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SAR Image Classification Using Fully Connected Conditional Random Fields Combined with Deep Learning and Superpixel Boundary Constraint

Zhensheng Sun, Miao Liu, Peng Liu, Juan Li, Tao Yu, Xingfa Gu, Jian Yang, Xiaofei Mi, Weijia Cao, Zhouwei Zhang
2021 Remote Sensing  
The proposed algorithm can successfully combine the local and global advantages of fully connected conditional random fields and deep models.  ...  To mitigate some of the issues and to improve the pattern recognition of high-resolution SAR images, a ConvCRF model combined with superpixel boundary constraint is developed.  ...  The authors also thank the National Demonstration Center of Spaceborne Remote Sensing for their generous provision of GF-3 SAR images.  ... 
doi:10.3390/rs13020271 fatcat:f77fbdol6radxptnua7yrewlby

Automated High-resolution Earth Observation Image Interpretation: Outcome of the 2020 Gaofen Challenge

Xian Sun, Peijin Wang, Zhiyuan Yan, Wenhui Diao, Xiaonan Lu, Zhujun Yang, Yidan Zhang, Deliang Xiang, Chen Yan, Jie Guo, Bo Dang, Wei Wei (+6 others)
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
With the development of convolutional neural networks, deep learning-based methods have achieved good performance on image interpretation.  ...  Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation.  ...  ACKNOWLEDGMENT We are very grateful for the support of the IEEE Geoscience and Remote Sensing Society, especially to Professor Paolo Gamba, Professor Jun Li, and the Image Analysis and Data Fusion Technical  ... 
doi:10.1109/jstars.2021.3106941 fatcat:phyzqxnzevafjp4slc7lyhpdja

A survey on deep learning-based precise boundary recovery of semantic segmentation for images and point clouds

Rui Zhang, Guangyun Li, Thomas Wunderlich, Li Wang
2021 International Journal of Applied Earth Observation and Geoinformation  
A B S T R A C T Precise localization of semantic segmentation is attracting increasing attention, and salient performances are dominated by deep learning-based methods, especially deep convolutional neural  ...  Considering this, this paper conducts a comprehensive survey of precise boundary recovery for semantic segmentation, focusing mainly on 2D images and 3D point clouds.  ...  This study is undertaken with the financial support of the National Natural Science Foundation of China (NSFC) (Grant No. 42071454) and the Science and Technology Research Projects of Science and Technology  ... 
doi:10.1016/j.jag.2021.102411 fatcat:czgwporyvzhmjh254ff7rmz2ci

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?  ...  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  ...  The NN is then followed by a conditional random field (CRF) to decrease the effect of speckle noise inherent in SAR images.  ... 
doi:10.1109/mgrs.2017.2762307 fatcat:ec7b32lpdnhvzbdz2uoayw6anq

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  
Although deep learning-based methods have been successfully applied to polarimetric synthetic aperture radar (Pol-SAR) image classification tasks, most of the available techniques are not suitable to deal  ...  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  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their useful comments and constructive suggestions, which were of great help in improving this article, like to thank NASA/JPL  ... 
doi:10.1109/jstars.2021.3061418 fatcat:w2qo2zvoevc2zbxyte2kov766q

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 2240-2250 Superpixel Boundary-Based Edge Description Algorithm for SAR Image Segmentation.  ...  ., +, JSTARS 2020 227-240 Superpixel Boundary-Based Edge Description Algorithm for SAR Image Segmentation.  ...  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

RUF: Effective Sea Ice Floe Segmentation Using End-to-End RES-UNET-CRF with Dual Loss

Anmol Sharan Nagi, Devinder Kumar, Daniel Sola, K. Andrea Scott
2021 Remote Sensing  
The model exploits the advantages of both convolutional neural networks and convolutional conditional random field (Conv-CRF) in a combined manner.  ...  Machine learning and, recently, deep learning techniques are being explored by various researchers to process vast amounts of Synthetic Aperture Radar (SAR) data for detecting potential hazards in navigational  ...  All SAR images are copyrighted to MacDONALD, Dettwiler and Associates Ltd. (2010)-All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency.  ... 
doi:10.3390/rs13132460 fatcat:3wpbo4nj6nbrpfxek76ujtn3de

Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images

Jianming Hu, Xiyang Zhi, Wei Zhang, Longfei Ren, Lorenzo Bruzzone
2020 Remote Sensing  
Automatic ship detection in complicated maritime background is a challenging task in the field of optical remote sensing image interpretation and analysis.  ...  Firstly, we present a reliable background prior extraction method adaptive for the random locations of targets by computing boundary probability and then generate a saliency map based on the background  ...  Acknowledgments: The authors would like to thank the Kaggle competition organizers for providing the Airbus dataset and Antonio-Javier Gallego from the University of Alicante for providing the MASATI dataset  ... 
doi:10.3390/rs12203370 fatcat:xivcnyxtczc4ra62kj3zn3mlce

Table of Contents

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Mignotte, and M. Dahmane 588 Bayesian Pan-Sharpening With Multiorder Gradient-Based Deep Network Constraints . , and P.  ...  Li 5580 Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Nag 872 Special Section: Deep Learning for Remote Sensing Image Scene Classification Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities .  ... 
doi:10.1109/jstars.2020.3046663 fatcat:zqzyhnzacjfdjeejvzokfy4qze

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] .  ...  SAR Image Classification Meets Deep Learning.  ...  has been verified useful for SAR image classification in several pioneering studies.  ... 
doi:10.3390/rs10020342 fatcat:qvt47nwaw5ckdltjucw4oo4hoq

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.  ...  The main applications of ML-based RSP are then analysed and structured based on the application field.  ...  A multilayer AE, combining with Euclidean distance as a supervised constraint, to be used in [394] for SAR-ATR tasks with the limited training images.  ... 
arXiv:2009.13702v1 fatcat:m6am73324zdwba736sn3vmph3i

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

2019 IEEE Transactions on Geoscience and Remote Sensing  
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  ...  Hu, C., Zhang, B., Dong, X., and Li, Y., Geosynchronous SAR Tomography: Theory and First Experimental Verification Using Beidou IGSO Satellite; TGRS Sept. 2019 6591-6607 Hu, F., Wu, J., Chang, L.,  ...  ., +, TGRS Nov. 2019 9236-9241 Conditional Random Field and Deep Feature Learning for Hyperspectral Image Classification.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

2021 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 14

2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, JSTARS 2021 7310-7323 Tropical Cyclone Intensity Classification and Estimation Using Infrared Satellite Images With Deep Learning.  ... 
doi:10.1109/jstars.2022.3143012 fatcat:dnetkulbyvdyne7zxlblmek2qy

Waterline Extraction for Artificial Coast With Vision Transformers

Le Yang, Xing Wang, Jingsheng Zhai
2022 Frontiers in Environmental Science  
To estimate the effects of the two methods, we collect the high-resolution images from the web map services, and the annotations are created manually for training and test.  ...  However, waterline extraction from high-resolution images is a very challenging task because it is easily influenced by the complex background.  ...  Sun et al. (2019) built a superpixel-based conditional random field model to segment the sea and land area.  ... 
doi:10.3389/fenvs.2022.799250 fatcat:ap2j2vighjg6pkpfsrt4hda2fa

A Two-Stage Gradient Ascent-Based Superpixel Framework for Adaptive Segmentation

He, Li, Guo, Wei, Guo
2019 Applied Sciences  
Experimental results show that the framework achieves better performance on detail-rich regions than previous superpixel approaches with satisfactory efficiency.  ...  In this work, a novel coarse-to-fine gradient ascent framework is proposed for superpixel-based color image adaptive segmentation.  ...  continuous Conditional Random Field (C-CRF) on features of full resolution.  ... 
doi:10.3390/app9122421 fatcat:flzeoyfwd5aexf6efqkg2b6id4
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