基于多尺度循环注意力网络的遥感影像场景分类方法

马欣悦; 王梨名; 祁昆仑; 郑贵洲

doi:10.3799/dqkx.2020.365

Volume 46 Issue 10

Nov. 2021

Turn off MathJax

Article Contents

Article Navigation > Earth Science > 2021 > 46(10): 3740-3752

Ma Xinyue, Wang Liming, Qi Kunlun, Zheng Guizhou, 2021. Remote Sensing Image Scene Classification Method Based on Multi-Scale Cyclic Attention Network. Earth Science, 46(10): 3740-3752. doi: 10.3799/dqkx.2020.365

Citation:

Ma Xinyue, Wang Liming, Qi Kunlun, Zheng Guizhou, 2021. Remote Sensing Image Scene Classification Method Based on Multi-Scale Cyclic Attention Network. Earth Science, 46(10): 3740-3752. doi: 10.3799/dqkx.2020.365

Citation:

PDF( 22422 KB)

Remote Sensing Image Scene Classification Method Based on Multi-Scale Cyclic Attention Network

doi: 10.3799/dqkx.2020.365

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China

Received Date: 2020-11-11
Available Online: 2021-11-03
Publish Date: 2021-11-03

Abstract

Abstract

Scene classification of high-resolution remote sensing images has always been a research hotspot in the field of remote sensing. In view of the diversity of scale requirements of remote sensing scenes, in this paper it proposes a remote sensing image scene classification method based on multi-scale cyclic attention network. Firstly, the features of multiple scales of remote sensing scene image are extracted by Resnet50 network, the attention mechanism is used to obtain the region of interest of the image, and the region of interest is clipped and scaled. Then, the features of different scales of the original image and the features of different scale cropped images are fused, input to the full connection layer for classification prediction. The proposed method is validated in UC Merced Land-Use and NWPU-RESISC45, the average classification accuracy is improved by 1.89% and 2.70% respectively compared with Resnet50.The results show that the multi-scale cyclic attention network can further improve the accuracy of remote sensing image scene classification.
- remote sensing,
- scene classification,
- multi-scale,
- convolutional neural network,
- attention mechanism

FullText(HTML)

References(37)

References

Bahdanau, D., Cho, K., Bengio, Y., 2014. Neural Machine Translation by Jointly Learning to Align and Translate. Computer Science, arXiv: 1409.0473. https://arxiv.org/abs/1409.0473

Castelluccio, M., Poggi, G., Sansone, C., et al., 2015. Land Use Classification in Remote Sensing Images by Convolutional Neural Networks. Acta Ecologica Sinica, 28(2): 627-635. http://pdfs.semanticscholar.org/4191/fe93bfd883740a881e6a60e54b371c2f241d.pdf

Chen, Q.H., Liu, Z.M., Liu, X.G., et al., 2010. Element-Oriented Land-Use Classification of Mining Area by High Spatial Resolution Remote Sensing Image. Earth Science, 35(3): 453-458(in Chinese with English abstract). http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5631116

Chen, S.Z., Tian, Y.L., 2014. Pyramid of Spatial Relatons for Scene-Level Land Use Classification. IEEE Transactions on Geoscience and Remote Sensing, 53(4): 1947-1957. https://doi.org/10.1109/TGRS.2014.2351395

Cheng, G., Han, J., Lu, X., 2017. Remote Sensing Image Scene Classification: Benchmark and State of the Art. Proceedings of the IEEE, 105(10): 1865-1883. https://doi.org/10.1109/JPROC.2017.2675998

Cheng, G., Ma, C. C., Zhou, P. C., et al., 2016. Scene Classification of High Resolution Remote Sensing Images Using Convolutional Neural Networks. In Proceedings 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, 767-770. https://doi.org/10.1109/IGARSS.2016.7729193

Cheng, G.X., Niu, R.Q., Zhang, K.X., et al., 2018. Opencast Mining Area Recognition in High-Resolution Remote Sensing Images Using Convolutional Neural Networks. Earth Science, 43(Suppl. 2): 256-262(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-DQKX2018S2021.htm

Fu, J.L., Zheng, H.L., Mei, T., 2017. Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu Hawaii, 4476-4484. https://doi.org/10.1109/CVPR.2017.476

Gómez-Chova, L., Tuia, D., Moser, G., et al., 2015. Multimodal Classification of Remote Sensing Images: A Review and Future Directions. Proceedings of the IEEE, 103(9): 1560-1584. https://doi.org/10.1109/JPROC.2015.2449668

Han, X.B., Zhong, Y.F., Cao, L.Q., et al., 2017. Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification. Remote Sensing, 9(8): 848. https://doi.org/10.3390/rs9080848

He, K.M., Zhang, X.Y., Ren, S Q., et al., 2016. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas Nevada, 770-778. https://doi.org/10.1109/CVPR.2016.90

He, N.J., Fang, L.Y., Li, S.T., et al., 2018. Remote Sensing Scene Classification Using Multilayer Stacked Covariance Pooling. IEEE Transactions on Geoscience and Remote Sensing, 56(12): 6899-6910. https://doi.org/10.1109/TGRS.2018.2845668

Jia, Y.Q., Shelhamer, E., Donahue, J., et al., 2014. Caffe: Convolutional Architecture for Fast Feature Embedding. In Proceedings of the 22nd ACM International Conference on Multimedia, Orlando Florida USA, 675-678. https://doi.org/10.1145/2647868.2654889

Ketkar, N., 2017. Introduction to PyTorch. Deep Learning with Python. Apress, Berkeley, CA, 195-208. https://doi.org/10.1007/978-1-4842-2766-4_12

Li, G.D., Zhang, C.J., Wang, M.K., et al., 2019. Transfer Learning Using Convolutional Neural Network for Scene Classification within High Resolution Remote Sensing Image. Science of Surveying and Mapping, 44(4): 116-123, 174(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-CHKD201904021.htm

Li, W.K., Zhang, W., Qin, J.H., et al., 2020. "Expansion-Fusion" Extraction of Surface Gully Area Based on DEM and High-Resolution Remote Sensing Images. Earth Science, 45(6): 1948-1955(in Chinese with English abstract).

Lienou, M., Maitre, H., Datcu, M., 2009. Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation. IEEE Geoscience and Remote Sensing Letters, 7(1): 28-32. https://doi.org/10.1109/LGRS.2009.2023536

Luo, W., Li, H. L., Liu, G. H., 2011. Automatic Annotation of Multispectral Satellite Images Using Author-Topic Model. IEEE Geoscience and Remote Sensing Letters, 9(4): 634-638. https://doi.org/10.1109/LGRS.2011.2177064

Nogueira, K., Penatti, O. A. B., dos Santos, J.A., 2017. Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification. Pattern Recognition, 61: 539-556. https://doi.org/10.1016/j.patcog.2016.07.001

Oliva, A., Torralba, A., 2001. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. International Journal of Computer Vision, 42(3): 145-175. https://doi.org/10.1023/A:1011139631724

Pan, S. J., Yang, Q., 2009. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10): 1345-1359. https://doi.org/10.1109/TKDE.2009.191

Simonyan, K., Zisserman, A., 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR. Computer Science, arXiv: 1409.1556.

Szegedy, C., Liu, W., Jia, Y.Q., et al., 2015. Going Deeper with Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA, IEEE, 1-9. https://doi.org/10.1109/CVPR.2015.7298594

Xia, G. S., Hu, J. W., Hu, F., et al., 2017. AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3965-3981. https://doi.org/10.1109/TGRS.2017.2685945

Yang, Y., Newsam, S., 2010. Bag-of-Visual-Words and Spatial Extensions for Land-Use Classification. In Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, San Jose California, 270-279. https://doi.org/10.1145/1869790.1869829

Yang, Y., Newsam, S., 2013. Geographic Image Retrieval Using Local Invariant Features. IEEE Transactions on Geoscience and Remote Sensing, 51(2): 818-832. https://doi.org/10.1109/TGRS.2012.2205158

Yu, D.H., Zhang, B.M., Zhao, C., et al., 2020. Scene Classification of Remote Sensing Image Using Ensemble Convolutional Neural Network. Journal of Remote Sensing, 24(6): 717-727(in Chinese with English abstract).

Yu, S.C., Yu, D.Q., Wang, L.C., et al., 2019. Remote Sensing Study of Dongting Lake Beach Changes before and after Operation of Three Gorges Reservoir. Earth Science, 44(12): 4275-4283(in Chinese with English abstract). http://en.cnki.com.cn/Article_en/CJFDTotal-DQKX201912037.htm

Yuan, Y., Fang, J., Lu, X.Q., et al., 2019. Remote Sensing Image Scene Classification Using Rearranged Local Features. IEEE Transactions on Geoscience and Remote Sensing, 57(3): 1779-1792. https://doi.org/10.1109/TGRS.2018.2869101

Zhang, D., Li, N., Ye, Q.L., 2019. Positional Context Aggregation Network for Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters, 17(6): 943-947. https://doi.org/10.1109/LGRS.2019.2937811

Zhao, Z.C., Li, J.Q., Luo, Z., et al., 2020. Remote Sensing Image Scene Classification Based on an Enhanced Attention Module. IEEE Geoscience and Remote Sensing Letters, (99): 1-5. https://doi.org/10.1109/LGRS.2020.3011405

陈启浩, 刘志敏, 刘修国, 等, 2010. 面向基元的高空间分辨率矿区遥感影像土地利用分类. 地球科学, 35(3): 453-458. doi: 10.3799/dqkx.2010.055

程国轩, 牛瑞卿, 张凯翔, 等, 2018. 基于卷积神经网络的高分遥感影像露天采矿场识别. 地球科学, 43(增刊2): 256-262. doi: 10.3799/dqkx.2018.987

李冠东, 张春菊, 王铭恺, 等, 2019. 卷积神经网络迁移的高分影像场景分类学习. 测绘科学, 444): 116-123, 174. https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD201904021.htm

李文凯, 张唯, 秦家豪, 等, 2020. 基于DEM和高分辨率遥感影像的"膨胀-融合"式地表沟壑提取. 地球科学, 45(6): 1948-1955. doi: 10.3799/dqkx.2020.004

余东行, 张保明, 赵传, 等, 2020. 联合卷积神经网络与集成学习的遥感影像场景分类. 遥感学报, 24(6): 717-727. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB202006006.htm

余姝辰, 余德清, 王伦澈, 等, 2019. 三峡水库运行前后洞庭湖洲滩面积变化遥感认识. 地球科学, 44(12): 4275-4283. doi: 10.3799/dqkx.2019.182

Relative Articles

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(11) / Tables(6)

Get Citation

PDF

XML

Article views (1571) PDF downloads(63)

Remote Sensing Image Scene Classification Method Based on Multi-Scale Cyclic Attention Network

doi: 10.3799/dqkx.2020.365

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Proportional views

Export File

Citation

Format

Content