High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network

@article{Xu2018HighQR,
  title={High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network},
  author={Wenjia Xu and Guangluan Xu and Yang Wang and Xian Sun and Daoyu Lin and Yirong Wu},
  journal={IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium},
  year={2018},
  pages={8889-8892}
}
  • Wenjia Xu, Guangluan Xu, Yirong Wu
  • Published 1 July 2018
  • Environmental Science, Computer Science
  • IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Single image super-resolution is an effective way to enhance the spatial resolution of remote sensing image, which is crucial for many applications such as target detection and image classification. However, existing methods based on the neural network usually have small receptive fields and ignore the image detail. We propose a novel method named deep memory connected network (DMCN) based on a convolutional neural network to reconstruct high-quality super-resolution images. We build local and… 

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References

SHOWING 1-7 OF 7 REFERENCES
Super-Resolution for Remote Sensing Images via Local–Global Combined Network
TLDR
This letter proposes a new single-image super-resolution algorithm named local–global combined networks (LGCNet) for remote sensing images based on the deep CNNs, elaborately designed with its “multifork” structure to learn multilevel representations ofRemote sensing images including both local details and global environmental priors.
SINGLE-IMAGE SUPER RESOLUTION FOR MULTISPECTRAL REMOTE SENSING DATA USING CONVOLUTIONAL NEURAL NETWORKS
Abstract. In optical remote sensing, spatial resolution of images is crucial for numerous applications. Space-borne systems are most likely to be affected by a lack of spatial resolution, due to
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
TLDR
This work presents a highly accurate single-image superresolution (SR) method using a very deep convolutional network inspired by VGG-net used for ImageNet classification and uses extremely high learning rates enabled by adjustable gradient clipping.
Learning a Deep Convolutional Network for Image Super-Resolution
TLDR
This work proposes a deep learning method for single image super-resolution (SR) that directly learns an end-to-end mapping between the low/high-resolution images and shows that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network.
Remote Sensing Image Scene Classification: Benchmark and State of the Art
TLDR
A large-scale data set, termed “NWPU-RESISC45,” is proposed, which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU).
Bag-of-visual-words and spatial extensions for land-use classification
TLDR
This work considers a standard non-spatial representation in which the frequencies but not the locations of quantized image features are used to discriminate between classes analogous to how words are used for text document classification without regard to their order of occurrence, and considers two spatial extensions.
SSIM) Bicubic (26.30, 0.4970) SRCNN (26.52, 0.5252) vdsr (27.29, 0.5549) MBSR (27.52,0.5858) (b) dataset: NWPU-RESISC45 image