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In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities.
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“Semisupervised neural networks for efficient hyperspectral image classification”
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“Principal component analysis”
Herv“’e Abdi and Lynne Williams · 2010
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“Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.”
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“Noise-contrastive estimation: A new estimation principle for unnormalized statistical models”
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“Fuzzy clustering algorithms for unsupervised change detection in remote sensing images”
Ashish Ghosh, Niladri Mishra and Susmita Ghosh · 2011
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“Sparse autoencoder”
Andrew Ng · 2011
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“Semisupervised self-learning for hyperspectral image classification”
Inmaculada D“’opido et al · 2013
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“Unsupervised feature learning for aerial scene classification”
Anil Cheriyadat · 2013
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“Auto-encoding variational bayes”
Diederik Kingma and Max Welling · 2013
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“Robust and adaptive network flows”
Dimitris Bertsimas, Ebrahim Nasrabadi and Sebastian Stiller · 2013
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“Efficient estimation of word representations in vector space”
Tomas Mikolov, Kai Chen, Greg Corrado and Jeffrey Dean · 2013
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“Distributed representations of words and phrases and their compositionality”
Tomas Mikolov et al · 2013
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“Metric learning: A survey”
Brian Kulis · 2013
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“Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing”
Jaime Zabalza et al · 2014
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“Generative adversarial nets”
Ian Goodfellow et al · 2014
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“Discriminative unsupervised feature learning with convolutional neural networks”
Alexey Dosovitskiy, Jost Springenberg, Martin Riedmiller and Thomas Brox · 2014
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“Open access to Earth land-cover map”
Chen Jun, Yifang Ban and Songnian Li · 2014
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“Deep learning”
Yann LeCun, Yoshua Bengio and Geoffrey Hinton · 2015
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“Unsupervised deep feature extraction for remote sensing image classification”
Adriana Romero, Carlo Gatta and Gustau Camps-Valls · 2015
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Alireza Makhzani et al · 2015
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“Unsupervised visual representation learning by context prediction”
Carl Doersch, Abhinav Gupta and Alexei Efros · 2015
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“Unsupervised representation learning with deep convolutional generative adversarial networks”
Alec Radford, Luke Metz and Soumith Chintala · 2015
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“Unsupervised learning of video representations using lstms”
Nitish Srivastava, Elman Mansimov and Ruslan Salakhudinov · 2015
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“Optical flow modeling and computation: A survey”
Denis Fortun, Patrick Bouthemy and Charles Kervrann · 2015
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“Facenet: A unified embedding for face recognition and clustering”
Florian Schroff, Dmitry Kalenichenko and James Philbin · 2015
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“Distilling the knowledge in a neural network”
Geoffrey Hinton, Oriol Vinyals and Jeff Dean · 2015
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“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
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“Unsupervised multilayer feature learning for satellite image scene classification”
Yansheng Li et al · 2016
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“Adversarial feature learning”
Jeff Donahue, Philipp Kr“”ahenb“”uhl and Trevor Darrell · 2016
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“Unsupervised learning of visual representations by solving jigsaw puzzles”
Mehdi Noroozi and Paolo Favaro · 2016
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“Context encoders: Feature learning by inpainting”
Deepak Pathak et al · 2016
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“Colorful image colorization”
Richard Zhang, Phillip Isola and Alexei Efros · 2016
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“Ambient sound provides supervision for visual learning”
Andrew Owens et al · 2016
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“Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images”
Puzhao Zhang et al · 2016
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“Urban Flood Mapping With Bitemporal Multispectral Imagery Via a Self-Supervised Learning Framework”
Bo Peng et al · 2016
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“Adversarially learned inference”
Vincent Dumoulin et al · 2016
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“Learning representations for automatic colorization”
Gustav Larsson, Michael Maire and Gregory Shakhnarovich · 2016
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“Unsupervised learning for physical interaction through video prediction”
Chelsea Finn, Ian Goodfellow and Sergey Levine · 2016
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“Shuffle and learn: unsupervised learning using temporal order verification”
Ishan Misra, C Zitnick and Martial Hebert · 2016
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“Unsupervised learning using sequential verification for action recognition”
Ishan Misra, C Zitnick and Martial Hebert · 2016
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“Deep learning in remote sensing: A comprehensive review and list of resources”
Xiao Zhu et al · 2017
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“Representation learning by learning to count”
Mehdi Noroozi, Hamed Pirsiavash and Paolo Favaro · 2017
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“Variational inference: A review for statisticians”
David Blei, Alp Kucukelbir and Jon McAuliffe · 2017
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“Remote sensing scene classification by unsupervised representation learning”
Xiaoqiang Lu, Xiangtao Zheng and Yuan Yuan · 2017
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“Self-taught feature learning for hyperspectral image classification”
Ronald Kemker and Christopher Kanan · 2017
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“Unsupervised spectral–spatial feature learning via deep residual Conv–Deconv network for hyperspectral image classification”
Lichao Mou, Pedram Ghamisi and Xiao Zhu · 2017
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“Globally and locally consistent image completion”
Satoshi Iizuka, Edgar Simo-Serra and Hiroshi Ishikawa · 2017
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“Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction”
Richard Zhang, Phillip Isola and Alexei. Efros · 2017
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“Colorization as a proxy task for visual understanding”
Gustav Larsson, Michael Maire and Gregory Shakhnarovich · 2017
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“Decomposing motion and content for natural video sequence prediction”
Ruben Villegas et al · 2017
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“Self-supervised video representation learning with odd-one-out networks”
Basura Fernando, Hakan Bilen, Efstratios Gavves and Stephen Gould · 2017
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“Unsupervised representation learning by sorting sequences”
Hsin-Ying Lee, Jia-Bin Huang, Maneesh Singh and Ming-Hsuan Yang · 2017
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“Learning features by watching objects move”
Deepak Pathak et al · 2017
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Antti Tarvainen and Harri Valpola · 2017
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“Unsupervised learning based on artificial neural network: A review”
Happiness Dike, Yimin Zhou, Kranthi Deveerasetty and Qingtian Wu · 2018
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“A brief introduction to weakly supervised learning”
Zhi-Hua Zhou · 2018
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“Self-supervised low-rank representation (SSLRR) for hyperspectral image classification”
Yuebin Wang et al · 2018
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“Unsupervised representation learning by predicting image rotations”
Spyros Gidaris, Praveer Singh and Nikos Komodakis · 2018
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“Boosting self-supervised learning via knowledge transfer”
Mehdi Noroozi, Ananth Vinjimoor, Paolo Favaro and Hamed Pirsiavash · 2018
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“Deep clustering for unsupervised learning of visual features”
Mathilde Caron, Piotr Bojanowski, Armand Joulin and Matthijs Douze · 2018
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“Self-supervised feature learning by learning to spot artifacts”
Simon Jenni and Paolo Favaro · 2018
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“Representation learning with contrastive predictive coding”
Aaron van Oord, Yazhe Li and Oriol Vinyals · 2018
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“Learning deep representations by mutual information estimation and maximization”
R Hjelm et al · 2018
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“Unsupervised feature learning via non-parametric instance discrimination”
Zhirong Wu, Yuanjun Xiong, Stella Yu and Dahua Lin · 2018
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“From principal subspaces to principal components with linear autoencoders”
Elad Plaut · 2018
Earlier work this paper cites.
“Endnet: Sparse autoencoder network for endmember extraction and hyperspectral unmixing”
Savas Ozkan, Berk Kaya and Gozde Akar · 2018
Earlier work this paper cites.
“Mining hard negative samples for SAR-optical image matching using generative adversarial networks”
Lloyd Hughes, Michael Schmitt and Xiao Zhu · 2018
Earlier work this paper cites.
“Generative adversarial networks for hyperspectral image classification”
Lin Zhu, Yushi Chen, Pedram Ghamisi and J“’on Benediktsson · 2018
Earlier work this paper cites.
“Visual permutation learning”
Rodrigo Santa, Basura Fernando, Anoop Cherian and Stephen Gould · 2018
Earlier work this paper cites.
“Self-Supervised Feature Learning for Semantic Segmentation of Overhead Imagery”
Suriya Singh et al · 2018
Earlier work this paper cites.
“Learning and using the arrow of time”
Donglai Wei, Joseph Lim, Andrew Zisserman and William Freeman · 2018
Cited alongside, same era.
“Cross-domain self-supervised multi-task feature learning using synthetic imagery”
Zhongzheng Ren and Yong Lee · 2018
Cited alongside, same era.
“When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs”
Gong Cheng et al · 2018
Cited alongside, same era.
“Deep learning for remote sensing image classification: A survey”
Ying Li et al · 2018
Cited alongside, same era.
“Improvements to context based self-supervised learning”
T Mundhenk, Daniel Ho and Barry Chen · 2018
Cited alongside, same era.
“Meta-learning”
Joaquin Vanschoren · 2019
Cited alongside, same era.
“Barlow twins: Self-supervised learning via redundancy reduction”
Jure Zbontar et al · 2021
Later among the works it cites.
“Vicreg: Variance-invariance-covariance regularization for self-supervised learning”
Adrien Bardes, Jean Ponce and Yann LeCun · 2021
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“Self-Supervised Learning of Satellite-Derived Vegetation Indices for Clustering and Visualization of Vegetation Types”
Ram Sharma and Keitarou Hara · 2021
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“Self-supervised monocular depth estimation from oblique UAV videos”
Logambal Madhuanand, Francesco Nex and Michael Yang · 2021
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“Self-Supervised Multi-Image Super-Resolution for Push-Frame Satellite Images”
Ngoc Nguyen et al · 2021
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“Bigearthnet: A large-scale benchmark archive for remote sensing image understanding”
Gencer Sumbul, Marcela Charfuelan, Beg“”um Demir and Volker Markl · 2019
Cited alongside, same era.
Michael Schmitt, Lloyd Hughes, Chunping Qiu and Xiao Zhu · 2019
Cited alongside, same era.
“So2Sat LCZ42: A benchmark dataset for global local climate zones classification”
Xiao Zhu et al · 2019
Cited alongside, same era.
“Wu-net: A weakly-supervised unmixing network for remotely sensed hyperspectral imagery”
Danfeng Hong et al · 2019
Cited alongside, same era.
“Learning to propagate labels on graphs: An iterative multitask regression framework for semi-supervised hyperspectral dimensionality reduction”
Danfeng Hong et al · 2019
Cited alongside, same era.
“CoSpace: Common subspace learning from hyperspectral-multispectral correspondences”
Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot and Xiao Zhu · 2019
Cited alongside, same era.
“Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing”
Danfeng Hong et al · 2021
Later among the works it cites.
“Hyperspectral image super-resolution with self-supervised spectral-spatial residual network”
Wenjing Chen, Xiangtao Zheng and Xiaoqiang Lu · 2021
Later among the works it cites.
“Self-Supervised Deep Subspace Clustering for Hyperspectral Images With Adaptive Self-Expressive Coefficient Matrix Initialization”
Kun Li et al · 2021
Later among the works it cites.
“Speckle2Void: Deep self-supervised SAR despeckling with blind-spot convolutional neural networks”
Andrea Molini, Diego Valsesia, Giulia Fracastoro and Enrico Magli · 2021
Later among the works it cites.
“Hyperspectral Image Restoration With Self-Supervised Learning: A Two-Stage Training Approach”
Yuntao Qian, Honglin Zhu, Ling Chen and Jun Zhou · 2021
Later among the works it cites.
“Adversarial autoencoder network for hyperspectral unmixing”
Qiwen Jin et al · 2021
Later among the works it cites.
“Perturbation-seeking generative adversarial networks: A defense framework for remote sensing image scene classification”
Gong Cheng et al · 2021
Later among the works it cites.
“Jigsaw Clustering for Unsupervised Visual Representation Learning”
Pengguang Chen, Shu Liu and Jiaya Jia · 2021
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“The color out of space: learning self-supervised representations for Earth Observation imagery”
Stefano Vincenzi et al · 2021
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“Hyper-Embedder: Learning a Deep Embedder for Self-Supervised Hyperspectral Dimensionality Reduction”
Xin Wu, Danfeng Hong and Di Zhao · 2021
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“Geographical Knowledge-driven Representation Learning for Remote Sensing Images”
Wenyuan Li, Keyan Chen, Hao Chen and Zhenwei Shi · 2021
Later among the works it cites.
“Self-supervised learning with randomised layers for remote sensing”
Heechul Jung and Taegyun Jeon · 2021
Later among the works it cites.
“Self-supervised pre-training enhances change detection in Sentinel-2 imagery”
Marrit Leenstra, Diego Marcos, Francesca Bovolo and Devis Tuia · 2021
Later among the works it cites.
“Hyperspectral Imagery Classification Based on Contrastive Learning”
Sikang Hou et al · 2021
Later among the works it cites.
“SC-EADNet: A Self-Supervised Contrastive Efficient Asymmetric Dilated Network for Hyperspectral Image Classification”
Mingzhen Zhu, Jiayuan Fan, Qihang Yang and Tao Chen · 2021
Later among the works it cites.
“Contrastive Self-Supervised Learning With Smoothed Representation for Remote Sensing”
Heechul Jung et al · 2021
Later among the works it cites.
“Self-supervised learning for joint SAR and multispectral land cover classification”
Antonio Montanaro, Diego Valsesia, Giulia Fracastoro and Enrico Magli · 2021
Later among the works it cites.
Haifeng Li et al · 2021
Later among the works it cites.
“Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding”
Vladan Stojnic and Vladimir Risojevic · 2021
Later among the works it cites.
“Self-supervised Change Detection in Multi-view Remote Sensing Images”
Yuxing Chen and Lorenzo Bruzzone · 2021
Later among the works it cites.
“Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data”
Oscar Ma“˜nas et al · 2021
Later among the works it cites.
“Self-supervised Audiovisual Representation Learning for Remote Sensing Data”
Konrad Heidler et al · 2021
Later among the works it cites.
“Contrastive clustering”
Yunfan Li et al · 2021
Later among the works it cites.
“Self-supervised Multisensor Change Detection”
Sudipan Saha, Patrick Ebel and Xiao Zhu · 2021
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“ContrastNet: Unsupervised feature learning by autoencoder and prototypical contrastive learning for hyperspectral imagery classification”
Zeyu Cao et al · 2021
Later among the works it cites.
“Deep Spatial-Spectral Subspace Clustering for Hyperspectral Images Based on Contrastive Learning”
Xiang Hu, Teng Li, Tong Zhou and Yuanxi Peng · 2021
Later among the works it cites.
“Self-Supervised GANs With Similarity Loss for Remote Sensing Image Scene Classification”
Dongen Guo, Ying Xia and Xiaobo Luo · 2021
Later among the works it cites.
“Self-supervised SAR-optical Data Fusion of Sentinel-1/-2 Images”
Yuxing Chen and Lorenzo Bruzzone · 2021
Later among the works it cites.
“Self-supervised Remote Sensing Images Change Detection at Pixel-level”
Yuxing Chen and Lorenzo Bruzzone · 2021
Later among the works it cites.
“Contrastive Learning Based on Transformer for Hyperspectral Image Classification”
Xiang Hu et al · 2021
Later among the works it cites.
“Exploring vision transformers for polarimetric SAR image classification”
Hongwei Dong, Lamei Zhang and Bin Zou · 2021
Later among the works it cites.
“Representation Learning for Remote Sensing: An Unsupervised Sensor Fusion Approach”
Aidan Swope, Xander Rudelis and Kyle Story · 2021
Later among the works it cites.
“Semantic Segmentation of Remote Sensing Images With Self-Supervised Multitask Representation Learning”
Wenyuan Li, Hao Chen and Zhenwei Shi · 2021
Later among the works it cites.
“Task-related self-supervised learning for remote sensing image change detection”
Zhinan Cai, Zhiyu Jiang and Yuan Yuan · 2021
Later among the works it cites.
“Unsupervised Pretraining for Object Detection by Patch Reidentification”
Jian Ding et al · 2021
Later among the works it cites.
“Self-supervised spectral matching network for hyperspectral target detection”
Can Yao, Yuan Yuan and Zhiyu Jiang · 2021
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“A Self-Supervised Denoising Network for Satellite-Airborne-Ground Hyperspectral Imagery”
Xinyu Wang et al · 2021
Later among the works it cites.
“SAR Image Classification Using Contrastive Learning and Pseudo-Labels With Limited Data”
Chenchen Wang, Hong Gu and Weimin Su · 2021
Later among the works it cites.
“A Mutual Information-Based Self-Supervised Learning Model for PolSAR Land Cover Classification”
Bo Ren et al · 2021
Later among the works it cites.
“Homography augumented momentum constrastive learning for SAR image retrieval”
Seonho Park, Maciej Rysz, Kathleen Dipple and Panos Pardalos · 2021
Later among the works it cites.
“Adversarial Self-Supervised Learning for Robust SAR Target Recognition”
Yanjie Xu et al · 2021
Later among the works it cites.
“Contrastive Multiview Coding With Electro-Optics for SAR Semantic Segmentation”
Keumgang Cha, Junghoon Seo and Yeji Choi · 2021
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“A review of deep learning methods for semantic segmentation of remote sensing imagery”
Xiaohui Yuan, Jianfang Shi and Lichuan Gu · 2021
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“Deep learning meets SAR: Concepts, models, pitfalls, and perspectives”
Xiao Zhu et al · 2021
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“H2O-Net: Self-Supervised Flood Segmentation via Adversarial Domain Adaptation and Label Refinement”
Peri Akiva et al · 2021
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“Patch-level unsupervised planetary change detection”
Sudipan Saha and Xiao Zhu · 2021
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“Unsupervised Change Detection of Extreme Events Using ML On-Board”
V“’t Ruzicka et al · 2021
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“Understanding self-supervised learning dynamics without contrastive pairs”
Yuandong Tian, Xinlei Chen and Surya Ganguli · 2021
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Chaitanya Ryali, David Schwab and Ari Morcos · 2021
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“Rethinking self-supervised learning: Small is beautiful”
Yun-Hao Cao and Jianxin Wu · 2021
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“Blind hyperspectral unmixing using autoencoders: A critical comparison”
Burkni Palsson, Johannes Sveinsson and Magnus Ulfarsson · 2022
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“SAE-net: A deep neural network for SAR autofocus”
Wei Pu · 2022
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“A Self-Supervised Denoising Network for Satellite-Airborne-Ground Hyperspectral Imagery”
Xinyu Wang et al · 2022
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“Few-shot Scene Classification of Optical Remote Sensing Images Leveraging Calibrated Pretext Tasks”
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“SITS-Former: A pre-trained spatio-spectral-temporal representation model for Sentinel-2 time series classification”
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“Self-Supervised Deep Learning For Nonlinear Seismic Full Waveform Inversion”
Zhaoqi Gao et al · 2022
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“Self-Supervised Deep Learning to Reconstruct Seismic Data With Consecutively Missing Traces”
He Huang et al · 2022
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“A Semisupervised Convolution Neural Network for Partial Unlabeled Remote-Sensing Image Segmentation”
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