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Remote Sensing Image Denoising, Restoration and Reconstruction
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Remote Sensing Image Denoising, Restoration and Reconstruction

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 39576

Special Issue Editors

Prof. Dr. Karen Egiazarian

E-Mail Website
Guest Editor
Tampere University; Korkeakoulunkatu 1, 33720 Tampere, Finland
Interests: computational imaging; compressed sensing; efficient signal processing algorithms; image/video restoration and compression
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Aleksandra Pizurica

E-Mail Website
Guest Editor
Ghent University, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium
Interests: statistical image modeling; sparse representation; image restoration and reconstruction; analysis of high-dimensional data; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vladimir Lukin

E-Mail
Guest Editor
Department of Information-Communication Technologies, National Aerospace University, Chkalova Str., 61070 Kharkov, Ukraine
Interests: multichannel remote sensing; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During image acquisition process remote sensing images are corrupted by various kinds of degradations, such as noise, geometric distortions, changes in illumination, blur (motion, atmospheric turbulence, out-of-focus), etc. Image restoration/reconstruction (IR) is an inverse imaging problem aiming at estimating original images from the observed distorted ones. IR can be applied on a sensor data at the pre-processing stage, to improve image quality and to support further stages of data analysis, object detection and classification, or at the post-processing stage, to reduce distortions caused by lossless coding of images (blocking and ringing artifacts).

This Special Issue will present recent advances in inverse imaging of remote sensing data. Specifically, novel model-based, machine learning methods, or hybrid methods of image restoration, image denoising, deblurring (blind and non-blind), image super-resolution will be of special attention.

Topics of interest include but are not limited to:

  • Image denoising
  • Image deblurring (blind and non-blind)
  • Image super-resolution
  • Image dehazing and de-raining
  • Image compression artifacts reduction
  • The effect of image restoration on clustering, classification and target detection
  • Sparse representation and low-rank approximation for image restoration in remote sensing
  • Deep learning models for image restoration, with emphasis on robustness to adversarial attacks and data variation
  • Multimodal image restoration and joint restoration and fusion of multi-sensor data

Prof. Dr. Karen Egiazarian
Prof. Dr. Aleksandra Pizurica
Prof. Dr. Vladimir Lukin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Image denoising and enhancement
  • Image deblurring (blind and non-blind)
  • Image super-resolution
  • Image dehazing and de-raining
  • Image compression artifacts reduction
  • Restoration of multi-modal images and multi-sensor data

Published Papers (15 papers)

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Editorial

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5 pages, 193 KiB  
Editorial
Editorial to Special Issue “Remote Sensing Image Denoising, Restoration and Reconstruction”
by Karen Egiazarian, Aleksandra Pižurica and Vladimir Lukin
Remote Sens. 2022, 14(20), 5228; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205228 - 19 Oct 2022
Viewed by 1049
Abstract
The motivations behind this Special Issue, announced in 18 August 2020, were the following [...] Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)

Research

Jump to: Editorial, Other

21 pages, 8851 KiB  
Article
Zero-Shot Remote Sensing Image Dehazing Based on a Re-Degradation Haze Imaging Model
by Jianchong Wei, Yi Wu, Liang Chen, Kunping Yang and Renbao Lian
Remote Sens. 2022, 14(22), 5737; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14225737 - 13 Nov 2022
Cited by 3 | Viewed by 1812
Abstract
Image dehazing is crucial for improving the advanced applications on remote sensing (RS) images. However, collecting paired RS images to train the deep neural networks (DNNs) is scarcely available, and the synthetic datasets may suffer from domain-shift issues. In this paper, we propose [...] Read more.
Image dehazing is crucial for improving the advanced applications on remote sensing (RS) images. However, collecting paired RS images to train the deep neural networks (DNNs) is scarcely available, and the synthetic datasets may suffer from domain-shift issues. In this paper, we propose a zero-shot RS image dehazing method based on a re-degradation haze imaging model, which directly restores the haze-free image from a single hazy image. Based on layer disentanglement, we design a dehazing framework consisting of three joint sub-modules to disentangle the hazy input image into three components: the atmospheric light, the transmission map, and the recovered haze-free image. We then generate a re-degraded hazy image by mixing up the hazy input image and the recovered haze-free image. By the proposed re-degradation haze imaging model, we theoretically demonstrate that the hazy input and the re-degraded hazy image follow a similar haze imaging model. This finding helps us to train the dehazing network in a zero-shot manner. The dehazing network is optimized to generate outputs that satisfy the relationship between the hazy input image and the re-degraded hazy image in the re-degradation haze imaging model. Therefore, given a hazy RS image, the dehazing network directly infers the haze-free image by minimizing a specific loss function. Using uniform hazy datasets, non-uniform hazy datasets, and real-world hazy images, we conducted comprehensive experiments to show that our method outperforms many state-of-the-art (SOTA) methods in processing uniform or slight/moderate non-uniform RS hazy images. In addition, evaluation on a high-level vision task (RS image road extraction) further demonstrates the effectiveness and promising performance of the proposed zero-shot dehazing method. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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24 pages, 8601 KiB  
Article
Reconstruction of Sentinel-2 Image Time Series Using Google Earth Engine
by Kaixiang Yang, Youming Luo, Mengyao Li, Shouyi Zhong, Qiang Liu and Xiuhong Li
Remote Sens. 2022, 14(17), 4395; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174395 - 04 Sep 2022
Cited by 12 | Viewed by 5282
Abstract
Sentinel-2 NDVI and surface reflectance time series have been widely used in various geoscience research, but the data is deteriorated or missing due to the cloud contamination, so it is necessary to reconstruct the Sentinel-2 NDVI and surface reflectance time series. At present, [...] Read more.
Sentinel-2 NDVI and surface reflectance time series have been widely used in various geoscience research, but the data is deteriorated or missing due to the cloud contamination, so it is necessary to reconstruct the Sentinel-2 NDVI and surface reflectance time series. At present, there are few studies on reconstructing the Sentinel-2 NDVI or surface reflectance time series, and these existing reconstruction methods have some shortcomings. We proposed a new method to reconstruct the Sentinel-2 NDVI and surface reflectance time series using the penalized least-square regression based on discrete cosine transform (DCT-PLS) method. This method iteratively identifies cloud-contaminated NDVI over NDVI time series from the Sentinel-2 surface reflectance data by adjusting the weights. The NDVI and surface reflectance time series are then reconstructed from cloud-free NDVI and surface reflectance using the adjusted weights as constraints. We have made some improvements to the DCT-PLS method. First, the traditional discrete cosine transformation (DCT) in the DCT-PLS method is matrix generated from discrete and equally spaced data, we reconfigured the DCT formulas to adapt for irregular interval time series, and optimized the control parameters N and s according to the typical vegetation samples in China. Second, the DCT-PLS method was deployed in the Google Earth Engine (GEE) platform for the efficiency and convenience of data users. We used the DCT-PLS method to reconstruct the Sentinel-2 NDVI time series and surface reflectance time series in the blue, green, red, and near infrared (NIR) bands in typical vegetation samples and the Zhangjiakou and Hangzhou study area. We found that this method performed better than the SG filter method in reconstructing the NDVI time series, and can identify and reconstruct the contaminated NDVI as well as surface reflectance with low root mean square error (RMSE) and high coefficient of determination (R2). However, in cases of a long range of cloud contamination, or above water surface, it may be necessary to increase the control parameter s for a more stable performance. The GEE code is freely available online and the link is in the conclusions of this article, researchers are welcome to use this method to generate cloudless Sentinel-2 NDVI and surface reflectance time series with 10 m spatial resolution, which is convenient for landcover classification and many other types of research. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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23 pages, 10346 KiB  
Article
Column-Spatial Correction Network for Remote Sensing Image Destriping
by Jia Li, Dan Zeng, Junjie Zhang, Jungong Han and Tao Mei
Remote Sens. 2022, 14(14), 3376; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143376 - 13 Jul 2022
Cited by 5 | Viewed by 1602
Abstract
The stripe noise in the multispectral remote sensing images, possibly resulting from the instrument instability, slit contamination, and light interference, significantly degrades the imaging quality and impairs high-level visual tasks. The local consistency of homogeneous region in striped images is damaged because of [...] Read more.
The stripe noise in the multispectral remote sensing images, possibly resulting from the instrument instability, slit contamination, and light interference, significantly degrades the imaging quality and impairs high-level visual tasks. The local consistency of homogeneous region in striped images is damaged because of the different gains and offsets of adjacent sensors regarding the same ground object, which leads to the structural characteristics of stripe noise. This can be characterized by the increased differences between columns in the remote sensing image. Therefore, the destriping can be viewed as a process of improving the local consistency of homogeneous region and the global uniformity of whole image. In recent years, convolutional neural network (CNN)-based models have been introduced to destriping tasks, and have achieved advanced results, relying on their powerful representation ability. Therefore, to effectively leverage both CNNs and the structural characteristics of stripe noise, we propose a multi-scaled column-spatial correction network (CSCNet) for remote sensing image destriping, in which the local structural characteristic of stripe noise and the global contextual information of the image are both explored at multiple feature scales. More specifically, the column-based correction module (CCM) and spatial-based correction module (SCM) were designed to improve the local consistency and global uniformity from the perspectives of column correction and full image correction, respectively. Moreover, a feature fusion module based on the channel attention mechanism was created to obtain discriminative features derived from different modules and scales. We compared the proposed model against both traditional and deep learning methods on simulated and real remote sensing images. The promising results indicate that CSCNet effectively removes image stripes and outperforms state-of-the-art methods in terms of qualitative and quantitative assessments. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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24 pages, 64647 KiB  
Article
Self-Supervised Denoising for Real Satellite Hyperspectral Imagery
by Jinchun Qin, Hongrui Zhao and Bing Liu
Remote Sens. 2022, 14(13), 3083; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133083 - 27 Jun 2022
Cited by 4 | Viewed by 1813
Abstract
Satellite hyperspectral remote sensing has gradually become an important means of Earth observation, but the existence of various types of noise seriously limits the application value of satellite hyperspectral images. With the continuous development of deep learning technology, breakthroughs have been made in [...] Read more.
Satellite hyperspectral remote sensing has gradually become an important means of Earth observation, but the existence of various types of noise seriously limits the application value of satellite hyperspectral images. With the continuous development of deep learning technology, breakthroughs have been made in improving hyperspectral image denoising algorithms based on supervised learning; however, these methods usually require a large number of clean/noisy training pairs, a target that is difficult to meet for real satellite hyperspectral imagery. In this paper, we propose a self-supervised learning-based algorithm, 3S-HSID, for denoising real satellite hyperspectral images without requiring external data support. The 3S-HSID framework can perform robust denoising of a single satellite hyperspectral image in all bands simultaneously. It first conducts a Bernoulli sampling of the input data, then uses the Bernoulli sampling results to construct the training pairs. Furthermore, the global spectral consistency and minimum local variance are used in the loss function to train the network. We use the training model to predict different Bernoulli sampling results, and the average of multiple predicted values is used as the denoising result. To prevent overfitting, we adopt a dropout strategy during training and testing. The results of denoising experiments on the simulated hyperspectral data show that the denoising performance of 3S-HSID is better than most state-of-the-art algorithms, especially in terms of maintaining the spectral characteristics of hyperspectral images. The denoising results for different types of real satellite hyperspectral data also demonstrate the reliability of the proposed method. The 3S-HSID framework provides a new technical means for real satellite hyperspectral image preprocessing. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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18 pages, 5696 KiB  
Article
Reconstruction of Vegetation Index Time Series Based on Self-Weighting Function Fitting from Curve Features
by Wenquan Zhu, Bangke He, Zhiying Xie, Cenliang Zhao, Huimin Zhuang and Peixian Li
Remote Sens. 2022, 14(9), 2247; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092247 - 07 May 2022
Cited by 3 | Viewed by 2320
Abstract
Vegetation index (VI) time series derived from satellite sensors have been widely used in the estimation of vegetation parameters, but the quality of VI time series is easily affected by clouds and poor atmospheric conditions. The function fitting method is a widely used [...] Read more.
Vegetation index (VI) time series derived from satellite sensors have been widely used in the estimation of vegetation parameters, but the quality of VI time series is easily affected by clouds and poor atmospheric conditions. The function fitting method is a widely used effective noise reduction technique for VI time series, but it is vulnerable to noise. Thus, ancillary data about VI quality are utilized to alleviate the interference of noise. However, this approach is limited by the availability, accuracy, and application rules of ancillary data. In this paper, we aimed to develop a new reconstruction method that does not require ancillary data. Based on the assumptions that VI time series follow the gradual growth and decline pattern of vegetation dynamics, and that clouds or poor atmospheric conditions usually depress VI values, we proposed a reconstruction method for VI time series based on self-weighting function fitting from curve features (SWCF). SWCF consists of two major procedures: (1) determining a fitting weight for each VI point based on the curve features of the VI time series and (2) implementing the weighted function fitting to reconstruct the VI time series. The double logistic function, double Gaussian function, and polynomial function were tested in SWCF based on a simulated dataset. The results indicate that the weighted function fitting with SWCF outperformed the corresponding unweighted function fitting with the root-mean-square error (RMSE) significantly reduced by 26.82–52.44% (p < 0.05), and it also outperformed the Savitzky–Golay filtering with the RMSE significantly reduced by 13.98–54.04% (p < 0.05) for 270 sample points selected in mid-high latitudes of the Northern Hemisphere. Moreover, SWCF showed excellent robustness and applicability in regional applications. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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23 pages, 12316 KiB  
Article
Hyperspectral Image Denoising via Adversarial Learning
by Junjie Zhang, Zhouyin Cai, Fansheng Chen and Dan Zeng
Remote Sens. 2022, 14(8), 1790; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081790 - 07 Apr 2022
Cited by 15 | Viewed by 2373
Abstract
Due to sensor instability and atmospheric interference, hyperspectral images (HSIs) often suffer from different kinds of noise which degrade the performance of downstream tasks. Therefore, HSI denoising has become an essential part of HSI preprocessing. Traditional methods tend to tackle one specific type [...] Read more.
Due to sensor instability and atmospheric interference, hyperspectral images (HSIs) often suffer from different kinds of noise which degrade the performance of downstream tasks. Therefore, HSI denoising has become an essential part of HSI preprocessing. Traditional methods tend to tackle one specific type of noise and remove it iteratively, resulting in drawbacks including inefficiency when dealing with mixed noise. Most recently, deep neural network-based models, especially generative adversarial networks, have demonstrated promising performance in generic image denoising. However, in contrast to generic RGB images, HSIs often possess abundant spectral information; thus, it is non-trivial to design a denoising network to effectively explore both spatial and spectral characteristics simultaneously. To address the above issues, in this paper, we propose an end-to-end HSI denoising model via adversarial learning. More specifically, to capture the subtle noise distribution from both spatial and spectral dimensions, we designed a Residual Spatial-Spectral Module (RSSM) and embed it in an UNet-like structure as the generator to obtain clean images. To distinguish the real image from the generated one, we designed a discriminator based on the Multiscale Feature Fusion Module (MFFM) to further improve the quality of the denoising results. The generator was trained with joint loss functions, including reconstruction loss, structural loss and adversarial loss. Moreover, considering the lack of publicly available training data for the HSI denoising task, we collected an additional benchmark dataset denoted as the Shandong Feicheng Denoising (SFD) dataset. We evaluated five types of mixed noise across several datasets in comparative experiments, and comprehensive experimental results on both simulated and real data demonstrate that the proposed model achieves competitive results against state-of-the-art methods. For ablation studies, we investigated the structure of the generator as well as the training process with joint losses and different amounts of training data, further validating the rationality and effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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26 pages, 48240 KiB  
Article
Hyperspectral Image Restoration via Spatial-Spectral Residual Total Variation Regularized Low-Rank Tensor Decomposition
by Xiangyang Kong, Yongqiang Zhao, Jonathan Cheung-Wai Chan and Jize Xue
Remote Sens. 2022, 14(3), 511; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030511 - 21 Jan 2022
Cited by 3 | Viewed by 2840
Abstract
To eliminate the mixed noise in hyperspectral images (HSIs), three-dimensional total variation (3DTV) regularization has been proven as an efficient tool. However, 3DTV regularization is prone to losing image details in restoration. To resolve this issue, we proposed a novel TV, named spatial [...] Read more.
To eliminate the mixed noise in hyperspectral images (HSIs), three-dimensional total variation (3DTV) regularization has been proven as an efficient tool. However, 3DTV regularization is prone to losing image details in restoration. To resolve this issue, we proposed a novel TV, named spatial domain spectral residual total variation (SSRTV). Considering that there is much residual texture information in spectral variation image, SSRTV first calculates the difference between the pixel values of adjacent bands and then calculates a 2DTV for the residual image. Experimental results demonstrated that the SSRTV regularization term is powerful at changing the structures of noises in an original HSI, thus allowing low-rank techniques to get rid of mixed noises more efficiently without treating them as low-rank features. The global low-rankness and spatial–spectral correlation of HSI is exploited by low-rank Tucker decomposition (LRTD). Moreover, it was demonstrated that the l2,1 norm is more effective to deal with sparse noise, especially the sample-specific noise such as stripes or deadlines. The augmented Lagrange multiplier (ALM) algorithm was adopted to solve the proposed model. Finally, experimental results with simulated and real data illustrated the validity of the proposed method. The proposed method outperformed state-of-the-art TV-regularized low-rank matrix/tensor decomposition methods in terms of quantitative metrics and visual inspection. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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20 pages, 15384 KiB  
Article
A Remote Sensing Image Destriping Model Based on Low-Rank and Directional Sparse Constraint
by Xiaobin Wu, Hongsong Qu, Liangliang Zheng, Tan Gao and Ziyu Zhang
Remote Sens. 2021, 13(24), 5126; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245126 - 17 Dec 2021
Cited by 5 | Viewed by 2381
Abstract
Stripe noise is a common condition that has a considerable impact on the quality of the images. Therefore, stripe noise removal (destriping) is a tremendously important step in image processing. Since the existing destriping models cause different degrees of ripple effects, in this [...] Read more.
Stripe noise is a common condition that has a considerable impact on the quality of the images. Therefore, stripe noise removal (destriping) is a tremendously important step in image processing. Since the existing destriping models cause different degrees of ripple effects, in this paper a new model, based on total variation (TV) regularization, global low rank and directional sparsity constraints, is proposed for the removal of vertical stripes. TV regularization is used to preserve details, and the global low rank and directional sparsity are used to constrain stripe noise. The directional and structural characteristics of stripe noise are fully utilized to achieve a better removal effect. Moreover, we designed an alternating minimization scheme to obtain the optimal solution. Simulation and actual experimental data show that the proposed model has strong robustness and is superior to existing competitive destriping models, both subjectively and objectively. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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46 pages, 25331 KiB  
Article
Guaranteed Robust Tensor Completion via ∗L-SVD with Applications to Remote Sensing Data
by Andong Wang, Guoxu Zhou and Qibin Zhao
Remote Sens. 2021, 13(18), 3671; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183671 - 14 Sep 2021
Cited by 5 | Viewed by 2962
Abstract
This paper conducts a rigorous analysis for the problem of robust tensor completion, which aims at recovering an unknown three-way tensor from incomplete observations corrupted by gross sparse outliers and small dense noises simultaneously due to various reasons such as sensor dead pixels, [...] Read more.
This paper conducts a rigorous analysis for the problem of robust tensor completion, which aims at recovering an unknown three-way tensor from incomplete observations corrupted by gross sparse outliers and small dense noises simultaneously due to various reasons such as sensor dead pixels, communication loss, electromagnetic interferences, cloud shadows, etc. To estimate the underlying tensor, a new penalized least squares estimator is first formulated by exploiting the low rankness of the signal tensor within the framework of tensor ∗L-Singular Value Decomposition (∗L-SVD) and leveraging the sparse structure of the outlier tensor. Then, an algorithm based on the Alternating Direction Method of Multipliers (ADMM) is designed to compute the estimator in an efficient way. Statistically, the non-asymptotic upper bound on the estimation error is established and further proved to be optimal (up to a log factor) in a minimax sense. Simulation studies on synthetic data demonstrate that the proposed error bound can predict the scaling behavior of the estimation error with problem parameters (i.e., tubal rank of the underlying tensor, sparsity of the outliers, and the number of uncorrupted observations). Both the effectiveness and efficiency of the proposed algorithm are evaluated through experiments for robust completion on seven different types of remote sensing data. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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18 pages, 12139 KiB  
Article
Unpaired Remote Sensing Image Super-Resolution with Multi-Stage Aggregation Networks
by Lize Zhang, Wen Lu, Yuanfei Huang, Xiaopeng Sun and Hongyi Zhang
Remote Sens. 2021, 13(16), 3167; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163167 - 10 Aug 2021
Cited by 1 | Viewed by 2048
Abstract
Mainstream image super-resolution (SR) methods are generally based on paired training samples. As the high-resolution (HR) remote sensing images are difficult to collect with a limited imaging device, most of the existing remote sensing super-resolution methods try to down-sample the collected original images [...] Read more.
Mainstream image super-resolution (SR) methods are generally based on paired training samples. As the high-resolution (HR) remote sensing images are difficult to collect with a limited imaging device, most of the existing remote sensing super-resolution methods try to down-sample the collected original images to generate an auxiliary low-resolution (LR) image and form a paired pseudo HR-LR dataset for training. However, the distribution of the generated LR images is generally inconsistent with the real images due to the limitation of remote sensing imaging devices. In this paper, we propose a perceptually unpaired super-resolution method by constructing a multi-stage aggregation network (MSAN). The optimization of the network depends on consistency losses. In particular, the first phase is to preserve the contents of the super-resolved results, by constraining the content consistency between the down-scaled SR results and the low-quality low-resolution inputs. The second stage minimizes perceptual feature loss between the current result and LR input to constrain perceptual-content consistency. The final phase employs the generative adversarial network (GAN) to adding photo-realistic textures by constraining perceptual-distribution consistency. Numerous experiments on synthetic remote sensing datasets and real remote sensing images show that our method obtains more plausible results than other SR methods quantitatively and qualitatively. The PSNR of our network is 0.06dB higher than the SOTA method—HAN on the UC Merced test set with complex degradation. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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18 pages, 3655 KiB  
Article
Mid-Infrared Compressive Hyperspectral Imaging
by Shuowen Yang, Xiang Yan, Hanlin Qin, Qingjie Zeng, Yi Liang, Henry Arguello and Xin Yuan
Remote Sens. 2021, 13(4), 741; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040741 - 17 Feb 2021
Cited by 8 | Viewed by 2892
Abstract
Hyperspectral imaging (HSI) has been widely investigated within the context of computational imaging due to the high dimensional challenges for direct imaging. However, existing computational HSI approaches are mostly designed for the visible to near-infrared waveband, whereas less attention has been paid to [...] Read more.
Hyperspectral imaging (HSI) has been widely investigated within the context of computational imaging due to the high dimensional challenges for direct imaging. However, existing computational HSI approaches are mostly designed for the visible to near-infrared waveband, whereas less attention has been paid to the mid-infrared spectral range. In this paper, we report a novel mid-infrared compressive HSI system to extend the application domain of mid-infrared digital micromirror device (MIR-DMD). In our system, a modified MIR-DMD is combined with an off-the-shelf infrared spectroradiometer to capture the spatial modulated and compressed measurements at different spectral channels. Following this, a dual-stage image reconstruction method is developed to recover infrared hyperspectral images from these measurements. In addition, a measurement without any coding is used as the side information to aid the reconstruction to enhance the reconstruction quality of the infrared hyperspectral images. A proof-of-concept setup is built to capture the mid-infrared hyperspectral data of 64 pixels × 48 pixels × 100 spectral channels ranging from 3 to 5 μm, with the acquisition time within one minute. To the best of our knowledge, this is the first mid-infrared compressive hyperspectral imaging approach that could offer a less expensive alternative to conventional mid-infrared hyperspectral imaging systems. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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16 pages, 6442 KiB  
Article
Airborne Radar Super-Resolution Imaging Based on Fast Total Variation Method
by Qiping Zhang, Yin Zhang, Yongchao Zhang, Yulin Huang and Jianyu Yang
Remote Sens. 2021, 13(4), 549; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040549 - 04 Feb 2021
Cited by 7 | Viewed by 2208
Abstract
Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O(N3) [...] Read more.
Total variation (TV) is an effective super-resolution method to improve the azimuth resolution and preserve the contour information of the target in airborne radar imaging. However, the computational complexity is very high because of the matrix inversion, reaching O(N3). In this paper, a Gohberg–Semencul (GS) representation based fast TV (GSFTV) method is proposed to make up for the shortcoming. The proposed GSFTV method fist utilizes a one-dimensional TV norm as the regular term under regularization framework, which is conducive to achieve super-resolution while preserving the target contour. Then, aiming at the very high computational complexity caused by matrix inversion when minimizing the TV regularization problem, we use the low displacement rank feature of Toeplitz matrix to achieve fast inversion through GS representation. This reduces the computational complexity from O(N3) to O(N2), benefiting efficiency improvement for airborne radar imaging. Finally, the simulation and real data processing results demonstrate that the proposed GSFTV method can simultaneously improve the resolution and preserve the target contour. Moreover, the very high computational efficiency of the proposed GSFTV method is tested by hardware platform. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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26 pages, 77415 KiB  
Article
Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity Regularized Tensor Optimization
by Chenxi Duan, Jun Pan and Rui Li
Remote Sens. 2020, 12(20), 3446; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203446 - 20 Oct 2020
Cited by 25 | Viewed by 3427
Abstract
In remote sensing images, the presence of thick cloud accompanying shadow can affect the quality of subsequent processing and limit the scenarios of application. Hence, to make good use of such images, it is indispensable to remove the thick cloud and cloud shadow [...] Read more.
In remote sensing images, the presence of thick cloud accompanying shadow can affect the quality of subsequent processing and limit the scenarios of application. Hence, to make good use of such images, it is indispensable to remove the thick cloud and cloud shadow as well as recover the cloud-contaminated pixels. Generally, the thick cloud and cloud shadow element are not only sparse but also smooth along the spatial horizontal and vertical direction, while the clean element is smooth along the temporal direction. Guided by the above insight, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity regularized tensor optimization (TSSTO) is proposed in this paper. Firstly, the sparsity norm is utilized to boost the sparsity of the cloud and cloud shadow element, and unidirectional total variation (UTV) regularizers are applied to ensure the smoothness in different directions. Then, through thresholding, the cloud mask and the cloud shadow mask can be acquired and used to guide the substitution. Finally, the reference image is selected to reconstruct details of the repairing area. A series of experiments are conducted both on simulated and real cloud-contaminated images from different sensors and with different resolutions, and the results demonstrate the potential of the proposed TSSTO method for removing cloud and cloud shadow from both qualitative and quantitative viewpoints. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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16 pages, 3965 KiB  
Technical Note
MODIS Land Surface Temperature Product Reconstruction Based on the SSA-BiLSTM Model
by Jianyong Cui, Manyu Zhang, Dongmei Song, Xinjian Shan and Bin Wang
Remote Sens. 2022, 14(4), 958; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14040958 - 16 Feb 2022
Cited by 6 | Viewed by 1718
Abstract
Land surface temperature (LST) is an important parameter indispensable for studying the substance and energy exchanges between the land surface and the atmosphere, climate changes, and other related aspects. However, due to cloud cover, there are many null values in MODIS (Moderate Resolution [...] Read more.
Land surface temperature (LST) is an important parameter indispensable for studying the substance and energy exchanges between the land surface and the atmosphere, climate changes, and other related aspects. However, due to cloud cover, there are many null values in MODIS (Moderate Resolution Imaging Spectroradiometer) LST data, which prevents such data from being widely used. Therefore, an LST reconstruction method is proposed by combining data decomposition with data prediction—SSA (Singular Spectrum Analysis) and BiLSTM (Bidirectional Long Short-Term Memory). This method consists of two major processes, namely, rough LST reconstruction based on the SSA model and refined LST reconstruction based on the BiLSTM model. The accuracy of the proposed method is verified through “removal–reconstruction–comparison” using remote sensing data and measured data. The verification results show that when the rate of original missing values in the LST time series for the study area is lower than 10%, the RMSE is smaller than 1.1 K, and the correlation coefficient is more significant than 0.98. Even when the rate of missing data is 40% and 50%, the proposed method remains accurate, the values of RMSE are 1.8331 K and 2.2929 K, and the importance of R2 are 0.9856 and 0.9800, respectively. The proposed method is compared with other existing LST reconstruction methods. The results of the comparative analysis indicate that the proposed method is superior to other methods in terms of reconstruction accuracy and stability. Moreover, the LST data reconstructed using the proposed method are highly consistent with the measured data, which further proves the accuracy of this method in LST reconstruction. The research findings provide a new technique and idea for accurate LST reconstruction. Full article
(This article belongs to the Special Issue Remote Sensing Image Denoising, Restoration and Reconstruction)
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