Fast unsupervised deep fusion network for change detection of multitemporal SAR images-Reference-Cited by-同舟云学术

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Fast unsupervised deep fusion network for change detection of multitemporal SAR images

Published:2019-03 Issue: Volume:332 Page:56-70
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Chen Huan^ORCID,Jiao Licheng,Liang Miaomiao,Liu Fang,Yang Shuyuan,Hou Biao

Funder

State Key Program of National Natural Science of China

Innovative Research Groups of the National Natural Science Foundation of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference49 articles.

1. A composed supervised/unsupervised approach to improve change detection from remote sensing;Castellana;Pattern Recognit. Lett.,2007

2. An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images;Bruzzone;IEEE Trans. Geosci. Remote Sens.,1997

3. A novel approach for disaster monitoring: Fractal models and tools;Martino;IEEE Trans. Geosci. Remote Sens.,2007

4. Unsupervised change detection in multitemporal sar images over large urban areas;Hu;IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.,2014

5. Image change detection algorithms: a systematic survey;Radke;IEEE Trans. Image Process.,2005

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