Unsupervised change detection in high spatial resolution remote sensing images based on a conditional random field model
Author:
Affiliation:
1. School of Computer Science and Technology, Nanjing University of Science and Technology, 210094, Nanjing, China
Publisher
Informa UK Limited
Subject
Applied Mathematics,Atmospheric Science,Computers in Earth Sciences,General Environmental Science
Link
https://www.tandfonline.com/doi/pdf/10.5721/EuJRS20164913
Reference32 articles.
1. Automatic three-dimensional features extraction: The case study of L'Aquila for collapse identification after April 06, 2009 earthquake
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4. Automatic analysis of the difference image for unsupervised change detection
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