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European Journal of Remote Sensing Volume 49, 2016 - Issue 1
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Original Articles

Unsupervised change detection in high spatial resolution remote sensing images based on a conditional random field model

Guo CaoSchool of Computer Science and Technology, Nanjing University of Science and Technology, 210094, Nanjing, ChinaCorrespondence[email protected]
,
Xuesong LiSchool of Computer Science and Technology, Nanjing University of Science and Technology, 210094, Nanjing, China
&
Licun ZhouSchool of Computer Science and Technology, Nanjing University of Science and Technology, 210094, Nanjing, China
Pages 225-237 | Received 11 Jul 2015, Accepted 09 May 2016, Published online: 17 Feb 2017

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Lidong Zou, Muyi Li, Sen Cao, Feng Yue, Xiufang Zhu, Yizhan Li & Zaichun Zhu. (2022) Object-Oriented Unsupervised Change Detection Based on Neighborhood Correlation Images and k-Means Clustering for the Multispectral and High Spatial Resolution Images . Canadian Journal of Remote Sensing 48:3, pages 441-451.
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Amin Mohsenifar, Ali Mohammadzadeh, Armin Moghimi & Bahram Salehi. (2021) A novel unsupervised forest change detection method based on the integration of a multiresolution singular value decomposition fusion and an edge-aware Markov Random Field algorithm. International Journal of Remote Sensing 42:24, pages 9376-9404.
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