Unsupervised Change Detection of SAR Images Based on an Improved NSST Algorithm-Reference-Cited by-同舟云学术

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Unsupervised Change Detection of SAR Images Based on an Improved NSST Algorithm

Published:2018-03-08 Issue:5 Volume:46 Page:801-808
ISSN:0255-660X
Container-title:Journal of the Indian Society of Remote Sensing
language:en
Short-container-title:J Indian Soc Remote Sens

Author:

Chen Pengyun,Jia Zhenhong,Yang Jie,Kasabov Nikola

Funder

International Cooperative Research and Personnel Training Projects of the Ministry of Education of the People’s Republic of China

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Geography, Planning and Development

Link

http://link.springer.com/article/10.1007/s12524-017-0740-4/fulltext.html

Reference19 articles.

1. Bazi, Y., Melgani, F., & Al-Sharari, H. D. (2010). Unsupervised change detection in multispectral remotely sensed imagery with level set methods. Geoscience & Remote Sensing IEEE Transactions on, 48(8), 3178–3187.

2. Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. Pattern recognition with fuzzy objective function algorithms (pp. 203–239). New York: Plenum Press.

3. Carincotte, C., Derrode, S., & Bourennane, S. (2006). Unsupervised change detection on SAR images using fuzzy hidden Markov chains. IEEE Transactions on Geoscience and Remote Sensing, 44(2), 432–441.

4. Celik, T. (2010). Change detection in satellite images using a genetic algorithm approach. IEEE Geoscience and Remote Sensing Letters, 7(2), 386–390.

5. Cui, S., & Datcu, M. (2012). Statistical wavelet subband modeling for multi-temporal SAR change detection. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 5(4), 1095–1109.

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5. Change Detection Algorithm for Multi-Temporal Remote Sensing Images Based on Adaptive Parameter Estimation;IEEE Access;2020

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