Proceedings ArticleDOI
PanNet: A Deep Network Architecture for Pan-Sharpening
Junfeng Yang,Xueyang Fu,Yuwen Hu,Yue Huang,Xinghao Ding,John Paisley +5 more
- pp 1753-1761
TLDR
This work incorporates domain-specific knowledge to design the PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation, and shows that the trained network generalizes well to images from different satellites without needing retraining.Abstract:
We propose a deep network architecture for the pan-sharpening problem called PanNet. We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation. For spectral preservation, we add up-sampled multispectral images to the network output, which directly propagates the spectral information to the reconstructed image. To preserve spatial structure, we train our network parameters in the high-pass filtering domain rather than the image domain. We show that the trained network generalizes well to images from different satellites without needing retraining. Experiments show significant improvement over state-of-the-art methods visually and in terms of standard quality metrics.read more
Citations
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Journal ArticleDOI
Deep Hyperspectral Image Sharpening
TL;DR: A deep HSI sharpening method is presented for the fusion of an LR-HSI with an HR-MSI, which directly learns the image priors via deep convolutional neural network-based residual learning.
Journal ArticleDOI
Pan-GAN: An unsupervised pan-sharpening method for remote sensing image fusion
TL;DR: This work proposes a novel unsupervised framework for pan-sharpening based on a generative adversarial network, termed as Pan-GAN, which does not rely on the so-called ground-truth during network training and has shown promising performance in terms of qualitative visual effects and quantitative evaluation metrics.
Journal ArticleDOI
Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
Charis Lanaras,Jose M. Bioucas-Dias,Silvano Galliani,Emmanuel P. Baltsavias,Konrad Schindler +4 more
TL;DR: This research uses data sampled globally over a wide range of geographical locations, to obtain a network that generalises across different climate zones and land-cover types, and can super-resolve arbitrary Sentinel-2 images without the need of retraining.
Journal ArticleDOI
A review of multimodal image matching: Methods and applications
TL;DR: This survey provides a comprehensive review of multimodal image matching methods from handcrafted to deep methods for each research field according to their imaging nature, including medical, remote sensing and computer vision.
Journal ArticleDOI
Image fusion meets deep learning: A survey and perspective
TL;DR: In this paper, a comprehensive review and analysis of latest deep learning methods in different image fusion scenarios is provided, and the evaluation for some representative methods in specific fusion tasks are performed qualitatively and quantitatively.
References
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TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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TL;DR: Caffe as discussed by the authors is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
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TL;DR: Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
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Image Super-Resolution Using Deep Convolutional Networks
TL;DR: Zhang et al. as discussed by the authors proposed a deep learning method for single image super-resolution (SR), which directly learns an end-to-end mapping between the low/high-resolution images.
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A universal image quality index
Zhou Wang,Alan C. Bovik +1 more
TL;DR: Although the new index is mathematically defined and no human visual system model is explicitly employed, experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error.