Abstract
Detection of change in synthetic aperture radar images (SAR) has been considering an area of study in remote sensing and computer vision. It is important to classify the modified feature in the different SAR images obtained during various observing periods. Imaging of synthetic aperture radar (SAR) has been recognized as effective in the recognition of change than other imagery sensors, particularly when the earth is observed with cloudy, rainy, or hazy weather. The images taken by the synthetic aperture radar are low contrast and low brightness grayscale image. So, it is difficult to find the changes in SAR images. In this article, we proposed a detection of changes system where we use a convolution neural network (CNN) as a classification model, Modified Gauss-Log Ratio used to find the difference image of the two sample images. The classification result from CNN and the preclassification outcome is used to find out the final change map. The reliability and robustness of the proposed approach were shown by investigative findings using two SAR image data sets.
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
References
Kusetogullari, H., Yavariabdi, A.: Unsupervised change detection in landsat images with atmospheric artifacts: a fuzzy multiobjective approach. Math. Probl. Eng. 2018(2), 24–26 (2018). https://doi.org/10.1155/2018/7274141
Jia, L., et al.: SAR image change detection based on iterative label information composite kernel supervised by anisotropic texture. IEEE Trans. Geosci. Remote Sens. 53(7), 3960–3973 (2015)
Hou, B., et al.: Unsupervised change detection in SAR image based on gauss-log ratio image fusion and compressed projection. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(8), 3297–3317 (2014)
Gao, G., et al.: Modified log-ratio operator for change detection of synthetic aperture radar targets in forest concealment. J. Appl. Remote Sens. 8(1), (2014)
Zheng, Y., et al.: Using combined difference image and k-means clustering for SAR image change detection. IEEE Geosci. Remote Sens. Lett. 11(3), 691–695 (2014)
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)
Bazi, Y., Melgani, F., Bruzzone, L.: A genetic expectation maximization method for unsupervised change detection in multitemporal SAR imagery. Int. J. Remote Sens. 30(24), 6591–6610 (2009)
Bazi, Y., Bruzzone, L., Melgani, F.: Automatic identification of the number and values of decision thresholds in the log-ratio image for change detection in SAR images. IEEE Geosci. Remote Sens. Lett. 3(3), 349–353 (2006)
Bovolo, F., Bruzzone, L.: A split-based approach to unsupervised change detection in large-size multitemporal images: application to tsunami-damage assessment. IEEE Trans. Geosci. Remote Sens. 45(6), 1658–1670 (2007)
Gong, M., Zhou, Z., Ma, J.: Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans. Image Process. 21(4), 2141–2151 (2012)
Li, H.C., et al.: Gabor feature based unsupervised change detection of multitemporal SAR images based on two-level clustering. IEEE Geosci. Remote Sens. Lett. 12(12), 2458–2462 (2015)
Gong, M., Su, L., Jia, M., Chen, W.: Fuzzy clustering with a modified MRF energy function for change detection in synthetic aperture radar images. IEEE Trans. Fuzzy Syst. 22(1), 98–109 (2014)
Wang, Q., Yuan, Z., Du, Q., Li, X.: GETNET: a general end-to end 2-D CNN framework for hyperspectral image change detection. IEEE Trans. Geosci. Remote Sens. 57(1), 3–13 (2019). https://doi.org/10.1109/TGRS.2018.2849692
Gao, F., Dong, J., Li, B., Xu, Q., Xie, C.: Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine. J. Appl. Remote Sens. 10(4), (2016). https://doi.org/10.1117/1.jrs.10.046019
Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K.: Convolutional neural networks: an overview and application in radiology. Insights Imaging 9(4), 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9
Earthnet Online. http://earth.esa.int/ers/ersaction/SanFrancisco_SAR_IM_Orbit47426_20040516.html (2017). Accessed 11 May 2017
Celik, T.: Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geosci. Remote Sens. Lett. 6(4), 772–776 (2009)
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 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ghosh, C., Majumdar, D., Mondal, B. (2021). SAR Image Change Detection Using Modified Gauss-Log Ratio Operator and Convolution Neural Network. In: Pan, I., Mukherjee, A., Piuri, V. (eds) Proceedings of Research and Applications in Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1355. Springer, Singapore. https://doi.org/10.1007/978-981-16-1543-6_21
Download citation
DOI: https://doi.org/10.1007/978-981-16-1543-6_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1542-9
Online ISBN: 978-981-16-1543-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)