Proceedings ArticleDOI
Video Satellite Imagery Super Resolution for ‘Jilin-1’ via a Single-and-Multi Frame Ensembled Framework
Shu Zhang,Qiangqiang Yuan,Jie Li +2 more
- pp 2731-2734
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TLDR
In this paper, a single-and-multi-frame ensembled framework called SMFE was proposed for remote sensing videos super-resolution, which combines a non-local based single image super resolution (SISR) network and a state-of-the-art multi-frame super resolution network EDVR.Abstract:
Compared with traditional remote sensing images, satellite remote sensing video contains more useful information and can capture continuous dynamic video. Recently, many deep-learning based methods have been proposed for video super resolution. However, these methods tend to ignore the structural information and characteristics for video satellite imagery such as small ground targets, a wide range of scales and weak textures. To this end, this paper proposes a single-and-multi-frame ensembled framework called SMFE for remote sensing videos super-resolution. The SMFE framework combines a non-local based single image super resolution (SISR) network and a state-of-the-arts multi-frame super resolution (MFSR) network EDVR. Experiments have been performed to demonstrate the effectiveness of the proposed method on Jilin-1.read more
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Journal ArticleDOI
Multitarget Detection and Tracking Method in Remote Sensing Satellite Video.
Lei Lei,Dongen Guo +1 more
TL;DR: In this article, a remote sensing video satellite multiple object detection and tracking method based on road masking, Gaussian mixture model (GMM), and data association is proposed, which extracts the road network from the remote sensing videos based on deep learning.
Journal ArticleDOI
Blind Superresolution of Satellite Videos by Ghost Module-Based Convolutional Networks
TL;DR: Wang et al. as discussed by the authors proposed a Ghost module-based convolution network model for blind SR of satellite videos, which assumes that the blur kernel is unknown and consists of two main modules, i.e., the preliminary image generation module and the SR results' reconstruction module.
Journal ArticleDOI
Blind Superresolution of Satellite Videos by Ghost Module-Based Convolutional Networks
TL;DR: Wang et al. as discussed by the authors proposed a Ghost module-based convolution network model for blind SR of satellite videos, which assumes that the blur kernel is unknown and consists of two main modules, i.e., the preliminary image generation module and the SR results' reconstruction module.
Journal ArticleDOI
Video Satellite Imagery Super-Resolution via Model-Based Deep Neural Networks
Zhi He,Xiaofang Li,Rongning Qu +2 more
TL;DR: Wang et al. as mentioned in this paper proposed a model-based deep neural networks for video satellite imagery SR (VSSR), which is composed of three main modules: degradation estimation module, intermediate image generation module, and multi-frame feature fusion module.
References
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Proceedings ArticleDOI
Enhanced Deep Residual Networks for Single Image Super-Resolution
TL;DR: This paper develops an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods, and proposes a new multi-scale deepsuper-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model.
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Image Super-Resolution Using Very Deep Residual Channel Attention Networks
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Proceedings ArticleDOI
EDVR: Video Restoration With Enhanced Deformable Convolutional Networks
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Proceedings ArticleDOI
Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation
TL;DR: A novel end-to-end deep neural network that generates dynamic upsampling filters and a residual image, which are computed depending on the local spatio-temporal neighborhood of each pixel to avoid explicit motion compensation is proposed.
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