通信学报 ›› 2021, Vol. 42 ›› Issue (2): 92-102.doi: 10.11959/j.issn.1000-436x.2021024
高红民, 曹雪莹, 陈忠昊, 花再军, 李臣明, 陈月
修回日期:
2020-10-20
出版日期:
2021-02-25
发布日期:
2021-02-01
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。基金资助:
Hongmin GAO, Xueying CAO, Zhonghao CHEN, Zaijun HUA, Chenming LI, Yue CHEN
Revised:
2020-10-20
Online:
2021-02-25
Published:
2021-02-01
Supported by:
摘要:
针对基于传统卷积神经网络模型的高光谱图像分类算法细节表现力不强及网络结构过于复杂的问题,设计了一种基于多尺度近端特征拼接网络的高光谱图像分类方法。通过引入多尺度滤波器和空洞卷积,在保持模型轻量化的同时可以获取更丰富的空间-光谱判别特征,并提出利用卷积神经网络近端特征间的相互联系进一步增强细节表现力。在3个基准高光谱图像数据集上的实验结果表明,所提方法优于其他分类模型。
中图分类号:
高红民, 曹雪莹, 陈忠昊, 花再军, 李臣明, 陈月. 基于多尺度近端特征拼接网络的高光谱图像分类方法[J]. 通信学报, 2021, 42(2): 92-102.
Hongmin GAO, Xueying CAO, Zhonghao CHEN, Zaijun HUA, Chenming LI, Yue CHEN. Hyperspectral image classification method based on multi-scale proximal feature concatenate network[J]. Journal on Communications, 2021, 42(2): 92-102.
表1
IP数据集、PU数据集和SA数据集真实地物信息类别"
数据集 | 类别 | 名称 | 样本数/个 | 样本总数/个 |
C1 | Alfalfa | 46 | ||
C2 | Corn-notill | 1 428 | ||
C3 | Corn-mintill | 830 | ||
C4 | Corn | 237 | ||
C5 | Grass-pasture | 483 | ||
C6 | Grass-trees | 730 | ||
C7 | Grass-pasture-mowed | 28 | ||
IP | C8 | Hay-windrowed | 478 | 10 249 |
C9 | Oats | 20 | ||
C10 | Soybean-notill | 972 | ||
C11 | Soybean-mintill | 2 455 | ||
C12 | Soybean-clean | 593 | ||
C13 | Wheat | 205 | ||
C14 | Woods | 1 265 | ||
C15 | Buildings-Grass-Trees_Drives | 386 | ||
C16 | Stone-Steel-Towers | 93 | ||
C1 | Asphalt | 6 631 | ||
C2 | Meadows | 18 649 | ||
C3 | Gravel | 2 099 | ||
C4 | Trees | 3 064 | ||
PU | C5 | Painted metal sheets | 1 345 | 42 776 |
C6 | Bare Soil | 5 029 | ||
C7 | Bitumen | 1 330 | ||
C8 | Self-Blocking Bricks | 3 682 | ||
C9 | Shadows | 947 | ||
C1 | Brocoli_green_weeds_1 | 2 009 | ||
C2 | Brocoli_green_weeds_2 | 3 726 | ||
C3 | Fallow | 1 976 | ||
C4 | Fallow_rough_plow | 1 394 | ||
C5 | Fallow_smooth | 2 678 | ||
C6 | Stubble | 3 959 | ||
C7 | Celery | 3 579 | ||
SA | C8 | Grapes_untrained | 11 271 | 54 129 |
C9 | Soil_vinyard_develop | 6 203 | ||
C10 | Corn_senesced_green_weeds | 3 278 | ||
C11 | Lettuce_romaine_4wk | 1 068 | ||
C12 | Lettuce_romaine_5wk | 1 927 | ||
C13 | Lettuce_romaine_6wk | 916 | ||
C14 | Lettuce_romaine_7wk | 1 070 | ||
C15 | Vinyard_untrained | 7 268 | ||
C16 | Vinyard_vertical_trellis | 1 807 |
表5
不同分类方法针对IP数据集的定量分析结果"
类别 | SVM | DCNN | ResNet | MCNN | MPFCN |
Alfalfa | 37.21% | 100% | 96.34% | 95.93% | 97.93% |
Corn-notill | 77.60% | 98.17% | 97.20% | 97.17% | 98.52% |
Corn-mintill | 57.54% | 93.04% | 97.05% | 94.24% | 96.90% |
Corn | 60.29% | 100% | 99.77% | 98.59% | 99.84% |
Grass-pasture | 90.40% | 97.01% | 99.20% | 96.55% | 96.27% |
Grass-trees | 92.89% | 97.49% | 97.95% | 99.49% | 99.59% |
Grass-pasture-mowed | 86.36% | 94.00% | 100% | 94.67% | 99.8% |
Hay-windrowed | 97.90% | 99.88% | 97.90% | 99.84% | 100% |
Oats | 55.05% | 96.875% | 100% | 97.92% | 100% |
Soybean-notill | 50.45% | 96.34% | 96.80% | 96.42% | 97.75% |
Soybean-mintill | 72.16% | 98.44% | 99.30% | 98.43% | 98.30% |
Soybean-clean | 50.56% | 93.16% | 92.98% | 95.94% | 96.50% |
Wheat | 93.86% | 100% | 98.91% | 95.83% | 98.73% |
Woods | 89.00% | 99.12 | 99.52% | 98.60% | 99.94% |
Buildings-Grass-Trees-Drives | 48.02% | 99.14% | 99.86% | 99.90% | 98.27% |
Stone-Steel-Towers | 91.67% | 91.67% | 88.71% | 96.03% | 97.62% |
表7
不同分类方法针对PU数据集的定量分析结果"
类别 | SVM | DCNN | ResNet | MCNN | MPFCN |
Asphalt | 91.66% | 99.68% | 99.99% | 99.35% | 99.64% |
Meadows | 94.91% | 99.92% | 99.93% | 99.20% | 99.92% |
Gravel | 71.37% | 98.81% | 97.94% | 96.67% | 99.06% |
Trees | 85.00% | 90.38% | 92.39% | 98.22% | 98.07% |
Painted metal sheets | 99.00% | 96.39% | 95.91% | 99.77% | 99.76% |
Bare Soil | 68.09% | 100% | 99.81% | 99.15% | 100% |
Bitumen | 49.41% | 99.40% | 99.78% | 98.72% | 99.10% |
Self-Blocking Bricks | 86.76% | 97.90% | 97.93% | 98.92% | 99.24% |
Shadows | 99.89% | 85.77% | 85.12% | 98.17% | 97.52% |
表9
不同分类方法针对SA数据集的定量分析结果"
类别 | SVM | DCNN | ResNet | MCNN | MPFCN |
Brocoli_green_weeds_1 | 98.62% | 100% | 99.78% | 100% | 100% |
Brocoli_green_weeds_2 | 99.86% | 98.83% | 99.87% | 99.98% | 100% |
Fallow | 98.86% | 99.63% | 99.16% | 100% | 100% |
Fallow_rough_plow | 99.34% | 99.74% | 99.85% | 99.82% | 99.96% |
Fallow_smooth | 97.25% | 99.56% | 97.68% | 99.79% | 99.74% |
Stubble | 99.82% | 99.74% | 99.75% | 99.98% | 99.89% |
Celery | 99.40% | 99.64% | 99.86% | 99.86% | 99.58% |
Grapes_untrained | 89.69% | 99.49% | 94.28% | 96.26% | 99.45% |
Soil_vinyard_develop | 99.21% | 100% | 100% | 99.97% | 99.84% |
Corn_senesced_green_weeds | 92.75% | 99.98% | 99.76% | 99.09% | 99.82% |
Lettuce_romaine_4wk | 91.51% | 97.76% | 97.87% | 98.62% | 99.14% |
Lettuce_romaine_5wk | 97.72% | 99.68% | 99.81% | 99.62% | 99.87% |
Lettuce_romaine_6wk | 98.56% | 99.58% | 97.29% | 99.83% | 99.86% |
Lettuce_romaine_7wk | 91.78% | 99.45% | 99.49% | 98.67% | 99.18% |
Vinyard_untrained | 45.70% | 94.51% | 98.99% | 97.37% | 99.52% |
Vinyard_vertical_trellis | 98.26% | 100% | 99.98% | 99.77% | 99.48% |
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