Missing information reconstruction of remote sensing data: A technical review
Because of sensor malfunction and poor atmospheric conditions, there is usually a great
deal of missing information in optical remote sensing data, which reduces the usage rate …
deal of missing information in optical remote sensing data, which reduces the usage rate …
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 …
such as thick cloud, the acquired remote sensing data often suffer from missing information …
Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations
L Zhuang, JM Bioucas-Dias - IEEE Journal of Selected Topics …, 2018 - ieeexplore.ieee.org
This paper introduces two very fast and competitive hyperspectral image (HSI) restoration
algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with …
algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with …
Deep hyperspectral prior: Single-image denoising, inpainting, super-resolution
O Sidorov, J Yngve Hardeberg - Proceedings of the IEEE …, 2019 - openaccess.thecvf.com
Deep learning algorithms have demonstrated state-of-the-art performance in various tasks of
image restoration. This was made possible through the ability of CNNs to learn from large …
image restoration. This was made possible through the ability of CNNs to learn from large …
Cloud removal in remote sensing images using nonnegative matrix factorization and error correction
In the imaging process of optical remote sensing platforms, clouds are an inevitable barrier
to the effective observation of sensors. To recover the original information covered by the …
to the effective observation of sensors. To recover the original information covered by the …
Recovering quantitative remote sensing products contaminated by thick clouds and shadows using multitemporal dictionary learning
With regard to quantitative remote sensing products in the visible and infrared ranges, thick
clouds and accompanying shadows are an inevitable source of noise. Due to the absence of …
clouds and accompanying shadows are an inevitable source of noise. Due to the absence of …
Cloud removal with fusion of high resolution optical and SAR images using generative adversarial networks
The existence of clouds is one of the main factors that contributes to missing information in
optical remote sensing images, restricting their further applications for Earth observation, so …
optical remote sensing images, restricting their further applications for Earth observation, so …
SAR-to-optical image translation using supervised cycle-consistent adversarial networks
Optical remote sensing (RS) data suffer from the limitation of bad weather and cloud
contamination, whereas synthetic aperture radar (SAR) can work under all weather …
contamination, whereas synthetic aperture radar (SAR) can work under all weather …
Exemplar-based image inpainting using angle-aware patch matching
N Zhang, H Ji, L Liu, G Wang - EURASIP Journal on Image and Video …, 2019 - Springer
Image inpainting has been presented to complete missing content according to the content
of the known region. This paper proposes a novel and efficient algorithm for image …
of the known region. This paper proposes a novel and efficient algorithm for image …
Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model
Cloud cover is generally present in remotely sensed images, which limits the potential of the
images for ground information extraction. Therefore, removing the clouds and recovering the …
images for ground information extraction. Therefore, removing the clouds and recovering the …