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Overview of SAR Image Change Detection

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3D Imaging Technologies—Multi-dimensional Signal Processing and Deep Learning

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 234))

Abstract

Compared with the traditional optical remote sensing imaging, SAR imaging has the advantages of all-weather and round-the-clock. Therefore, it becomes important data source in change detection. SAR image change detection has been widely used in urban construction, disaster assessment, crop growth, environmental monitoring, and so on. In this paper, the process of SAR change detection is introduced first. Then we introduce the generation of the traditional SAR difference image and fusion difference image and mainly analyze the difference image from four aspects to obtain the detection, which include feature, threshold, clustering, and deep learning. Finally, some typical methods are simulated, and their accuracy is evaluated.

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References

  1. Deng, X., Wang, L., Peng, H., Wu, Y.: The History and tendency of synthetic aperture radars. J. Test Measur. Technol. 14(2), 80–86 (2000)

    Google Scholar 

  2. El-Darymli, K., Gill, E.W., Mcguire, P., Power, D., Moloney, C.: Automatic target recognition in synthetic aperture radar imagery: a state-of-the-art review. IEEE Access 6014–6058 (2016)

    Google Scholar 

  3. Dong, G., Kuang, G.: Classification on the monogenic scale space: application to target recognition in SAR image. IEEE Trans. Image Process. 24(8), 2527–2539 (2015)

    Article  MathSciNet  Google Scholar 

  4. Cohen, M.B., Elder, S., Musco, S., Musco, C., Persu, M.: Dimensionality reduction for k-means clustering and low rank approximation. Forty-Seventh ACM Symp. Theory Comput. 46(8), 163–172 (2015)

    Article  MathSciNet  Google Scholar 

  5. Shi, X., Guo, Z., Lai, Z., Yang, Y., Bao, Z., Zhang, D.: A Framework of joint graph embedding and sparse regression for dimensionality reduction. IEEE Trans. Image Process. 24(4), 1341–1355 (2015)

    Article  MathSciNet  Google Scholar 

  6. Ohkura, H.: Application of SAR data to monitoring Earth surface changes and displacement. Adv. Space Res. 21(3), 485–492 (1998)

    Article  Google Scholar 

  7. Weydahl, D.J.: Analysis of ERS Tandem SAR Coherence from Glaciers, Valleys, and Fjord Ice on Svalbard. IEEE Trans. Geosci. Remote Sens. 39(9), 2029–2039 (2001)

    Article  Google Scholar 

  8. Gong, M., Cao, Y., Wu, Q.: A neighborhood-based ratio approach for change detection in SAR images. IEEE Geosci. Remote Sens. Lett. 9(2), 307–311 (2012)

    Article  Google Scholar 

  9. Ma, J., Gong, M., Zhou, Z.: Wavelet fusion on ratio images for change detection in SAR images. IEEE Geosci. Remote Sens. Lett. 9(6), 1122–1126 (2012)

    Article  Google Scholar 

  10. Huang, P., Duan, Y., Tan, W., Xu, W.: Change detection method based on fusion difference map in flood disaster. J. Radars (in press)

    Google Scholar 

  11. Qu, C., Li, Z., Zhou, Q., Liu, C., Deng, B.: SAR image change detection algorithm based on CCA difference graph fusion. Fire Control Command Control 43(12), 1–4 (2018)

    Google Scholar 

  12. Chen, Z., Deng, P., Zhong, J., Wang, H.: Application of textural features to change detection in SAR image. Remote Sens. Technol. Appl. 17(3), 162–166 (2002)

    Google Scholar 

  13. Wu, X., Yang, F., Lishman, R.: Land cover change detection using texture analysis. J. Comput. Sci. 6(1), 92–100 (2010)

    Article  Google Scholar 

  14. Bruzzone, L., Prieto, D.F.: Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Remote Sens. 38(3), 1171–1182 (2000)

    Article  Google Scholar 

  15. Bazi, Y., Bruzzone, L., Melgani, F.: An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Trans. Geosci. Remote Sens. 43(4), 874–887 (2005)

    Article  Google Scholar 

  16. Moser, G., Serpico, S.B.: Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery. IEEE Trans. Geosci. Remote Sens. 44(10), 2972–2982 (2006)

    Article  Google Scholar 

  17. Zhuang, H., Deng, K., Yu, M., Fan, H.: A novel approach combining KI criterion and inverse Gaussian model to unsupervised change in SAR images. Geomatics Inf. Sci. Wuhan Univ. 43(2), 282–288 (2018)

    Google Scholar 

  18. Jakka, T.K., Reddy, Y.M., Rao, B.P.: Change detection in SAR images using adaptive discrete wavelet transform with fuzzy C-mean clustering. J. Indian Soc. Remote Sens. 47(3), 379–390 (2018)

    Article  Google Scholar 

  19. Li, Y., Lu, G., Jiao, L.: A Memetic Kernel clustering algorithm for change detection in SAR images. In: Editor, Gong, M., Pan, L., Song, T., Zhang, G. (eds.) Bio-inspired Computing—Theories and Applications. BIC-TA 2016, CCIS, vol. 682, pp. 388–393. Springer, Singapore (2016)

    Google Scholar 

  20. Shang, R., Zhang, W., Jiao, L.: Detection in SAR images based on histogram and improved Elitist genetic fuzzy clustering. In: Editor, Sun, X., Chao, H.C., You, X., Bertino, E. (eds.) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science, LNCS, vol. 10603, pp. 541–553. Springer, Cham (2017)

    Google Scholar 

  21. Liu, J., Gong, M., Zhao, J., Li, H., Jiao, L.: Difference representation learning using stacked restricted Boltzmann machines for change detection in SAR images. Soft. Comput. 20(12), 4645–4657 (2016)

    Article  Google Scholar 

  22. Gong, M., Zhao, J., Liu, J., Miao, Q., Jiao, L.: Change detection in synthetic aperture radar images based on deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. 27(1), 125–138 (2017)

    Article  MathSciNet  Google Scholar 

  23. Gao, F., Dong, J., Li, B., Xu, Q.: Automatic change detection in synthetic aperture radar images based on PCANet. IEEE Geosci. Remote Sens. Lett. 13(12), 1792–1796 (2017)

    Article  Google Scholar 

  24. Feng, C., Fan, H., Wen, B., Ma, S.: Change detection of SAR images based on stacked sparse automatic encoder. Laser J. 39(11), 29–33 (2018)

    Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (61801419) and the Natural Science Foundation of Yunnan Province (2019FD114). The authors gratefully acknowledge the support of Yunnan Key Lab of Opto-electronic Information Technology.

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Authors and Affiliations

  1. Yunnan Key Lab of Opto-Electronic Information Technology, Yunnan Normal University, Kunming, China

    Jiayu Xuan, Zhihui Xin, Xiaoqiao Huang, Zhixu Wang & Yu Sun

Authors
  1. Jiayu Xuan
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  2. Zhihui Xin
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  3. Xiaoqiao Huang
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  4. Zhixu Wang
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  5. Yu Sun
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Corresponding authors

Correspondence to Zhihui Xin or Xiaoqiao Huang .

Editor information

Editors and Affiliations

  1. KES International, Shoreham-by-Sea, UK

    Lakhmi C. Jain

  2. Faculty of Telecommunications, Technical University of Sofia, Sofia, Bulgaria

    Roumen Kountchev

  3. Department of Electro-Optical Engineering, Yunnan Normal University, Kunming, Yunnan, China

    Junsheng Shi

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Xuan, J., Xin, Z., Huang, X., Wang, Z., Sun, Y. (2021). Overview of SAR Image Change Detection. In: Jain, L.C., Kountchev, R., Shi, J. (eds) 3D Imaging Technologies—Multi-dimensional Signal Processing and Deep Learning. Smart Innovation, Systems and Technologies, vol 234. Springer, Singapore. https://doi.org/10.1007/978-981-16-3391-1_4

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