Unsupervised change detection of VHR remote sensing images based on multi-resolution Markov Random Field in wavelet domain
Author:
Affiliation:
1. School Of Civil Engineering, Chongqing Jiaotong University, Chongqing, China
2. School of Land Science and Technology, China University of Geosciences, Beijing, China
3. Faculty of Mathematics, University of Waterloo, Waterloo, Canada
Funder
Key projects of basic science and frontier technology research of chongqing
the National Natural Science of China
Publisher
Informa UK Limited
Subject
General Earth and Planetary Sciences
Link
https://www.tandfonline.com/doi/pdf/10.1080/01431161.2019.1602792
Reference31 articles.
1. Hyperspectral image classification based on spectral-spatial features using probabilistic SVM and locally weighted Markov Random Fields
2. A multiscale random field model for Bayesian image segmentation
3. Automatic analysis of the difference image for unsupervised change detection
4. Unsupervised Change Detection for Satellite Images Using Dual-Tree Complex Wavelet Transform
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