Filters








3,923 Hits in 8.4 sec

Remote Sensing Image Super-Resolution Based on Dense Channel Attention Network

Yunchuan Ma, Pengyuan Lv, Hao Liu, Xuehong Sun, Yanfei Zhong
2021 Remote Sensing  
To address this problem, we propose a dense channel attention network (DCAN) to reconstruct high-resolution (HR) remote sensing images.  ...  In the recent years, convolutional neural networks (CNN)-based super resolution (SR) methods are widely used in the field of remote sensing.  ...  Recently, deep learning-based methods attracted a lot of attention in the remote sensing images super resolution task.  ... 
doi:10.3390/rs13152966 fatcat:r2m275vddfctza63cxacshyukm

Lightweight Feedback Convolution Neural Network for Remote Sensing Images Super-Resolution

Jin Wang, Yiming Wu, Liu Wang, Lei Wang, Osama Alfarraj, Amr Tolba
2021 IEEE Access  
INDEX TERMS Remote sensing, super-resolution, feedback mechanism, ghost module, attention mechanism.  ...  There are lots of image data in the field of remote sensing, most of which have low-resolution due to the limited image sensor.  ...  Therefore, super-resolution technology has great research value in remote sensing image processing [5] .  ... 
doi:10.1109/access.2021.3052946 fatcat:zndked7xizfrrjdxwe7fihthnm

Remote Sensing Image Super-Resolution via Residual Aggregation and Split Attentional Fusion Network

Long Chen, Hui Liu, Minhang Yang, Yurong Qian, Zhengqing Xiao, Xiwu Zhong
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
To achieve high-quality super-resolution of remote sensing images, a residual aggregation and split attentional fusion network (RASAF) is proposed in this article.  ...  Remote sensing images contain various land surface scenes and different scales of ground objects, which greatly increases the difficulty of super-resolution tasks.  ...  Multi-label remote sensing image classification performance To validate the impact of super-resolution algorithms on multi-classification tasks of remote sensing images.  ... 
doi:10.1109/jstars.2021.3113658 fatcat:pfjvc3kojndndpgvdhhp7vqela

Non-locally up-down convolutional attention network for remote sensing image super-resolution

Huan Wang, Qian Hu, Chengdong Wu, Jianning Chi, Xiaosheng Yu
2020 IEEE Access  
INDEX TERMS Single image super-resolution (SISR), channel-wise and space-wise attention mechanisms, deep learning, remote sensing image processing. H. Wang et al.: NLASR Sensing Image Super-Resolution  ...  To solve these problems, we propose a nonlocally up-down convolutional attention network (NLASR) for remote sensing image super-resolution.  ...  In Section III, we propose the novel NLASR network for remote sensing image super-resolution.  ... 
doi:10.1109/access.2020.3022882 fatcat:rwfs2hiydncm5cexfx46dhzq6u

Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution [article]

Meng Xu, Zhihao Wang, Jiasong Zhu, Xiuping Jia, Sen Jia
2021 arXiv   pre-print
In this paper, we propose a network based on the generative adversarial network (GAN) to generate high resolution remote sensing images, named the multi-attention generative adversarial network (MA-GAN  ...  Image super-resolution (SR) methods can generate remote sensing images with high spatial resolution without increasing the cost, thereby providing a feasible way to acquire high-resolution remote sensing  ...  In this paper, we propose a network based on the generative adversarial network (GAN) to generate high resolution remote sensing images, named the multi-attention generative adversarial network (MA-GAN  ... 
arXiv:2107.06536v1 fatcat:jfwnfljc4bf5pjzqx6qer7okai

Cross-Dimension Attention Guided Self-Supervised Remote Sensing Single-Image Super-Resolution

Wenzong Jiang, Lifei Zhao, Yanjiang Wang, Weifeng Liu, Baodi Liu
2021 Remote Sensing  
Inspired by the self-supervised methods, this paper proposes a cross-dimension attention guided self-supervised remote sensing single-image super-resolution method (CASSISR).  ...  In recent years, the application of deep learning has achieved a huge leap in the performance of remote sensing image super-resolution (SR).  ...  HR high-resolution LR low-resolution CASSISR cross-dimension attention guided self-supervised remote sensing single image super-resolution CDAN cross-dimension attention network CDAM cross-dimension  ... 
doi:10.3390/rs13193835 fatcat:25bezdek4zdrfmlphp2xqduxty

Research on super-resolution reconstruction of remote sensing images: a comprehensive review

Hui Liu, Yurong Qian, Xiwu Zhong, Long Chen, Guangqi Yang
2021 Optical Engineering: The Journal of SPIE  
The super-resolution (SR) reconstruction of remote sensing images is a low-cost and efficient method to improve their resolution, and it is often used for further image analysis.  ...  To understand the development of SR reconstruction of remote sensing images and research hotspots and trends, we examined its history and reviewed existing methods categorized into traditional, learning-based  ...  In the SR reconstruction of single-frame remote sensing image based on deep dense convolutional network, Pan et al. 74 proposed an single image super resolution (SISR) method based on residual dense  ... 
doi:10.1117/1.oe.60.10.100901 fatcat:44ssaq55ebfkrghyatvo5lbr2m

Wider Channel Attention Network for Remote Sensing Image Super-resolution [article]

Jun Gu, Guangluan Xu, Yue Zhang, Xian Sun, Ran Wen, Lei Wang
2019 arXiv   pre-print
In this letter, we propose a novel single-image super-resolution (SISR) algorithm named Wider Channel Attention Network (WCAN) for remote sensing images.  ...  Recently, deep convolutional neural networks (CNNs) have obtained promising results in image processing tasks including super-resolution (SR).  ...  CONCLUSION In this letter, we propose a novel network named WCAN for remote sensing image super-resolution.  ... 
arXiv:1812.05329v2 fatcat:tmgcvfvitjeffoidrap5ovrqia

Residual Dense Network Based on Channel-Spatial Attention for the Scene Classification of a High-Resolution Remote Sensing Image

Xiaolei Zhao, Jing Zhang, Jimiao Tian, Li Zhuo, Jie Zhang
2020 Remote Sensing  
Therefore, we design an RDN based on channel-spatial attention for scene classification of a high-resolution remote sensing image.  ...  In recent years, convolutional neural networks (CNNs) have achieved excellent performance in remote sensing image classification, especially the residual dense network (RDN) as one of the representative  ...  Conclusions We proposed an RDN based on channel-spatial attention for the scene classification of a high-resolution remote sensing image.  ... 
doi:10.3390/rs12111887 fatcat:zcebi6ahxjavbcpowtbsetnu5a

Edge Loss for Remote Sensing Image Super-Resolution [chapter]

Jiaoyue Li, Weifeng Liu, Kai Zhang, Baodi Liu
2021 Frontiers in Artificial Intelligence and Applications  
Recently, remote sensing image super-resolution methods based on deep learning have shown remarkable performance.  ...  Remote sensing image super-resolution (SR) plays an essential role in many remote sensing applications.  ...  Acknowledg The Natural Science Foundation of Shandong Province, China(Grant No.ZR2019MF073, ZR2018MF017, No.ZR2017MF069), the Open Research Fund from Shandong Provincial Key Laboratory of Computer Network  ... 
doi:10.3233/faia210411 fatcat:h5nivpiamnbovppri3gftbzm4a

Transferred Multi-Perception Attention Networks for Remote Sensing Image Super-Resolution

Xiaoyu Dong, Zhihong Xi, Xu Sun, Lianru Gao
2019 Remote Sensing  
Image super-resolution (SR) reconstruction plays a key role in coping with the increasing demand on remote sensing imaging applications with high spatial resolution requirements.  ...  By incorporating the proposed enhanced residual block (ERB) and residual channel attention group (RCAG), MPSR can super-resolve low-resolution remote sensing images via multi-perception learning and multi-level  ...  Acknowledgments: The author Xiaoyu Dong is thankful for the careful guidance from Xu Sun and the funding supported by the Institute of Remote Sensing and Digital Earth.  ... 
doi:10.3390/rs11232857 fatcat:stnmwoxlwjh5fjunm74duckcym

In-Orbit Lunar Satellite Image Super Resolution for Selective Data Transmission [article]

Atal Tewari, Chennuri Prateek, Nitin Khanna
2021 arXiv   pre-print
We present a residual dense non-local attention network (RDNLA) that provides enhanced super-resolution outputs to improve the SR performance.  ...  As the resolution of images plays a critical role in making precise inferences, we also include in-orbit super-resolution (SR) in the system design.  ...  of remote sensing images.  ... 
arXiv:2110.10109v1 fatcat:t3d7udh5onea5cevb7mno4j5s4

Deep learning approaches for real-time image super-resolution

Pourya Shamsolmoali, M. Emre Celebi, Ruili Wang
2020 Neural computing & applications (Print)  
Generating a high-resolution (HR) image from its corresponding low-resolution (LR) input is referred to image super-resolution (SR).  ...  Image SR has already shown significant performance in many applications, such as video surveillance, remote sensing, face recognition, and medical images.  ...  The paper ''Arbitrary-oriented object detection via dense feature fusion and attention model for remote sensing super-resolution image'' aims at developing a new arbitrary-oriented object detection method  ... 
doi:10.1007/s00521-020-05176-z fatcat:gvmbzve6kvfmzfwumblipqzaji

S2A: Wasserstein GAN with Spatio-Spectral Laplacian Attention for Multi-Spectral Band Synthesis [article]

Litu Rout, Indranil Misra, S Manthira Moorthi, Debajyoti Dhar
2020 arXiv   pre-print
Intersection of adversarial learning and satellite image processing is an emerging field in remote sensing.  ...  In this regard, we introduce a new cost function for the discriminator based on spatial attention and domain adaptation loss.  ...  [16] used residual learning modules in remote sensing image super-resolution. Luo et al. [25] designed CNN based video satellite image super-resolution. Lei et al.  ... 
arXiv:2004.03867v1 fatcat:6n7jdgf7r5crnkrkqa4ulnt5ya

Image super-resolution algorithm based on RRDB model

Huan Li
2021 IEEE Access  
This work was financially supported in part by Research on the influence of the evolution of new generation network and information technology on Zhejiang media and its development trend (20NDYD022YB).  ...  The existing super-resolution network models based on the attention mechanism usually use channel attention and spatial attention networks.  ...  Attention based on the multi-channel attention mechanism,SRCA).  ... 
doi:10.1109/access.2021.3118444 fatcat:u26grayknrh2ti4ngi3ofyz6si
« Previous Showing results 1 — 15 out of 3,923 results