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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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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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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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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Water Body Extraction From Very High-Resolution Remote Sensing Imagery Using Deep U-Net and a Superpixel-Based Conditional Random Field Model

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2018.2879492 ◽

2019 ◽

Vol 16 (4) ◽

pp. 618-622 ◽

Author(s):

Wenqing Feng ◽

Haigang Sui ◽

Weiming Huang ◽

Chuan Xu ◽

Kaiqiang An

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Random Field ◽

Field Model ◽

Conditional Random Field ◽

Random Field Model ◽

Remote Sensing Imagery ◽

Conditional Random Field Model ◽

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A new change-detection method in high-resolution remote sensing images based on a conditional random field model

International Journal of Remote Sensing ◽

10.1080/01431161.2016.1148284 ◽

2016 ◽

Vol 37 (5) ◽

pp. 1173-1189 ◽

Author(s):

Guo Cao ◽

Licun Zhou ◽

Yupeng Li

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Change Detection ◽

Random Field ◽

Field Model ◽

Detection Method ◽

Conditional Random Field ◽

Remote Sensing Images ◽

Random Field Model ◽

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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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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MULTI-SOURCE HIERARCHICAL CONDITIONAL RANDOM FIELD MODEL FOR FEATURE FUSION OF REMOTE SENSING IMAGES AND LIDAR DATA

ISPRS - International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-1-w1-389-2013 ◽

2013 ◽

Vol XL-1/W1 ◽

pp. 389-392

Author(s):

Z. Zhang ◽

M. Y. Yang ◽

M. Zhou

Keyword(s):

Remote Sensing ◽

Random Field ◽

Field Model ◽

Feature Fusion ◽

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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Semantic Segmentation of PolSAR Images Using Conditional Random Field Model Based on Deep Features

Journal of Physics Conference Series ◽

10.1088/1742-6596/1168/4/042008 ◽

2019 ◽

Vol 1168 ◽

pp. 042008

Author(s):

Tao Hu ◽

Wei-hua Li ◽

Xian-xiang Qin

Keyword(s):

Random Field ◽

Field Model ◽

Conditional Random Field ◽

Semantic Segmentation ◽

Random Field Model ◽

Model Based ◽

Conditional Random Field Model

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