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A Novel Query Strategy-Based Rank Batch-Mode Active Learning Method for High-Resolution Remote Sensing Image Classification

Xin Luo, Huaqiang Du, Guomo Zhou, Xuejian Li, Fangjie Mao, Di'en Zhu, Yanxin Xu, Meng Zhang, Shaobai He, Zihao Huang
2021 Remote Sensing  
Based on ranked batch-mode active learning (RBMAL), this paper proposes a novel combined query strategy of spectral information divergence lowest confidence uncertainty sampling (SIDLC), called RBSIDLC  ...  An informative training set is necessary for ensuring the robust performance of the classification of very-high-resolution remote sensing (VHRRS) images, but labeling work is often difficult, expensive  ...  Algorithm 1 : 1 Ranked batch-mode active learning algorithm.  ... 
doi:10.3390/rs13112234 fatcat:jqao3enzhndndldujy3uxchfxi

Batch Mode Active Learning for Multimedia Pattern Recognition

Shayok Chakraborty, Vineeth Balasubramanian, Sethuraman Panchanathan
2012 2012 IEEE International Symposium on Multimedia  
I could not have asked for a better role model, who is inspirational, supportive and patient.  ...  This has expanded the possibility of solving real world problems using computational learning frameworks.  ...  ACKNOWLEDGEMENTS My tenure at Arizona State University has been influenced and guided by a number of people to whom I am deeply indebted.  ... 
doi:10.1109/ism.2012.101 dblp:conf/ism/ChakrabortyBP12 fatcat:kvr4sjlulrcv5cdwtrapadskm4

A Survey of Deep Active Learning [article]

Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B. Gupta, Xiaojiang Chen, Xin Wang
2021 arXiv   pre-print
This article is to fill this gap, we provide a formal classification method for the existing work, and a comprehensive and systematic overview.  ...  Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features.  ...  Multi-class batch-mode active learning for image classification.  ... 
arXiv:2009.00236v2 fatcat:zuk2doushzhlfaufcyhoktxj7e

Collaborative Active and Semisupervised Learning for Hyperspectral Remote Sensing Image Classification

Lunjun Wan, Ke Tang, Mingzhi Li, Yanfei Zhong, A. K. Qin
2015 IEEE Transactions on Geoscience and Remote Sensing  
Index Terms-Active learning (AL), hyperspectral image classification, remote sensing, semisupervised learning (SSL).  ...  To overcome these drawbacks, a novel approach named collaborative active and semisupervised learning (CASSL) is proposed in this paper.  ...  Query by committee (QBC) is another type of query heuristics. In [8] , a QBC-based method, i.e., entropy query by bagging (EQB), was introduced to remote sensing image classification.  ... 
doi:10.1109/tgrs.2014.2359933 fatcat:ucvl7nkynrh4bptefvro2hpmvu

Bayesian Active Remote Sensing Image Classification

Pablo Ruiz, Javier Mateos, Gustavo Camps-Valls, Rafael Molina, Aggelos K. Katsaggelos
2014 IEEE Transactions on Geoscience and Remote Sensing  
In this context, the emerging field of nonparametric Bayesian methods constitutes a proper theoretical framework to tackle the remote sensing image classification problem.  ...  Their properties make them appropriate for dealing with a high number of image features and a low number of available labeled spectra.  ...  Devis Tuia from the EPFL (Switzerland) for sharing the code of the RS, MS, and EQB-SVM active learning methods compared in this paper; Dr. L.  ... 
doi:10.1109/tgrs.2013.2258468 fatcat:4u2siizn4jfgpoc7uxuehj5sqi

Active Learning via Multi-View and Local Proximity Co-Regularization for Hyperspectral Image Classification

Wei Di, Melba M. Crawford
2011 IEEE Journal on Selected Topics in Signal Processing  
A novel co-regularization framework for active learning is proposed for hyperspectral image classification.  ...  Incorporating manifold learning into the active learning process enforces the clustering assumption and avoids the degradation of the distance measure associated with the original high-dimensional spectral  ...  Crawford, Fellow, IEEE Abstract-A novel co-regularization framework for active learning is proposed for hyperspectral image classification.  ... 
doi:10.1109/jstsp.2011.2123077 fatcat:rqv53imawvde3ozcwqjfihzhzi

TextRS: Deep Bidirectional Triplet Network for Matching Text to Remote Sensing Images

Abdullah, Bazi, Al Rahhal, Mekhalfi, Rangarajan, Zuair
2020 Remote Sensing  
Second, we put forth a novel Deep Bidirectional Triplet Network (DBTN) for text to image matching.  ...  Unlike traditional remote sensing image-to-image retrieval, our paradigm seeks to carry out the retrieval by matching text to image representations.  ...  Introduction The steady accessibility of remote sensing data, particularly high resolution images, has animated remarkable research outputs in the remote sensing community.  ... 
doi:10.3390/rs12030405 fatcat:hysgiroqz5d3rc6oqvaoh4w7ku

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 Aug. 2014 3491-3501 A Neural Approach Under Active Learning Mode for Change Detection in Remotely Sensed Images.  ...  ., +, JSTARS Aug. 2014 3491-3501 A Neural Approach Under Active Learning Mode for Change Detection in Remotely Sensed Images.  ... 
doi:10.1109/jstars.2015.2397347 fatcat:ib3tjwsjsnd6ri6kkklq5ov37a

Active Learning Plus Deep Learning Can Establish Cost-Effective and Robust Model for Multichannel Image: A Case on Hyperspectral Image Classification

Fangyu Shi, Zhaodi Wang, Menghan Hu, Guangtao Zhai
2020 Sensors  
In this paper, we present a framework using active learning and deep learning for multichannel image classification.  ...  In comparison, using active learning algorithm of entropy and image pool achieves a similar accuracy with only part of the whole training set manually annotated.  ...  Acknowledgments: The authors would like to acknowledge Wei Wang and Shuping Li for providing assistance with the English language revision.  ... 
doi:10.3390/s20174975 pmid:32887391 pmcid:PMC7506905 fatcat:4i2dwd2dpbg6zp5sxtffwxstiu

Hyperspectral Image Classification – Traditional to Deep Models: A Survey for Future Prospects [article]

Muhammad Ahmad, Sidrah Shabbir, Swalpa Kumar Roy, Danfeng Hong, Xin Wu, Jing Yao, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Jocelyn Chanussot
2021 arXiv   pre-print
., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods.  ...  This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic.  ...  ACKNOWLEDGMENT The authors thanks to Ganesan Narayanasamy who is leading IBM OpenPOWER/POWER enablement and ecosystem worldwide for his support to get the IBM AC922 system's access.  ... 
arXiv:2101.06116v2 fatcat:2duwvojkybgufo4kf6sbc6hdva

TRS: Transformers for Remote Sensing Scene Classification

Jianrong Zhang, Hongwei Zhao, Jiao Li
2021 Remote Sensing  
In this paper, we propose a new remote sensing scene classification method, Remote Sensing Transformer (TRS), a powerful "pure CNNs→Convolution + Transformer → pure Transformers" structure.  ...  With the development of attention-based methods, Convolutional Neural Networks (CNNs) have achieved competitive performance in remote sensing scene classification tasks.  ...  The traditional remote sensing scene classification method mainly relies on the spatial features of images [5, 6] . However, the error rate is high in the complex remote sensing scene.  ... 
doi:10.3390/rs13204143 fatcat:svpolh6htjc4ndqpqfq4isx54q

A Discriminative Feature Learning Approach for Remote Sensing Image Retrieval

Wei Xiong, Yafei Lv, Yaqi Cui, Xiaohan Zhang, Xiangqi Gu
2019 Remote Sensing  
Then, a new method for constructing more challenging datasets is first used for remote sensing image retrieval, to better validate our schemes.  ...  Effective feature representations play a decisive role in content-based remote sensing image retrieval (CBRSIR).  ...  Acknowledgments: The authors would like to thank the anonymous reviewers for their hard work. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs11030281 fatcat:4glhbdsc4jcjnof7edmobjixbi

ExtremeEarth Meets Satellite Data From Space

Desta Haileselassie Hagos, Theofilos Kakantousis, Vladimir Vlassov, Sina Sheikholeslami, Tianze Wang, Jim Dowling, Claudia Paris, Daniele Marinelli, Giulio Weikmann, Lorenzo Bruzzone, Salman Khaleghian, Thomas Krmer (+14 others)
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
New techniques in the areas of remote sensing and artificial intelligence with an emphasis on deep learning are developed.  ...  that enables scalable data processing, machine learning, and deep learning on Copernicus data, and development of very large training datasets for deep learning architectures targeting the classification  ...  His current research interests are on algorithms for automated analysis of SAR images for sea ice applications.  ... 
doi:10.1109/jstars.2021.3107982 fatcat:fxmpayska5bvlj7ibw3peqhuzu

ReSFlow: A Remote Sensing Imagery Data-Flow for Improved Model Generalization

Dalton Lunga, Jacob Arndt, Jonathan Gerrand, Robert Stewart
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Scaling computational activities demand models that generalize well across various challenges that can hamper progress, including 1) diverse imaging and geographic conditions, 2) sampling bias in training  ...  Furthermore, custom model architectures and rich training strategies effective for within bucket conditions can be developed.  ...  Heterogeneity in remote sensing instruments: Global satellite image datasets contain high-resolution imagery collected from multiple sensing instruments at different spatial, spectral, and temporal resolutions  ... 
doi:10.1109/jstars.2021.3119001 fatcat:fuap2dqe3jayvgkcwvvj6sqe6i

How can big data and machine learning benefit environment and water management: A survey of methods, applications, and future directions

Alexander Y. Sun, Bridget R Scanlon
2019 Environmental Research Letters  
The authors are grateful to Dr Michael Fienen and an anonymous reviewer for their constructive comments on the original manuscript.  ...  in the areas of remote sensing image classification, high-dimensional spatial and temporal data fusion, and multisource data predictive analytics.  ...  networks, image classification, deep learning, classification, and Big Data.  ... 
doi:10.1088/1748-9326/ab1b7d fatcat:vx4thuy45vhlnmhu7bk2hwh2g4
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