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Learning Rotation Domain Deep Mutual Information Using Convolutional LSTM for Unsupervised PolSAR Image Classification

Lei Wang, Xin Xu, Rong Gui, Rui Yang, Fangling Pu
2020 Remote Sensing  
to learn the deep mutual information of polarimetric coherent matrices in the rotation domain with different polarimetric orientation angles (POAs) for unsupervised PolSAR image classification.  ...  Therefore, unsupervised PolSAR image classification is worthy of further investigation that is based on deep learning.  ...  .; supervision, X.X.; funding acquisition, X.X., F.P. All authors have read and agreed to the published version of the manuscript.  ... 
doi:10.3390/rs12244075 fatcat:2au5xjfaybcu7guqebi5pzznya

Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

Yangyang Li, Ruoting Xing, Licheng Jiao, Yanqiao Chen, Yingte Chai, Naresh Marturi, Ronghua Shang
2019 Remote Sensing  
To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed.  ...  In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results.  ...  Acknowledgments: The author would like to show their gratitude to the editors and the anonymous reviewers for their insightful comments.  ... 
doi:10.3390/rs11161933 fatcat:hxen3wfavzh4xlayz3gursggyy

Self-Paced Convolutional Neural Network for PolSAR Images Classification

Changzhe Jiao, Xinlin Wang, Shuiping Gou, Wenshuai Chen, Debo Li, Chao Chen, Xiaofeng Li
2019 Remote Sensing  
In this paper, a novel classification method for PolSAR images using self-paced convolutional neural network (SPCNN) is proposed.  ...  Utilizing the information between the four channels can considerably improve the performance of PolSAR image classification.  ...  Nonetheless, pooling operation may also lose useful information. Differing from the image-based classification model, SPCNN is utilized for pixel-based classification.  ... 
doi:10.3390/rs11040424 fatcat:vrmdmbbz65c4xfab64vr3oxole

PCLNet: A Practical Way for Unsupervised Deep PolSAR Representations and Few-Shot Classification [article]

Lamei Zhang and Siyu Zhang and Bin Zou and Hongwei Dong
2020 arXiv   pre-print
Specifically, a PolSAR-tailored contrastive learning network (PCLNet) is proposed for unsupervised deep PolSAR representation learning and few-shot classification.  ...  It is well-known that following the supervised learning paradigm may lead to the overfitting of training data, and the lack of supervision information of PolSAR images undoubtedly aggravates this problem  ...  Greatly inspired by the success in natural language processing [44] , self-supervised learning (SSL) [45] provides a promising way for unsupervised representation learning, which follows the supervised  ... 
arXiv:2006.15351v1 fatcat:seb46jshpjdgdbqepp45lln7pi

Representative Learning via Span-Based Mutual Information for PolSAR Image Classification

Jianlong Wang, Biao Hou, Licheng Jiao, Shuang Wang
2021 Remote Sensing  
Therefore, span-based mutual information (Sp-MI) is proposed to lighten the dependence on labeling information, and then a heuristic representative learning scheme is also given by artificial neural network  ...  Except for the support vector machine, three ANN-based classifiers are implemented to verify the rationality and effectiveness of the proposed representative learning and normalization scheme.  ...  Further, a mutual information-based self-supervised learning model is given to learn an implicit representation from unlabeled samples of PolSAR data in [32] .  ... 
doi:10.3390/rs13091609 fatcat:epf43nj65veq7duc3juicwxw7e

2014 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 7

2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., Bronstert, A., and Foerster, S  ...  ., +, JSTARS Feb. 2014 491-502 A Generic Land-Cover Classification Framework for Polarimetric SAR Images Using the Optimum Touzi Decomposition Parameter Subset-An Insight on Mutual Information-Based  ...  ., +, JSTARS Feb. 2014 491-502 A Generic Land-Cover Classification Framework for Polarimetric SAR Images Using the Optimum Touzi Decomposition Parameter Subset-An Insight on Mutual Information-Based  ... 
doi:10.1109/jstars.2015.2397347 fatcat:ib3tjwsjsnd6ri6kkklq5ov37a

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing [article]

Ping Lang, Xiongjun Fu, Marco Martorella, Jian Dong, Rui Qin, Xianpeng Meng, Min Xie
2020 arXiv   pre-print
With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems.  ...  of ML-based RSP techniques.  ...  performance levels for SAR land use/cover classification.  ... 
arXiv:2009.13702v1 fatcat:m6am73324zdwba736sn3vmph3i

The 2011 Tohoku Tsunami from the Sky: A Review on the Evolution of Artificial Intelligence Methods for Damage Assessment

Jérémie Sublime
2021 Geosciences  
The Tohoku tsunami was a devastating event that struck North-East Japan in 2011 and remained in the memory of people worldwide.  ...  This paper provides a review of how artificial intelligence methods applied to case studies about the Tohoku tsunami have evolved since 2011.  ...  Their method is based on a modified logistic regression model-a normally supervised algorithm used for binary classification-that the authors have modified so that the parameters that are normally trained  ... 
doi:10.3390/geosciences11030133 fatcat:q4e4d73xxff3bcxeiik5byqr7e

2015 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 53

2015 IEEE Transactions on Geoscience and Remote Sensing  
Meka, A., +, TGRS April 2015 1707-1717 Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy.  ...  ., +, TGRS Oct. 2015 5495-5503 Mutual-Information-Based Semi-Supervised Hyperspectral Band Selection With High Discrimination, High Information, and Low Redundancy.  ...  Radiofrequency interference A Methodology to Determine Radio-Frequency Interference in AMSR2 Observations.  ... 
doi:10.1109/tgrs.2015.2513444 fatcat:zuklkpk4gjdxjegoym5oagotzq

GPU-Accelerated CatBoost-Forest for Hyperspectral Image Classification Via Parallelized mRMR Ensemble Subspace Feature Selection

Alim Samat, Erzhu Li, Peijun Du, Sicong Liu, Junshi Xia
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Index Terms-CatBoost, ensemble learning (EL), feature selection (FS), gradient boosted decision tree (GBDT), histogram-based gradient boosting trees (histGBT), hyperspectral image classification, lightGBM  ...  Experimental results on three widely acknowledged hyperspectral benchmarks showed that: 1) GPU-CatBoost is also an advanced ensemble learning (EL) algorithm for hyperspectral image classification using  ...  Gamba, the IEEE GRSS Image Analysis and DFTC, and the NCALM and the Hyperspectral Image Analysis Laboratory, University of Houston, for freely providing the Pavia University, GRSS-DFC2013 Houston, and  ... 
doi:10.1109/jstars.2021.3063507 fatcat:bql22z6tb5fwrhptosdaksufbm

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  This index covers all technical items-papers, correspondence, reviews, etc.-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  ., +, TIP 2021 572-587 A Supervised Segmentation Network for Hyperspectral Image Classification.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

Knowledge Extracted from Copernicus Satellite Data

Dumitru Octavian, Schwarz Gottfried, Eltoft Torbjørn, Kræmer Thomas, Wagner Penelope, Hughes Nick, Arthus David, Fleming Andrew, Koubarakis Manolis, Datcu Mihai
2019 Zenodo  
Such a combination approach already demonstrated its applicability for monitoring seasonal snow cover [1].  ...  By applying an already established active learning approach based on a Support Vector Machine with relevance feedback [2], we can limit ourselves to a limited number of typical satellite images to extract  ...  The next task is connected to find more applications and reduce the time and all machine and human sources for final model creation and visualization.  ... 
doi:10.5281/zenodo.3941573 fatcat:zzifwgljifck5bpjnboetsftfu

Image splicing detection with local illumination estimation

Yu Fan, Philippe Carre, Christine Fernandez-Maloigne
2015 2015 IEEE International Conference on Image Processing (ICIP)  
the land of deep unsupervised learning .  ...  University of Bolton 82 | ICIP 2015 -Technical Program TEC-P15.6 -A LOCAL MUTUAL INFORMATION-BASED METHOD FOR LARGE SCALE ACTIVE LEARNING Farid OVEISI, University of Bolton Shahrzad OVEISI, Azad  ...  Concluding the Show & Tell session will be a section concentrating on why the sensory gap is important to consider for practical applications, such as annotation tools, and image learning and mining systems  ... 
doi:10.1109/icip.2015.7351341 dblp:conf/icip/FanCF15 fatcat:7ja5gjnp5rafvedc2nman7xcru

ICCEM 2020 Front Matter

2020 2020 IEEE International Conference on Computational Electromagnetics (ICCEM)  
Classification Based on SAR Data Using Superpixel and Cosine Similarity Xueyue Mao, Yilong Lu and Xiao Xiao 10.55AM T-AM2-R3.2 [#1570611007] Application of Supervised Descent Method to Inverse Problems  ...  Deep Learning Based Through Wall Imaging Method Yukai Bai and Xiuzhu Ye 11.40AM T-AM2-R3.5 [#1570611110] A Machine Learning Assisted Compressive Sensing Approach for Sparse Electromagnetic Imaging  ... 
doi:10.1109/iccem47450.2020.9219463 fatcat:brgvy2m7t5exhiqblr444i2m7e

More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification [article]

Danfeng Hong and Lianru Gao and Naoto Yokoya and Jing Yao and Jocelyn Chanussot and Qian Du and Bing Zhang
2020 pre-print
More significantly, our framework is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs).  ...  In particular, we also investigate a special case of multi-modality learning (MML) -- cross-modality learning (CML) that exists widely in RS image classification applications.  ...  The authors also would like to thank the IEEE GRSS DFC2017 for providing Sentinel-2 MS datasets for the LCZ classification task.  ... 
doi:10.1109/tgrs.2020.3016820 arXiv:2008.05457v1 fatcat:4ddnh2yevrbwtea5hlrj7rx7tm
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