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PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field [article]

Haixia Bi, Jing Yao, Zhiqiang Wei, Danfeng Hong, Jocelyn Chanussot
2020 arXiv   pre-print
To this end, we present a novel PolSAR image classification method, which removes speckle noise via low-rank (LR) feature extraction and enforces smoothness priors via Markov random field (MRF).  ...  Specifically, we employ the mixture of Gaussian-based robust LR matrix factorization to simultaneously extract discriminative features and remove complex noises.  ...  Figure 1 . 1 Illustrating the PolSAR image classification method via robust low-rank feature extraction and Markov random field. ) , α s as the label smoothness factor, and N (i) as the neighboring pixel  ... 
arXiv:2009.05942v1 fatcat:otl2ybfxirf4fptl2ps7lddxra

A Hierarchical Fully Convolutional Network Integrated with Sparse and Low-Rank Subspace Representations for PolSAR Imagery Classification

Yan Wang, Chu He, Xinlong Liu, Mingsheng Liao
2018 Remote Sensing  
A common way is to use the Markov random field and conditional random field to model the spatial interactions [9-11], or segment data into homogeneous objects [12] .  ...  is transferred to extract the nonlinear deep multi-scale spatial information of PolSAR image; and (3) the shallow sparse and low-rank subspace features are integrated to boost the discrimination of deep  ...  sparse and low-rank subspace of high-dimensional PolSAR data based upon graph embedding.  ... 
doi:10.3390/rs10020342 fatcat:qvt47nwaw5ckdltjucw4oo4hoq

Synthetic Aperture Radar Image Classification: a Survey

Aseel Sami, Matheel E. Abdulmunem
2020 Iraqi Journal of Science  
The most prominent use of Convolutional Neural Networks (CNN) was successful in extracting features from the images and training the neural network to analyze and classify them into classes according to  ...  In this review paper, several studies and researches were surveyed for assisting future researchers to identify available techniques in the field of classification of Synthetic Aperture Radar (SAR) images  ...  Images Based on Generalized Mean Shift. 2018 [18] A Hierarchical Fully Convolutional Network Integrated with Sparse and Low-Rank Subspace Representations for PolSAR Imagery Classification. 2020 [19  ... 
doi:10.24996/ijs.2020.61.5.29 fatcat:oc7gh3pblzfq7cknssnvq5s46u

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  ...  Finally, we propose a graph integration module based on self-attention to perform robust hierarchical feature extraction and learn an optimal linear combination of various scales to exploit effective feature  ...  , DLR, and CSA for providing PolSAR datasets for free download, and also like to thank M.  ... 
doi:10.1109/jstars.2021.3061418 fatcat:w2qo2zvoevc2zbxyte2kov766q

Random Region Matting for High-Resolution PolSAR Image Semantic Segmentation

Jun Ni, Fan Zhang, Fei Ma, Qiang Yin, Deliang Xiang
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
A special polarimetric decomposition method is used to extract the features, and the filter and the data truncation are implemented to enhance local and global information of images.  ...  To bridge the PolSAR data and application, the 2020 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation provides a set of high-quality PolSAR semantic segmentation dataset  ...  In earlier PolSAR image segmentation algorithm, some methods have been proposed for PolSAR imagery [7] , such as the Markov random field [8] , [9] , the conditional random field [10] , [11] .  ... 
doi:10.1109/jstars.2021.3062447 fatcat:yd2v7j3bjfgurj7h5zush7ftpy

Integrating Color Features in Polarimetric SAR Image Classification

Stefan Uhlmann, Serkan Kiranyaz
2014 IEEE Transactions on Geoscience and Remote Sensing  
It is a common practice to visualize PolSAR data by color coding methods and thus, it is possible to extract powerful color features from such pseudocolor images so as to provide additional data for a  ...  However, one source of information has so far been neglected from PolSAR classification: Color.  ...  RADARSAT-2 Data and Products MacDONALD, DETTWILER, and ASSOCIATES LTD. (2008)-all rights reserved.  ... 
doi:10.1109/tgrs.2013.2258675 fatcat:s4imvqz4ojcjtcx7vfdqhfbgky

Polarimetric Hierarchical Semantic Model and Scattering Mechanism Based PolSAR Image Classification [article]

Fang Liu, Junfei Shi, Licheng Jiao, Hongying Liu, Shuyuan Yang, Jie Wu, Hongxia Hao, Jialing Yuan
2015 arXiv   pre-print
In this paper, a polarimetric hierarchical semantic model (PHSM) is firstly proposed to overcome this disadvantage based on the constructions of a primal-level and a middle-level semantic.  ...  Experimental results on PolSAR data sets with different bands and sensors demonstrate that the proposed method is superior to the state-of-the-art methods in region homogeneity and edge preservation for  ...  wavelet features [19] ; 3) approaches with regularization criterion, such as Markov Random Field(MRF) [20] [21] and contour criterion [22] [23] ; 4) approaches based on statistical modeling,  ... 
arXiv:1507.00110v1 fatcat:hh5gez3acrderizf2ej6b4dlua

Polarimetric SAR Image Classification Using Multifeatures Combination and Extremely Randomized Clustering Forests

Tongyuan Zou, Wen Yang, Dengxin Dai, Hong Sun
2009 EURASIP Journal on Advances in Signal Processing  
Finally, we introduce extremely randomized clustering forests (ERCFs) to polarimetric SAR image classification and compare it with other competitive classifiers.  ...  Then, we propose two strategies for feature combination: manual selection according to heuristic rules and automatic combination based on a simple but efficient criterion.  ...  Then we apply a Potts model Markov Random Field (MRF) smoothing process using graph cut optimization [39] on the final pixels labels to obtain final classification result.  ... 
doi:10.1155/2010/465612 fatcat:6afemcd3x5ew5kjxk4g6grbffm

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 Nov. 2019 9236-9241 Conditional Random Field and Deep Feature Learning for Hyperspectral Image Classification.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

Deep Learning Meets SAR [article]

Xiao Xiang Zhu, Sina Montazeri, Mohsin Ali, Yuansheng Hua, Yuanyuan Wang, Lichao Mou, Yilei Shi, Feng Xu, Richard Bamler
2021 arXiv   pre-print
With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows.  ...  of deep learning applied to SAR in depth, summarize available benchmarks, and recommend some important future research directions.  ...  For terrain surface classification from SAR and Polarimetric SAR (PolSAR) images, effective feature extraction is essential.  ... 
arXiv:2006.10027v2 fatcat:s3tiroz4qve6nbhavtz77fbis4

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 feature extraction dictionary was used to extract the local and global features of target's HRRP [512] , [523] for multifeature joint learning method based on sparse representation and low-rank representation  ... 
arXiv:2009.13702v1 fatcat:m6am73324zdwba736sn3vmph3i

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

2015 IEEE Transactions on Geoscience and Remote Sensing  
., +, TGRS Nov. 2015 6315 Markov processes An Automatic -Distribution and Markov Random Field Segmentation Al- gorithm for PolSAR Images.  ...  ., +, TGRS April 2015 1707-1717 An Automatic -Distribution and Markov Random Field Segmentation Al- gorithm for PolSAR Images.  ... 
doi:10.1109/tgrs.2015.2513444 fatcat:zuklkpk4gjdxjegoym5oagotzq

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  
In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all?  ...  such as climate change and urbanization.  ...  [56] first introduced multilayer feature learning for PolSAR classification; here, an SAE is employed to extract useful features from a channel PolSAR image. Geng et al.  ... 
doi:10.1109/mgrs.2017.2762307 fatcat:ec7b32lpdnhvzbdz2uoayw6anq

2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12

2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., and Lopez, J.F  ...  ., +, JSTARS July 2019 2565-2574 Water Detection in SWOT HR Images Based on Multiple Markov Random Fields.  ...  ., +, JSTARS Dec. 2019 5086-5100 Spectral-Spatial Hyperspectral Image Classification Using Cascaded Markov Random Fields.  ... 
doi:10.1109/jstars.2020.2973794 fatcat:sncrozq3fjg4bgjf4lnkslbz3u

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  
., and Foerster, S  ...  ., +, JSTARS Dec. 2014 4653-4669 Pansharpening Based on Low-Rank and Sparse Decomposition.  ...  Villa, P., +, JSTARS July 2014 3117-3127 Pansharpening Based on Low-Rank and Sparse Decomposition.  ... 
doi:10.1109/jstars.2015.2397347 fatcat:ib3tjwsjsnd6ri6kkklq5ov37a
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