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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.  ...  Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.  ...  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

Semi-supervised Classication for PolSAR Data with Multi-scale Evolving Weighted Graph Convolutional Network

Shijie Ren, Feng Zhou
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
To overcome this limitation and achieve robust PolSAR image classification, this article proposes the multiscale evolving weighted graph convolutional network, where weighted graphs based on superpixel  ...  with PolSAR data on irregular domains, e.g., superpixel graphs, because they are naturally designed as grid-based architectures in Euclidean space.  ...  , DLR, and CSA for providing PolSAR datasets for free download, and also like to thank M.  ... 
doi:10.1109/jstars.2021.3061418 fatcat:w2qo2zvoevc2zbxyte2kov766q

Semi-Supervised Learning for Ill-Posed Polarimetric SAR Classification

Stefan Uhlmann, Serkan Kiranyaz, Moncef Gabbouj
2014 Remote Sensing  
We propose different strategies within self-training on how to select more reliable candidates from the pool of unlabeled samples to speed-up the learning process and to improve the classification performance  ...  In this paper, we investigate the application of semi-supervised learning approaches and particularly focus on the small sample size problem.  ...  Stefan Uhlmann and Serkan Kiranyaz carried out the evaluation and wrote the manuscript. Moncef Gabbouj supervised the research efforts. All authors compiled and approved the final manuscript  ... 
doi:10.3390/rs6064801 fatcat:nncnvxdhjrgrxoo5gzc5wpxvym

Multiple Classifiers Based Semi-Supervised Polarimetric SAR Image Classification Method

Lekun Zhu, Xiaoshuang Ma, Penghai Wu, Jiangong Xu
2021 Sensors  
In this paper, we propose a novel semi-supervised classification method for PolSAR images, which combines the deep learning technique with the traditional scattering trait-based classifiers.  ...  Experiments on two real PolSAR images on agricultural and forest areas indicate that, in most cases, significant improvements can be achieved with the proposed method, compared to the base classifiers,  ...  [38] proposed a semi-supervised algorithm based on self-training and superpixel, which used the segmentation and stacked sparse auto-encoder to expand the training set. Sun et al.  ... 
doi:10.3390/s21093006 pmid:33922957 fatcat:bn6ir2c53jdqdpv4cqk5iy5gsq

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  
Therefore, unsupervised PolSAR image classification is worthy of further investigation that is based on deep learning.  ...  Finally, the classification results can be output directly from the trained network model. The proposed method is trained in an end-to-end manner and does not have cumbersome pipelines.  ...  .; writing-review and editing, R.G., R.Y., X.X., and F.P.; 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

SSCV-GANs:Semi-Supervised Complex-Valued GANs for PolSAR Image Classification

Xiufang Li, Qigong Sun, LingLing Li, Xu Liu, Hongying Liu, Licheng Jiao, Fang Liu
2020 IEEE Access  
Polarimetric synthetic aperture radar (PolSAR) image classification has been widely applied in many fields, such as agriculture, meteorology and military.  ...  On the one hand, the complex-valued model conforms with the physical mechanism of PolSAR data and it plays an important role for retaining and utilizing amplitude and phase information of PolSAR data.  ...  They would also like to thank the NASA/JPL-Caltech and Canadian Space Agency for providing the polarimetric AIRSAR data.  ... 
doi:10.1109/access.2020.3004591 fatcat:zyh5fxaiczdc3n7q6x7uybauda

Graph Convolutional Networks by Architecture Search for PolSAR Image Classification

Hongying Liu, Derong Xu, Tianwen Zhu, Fanhua Shang, Yuanyuan Liu, Jianhua Lu, Ri Yang
2021 Remote Sensing  
In this paper, we propose a neural architecture search method based GCN (ASGCN) for the classification of PolSAR images.  ...  As one well-known class of semi-supervised learning methods, graph convolutional networks (GCNs) have gained much attention recently to address the classification problem with only a few labeled samples  ...  In [22] , a new superpixel generation method named as fuzzy super-pixel (FS) is proposed for PolSAR image classification.  ... 
doi:10.3390/rs13071404 fatcat:hyl2fomze5ejxggrplmcsm52yu

Semi-Supervised Remote Sensing Image Semantic Segmentation via Consistency Regularization and Average Update of Pseudo-Label

Jiaxin Wang, Chris H. Q. Ding, Sibao Chen, Chenggang He, Bin Luo
2020 Remote Sensing  
This paper proposes a method for remote sensing image segmentation based on semi-supervised learning.  ...  We first design a Consistency Regularization (CR) training method for semi-supervised training, then employ the new learned model for Average Update of Pseudo-label (AUP), and finally combine pseudo labels  ...  Acknowledgments: The authors thank the editor and anonymous reviewers for their valuable comments and suggestions, which were very helpful in improving this paper.  ... 
doi:10.3390/rs12213603 fatcat:jtg6jxaebvebpm35yunq5idziq

Sparse Subspace Clustering-Based Feature Extraction for PolSAR Imagery Classification

Bo Ren, Biao Hou, Jin Zhao, Licheng Jiao
2018 Remote Sensing  
In this paper, based on the subspace clustering algorithms, we combine sparse representation, low-rank representation, and manifold graphs to investigate the intrinsic property of PolSAR data.  ...  The proposed algorithms aim at constructing a projection matrix from the subspace clustering algorithms to achieve the features benefiting for the subsequent PolSAR image classification.  ...  Patel and Ehsan Elhamifar, which give us motivations to explore the PolSAR data. The codes of SSC, LRSC, and LRSSC related algorithms can be obtained from their homepage [59, 60] .  ... 
doi:10.3390/rs10030391 fatcat:mscslz6bureldmxrtq2haswv4q

Semi-Supervised Classification and Landscape Metrics for Mapping and Spatial Pattern Change Analysis of Tropical Forest Types in Thua Thien Hue Province, Vietnam

Tuong, Tani, Wang, Thang
2019 Forests  
Based on the classified images, forest transition was evaluated using certain landscape metrics at the class and landscape levels.  ...  Materials and Methods: A combination of Landsat data with PALSAR and PALSAR-2 was used for forest classification through the proposed semi-supervised model.  ...  Many semi-supervised classification algorithms such as expectation-maximization, co-training, and self-training have been developed.  ... 
doi:10.3390/f10080673 fatcat:ijeqm5z46zd6hke3uq75ju4jky

Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

Xiao Xiang Zhu, Devis Tuia, Lichao Mou, Gui-Song Xia, Liangpei Zhang, Feng Xu, Friedrich Fraundorfer
2017 IEEE Geoscience and Remote Sensing Magazine  
such as climate change and urbanization.  ...  In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously  ...  [59] tested DBN on urban land use and land cover classification using PolSAR data. Hou et al. [60] proposed an SAE combined with superpixels for PolSAR image classification.  ... 
doi:10.1109/mgrs.2017.2762307 fatcat:ec7b32lpdnhvzbdz2uoayw6anq

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
The main applications of ML-based RSP are then analysed and structured based on the application field.  ...  Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments.  ...  A spatial-anchor graph based fast semi-supervised classification algorithm for PolSAR image was introduced in [435] .  ... 
arXiv:2009.13702v1 fatcat:m6am73324zdwba736sn3vmph3i

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 4325-4338 A New Parallel Dual-Channel Fully Convolutional Network Via Semi-Supervised FCM for PolSAR Image Classification.  ...  ., +, JSTARS 2020 1271-1285 A New Parallel Dual-Channel Fully Convolutional Network Via Semi-Supervised FCM for PolSAR Image Classification.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
., and Drake, V.A., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674  ...  and Hanssen, R.F., Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging  ...  ., +, TGRS Jan. 2019 3-13 Hyperspectral Image Classification With Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

Alberto Signoroni, Mattia Savardi, Annalisa Baronio, Sergio Benini
2019 Journal of Imaging  
sectors that involve these imaging technologies.  ...  Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of  ...  CRF (CNN-RCRF) to perform high-resolution classification, refining the superpixel image into a pixel-based result.  ... 
doi:10.3390/jimaging5050052 pmid:34460490 fatcat:ledlmt42bfdtdhe7tvj2dl2rwm
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