Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (5): 1431-1439.DOI: 10.11772/j.issn.1001-9081.2021030464
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Zhen QU, Kunting LI, Zhixi FENG()
Received:
2021-03-26
Revised:
2021-07-16
Accepted:
2021-07-16
Online:
2022-06-11
Published:
2022-05-10
Contact:
Zhixi FENG
About author:
QU Zhen, born in 2000. His research interests include machinelearning,remote sensing image processing.Supported by:
通讯作者:
冯志玺
作者简介:
屈震(2000—),男,陕西榆林人,主要研究方向:机器学习、遥感图像处理基金资助:
CLC Number:
Zhen QU, Kunting LI, Zhixi FENG. Remote sensing image scene classification based on effective channel attention[J]. Journal of Computer Applications, 2022, 42(5): 1431-1439.
屈震, 李堃婷, 冯志玺. 基于有效通道注意力的遥感图像场景分类[J]. 《计算机应用》唯一官方网站, 2022, 42(5): 1431-1439.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021030464
网络 | 平均准确率/% | 每周期平均耗时/s | 参数量/106 | t/min |
---|---|---|---|---|
VGGNet16 | 60.01±0.12 | 15.32 | 138.0 | — |
ResNet50 | 79.53±0.10 | 13.21 | 25.5 | 24.40 |
ResNeXt50 | 81.22±0.08 | 14.31 | 25.0 | 5.31 |
SE-ResNeXt50 | 90.15±0.15 | 12.15 | 35.7 | 5.76 |
ECA-ResNeXt-8 | 93.86±0.12 | 10.28 | 22.8 | 1.01 |
Tab. 1 Result comparison of different indicators of different networks
网络 | 平均准确率/% | 每周期平均耗时/s | 参数量/106 | t/min |
---|---|---|---|---|
VGGNet16 | 60.01±0.12 | 15.32 | 138.0 | — |
ResNet50 | 79.53±0.10 | 13.21 | 25.5 | 24.40 |
ResNeXt50 | 81.22±0.08 | 14.31 | 25.0 | 5.31 |
SE-ResNeXt50 | 90.15±0.15 | 12.15 | 35.7 | 5.76 |
ECA-ResNeXt-8 | 93.86±0.12 | 10.28 | 22.8 | 1.01 |
分类器 | 准确率/% | 最后一层的平均训练时间/s |
---|---|---|
ECA-ResNeXt-8+Fully Connected | 93.86±0.12 | — |
ECA-ResNeXt-8+KNN | 89.22±0.22 | 5.5 |
ECA-ResNeXt-8+Naive Bayes | 91.93±0.10 | 10.2 |
ECA-ResNeXt-8+Random Forest | 87.52±0.15 | 11.4 |
ECA-ResNeXt-8+SVM | 95.81±0.12 | 15.8 |
Tab. 2 Performance comparison under different classifiers
分类器 | 准确率/% | 最后一层的平均训练时间/s |
---|---|---|
ECA-ResNeXt-8+Fully Connected | 93.86±0.12 | — |
ECA-ResNeXt-8+KNN | 89.22±0.22 | 5.5 |
ECA-ResNeXt-8+Naive Bayes | 91.93±0.10 | 10.2 |
ECA-ResNeXt-8+Random Forest | 87.52±0.15 | 11.4 |
ECA-ResNeXt-8+SVM | 95.81±0.12 | 15.8 |
数据集 | 图片总数 | 场景类别数 | 平均准确率/% |
---|---|---|---|
UC Merced-Land-Use | 2 100 | 21 | 95.81±0.12 |
KSA | 3 250 | 13 | 93.33±0.21 |
AID | 10 000 | 30 | 94.12±0.14 |
Optimal-31 | 1 860 | 31 | 92.41±0.16 |
WHU-RS19 | 1 005 | 19 | 93.56±0.11 |
RSSCN7 | 2 800 | 7 | 94.44±0.08 |
Tab. 3 Test results of proposed model on different datasets
数据集 | 图片总数 | 场景类别数 | 平均准确率/% |
---|---|---|---|
UC Merced-Land-Use | 2 100 | 21 | 95.81±0.12 |
KSA | 3 250 | 13 | 93.33±0.21 |
AID | 10 000 | 30 | 94.12±0.14 |
Optimal-31 | 1 860 | 31 | 92.41±0.16 |
WHU-RS19 | 1 005 | 19 | 93.56±0.11 |
RSSCN7 | 2 800 | 7 | 94.44±0.08 |
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