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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 5264-5271 Impact of Satellite Sounding Data on Virtual Visible Imagery Generation Using Conditional Generative Adversarial Network.  ...  ., +, JSTARS 2020 3701-3710 Impact of Satellite Sounding Data on Virtual Visible Imagery Generation Using Conditional Generative Adversarial Network.  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

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  
Qi, Y., +, JSTARS 2021 6792-6806 Semisupervised Remote Sensing Image Fusion Using Multiscale Conditional Generative Adversarial Network With Siamese Structure.  ...  ., +, JSTARS 2021 2995-3005 Semisupervised Remote Sensing Image Fusion Using Multiscale Condi-tional Generative Adversarial Network With Siamese Structure.  ...  ., Hyperspectral Image Superresolution via Deep Structure and Texture Interfusion; JSTARS 2021 8665-8678 Hu, J., see Feng, D., JSTARS 2021 12212-12223 Hu, J., Shen, X., Yu, H., Shang, X., Guo, Q.,  ... 
doi:10.1109/jstars.2022.3143012 fatcat:dnetkulbyvdyne7zxlblmek2qy

Table of Contents

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Orban 4085 Change Detection in Unlabeled Optical Remote Sensing Data Using Siamese CNN . and Z.  ...  Wu Structure Aware Generative Adversarial Networks for Hyperspectral Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Nian 4352 Hyperspectral Image Super Resolution Based on Multiscale Feature Fusion and Aggregation Network With 3-D Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jstars.2020.3046663 fatcat:zqzyhnzacjfdjeejvzokfy4qze

Change Detection in Unlabeled Optical Remote Sensing Data using Siamese CNN

Rachid Hedjam, Abdelhamid Abdessalem, Farid Melgani
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
We detect the changes by processing a pair of optical remote sensing images.  ...  The proposed method adopts a patch-based approach, whereby we use a Siamese convolutional neural network (S-CNN), trained with augmented data, to compare successive pairs of patches obtained from the input  ...  ACKNOWLEDGMENT The authors would like to thank the Archives of Quebec for providing historical aerial images of the city of Montreal.  ... 
doi:10.1109/jstars.2020.3009116 fatcat:q6libcvf4rdrtgz6osnj3lhcmu

A Review of Deep Learning in Multiscale Agricultural Sensing

Dashuai Wang, Wujing Cao, Fan Zhang, Zhuolin Li, Sheng Xu, Xinyu Wu
2022 Remote Sensing  
To provide an update on these studies, we conducted a comprehensive investigation with a special emphasis on deep learning in multiscale agricultural remote and proximal sensing.  ...  In precision agriculture (PA), non-destructive and non-invasive remote and proximal sensing methods have been widely used to observe crops in visible and invisible spectra.  ...  Inception V3 network, a Siamese network with two subnets and contrastive loss, and a Siamese network with three subnets and triplet loss) with transfer learning were used [31] .  ... 
doi:10.3390/rs14030559 fatcat:fcgpljr2tfhpjd3nvmi3kgp3bq

Deep Learning for Hyperspectral Image Classification: An Overview

Shutao Li, Weiwei Song, Leyuan Fang, Yushi Chen, Pedram Ghamisi, Jon Atli Benediktsson
2019 IEEE Transactions on Geoscience and Remote Sensing  
Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing.  ...  In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies  ...  In more detail, the well pretrained FCN-8 [72] was first used to explore deep multiscale spatial structural information.  ... 
doi:10.1109/tgrs.2019.2907932 fatcat:fepkjl3srnaqhakq4vlqvehfeu

2021 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 32

2021 IEEE Transactions on Neural Networks and Learning Systems  
., +, TNNLS March 2021 1124-1135 Image sequences Antidecay LSTM for Siamese Tracking With Adversarial Learning.  ...  ., +, TNNLS Feb. 2021 748-762 Condition monitoring A Neural Network-Based Joint Prognostic Model for Data Fusion and Remaining Useful Life Prediction.  ...  Image coding Deep Multiscale Detail Networks for Multiband Spectral Image Sharpening.  ... 
doi:10.1109/tnnls.2021.3134132 fatcat:2e7comcq2fhrziselptjubwjme

Deep Induction Network for Small Samples Classification of Hyperspectral Images

Kuiliang Gao, Wenyue Guo, Xuchu Yu, Liu Bing, Anzhu Yu, Xiangpo Wei
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
To address the problem, a deep model based on the induction network is designed in this article to improve the classification performance of HSI under the condition of small samples.  ...  Specifically, the typical meta-training strategy is adopted, enabling the model to acquire stronger generalization ability, so as to accurately distinguish the new classes with only a few labeled samples  ...  Yokoya for providing the HSI used in this article. The authors would also like to thank all the researchers for kindly providing the codes associated with the experiments.  ... 
doi:10.1109/jstars.2020.3002787 fatcat:vrh555bat5dxjnaa3c6e5a3yzy

2021 Index IEEE Transactions on Instrumentation and Measurement Vol. 70

2021 IEEE Transactions on Instrumentation and Measurement  
Article numbers are based on specified topic areas and corresponding codes associated with the publication.  ...  ., +, TIM 2021 5006011 Multiscale Convolutional Generative Adversarial Network for Anchorage Grout Defect Detection.  ...  Sample Augmentation for Intelligent Milling Tool Wear Condition Monitoring Using Numerical Simulation and Generative Adversarial Network.  ... 
doi:10.1109/tim.2022.3156705 fatcat:dmqderzenrcopoyipv3v4vh4ry

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
This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them.  ...  The main applications of ML-based RSP are then analysed and structured based on the application field.  ...  RFI radio frequency identification RMA range migration algorithm RNNs recurrent neural networks RS remote sensing RESISC remote sensing image scene classification RBF radial basis function REC radar emitter  ... 
arXiv:2009.13702v1 fatcat:m6am73324zdwba736sn3vmph3i

Hyperspectral Anomaly Detection Using Deep Learning: A Review

Xing Hu, Chun Xie, Zhe Fan, Qianqian Duan, Dawei Zhang, Linhua Jiang, Xian Wei, Danfeng Hong, Guoqiang Li, Xinhua Zeng, Wenming Chen, Dongfang Wu (+1 others)
2022 Remote Sensing  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY  ...  Introduction 1.Hyperspectral Image and Applications In the field of remote sensing, hyperspectral image (HSI) is a ground image collected by advanced sensor technology and imaging system mounted on satellites  ...  Conclusions HSI anomaly detection has received extensive attention in the field of remote sensing image processing.  ... 
doi:10.3390/rs14091973 dblp:journals/remotesensing/HuXFDZJWHLZCWC22 fatcat:vwb3azo7cjgopaj7lrbwp6wzki

2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31

2020 IEEE Transactions on Neural Networks and Learning Systems  
Yang, K., +, TNNLS April 2020 1387-1400 Image-Based Model Parameter Optimization Using Model-Assisted Generative Adversarial Networks.  ...  ., +, TNNLS Nov. 2020 5005-5013 Skip-Connected Covariance Network for Remote Sensing Scene Classification.  ...  ., +, TNNLS Oct. 2020 3777-3787 On the Working Principle of the Hopfield Neural Networks and its Equivalence to the GADIA in Optimization. Uykan, Z.,  ... 
doi:10.1109/tnnls.2020.3045307 fatcat:34qoykdtarewhdscxqj5jvovqy

2021 Index IEEE Journal of Biomedical and Health Informatics Vol. 25

2021 IEEE journal of biomedical and health informatics  
., +, JBHI July 2021 2376-2387 COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network.  ...  ., +, JBHI July 2021 2376-2387 COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network.  ... 
doi:10.1109/jbhi.2022.3140980 fatcat:ufig7b54gfftnj3mocspoqbzq4

2021 Index IEEE Transactions on Industrial Informatics Vol. 17

2021 IEEE Transactions on Industrial Informatics  
., +, TII Aug. 2021 5369-5379 Dual-Domain-Based Adversarial Defense With Conditional VAE and Bayesian Network.  ...  ., +, TII April 2021 2400-2410 Dual-Domain-Based Adversarial Defense With Conditional VAE and Bayes-ian Network.  ... 
doi:10.1109/tii.2021.3138206 fatcat:ulsazxgmpfdmlivigjqgyl7zre

Artificial Intelligence in the Creative Industries: A Review [article]

Nantheera Anantrasirichai, David Bull
2021 arXiv   pre-print
A brief background of AI, and specifically Machine Learning (ML) algorithms, is provided including Convolutional Neural Network (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks  ...  We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right.  ...  Image Enhancement Conditional Generative Adversarial Networks (IE-CGANs) designed to process both visible and infrared images have been proposed by Kuang et al. (2019) .  ... 
arXiv:2007.12391v5 fatcat:mn2xqeylyrbabbu5zwln3admtm
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