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SAR Scene Classification Based on Self-supervised Jigsaw Puzzles

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Intelligence Science IV (ICIS 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 659))

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Abstract

Scene classification is a hot issue in the field of SAR image interpretation. Many SAR image interpretation tasks can be promoted with the development of highly credible scene classification methods. But the fussy steps of traditional methods and the imperious demands of labeled samples in deep learning-based methods restrict the effective feature learning in SAR scene classification. Hence, a self-supervised learning method based on Jigsaw puzzles is proposed to address the problems. Concretely, the Jigsaw puzzle reassembly of the SAR image block is firstly taken as the upstream task without manual labels. Once the correct spatial arrangement is obtained from it, the learned high-level feature from the upstream task is used as the pre-training model for the downstream task, which is then fine-tuned with only a few labeled samples to enhance the performance of the SAR scene classification task. Experimental results on 25-class real SAR scenes confirm the proposed method can greatly improve the scene classification performance than directly training the network with the same number of labeled samples used in the downstream task.

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References

  1. Wang, H., Magagi, R., Goita, K.: Polarimetric decomposition for monitoring crop growth status. IEEE Geosci. Remote Sens. Lett. 13(6), 870–874 (2016)

    Article  Google Scholar 

  2. Del Frate, F., Latini, D., Scappiti, V.: On neural networks algorithms for oil spill detection when applied to C-and X-band SAR. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 5249–5251. IEEE (2017)

    Google Scholar 

  3. Dumitru, C.O., Cui, S., Schwarz, G., et al.: Information content of very-high-resolution SAR images: semantics, geospatial context, and ontologies. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(4), 1635–1650 (2014)

    Article  Google Scholar 

  4. Chapelle, O., Haffner, P., Vapnik, V.N.: Support vector machines for histogram-based image classification. IEEE Trans. Neural Netw. 10(5), 1055–1064 (1999)

    Article  Google Scholar 

  5. McNairn, H., Kross, A., Lapen, D., et al.: Early season monitoring of corn and soybeans with TerraSAR-X and RADARSAT-2. Int. J. Appl. Earth Obs. Geoinf. 28, 252–259 (2014)

    Google Scholar 

  6. Zhao, Z., Jia, M., Wang, L.: High-resolution SAR image classification via multiscale local Fisher patterns. IEEE Trans. Geosci. Remote Sens. 59(12), 10161–10178 (2020)

    Article  Google Scholar 

  7. Zhang, A., Yang, X., Fang, S., et al.: Region level SAR image classification using deep features and spatial constraints. ISPRS J. Photogramm. Remote Sens. 163, 36–48 (2020)

    Article  Google Scholar 

  8. Li, Y., Li, X., Sun, Q., et al.: SAR image classification using CNN embeddings and metric learning. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2020)

    MathSciNet  Google Scholar 

  9. Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1422–1430 (2015)

    Google Scholar 

  10. Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VI, pp. 69–84. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_5

    Chapter  Google Scholar 

  11. Richardson, J.T.E., Vecchi, T.: A jigsaw-puzzle imagery task for assessing active visuospatial processes in old and young people. Behav. Res. Meth. Instrum. Comput. 34(1), 69–82 (2002)

    Article  Google Scholar 

  12. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)

    Google Scholar 

Download references

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 62171347, 61877066, 61771379, 62001355, 62101405; the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61621005; the fundamental Research Funds for the Central Universities under Grant XJS211904; the Key Research and Development Program in Shaanxi Province of China under Grant 2019ZDLGY0305 and 2021ZDLGY0208; the Science and Technology Program in Xi’an of China under Grant XA2020-RGZNTJ-0021; 111 Project.

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Correspondence to Zhongle Ren .

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Ren, Z., Lu, Y., Wang, H., Zhang, Y., Hou, B. (2022). SAR Scene Classification Based on Self-supervised Jigsaw Puzzles. In: Shi, Z., Jin, Y., Zhang, X. (eds) Intelligence Science IV. ICIS 2022. IFIP Advances in Information and Communication Technology, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-14903-0_36

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  • DOI: https://doi.org/10.1007/978-3-031-14903-0_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14902-3

  • Online ISBN: 978-3-031-14903-0

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