Abstract
In this paper, we proposed a novel sea ice change detection method for Synthetic Aperture Radar (SAR) images based on Canonical Correlation Analysis (CCA) and Contractive Autoencoders (SCAEs). To alleviate the effect of multiplicative speckle noise, structured matrix decomposition is utilized for difference image enhancement, and therefore, better difference image with less noisy spots can be obtained. In order to get good data representations in changed and unchanged pixels classification, CCA and SCAEs are combined to exploit more effective changed features. Experiments on two real sea ice datasets demonstrate the robustness and efficiency of the proposed method in comparison with three other state-of-the-art methods.
This work was supported by the National Natural Science Foundation of China (Grant Nos. 41606198 and 41576011) and in part by the Shandong Province Natural Science Foundation of China under Grant No. ZR2016FB02.
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
Wang, L., Scott, K.A., Clausi, D.A.: Sea ice concentration estimation during freeze-up from SAR imagery using a convolutional neural network. Remote Sens. 9, 409 (2017)
Zhang, H., Gong, M., Zhang, P., Su, L., Shi, J.: Feature-level change detection using deep representation and feature change analysis for multispectral imagery. IEEE Geosci. Remote Sens. Lett. 13, 1666–1670 (2016)
Lopes, A., Nezry, E., Touzi, R., Laur, H.: Structure detection and statistical adaptive speckle filtering in SAR images. Int. J. Remote Sens. 14, 1735–1758 (1993)
Gomez, L., Munteanu, C., Berlles, J., Mejail, M.: Evolutionary expert-supervised despeckled SRAD filter design for enhancing SAR images. IEEE Geosci. Remote Sens. Lett. 8, 814–818 (2011)
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, 874–887 (2005)
Celik, T.: Unsupervised change detection in satellite images using principal component analysis and \(k\)-means clustering. IEEE Geosci. Remote Sens. Lett. 6, 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, 1792–1796 (2016)
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, 2141–2151 (2012)
Lin, K., Lu, J., Chen, C.-S., Zhou, J.: Learning compact binary descriptors with unsupervised deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1183–1192 (2016)
Sell, G., Garcia-Romero, D., McCree, A.: Speaker diarization with I-Vectors from DNN senone posteriors. In: Proceedings of Interspeech, pp. 3096–3099 (2015)
Wang, L., Scott, K.A., Xu, L., et al.: Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: a case study. IEEE Trans. Geosci. Remote Sens. 54, 4524–4533 (2016)
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. Lear. Syst. 27, 125–138 (2016)
Peng, H., Li, B., Ling, H., Hu, W., Xiong, W., Maybank, S.: Salient object detection via structured matrix decomposition. IEEE Trans. Patter. Anal. Mach. Intell. 39, 818–832 (2017)
Geng, J., Wang, H., Fan, J., Ma, X.: Deep supervised and contractive neural network for SAR image classificaiton. IEEE Trans. Geosci. Remote Sens. 55, 2442–2459 (2017)
Li, H., Celik, T., Longbotham, N.: Gabor feature based unsupervised change detection of multitemporal SAR images based on two-level clustering. IEEE Geosci. Remote Sens. Lett. 12, 2458–2462 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, X., Gao, F., Dong, J., Wang, S. (2018). Sea Ice Change Detection from SAR Images Based on Canonical Correlation Analysis and Contractive Autoencoders. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_69
Download citation
DOI: https://doi.org/10.1007/978-3-030-00767-6_69
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00766-9
Online ISBN: 978-3-030-00767-6
eBook Packages: Computer ScienceComputer Science (R0)