Approaching semi-supervised collaborative learning model for remote sensing image analysis
Author:
Affiliation:
1. Le Quy Don Technical University,Dept. Information Technology,Hanoi,Vietnam
2. Institute of Techniques for Special Engineering, Le Quy Don Technical University,Hanoi,Vietnam
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10013784/10013785/10013798.pdf?arnumber=10013798
Reference20 articles.
1. Semi-supervised fuzzy C-means clustering for change detection from the multispectral satel-lite image;mai;2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE),2015
2. On semi-supervised fuzzy c-means clustering;endo;IEEE International Confer-ence on Fuzzy Systems,2009
3. A novel approach for change detection of remotely sensed images using semi-supervised multiple classifier system
4. Semi-supervised change detection method for multi-temporal hyperspectral images
5. Semi-unsupervised MFISH chromosome image classification;karvelis;10th IEEE Int Cont on Digital Object Identifier,2000
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