Advertisement

Sensing and Imaging

, 20:31 | Cite as

Sample Selection Based Change Detection with Dilated Network Learning in Remote Sensing Images

  • N. VenugopalEmail author
Original Paper
First Online:
  • 91 Downloads

Abstract

The change detection system plays an active role in remote sensing images by detecting the changes in the image environment. Changes may be occur frequently due to the seasonal factors such as deforestation, natural disorders, etc. The objective of this paper is to develop an effective approach to find the changes that were experienced over a time period. The changes detected by difference image (DI) method is not efficient because it has the drawbacks in speckle noise suppression and classification, also the quality of DI affects the change map. This paper proposes a sample selection-multiple dilated convolutional neural network for the detection of changed and unchanged areas. In this, multiple dilation rates are incorporated, that overcome the existing gridding problems in network. The feature map determined at fully connected network extracts the global information by widening the receptive field that covers all the regions without any missing portions in the image. Hence, this type of architecture improves the learning in deep network by detecting the changes accurately. Finally, the trained network model achieves a feature map from the two images with the classified result of changed and unchanged pixels. The accuracy of the proposed change detection result provides improved results as compared with the existing algorithms. The performance metrics like false positive, false negative, overall error, the percentage of correct classification and Kappa are used for the estimation of the proposed image change detection task. Experiments are conducted on seven real datasets, and the result shows the better detection result than the other existing approaches.

Keywords

Change detection Remote sensing Convolutional neural network Supervised learning 
This is a preview of subscription content, log in to check access.

Notes

References

  1. 1.
    Sharma, A., & Gulati, T. (2017). Change detection in remotely sensed images based on image fusion and fuzzy clustering. International Journal of Electronics Engineering Research, 9(1), 141–150.Google Scholar
  2. 2.
    Khan, S. H., He, X., Porikli, F., & Bennamoun, M. (2017). Forest change detection in incomplete satellite images with deep neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(9), 5407–5423.CrossRefGoogle Scholar
  3. 3.
    Du, P., Liu, S., Gamba, P., Tan, K., & Xia, J. (2012). Fusion of difference images for change detection over urban areas. IEEE Journal on Selected Topics in Applied Earth Observations and Remote Sensing, 5(4), 1076–1086.CrossRefGoogle Scholar
  4. 4.
    Jia, L., Li, M., Zhang, P., & Wu, Y. (2016). SAR image change detection based on correlation kernel and multistage extreme learning machine. IEEE Transactions on Geoscience and Remote Sensing, 54(10), 5993–6006.CrossRefGoogle Scholar
  5. 5.
    Bovolo, F., Marchesi, S., & Bruzzone, L. (2012). A framework for automatic and unsupervised detection of multiple changes in multitemporal images. IEEE Transactions on Geoscience and Remote Sensing, 50(6), 2196–2212.CrossRefGoogle Scholar
  6. 6.
    Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91–106.CrossRefGoogle Scholar
  7. 7.
    Geng, J., Wang, H., Fan, J., & Ma, X. (2017). Change detection of SAR images based on supervised contractive autoencoders and fuzzy clustering. In 2017 International workshop on remote sensing with intelligent processing (RSIP) (pp. 1–3). IEEE.Google Scholar
  8. 8.
    Krylov, V. A., Moser, G., Serpico, S. B., & Zerubia, J. (2016). False discovery rate approach to unsupervised image change detection. IEEE Transactions on Image Processing, 25(10), 4704–4718.MathSciNetCrossRefGoogle Scholar
  9. 9.
    Liu, S., Bruzzone, L., Bovolo, F., & Du, P. (2015). Hierarchical unsupervised change detection in multitemporal hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 53(1), 244–260.CrossRefGoogle Scholar
  10. 10.
    Ghosh, S., Bruzzone, L., Patra, S., Bovolo, F., & Ghosh, A. (2007). A context-sensitive technique for unsupervised change detection based on Hopfield-type neural networks. IEEE Transactions on Geoscience and Remote Sensing, 45(3), 778–789.CrossRefGoogle Scholar
  11. 11.
    Bruzzone, L., & Bovolo, F. (2013). A novel framework for the design of change-detection systems for very-high-resolution remote sensing images. Proceedings of the IEEE, 101(3), 609–630.CrossRefGoogle Scholar
  12. 12.
    Li, C., & Xiong, H. (2017). A geometric and radiometric simultaneous correction model (GRSCM) framework for high-accuracy remotely sensed image preprocessing. Photogrammetric Engineering and Remote Sensing, 83(9), 621–632.CrossRefGoogle Scholar
  13. 13.
    Radke, R. J., Andra, S., Al-Kofahi, O., & Roysam, B. (2005). Image change detection algorithms: A systematic survey. IEEE Transactions on Image Processing, 14(3), 294–307.MathSciNetCrossRefGoogle Scholar
  14. 14.
    Gong, M., Zhao, J., Liu, J., Miao, Q., & Jiao, L. (2016). Change detection in synthetic aperture radar images based on deep neural networks. IEEE Transactions on Neural Networks and Learning Systems, 27(1), 125–138.MathSciNetCrossRefGoogle Scholar
  15. 15.
    Wen, D., Huang, X., Zhang, L., & Benediktsson, J. A. (2016). A novel automatic change detection method for urban high-resolution remotely sensed imagery based on multiindex scene representation. IEEE Transactions on Geoscience and Remote Sensing, 54(1), 609–625.CrossRefGoogle Scholar
  16. 16.
    Liu, J., Gong, M., Qin, K., & Zhang, P. (2018). A deep convolutional coupling network for change detection based on heterogeneous optical and radar images. IEEE transactions on neural networks and learning systems, 29(3), 545–559.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Jia, L., Li, M., Wu, Y., Zhang, P., Liu, G., Chen, H., et al. (2015). SAR image change detection based on iterative label-information composite kernel supervised by anisotropic texture. IEEE Transactions on Geoscience and Remote Sensing, 53(7), 3960–3973.CrossRefGoogle Scholar
  18. 18.
    Lu, X., Yuan, Y., & Zheng, X. (2017). Joint dictionary learning for multispectral change detection. IEEE Transactions on Cybernetics, 47(4), 884–897.CrossRefGoogle Scholar
  19. 19.
    Han, Y., Bovolo, F., & Bruzzone, L. (2017). Segmentation-based fine registration of very high resolution multitemporal images. IEEE Transactions on Geoscience and Remote Sensing, 55(5), 2884–2897.CrossRefGoogle Scholar
  20. 20.
    Dong, C., Loy, C. C., He, K., & Tang, X. (2014, September). Learning a deep convolutional network for image super-resolution. In European conference on computer vision (pp. 184–199). Springer, Cham.Google Scholar
  21. 21.
    Gong, M., Zhou, Z., & Ma, J. (2011). Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Transactions on Image Processing, 21(4), 2141–2151.MathSciNetCrossRefGoogle Scholar
  22. 22.
    Moser, G., & Serpico, S. B. (2006). Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery. IEEE Transactions on Geoscience and Remote Sensing, 44(10), 2972–2982.CrossRefGoogle Scholar
  23. 23.
    Celik, T. (2009). Unsupervised change detection in satellite images using principal component analysis and $ k $-means clustering. IEEE Geoscience and Remote Sensing Letters, 6(4), 772–776.CrossRefGoogle Scholar
  24. 24.
    Bazi, Y., Bruzzone, L., & Melgani, F. (2005). An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43(4), 874–887.CrossRefGoogle Scholar
  25. 25.
    Gong, M., Su, L., Jia, M., & Chen, W. (2013). Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images. IEEE Transactions on Fuzzy Systems, 22(1), 98–109.CrossRefGoogle Scholar
  26. 26.
    Gong, M., Jia, M., Su, L., Wang, S., & Jiao, L. (2014). Detecting changes of the Yellow River Estuary via SAR images based on a local fit-search model and kernel-induced graph cuts. International Journal of Remote Sensing, 35(11–12), 4009–4030.CrossRefGoogle Scholar
  27. 27.
    Selesnick, I. W. (2001). Hilbert transform pairs of wavelet bases. IEEE Signal Processing Letters, 8(6), 170–173.CrossRefGoogle Scholar
  28. 28.
    Bovolo, F., & Bruzzone, L. (2005). A detail-preserving scale-driven approach to change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43(12), 2963–2972.CrossRefGoogle Scholar
  29. 29.
    Selesnick, I. W., Baraniuk, R. G., & Kingsbury, N. G. (2005). The dual-tree complex wavelet transform. IEEE Signal Processing Magazine, 22(6), 123–151.CrossRefGoogle Scholar
  30. 30.
    Selesnick, I. W. (2004). The double-density dual-tree DWT. IEEE Transactions on Signal Processing, 52(5), 1304–1314.MathSciNetCrossRefGoogle Scholar
  31. 31.
    Ma, L., Li, M., Blaschke, T., Ma, X., Tiede, D., Cheng, L., et al. (2016). Object-based change detection in urban areas: The effects of segmentation strategy, scale, and feature space on unsupervised methods. Remote Sensing, 8(9), 761.CrossRefGoogle Scholar
  32. 32.
    Wu, C., Du, B., Cui, X., & Zhang, L. (2017). A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion. Remote Sensing of Environment, 199, 241–255.CrossRefGoogle Scholar
  33. 33.
    Gong, M., Zhao, J., Liu, J., Miao, Q., & Jiao, L. (2015). Change detection in synthetic aperture radar images based on deep neural networks. IEEE Transactions on Neural Networks and Learning Systems, 27(1), 125–138.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringPES UniversityBengaluruIndia

Personalised recommendations

Cite article

Buy options