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A Deep Multitask Convolutional Neural Network for Remote Sensing Image Super-resolution and Colorization

Jianan Feng, Qian Jiang, Ching-Hsun Tseng, Xin Jin, Ling Liu, Wei Zhou, Shaowen Yao
2022 IEEE Transactions on Geoscience and Remote Sensing  
A generic neural network, RSI-Net, is designed for remote sensing image SR, colorization, simultaneous SR colorization, and pansharpening.  ...  Abundant pan-sharpening and super-resolution (SR) methods based on deep learning have been proposed and have achieved remarkable performance.  ...  CONCLUSION We proposed a neural network to enhance the spatial and spectral resolution of remote sensing images.  ... 
doi:10.1109/tgrs.2022.3154435 fatcat:qot7ydkoojhlblohm3ern7rm6a

Table of contents

2021 IEEE Transactions on Geoscience and Remote Sensing  
Rosenblatt 5197 Super-Resolution-Guided Progressive Pansharpening Based on a Deep Convolutional Neural Network ................. ........................................................................  ...  Jiao 4957 Multiscale Cloud Detection in Remote Sensing Images Using a Dual Convolutional Neural Network .................. ..............................................................................  ... 
doi:10.1109/tgrs.2021.3075585 fatcat:467tqoqtufdf5lbdit6xq554na

On Coupling Classification and Super-Resolution in Remote Urban Sensing: An Integrated Deep Learning Approach

Yang Zhang, Ruohan Zong, Lanyu Shang, Dong Wang
2022 IEEE Transactions on Geoscience and Remote Sensing  
To address these challenges, we develop SCLearn, a novel deep convolutional neural network architecture, to couple the classification task with the super-resolution task in an integrated learning framework  ...  Motivated by the state-of-the-art optical sensing and image processing technologies, remote urban sensing (RUS) has emerged as a powerful sensing paradigm to capture abundant visual information about the  ...  [29] designed a novel deep neural network framework that applies the cycle-consistent convolutional network design to capture the complex mapping between low-and high-resolution satellite images in  ... 
doi:10.1109/tgrs.2022.3169703 fatcat:6gechw66f5hlfmntpdgwwrfn4m

Front Matter: Volume 11187

Qionghai Dai, Tsutomu Shimura, Zhenrong Zheng
2019 Optoelectronic Imaging and Multimedia Technology VI  
for virtual reality communication [11187-40] 11187 17 Cloud and snow detection from remote sensing imagery based on convolutional neural network [11187-41] 11187 18 Surface defect recognition of  ...  of videos and spatiotemporal slice images 11187 0A No-reference video quality assessment based on spatiotemporal slice images and deep convolutional neural networks 11187 0B An efficient stereo matching  ... 
doi:10.1117/12.2563101 fatcat:zsy4ucxvlfd4hjepdpolsjfeya

Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration

Wenjia Xu, Guangluan Xu, Yang Wang, Xian Sun, Daoyu Lin, Yirong Wu
2018 Remote Sensing  
To address such problem, we propose a novel method named deep memory connected network (DMCN) based on the convolutional neural network to reconstruct high-quality images.  ...  The spatial resolution and clarity of remote sensing images are crucial for many applications such as target detection and image classification.  ...  Acknowledgments: Our code is available at https://github.com/wenjiaXu/Optical-RemoteSensing-Image-Resolution. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs10121893 fatcat:n47qcad5mzhivekhfn5mwekxmu

VEHICLE DETECTION IN REMOTE SENSING IMAGES USING DEEP NEURAL NETWORKS AND MULTI-TASK LEARNING

M. Cao, H. Ji, Z. Gao, T. Mei
2020 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Furthermore, we simultaneously address super-resolution (SR) and detection problems of low-resolution (LR) image in an end-to-end manner.  ...  Due to the stunning success of deep learning techniques in object detection community, we consider to utilize CNNs for vehicle detection task in remote sensing image.  ...  In future, we plan on realizing instance segmentation of vehicle in remote sensing image. Figure 1 . 1 Examples of everyday images of vehicles (a) and remote sensing image of vehicles (b).  ... 
doi:10.5194/isprs-annals-v-2-2020-797-2020 fatcat:ewdicrl4dnceda7vr7hxhsajde

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 1567-1589 Super-Resolution for MIMO Array SAR 3-D Imaging Based on Compressive Sensing and Deep Neural Network.  ...  ., +, JSTARS 2020 5036-5047 Super-Resolution for MIMO Array SAR 3-D Imaging Based on Compres- sive Sensing and Deep Neural Network.  ...  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

Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image

Wentong Wu, Han Liu, Lingling Li, Yilin Long, Xiaodong Wang, Zhuohua Wang, Jinglun Li, Yi Chang, Haibin Lv
2021 PLoS ONE  
This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images.  ...  Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction  ...  Among them, 565 color remote sensing images from Google Earth, and the spatial resolution of these images is about 0.5-2 meters.  ... 
doi:10.1371/journal.pone.0259283 pmid:34714878 pmcid:PMC8555847 fatcat:qa6wt2gzhberdcntv7u7xfg27e

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  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  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.  ...  Remote Sensing and Social Sensing Data Fusion for Fine-Resolution Population Mapping With a Multimodel Neural Network.  ... 
doi:10.1109/jstars.2022.3143012 fatcat:dnetkulbyvdyne7zxlblmek2qy

Sentinel-2 Sharpening Using A Single Unsupervised Convolutional Neural Network With MTF-based Degradation Model

Han V. Nguyen, Magnus Orn Ulfarsson, Johannes R. Sveinsson, Mauro Dalla Mura
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Index Terms-Convolutional neural networks (CNNs), image fusion, MTF-based degradation, Sentinel-2 image sharpening, super-resolution, unsupervised deep learning.  ...  To break the gap, this article proposes a novel unsupervised DL-based S2 sharpening method using a single convolutional neural network (CNN) to sharpen the 20 and 60 m bands at the same time at full resolution  ...  Baltsavias, and K. Schindler for providing the SupReME code, Q. Wang, W. Shi, Z. Li, and P. M. Atkinson for providing the ATPRK code, and C.-H. Lin and J. M.  ... 
doi:10.1109/jstars.2021.3092286 fatcat:62fgqc6c2bc6noda7xyich5uea

A Lightweight Model of VGG-16 for Remote Sensing Image Classification

Ye Mu
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
that our model still has a high accuracy rate of 95% for the remote sensing image with ultra-low pixels and less feature points.Therefore, the model has a good application prospect in remote sensing image  ...  a lightweight model based on VGG-16, which can selectively extract some features of remote sensing images, remove redundant information, and recognize and classify remote sensing images.  ...  Modeling VGG is a convolutional neural network model proposed that "very deep convolutional networks for large scale image recognition" by simonyan and zisserman in the document.  ... 
doi:10.1109/jstars.2021.3090085 fatcat:m5hmzjc4jvch5h5uillvlihyqm

Special issue on video and imaging systems for critical engineering applications [SI 1096]

Gwanggil Jeon, Awais Ahmad, Abdellah Chehri, Salvatore Cuomo
2020 Multimedia tools and applications  
Moreover, artificial neural network when combined with pattern recognition techniques, such  ...  using remote sensing, underground and tunnel inspection, other and environmental monitoring are some of the areas that need to be addressed with urgency.  ...  Acknowledgments We would like to express our appreciation to all the authors for their informative contributions and the reviewers for their support and constructive critiques in making this special issue  ... 
doi:10.1007/s11042-020-08672-5 fatcat:dmusbepcancb5i6jqo7hhf6a2m

Research Contribution and Comprehensive Review towards the Semantic Segmentation of Aerial Images Using Deep Learning Techniques

P. Anilkumar, P. Venugopal, Mamoun Alazab
2022 Security and Communication Networks  
and remote-sensing images.  ...  to be used as a reference for developing the new semantic image segmentation models in the future.  ...  Also, the authors would like to thank the individual copyright holders for consent conceded to incorporate referred figures in this work. 26 Security and Communication Networks  ... 
doi:10.1155/2022/6010912 fatcat:qxoogfb3zneypkh5w3m5p3ts3e

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  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.  ...  ., +, TMM 2021 1696-1707 Large Factor Image Super-Resolution With Cascaded Convolutional Neural Networks.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

Generative Adversarial Networks for Image Super-Resolution: A Survey [article]

Chunwei Tian, Xuanyu Zhang, Jerry Chun-Wen Lin, Wangmeng Zuo, Yanning Zhang
2022 arXiv   pre-print
Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised  ...  Single image super-resolution (SISR) has played an important role in the field of image processing.  ...  In terms of remote sensing image super-resolution, Gong et al. used enlighten blocks to make a deep network achieve a reliable point and used self-supervised hierarchical perceptual loss to overcome effects  ... 
arXiv:2204.13620v1 fatcat:hlwdqith65cxrbqrnbphjz6u4u
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