A comprehensive review on deep learning based remote sensing image super-resolution methods
Satellite imageries are an important geoinformation source for different applications in the
Earth Science field. However, due to the limitation of the optic and sensor technologies and …
Earth Science field. However, due to the limitation of the optic and sensor technologies and …
A review of image super-resolution approaches based on deep learning and applications in remote sensing
At present, with the advance of satellite image processing technology, remote sensing
images are becoming more widely used in real scenes. However, due to the limitations of …
images are becoming more widely used in real scenes. However, due to the limitations of …
EDiffSR: An efficient diffusion probabilistic model for remote sensing image super-resolution
Recently, convolutional networks have achieved remarkable development in remote
sensing image (RSI) super-resolution (SR) by minimizing the regression objectives, eg, MSE …
sensing image (RSI) super-resolution (SR) by minimizing the regression objectives, eg, MSE …
From single-to multi-modal remote sensing imagery interpretation: A survey and taxonomy
Modality is a source or form of information. Through various modal information, humans can
perceive the world from multiple perspectives. Simultaneously, the observation of remote …
perceive the world from multiple perspectives. Simultaneously, the observation of remote …
Hybrid-scale self-similarity exploitation for remote sensing image super-resolution
Recently, deep convolutional neural networks (CNNs) have made great progress in remote
sensing image super-resolution (SR). The CNN-based methods can learn powerful feature …
sensing image super-resolution (SR). The CNN-based methods can learn powerful feature …
Consolidated convolutional neural network for hyperspectral image classification
The performance of hyperspectral image (HSI) classification is highly dependent on spatial
and spectral information, and is heavily affected by factors such as data redundancy and …
and spectral information, and is heavily affected by factors such as data redundancy and …
From artifact removal to super-resolution
Deep-learning-based super-resolution (SR) methods have been extensively studied and
have achieved significant performance with deep convolutional neural networks. However …
have achieved significant performance with deep convolutional neural networks. However …
Contextual transformation network for lightweight remote-sensing image super-resolution
Current super-resolution networks typically reduce network parameters and multiadds
operations by designing lightweight structures, but lightening the convolution layer is often …
operations by designing lightweight structures, but lightening the convolution layer is often …
Multiattention generative adversarial network for remote sensing image super-resolution
Image super-resolution (SR) methods can generate remote sensing images with high spatial
resolution without increasing the cost of acquisition equipment, thereby providing a feasible …
resolution without increasing the cost of acquisition equipment, thereby providing a feasible …
Enhancing remote sensing image super-resolution with efficient hybrid conditional diffusion model
L Han, Y Zhao, H Lv, Y Zhang, H Liu, G Bi, Q Han - Remote Sensing, 2023 - mdpi.com
Recently, optical remote-sensing images have been widely applied in fields such as
environmental monitoring and land cover classification. However, due to limitations in …
environmental monitoring and land cover classification. However, due to limitations in …