Abstract
Image classification is a hot spot in the field of pattern recognition and artificial intelligence. When there are apparent inter-class similarity and intra-class diversity, such as in the area of remote sensing, image classification becomes very challenge. With the continuous development of convolutional neural networks, a major breakthrough has been made in image classification. Although good performance have been achieved, there is still some room for improvement. First, in addition to global information, local features are crucial to image classification. Second, minimizing/maximizing the distance from the same/different classes allows the key points in image classification to be given full attention. In this paper, we propose an image classification method which is named multi-branch position attention network (MBPANet). We design a channel attention module containing position information, called Position Channel Attention Module (PCAM), and synthesize a new attention module Position Spatial Attention Module (PSAM) with a spatial attention module Local Spatial Attention Module (LSAM). The features obtained by the attention weighting method not only obtain local neighborhood semantic information but also contain global semantic information. Extensive experiments on three benchmark datasets show that our approach outperforms state-of-the-art methods.
Supported by Science and Technology Project of SGCC (No. 5500-202140127A).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11531–11539 (2020)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Yang, Y., Newsam, S.: Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 270–279 (2010)
Xia, G.S., et al.: AID: a benchmark data set for performance evaluation of aerial scene classification. IEEE Trans. Geosci. Remote Sens. 55(7), 3965–3981 (2017)
Cheng, G., Han, J., Lu, X.: Remote sensing image scene classification: benchmark and state of the art. Proc. IEEE 105(10), 1865–1883 (2017)
Huang, H., Xu, K.: Combing triple-part features of convolutional neural networks for scene classification in remote sensing. Remote Sens. 11(14), 1687 (2019)
Zhang, W., Tang, P., Zhao, L.: Remote sensing image scene classification using CNN-CapsNet. Remote Sens. 11(5), 494 (2019)
He, N., Fang, L., Li, S., Plaza, J., Plaza, A.: Skip-connected covariance network for remote sensing scene classification. IEEE Trans. Neural Netw. Learn. Syst. 31(5), 1461–1474 (2019)
Wang, Q., Liu, S., Chanussot, J., Li, X.: Scene classification with recurrent attention of VHR remote sensing images. IEEE Trans. Geosci. Remote Sens. 57(2), 1155–1167 (2018)
Sun, H., Li, S., Zheng, X., Lu, X.: Remote sensing scene classification by gated bidirectional network. IEEE Trans. Geosci. Remote Sens. 58(1), 82–96 (2019)
Zhao, Z., Li, J., Luo, Z., Li, J., Chen, C.: Remote sensing image scene classification based on an enhanced attention module. IEEE Geosci. Remote Sens. Lett. 18(11), 1926–1930 (2021)
Wang, S., Guan, Y., Shao, L.: Multi-granularity canonical appearance pooling for remote sensing scene classification. IEEE Trans. Image Process. 29, 5396–5407 (2020)
Yu, D., Xu, Q., Guo, H., Zhao, C., Lin, Y., Li, D.: An efficient and lightweight convolutional neural network for remote sensing image scene classification. Sensors 20(7), 1999 (2020)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Dosovitskiy, A., et al.: An image is worth 16 \(\times \) 16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Chen, S.B., Wei, Q.S., Wang, W.Z., Tang, J., Luo, B., Wang, Z.Y.: Remote sensing scene classification via multi-branch local attention network. IEEE Trans. Image Process. 31, 99–109 (2022)
Xu, K., Huang, H., Li, Y., Shi, G.: Multilayer feature fusion network for scene classification in remote sensing. IEEE Geosci. Remote Sens. Lett. 17(11), 1894–1898 (2020)
Yu, Y., Liu, F.: A two-stream deep fusion framework for high-resolution aerial scene classification. Comput. Intell. Neurosci. 2018(8639367), 1–13 (2018)
Guo, Y., Ji, J., Shi, D., Ye, Q., Xie, H.: Multi-view feature learning for VHR remote sensing image classification. Multimedia Tools Appl. 80(15), 23009–23021 (2020). https://doi.org/10.1007/s11042-020-08713-z
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, K., Yang, J., Yuan, K., Wei, QS., Chen, SB. (2022). Image Classification via Multi-branch Position Attention Network. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13363. Springer, Cham. https://doi.org/10.1007/978-3-031-09037-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-09037-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09036-3
Online ISBN: 978-3-031-09037-0
eBook Packages: Computer ScienceComputer Science (R0)