.47
    100*Kappa coefficient 85.45±0.98 72.13±7.75 90.94±4.72 94.67±1.84 91.03±4.66 91.12±3.06 95.13±1.07
    Training time /s 89.7 62.6 804.9 1674.7 1915.2 146.3 1216.3
    Testing time /s 44.9 79.2 219.8 265.8 839.7 94.9 621.5
    下载: 导出CSV

    表  6  KSC数据集的测试结果

    Table  6.   Test results for the KSC dataset

    Classification SVM CDCNN SSRN FDSSC DBMA DBDA Ours
    Scrub /% 92.43±1.09 92.76±4.62 97.86±2.57 97.89±5.12 99.90±0.23 1±0 99.99±0.04
    Willows wamp /% 87.14±4.68 61.03±21.39 94.69±4.72 90.02±10.00 91.00±8.81 1±0 96.51±4.32
    Camping hammock /% 72.47±8.64 48.62±16.58 72.33±32.83 65.08±19.52 88.27±11.90 96.11±3.05 93.38±5.57
    Slash pine /% 54.45±7.86 38.64±20.30 74.27±15.09 73.27±16.00 77.82±11.04 84.17±3.08 88.72±7.04
    Oak/broadleaf /% 64.11±13.72 10.20±15.95 62.28±33.65 55.44±38.30 63.51±15.17 80.90±10.37 85.21±8.27
    Hardwood /% 65.23±7.48 66.76±9.27 93.65±12.76 88.96±16.76 94.36±5.85 98.82±1.18 99.47±0.40
    Swap /% 75.50±3.79 38.53±41.39 85.14±28.75 88.84±13.54 85.52±14.65 85.75±10.12 92.33±9.86
    Graminoid marsh /% 87.33±4.95 60.31±19.36 97.71±2.80 97.83±3.73 94.66±3.18 99.24±0.76 99.19±0.86
    Spartina marsh /% 87.94±3.14 77.77±12.03 98.99±1.00 99.31±1.74 98.01±2.03 99.89±0.11 99.85±0.31
    Cattail marsh /% 97.01±4.64 84.44±16.67 99.62±1.04 1±0 97.06±3.82 1±0 1±0
    Salt marsh /% 96.03±1.88 98.79±1.22 98.71±1.23 99.05±1.26 1±0 1±0 98.96±1.69
    Mud flats /% 93.76±2.45 92.43±4.46 99.82±0.32 99.19±0.69 97.40±2.73 98.68±0.64 99.48±0.67
    Water /% 99.72±0.61 98.25±1.59 99.94±0.14 1±0 1±0 1±0 99.93±0.14
    OA /% 87.96±1.42 78.60±7.29 93.81±3.85 93.31±3.06 94.30±1.97 97.79±0.40 98.01±0.76
    AA /% 82.55±2.36 66.81±9.71 90.39±7.91 88.84±5.57 91.35±2.04 95.66±0.66 96.38±1.40
    100*Kappa coefficient 86.59±1.58 76.11±8.16 93.11±4.29 92.55±3.41 93.66±2.19 97.54±0.45 97.79±0.85
    Training time/s 47.7 72.0 671.2 979.8 1191.0 184.3 979.7
    Testing time/s 3.6 6.4 17.8 20.8 52.6 7.4 46.6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-01
  • 修回日期:  2022-09-13
  • 刊出日期:  2022-11-20

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