User profiles for Qiangqiang Yuan
 | Qiangqiang YuanFull Professor in School of Geodesy and Geomatics, Wuhan university Verified email at sgg.whu.edu.cn Cited by 11796 |
Deep learning in environmental remote sensing: Achievements and challenges
Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …
environmental remote sensing research. With an increasing amount of “big data” from earth …
Image super-resolution: The techniques, applications, and future
Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from
the observed LR images. As SR has been developed for more than three decades, both multi-…
the observed LR images. As SR has been developed for more than three decades, both multi-…
Hyperspectral image restoration using low-rank matrix recovery
Hyperspectral images (HSIs) are often degraded by a mixture of various kinds of noise in the
acquisition process, which can include Gaussian noise, impulse noise, dead lines, stripes, …
acquisition process, which can include Gaussian noise, impulse noise, dead lines, stripes, …
A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening
Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery
processing, in which high-resolution spatial details from panchromatic images are employed …
processing, in which high-resolution spatial details from panchromatic images are employed …
Ntire 2022 spectral recovery challenge and data set
This paper reviews the third biennial challenge on spectral reconstruction from RGB images,
ie, the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB …
ie, the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB …
Hyperspectral image denoising employing a spectral–spatial adaptive total variation model
The amount of noise included in a hyperspectral image limits its application and has a
negative impact on hyperspectral image classification, unmixing, target detection, and so on. In …
negative impact on hyperspectral image classification, unmixing, target detection, and so on. In …
From degrade to upgrade: Learning a self-supervised degradation guided adaptive network for blind remote sensing image super-resolution
Over the past few years, single image super-resolution (SR) has become a hotspot in the
remote sensing area, and numerous methods have made remarkable progress in this …
remote sensing area, and numerous methods have made remarkable progress in this …
Boosting the accuracy of multispectral image pansharpening by learning a deep residual network
In the field of multispectral (MS) and panchromatic image fusion (pansharpening), the
impressive effectiveness of deep neural networks has recently been employed to overcome the …
impressive effectiveness of deep neural networks has recently been employed to overcome the …
Hyperspectral image denoising employing a spatial–spectral deep residual convolutional neural network
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the
performance of the subsequent HSI interpretation and applications. In this paper, a novel deep …
performance of the subsequent HSI interpretation and applications. In this paper, a novel deep …
Missing data reconstruction in remote sensing image with a unified spatial–temporal–spectral deep convolutional neural network
Because of the internal malfunction of satellite sensors and poor atmospheric conditions such
as thick cloud, the acquired remote sensing data often suffer from missing information, ie, …
as thick cloud, the acquired remote sensing data often suffer from missing information, ie, …