Change detection based on structural conditional random field framework for high spatial resolution remote sensing imagery

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2017.8127138 ◽

2017 ◽

Author(s):

Pengyuan Lv ◽

Yanfei Zhong ◽

Ji Zhao ◽

Ailong Ma ◽

Liangpei Zhang

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Random Field ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Conditional Random Field ◽

Remote Sensing Imagery

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Unsupervised Change Detection Based on Hybrid Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2018.2819367 ◽

2018 ◽

Vol 56 (7) ◽

pp. 4002-4015 ◽

Author(s):

Pengyuan Lv ◽

Yanfei Zhong ◽

Ji Zhao ◽

Liangpei Zhang

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Random Field ◽

Spatial Resolution ◽

Field Model ◽

High Spatial Resolution ◽

Conditional Random Field ◽

Random Field Model ◽

Remote Sensing Imagery ◽

Conditional Random Field Model

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Unsupervised change detection model based on hybrid conditional random field for high spatial resolution remote sensing imagery

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2016.7729478 ◽

2016 ◽

Author(s):

Pengyuan Lv ◽

Yanfei Zhong ◽

Ji Zhao ◽

Liangpei Zhang

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Random Field ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Conditional Random Field ◽

Remote Sensing Imagery ◽

Detection Model ◽

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Change Detection Based on a Multifeature Probabilistic Ensemble Conditional Random Field Model for High Spatial Resolution Remote Sensing Imagery

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2016.2619163 ◽

2016 ◽

Vol 13 (12) ◽

pp. 1965-1969 ◽

Author(s):

Pengyuan Lv ◽

Yanfei Zhong ◽

Ji Zhao ◽

Hongzan Jiao ◽

Liangpei Zhang

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Random Field ◽

Spatial Resolution ◽

Field Model ◽

High Spatial Resolution ◽

Conditional Random Field ◽

Random Field Model ◽

Remote Sensing Imagery ◽

Conditional Random Field Model

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A Hybrid Object-Oriented Conditional Random Field Classification Framework for High Spatial Resolution Remote Sensing Imagery

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2014.2306692 ◽

2014 ◽

Vol 52 (11) ◽

pp. 7023-7037 ◽

Author(s):

Yanfei Zhong ◽

Ji Zhao ◽

Liangpei Zhang

Keyword(s):

Remote Sensing ◽

Random Field ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Conditional Random Field ◽

Object Oriented ◽

Remote Sensing Imagery ◽

Classification Framework

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

European Journal of Remote Sensing ◽

10.5721/eujrs20164913 ◽

2016 ◽

Vol 49 (1) ◽

pp. 225-237 ◽

Author(s):

Guo Cao ◽

Xuesong Li ◽

Licun Zhou

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Random Field ◽

Spatial Resolution ◽

Field Model ◽

High Spatial Resolution ◽

Conditional Random Field ◽

Remote Sensing Images ◽

Random Field Model ◽

Conditional Random Field Model

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Land-Use/Land-Cover Change Detection Based on Class-Prior Object-Oriented Conditional Random Field Framework for High Spatial Resolution Remote Sensing Imagery

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.3034373 ◽

2020 ◽

pp. 1-16

Author(s):

Sunan Shi ◽

Yanfei Zhong ◽

Ji Zhao ◽

Pengyuan Lv ◽

Yinhe Liu ◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

Random Field ◽

Land Cover Change ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Conditional Random Field ◽

Object Oriented ◽

Land Use Land Cover ◽

Land Cover Change Detection

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Urban area detection from high-spatial resolution remote sensing imagery using Markov random field-based region growing

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.8.083566 ◽

2014 ◽

Vol 8 (1) ◽

pp. 083566 ◽

Author(s):

Chen Zheng ◽

Leiguang Wang ◽

Hui Zhao ◽

Xiaohui Chen

Keyword(s):

Remote Sensing ◽

Random Field ◽

Spatial Resolution ◽

Markov Random Field ◽

High Spatial Resolution ◽

Region Growing ◽

Remote Sensing Imagery ◽

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A Markov Random Field Moel with Alternating Granularities for Segmentation of High Spatial Resolution Remote Sensing Imagery

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8900552 ◽

2019 ◽

Author(s):

Chen Zheng ◽

Min Zhang ◽

Xiaohui Chen ◽

Leiguang Wang

Keyword(s):

Remote Sensing ◽

Random Field ◽

Spatial Resolution ◽

Markov Random Field ◽

High Spatial Resolution ◽

Remote Sensing Imagery ◽

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Change Detection Based on Pulse-Coupled Neural Networks and the NMI Feature for High Spatial Resolution Remote Sensing Imagery

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2014.2349937 ◽

2015 ◽

Vol 12 (3) ◽

pp. 537-541 ◽

Author(s):

Yanfei Zhong ◽

Wenfeng Liu ◽

Ji Zhao ◽

Liangpei Zhang

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Change Detection ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Imagery ◽

Coupled Neural Networks ◽

Pulse Coupled Neural Networks

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Multiscale U-Shaped CNN Building Instance Extraction Framework With Edge Constraint for High-Spatial-Resolution Remote Sensing Imagery

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.3022410 ◽

2020 ◽

pp. 1-15

Author(s):

Yuanyuan Liu ◽

Dingyuan Chen ◽

Ailong Ma ◽

Yanfei Zhong ◽

Fang Fang ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Remote Sensing Imagery

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