通信学报 ›› 2020, Vol. 41 ›› Issue (11): 132-140.doi: 10.11959/j.issn.1000-436x.2020238
修回日期:
2020-09-07
出版日期:
2020-11-25
发布日期:
2020-12-19
作者简介:
高红民(1983- ),男,江苏仪征人,博士,河海大学教授,主要研究方向为深度学习等智能计算方法、图像大数据与人工智能、遥感影像处理等|曹雪莹(1994- ),女,江苏睢宁人,河海大学硕士生,主要研究方向为深度学习与图像处理等|杨耀(1995- ),男,江西宜春人,河海大学硕士生,主要研究方向为深度学习与图像处理等|花再军(1983- ),男,江苏姜堰人,河海大学实验师,主要研究方向为机器学习及图像处理等|李臣明(1969- ),男,内蒙古通辽人,河海大学教授,主要研究方向为智能信息处理、遥感技术与系统等
基金资助:
Hongmin GAO,Xueying CAO,Yao YANG,Zaijun HUA,Chenming LI()
Revised:
2020-09-07
Online:
2020-11-25
Published:
2020-12-19
Supported by:
摘要:
针对基于深度卷积神经网络的高光谱图像分类算法中存在的空间分辨率下降、池化操作引发特征丢失从而导致分类精度下降的问题,设计了一种由双边融合块构成的双边融合块网络。1×1卷积与超链接构成双边融合块上结构,传递局部空间特征,池化、卷积、反卷积、上采样组成下结构,强化高效判别特征。在3个基准高光谱图像数据集上的实验结果表明,该模型优于其他同类分类模型。
中图分类号:
高红民,曹雪莹,杨耀,花再军,李臣明. 基于CNN的双边融合网络在高光谱图像分类中的应用[J]. 通信学报, 2020, 41(11): 132-140.
Hongmin GAO,Xueying CAO,Yao YANG,Zaijun HUA,Chenming LI. Application of bilateral fusion model based on CNN in hyperspectral image classification[J]. Journal on Communications, 2020, 41(11): 132-140.
表1
双边融合块网络最优参数"
层 | 内核 | 输出尺寸 | ||
IP数据集 | PU数据集 | SA数据集 | ||
图像块 | — | 21像素×21像素,15层 | 21像素×21像素,15层 | 33像素×33像素,21层 |
卷积层1 | 5×5 | 21像素×21像素,16层 | 21像素×21像素,24层 | 33像素×33像素,16层 |
最大池化层 | 3×3 | 7像素×7像素,16层 | 7像素×7像素,24层 | 11像素×11像素,16层 |
转置卷积和上采样 | 3×3 | 21像素×21像素,16层 | 21像素×21像素,24层 | 33像素×33像素,16层 |
结合层 | — | 21像素×21像素,32层 | 21像素×21像素,48层 | 33像素×33像素,32层 |
卷积层2 | 5×5 | 21像素×21像素,64层 | 21像素×21像素,96层 | 33像素×33像素,64层 |
1×1卷积 | 1×1 | 21像素×21像素,64层 | 21像素×21像素,96层 | 33像素×33像素,64层 |
融合层 | — | 21像素×21像素,64层 | 21像素×21像素,96层 | 33像素×33像素,64层 |
全连接1 | Relu | 128 | ||
全连接2 | Relu | 64 | ||
训练参数 | — | 3 924 928 | 5 922 401 | 9 074 528 |
双边融合块个数 | — | 4 | 3 | 2 |
表6
在IP数据集下与其他分类模型的比较"
类别 | RPCA-CNN模型 | FCNN模型 | DCNN模型 | SSRN模型 | DRN模型 | DFBN模型 |
Alfalfa | 34.88% | 90.12% | 43.66% | 93.41% | 90.37% | 96.10% |
Corn-notill | 60.91% | 93.11% | 89.91% | 97.59% | 94.30% | 95.54% |
Corn-mintill | 57.19% | 97.03% | 93.68% | 98.71% | 96.14% | 99.35% |
Corn | 54.32% | 93.92% | 82.86% | 91.22% | 85.12% | 98.99% |
Grass-pasture | 92.08% | 96.34% | 97.31% | 99.79% | 98.31% | 98.77% |
Grass-trees | 97.63% | 97.60% | 99.09% | 99.70% | 98.86% | 99.06% |
Grass-pasture-mowed | 79.20% | 99.00% | 91.20% | 98.60% | 93.80% | 96.20% |
Hay-windrowed | 99.93% | 99.30% | 99.91% | 99.84% | 99.95% | 99.97% |
Oats | 57.22% | 82.78% | 69.17% | 95.28% | 78.06% | 73.89% |
Soybean-notill | 76.05% | 97.49% | 93.69% | 99.25% | 98.13% | 98.63% |
Soybean-mintill | 63.95% | 98.28% | 94.14% | 96.30% | 99.04% | 99.51% |
Soybean-clean | 48.16% | 91.83% | 91.22% | 97.21% | 92.36% | 96.47% |
Wheat | 99.30% | 96.32% | 99.76% | 99.78% | 98.57% | 97.49% |
Woods | 94.79% | 99.78% | 98.78% | 99.73% | 99.73% | 99.93% |
Buildings-Grass-Trees-Drives | 77.59% | 93.78% | 85.46% | 95.26% | 96.71% | 98.90% |
Stone-Steel-Towers | 99.82% | 87.20% | 96.13% | 98.75% | 89.17% | 89.11% |
表8
在PU数据集下与其他分类模型的比较"
类别 | RPCA-CNN模型 | FCNN模型 | DCNN模型 | SSRN模型 | DRN模型 | DFBN模型 |
Asphalt | 70.04% | 86.46% | 96.79% | 97.40% | 97.42% | 98.16% |
Meadows | 84.28% | 98.41% | 99.23% | 99.89% | 99.75% | 99.86% |
Gravel | 88.17% | 65.25% | 67.98% | 88.64% | 89.02% | 96.11% |
Trees | 92.97% | 71.72% | 97.03% | 97.08% | 95.81% | 96.60% |
Painted metal sheets | 100.00% | 84.11% | 96.50% | 99.91% | 94.94% | 97.22% |
Bare Soil | 75.27% | 91.61% | 92.10% | 99.54% | 99.73% | 99.61% |
Bitumen | 95.69% | 61.62% | 67.64% | 99.55% | 94.93% | 98.81% |
Self-Blocking Bricks | 60.09% | 86.58% | 88.08% | 96.44% | 88.13% | 97.52% |
Shadows | 99.88% | 59.14% | 99.41% | 99.98% | 96.75% | 95.78% |
表10
在SA数据集下与其他分类模型的比较"
类别 | RPCA-CNN模型 | FCNN模型 | DCNN模型 | SSRN模型 | DRN模型 | DFBN模型 |
Brocoli_green_weeds_1 | 99.99% | 57.71% | 77.03% | 95.48% | 97.36% | 99.45% |
Brocoli_green_weeds_2 | 81.90% | 78.83% | 98.25% | 97.97% | 99.34% | 99.96% |
Fallow | 22.50% | 85.04% | 82.33% | 87.17% | 94.87% | 99.97% |
Fallow_rough_plow | 91.23% | 81.69% | 98.76% | 97.45% | 93.77% | 98.34% |
Fallow_smooth | 59.10% | 97.54% | 92.29% | 95.11% | 93.54% | 98.57% |
Stubble | 99.53% | 93.73% | 99.94% | 99.90% | 98.23% | 99.81% |
Celery | 93.05% | 77.67% | 98.40% | 98.79% | 98.16% | 99.48% |
Grapes_untrained | 36.76% | 78.56% | 69.84% | 80.93% | 95.05% | 96.78% |
Soil_vinyard_develop | 72.10% | 97.90% | 99.12% | 99.55% | 99.70% | 100.00% |
Corn_senesced_green_weeds | 73.37% | 98.07% | 91.87% | 93.60% | 97.35% | 99.77% |
Lettuce_romaine_4wk | 60.86% | 84.15% | 25.40% | 86.31% | 92.99% | 98.25% |
Lettuce_romaine_5wk | 22.66% | 96.44% | 98.61% | 91.50% | 89.96% | 99.71% |
Lettuce_romaine_6wk | 41.34% | 84.12% | 98.98% | 98.09% | 89.46% | 93.87% |
Lettuce_romaine_7wk | 97.60% | 91.01% | 88.94% | 93.96% | 92.79% | 95.75% |
Vinyard_untrained | 63.91% | 55.72% | 74.50% | 72.31% | 89.33% | 98.62% |
Vinyard_vertical_trellis | 86.25% | 39.11% | 93.87% | 94.39% | 97.75% | 99.87% |
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