Sea Ice Change Detection from SAR Images Based on Canonical Correlation Analysis and Contractive Autoencoders-Reference-Cited by-同舟云学术

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Sea Ice Change Detection from SAR Images Based on Canonical Correlation Analysis and Contractive Autoencoders

Published:2018 Issue: Volume: Page:748-757
ISSN:0302-9743
Container-title:Advances in Multimedia Information Processing – PCM 2018
language:
Short-container-title:

Author:

Wang Xiao,Gao Feng,Dong Junyu,Wang Shengke

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-00767-6_69

Reference15 articles.

1. 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)

2. 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)

3. 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)

4. 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)

5. 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)

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