光学学报, 2018, 38 (11): 1128001, 网络出版: 2019-05-09 复制并引用该论文  本文已被引 10 次  被引通知

图 & 表

图 1. 集成神经网络结构

Fig. 1. Architecture of integrated neural network

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图 2. 集成网络构建流程

Fig. 2. Flow chart of integrated network construction

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图 3. NWPU-RESISC45数据集的场景图

Fig. 3. Scene images of NWPU-RESISC45 dataset

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图 4. ResNet-50训练过程中准确率、损失值和学习率随循环次数的变化。(a)准确率;(b)损失值;(c)学习率

Fig. 4. Accuracy, loss value and learning rate versus number of cycles in training process of ResNet-50.(a) Accuracy; (b) loss value; (c) learning rate

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图 5. CNN的分类结果

Fig. 5. Classification results based on CNN

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图 6. BP网络的训练过程中准确率和损失值随循环次数的变化曲线。(a)准确率;(b)损失值

Fig. 6. Accuracy and loss value versus number of cycles in training process of BP network. (a) Accuracy; (b) loss value

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图 7. 集成模型对数据集进行分类预测后得到的混淆矩阵

Fig. 7. Confusion matrix obtained after classification prediction of dataset by integrated model

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图 8. 与其他算法单类的准确率对比

Fig. 8. Single accuracy comparison with those of other algorithms

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图 9. 多种模型的分类准确率和预测时间

Fig. 9. Classification accuracies and prediction time of various models

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图 10. 使用AlexNet进行分类的场景类别数量对集成模型的性能影响

Fig. 10. Impact of number of scene categories classified by AlexNet on performance of integrated model

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表 1各个网络的训练参数及结果

Table1. Training parameters and results of each network

Model Input size /(pixel×pixel) Batch size /frame Number of cycles Training accuracy /% Experiment I Experiment II AlexNet 224×224 256 300 81.22 85.46 ResNet-50 224×224 256 300 86.52 90.52 ResNet-152 224×224 128 600 85.11 90.11 DenseNet-169 224×224 128 600 82.44 87.44 VGG-16[2] - - - 87.15 90.36 Proposed model - - - 88.47 92.53

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表 2几种算法的性能对比

Table2. Performance comparison among several algorithms

Method Color-histogram BoVW VGG-16 ResNet-50 Proposed Competition Accuracy /% 27.52 44.97 90.36 90.59 92.53 93.41 Standard deviation 0.2184 0.2051 0.0673 0.0657 0.0593 0.0451 Prediction time /s - - 0.62 0.47 0.41 2.26

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表 3与其他方法的平均准确率对比

Table3. Average accuracy comparison with those of other algorithms

Method Accuracy /%(experiment I) Accuracy /%(experiment II) GIST[2] 15.90 17.88 LBP[2] 19.20 21.74 Color histograms[2] 24.84 27.52 BoVW+SPM[2] 27.83 32.96 LLC[2] 38.81 40.03 BoVW[2] 41.72 44.97 GoogLeNet[2] 82.57 86.02 VGG-16[2] 87.15 90.36 AlexNet[2] 81.22 85.16 Two-streamDFF[13] 80.22 83.16 ResNet-50 87.69 90.59 Proposed model 89.34 92.53

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张晓男, 钟兴, 朱瑞飞, 高放, 张作省, 鲍松泽, 李竺强. 基于集成卷积神经网络的遥感影像场景分类[J]. 光学学报, 2018, 38(11): 1128001. Xiaonan Zhang, Xing Zhong, Ruifei Zhu, Fang Gao, Zuoxing Zhang, Songze Bao, Zhuqiang Li. Scene Classification of Remote Sensing Images Based on Integrated Convolutional Neural Networks[J]. Acta Optica Sinica, 2018, 38(11): 1128001.