• Journal of Infrared and Millimeter Waves
  • Vol. 40, Issue 4, 530 (2021)
Ruo-Yao LI1、2, Bo ZHANG1、2, and Bin WANG1、2、*
Author Affiliations
  • 1Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China
  • 2Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai 200433, China
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    DOI: 10.11972/j.issn.1001-9014.2021.04.012 Cite this Article
    Ruo-Yao LI, Bo ZHANG, Bin WANG. Remote sensing image scene classification based on multilayer feature context encoding network[J]. Journal of Infrared and Millimeter Waves, 2021, 40(4): 530 Copy Citation Text show less
    The illustration of the architecture of DenseNet
    Fig. 1. The illustration of the architecture of DenseNet
    The framework of the proposed MFCE network Note: ⊙ and ↑ denote the channel concatenation operation and the spatial up-sampling operation, respectively
    Fig. 2. The framework of the proposed MFCE network Note:  and  denote the channel concatenation operation and the spatial up-sampling operation, respectively
    Samples of remote sensing images (a) AID dataset,(b) NWPU-RESISC45 dataset
    Fig. 3. Samples of remote sensing images (a) AID dataset,(b) NWPU-RESISC45 dataset
    Test accuracy with MFCE network and Fine-tuned DenseNet-121 (a) AID dataset, (b) NWPU-RESISC45 dataset
    Fig. 4. Test accuracy with MFCE network and Fine-tuned DenseNet-121 (a) AID dataset, (b) NWPU-RESISC45 dataset
    Visual comparison of heatmaps among MFCE andFine-tuned DenseNet-121 for NWPU-RESISC45 dataset Note: (d-f) heatmaps of the baseline,and (g-i) MFCE network
    Fig. 5. Visual comparison of heatmaps among MFCE andFine-tuned DenseNet-121 for NWPU-RESISC45 dataset Note: (d-f) heatmaps of the baseline,and (g-i) MFCE network
    MethodOA
    Tr=20%Tr=50%
    VGG-VD-161786.59±0.2989.64±0.36
    Fine-tuned DenseNet-12194.75±0.1896.56±0.17
    FACNN12-95.45±0.11
    D-CNN with VGGNet-16790.82±0.1696.89±0.10
    VGG-VD16+MSCP+MRA1192.21±0.1796.56±0.18
    MFCE (ours)95.51±0.0997.14±0.19
    Table 1. OA of different methods on AID dataset with different training ratios
    MethodOA
    Tr=10%Tr=20%
    VGGNet-161887.15±0.4590.36±0.18
    Fine-tuned DenseNet-12191.56±0.2193.72±0.20
    FACNN12--
    VGG-VD16+MSCP+MRA1188.07±0.1890.81±0.13
    D-CNN with VGGNet-16789.22±0.5091.89±0.22
    MFCE (ours)92.42±0.2094.40±0.09
    Table 2. OA of different methods on NWPU-RESISC45 dataset with different training ratios
    MethodsOA
    AID (Tr=20%)NWPU-RESISC45 (Tr=10%)
    MFCE (2, 4, 6)95.16±0.2092.17±0.28
    MFCE (2, 4, 6, 8)95.51±0.0992.42±0.20
    Table 3. Results of MFCE network adopting different levels of multiscale pooling on AID dataset and NWPU-RESISC45 dataset
    MethodOA
    AID (Tr=20%)NWPU-RESISC45 (Tr=10%)
    Fine-tuned DenseNet-12194.75±0.1891.56±0.21
    MFCE without Context Encoding94.92±0.1991.52±0.30
    MFCE95.51±0.0992.42±0.20
    Table 4. Comparison of different methods on AID dataset and NWPU-RESISC45 dataset
    MethodOA
    Fine-tuned VGGNet-1690.19±0.38
    Fine-tuned ResNet-1893.10±0.35
    Fine-tuned DenseNet-12194.75±0.18
    VGGNet-16+MCE (ours)91.57±0.26
    ResNet-18+MCE (ours)94.08±0.20
    MFCE (ours)95.51±0.09
    Table 5. Results of MCE module combined with different backbones and baselines on AID dataset
    MethodsParametersMACs
    VGGNet-165138.36M15.48G
    ResNet-181911.69M1.82G
    DenseNet-121137.98M2.87G
    VGGNet-16+MCE (ours)15.52M20.10G
    ResNet-18+MCE (ours)11.98M2.42G
    MFCE (ours)10.14M3.91G
    Table 6. Parameters and MACs of different networks
    Ruo-Yao LI, Bo ZHANG, Bin WANG. Remote sensing image scene classification based on multilayer feature context encoding network[J]. Journal of Infrared and Millimeter Waves, 2021, 40(4): 530
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