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 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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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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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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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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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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High-Resolution SAR Image Classification Using Context-Aware Encoder Network and Hybrid Conditional Random Field Model

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2019.2963699 ◽

2020 ◽

Vol 58 (8) ◽

pp. 5317-5335

Author(s):

Wenkai Liang ◽

Yan Wu ◽

Ming Li ◽

Yice Cao

Keyword(s):

High Resolution ◽

Random Field ◽

Image Classification ◽

Field Model ◽

Conditional Random Field ◽

Context Aware ◽

Sar Image ◽

Random Field Model ◽

Conditional Random Field Model

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Automatic change detection based on conditional random field in high resolution remote sensing images

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

10.1109/igarss.2016.7729620 ◽

2016 ◽

Author(s):

Guo Cao ◽

Xuesong Li ◽

Yanfeng Shang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Change Detection ◽

Random Field ◽

Conditional Random Field ◽

Remote Sensing Images

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Change Detection Based on Conditional Random Field With Region Connection Constraints in High-Resolution Remote Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2016.2514610 ◽

2016 ◽

Vol 9 (8) ◽

pp. 3478-3488 ◽

Author(s):

Licun Zhou ◽

Guo Cao ◽

Yupeng Li ◽

Yanfeng Shang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Change Detection ◽

Random Field ◽

Conditional Random Field ◽

Remote Sensing Images

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Multimodal Change Detection in Remote Sensing Images Using an Unsupervised Pixel Pairwise Based Markov Random Field Model

IEEE Transactions on Image Processing ◽

10.1109/tip.2019.2933747 ◽

2019 ◽

pp. 1-1 ◽

Author(s):

Redha Touati ◽

Max Mignotte ◽

Mohamed Dahmane

Keyword(s):

Remote Sensing ◽

Change Detection ◽

Random Field ◽

Markov Random Field ◽

Field Model ◽

Remote Sensing Images ◽

Random Field Model ◽

Markov Random Field Model ◽

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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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