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Special Issue "Data Restoration and Denoising of Remote Sensing Data"

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 (31 August 2018)

Special Issue Editors

Guest Editor
Prof. Jocelyn Chanussot

GIPSA-lab, 11 rue des Mathématiques, Grenoble Campus BP46, F-38402 SAINT MARTIN D'HERES CEDEX, France
Website | E-Mail
Interests: image processing; machine learning; mathematical morphology; hyperspectral imaging; data fusion
Guest Editor
Dr. Giorgio Antonino Licciardi

Hypatia Research Consortium, Via del Politecnico SNC, C/O Italian Space Agency, 00133 Roma Italy
Website | E-Mail
Phone: +39 06 8567495
Interests: Hyperspactral image analysis; machine learning; deep learning techniques; dimensionality reduction; super-resolution; spectral unmixing; data fusion
Guest Editor
Dr. Samuel Foucher

Computer Research Institute of Montreal, Canada
Website | E-Mail
Interests: image processing; remote sensing; SAR polarimetry; machine learning
Guest Editor
Prof. Kun-Shan Chen

The Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Website | E-Mail
Interests: microwave scattering and propagation, imaging radar, intelligent signal and image processing

Special Issue Information

Dear Colleagues,

With very diverse available sensors and platforms, remote sensing is increasingly used in a number of applications with very high societal impact. However, the quality of the data remains a critical issue and can significantly impact the actual performances of the implemented processing algorithms. The noise corrupting the data can have very different causes, depending on the modality of acquisition (radar, optical, hyperspectral, thermal, LiDAR), the characteristics of the sensors and the used platform, from UAVs to satellites. It is, hence, usually highly desirable to reduce the noise before considering specific processing. The Special Issue, focused on noise reduction for remote sensing data, aims at gathering a variety of contributions, embracing this problem in its diversity. As a matter of fact, different natures of noise require different strategies, from physical modelling, sensor design, to advanced statistics and information processing. Contributions are welcome (but are not limited to) for the following topics:

  • speckle reduction in active imaging systems (SAR, PolSAR, InSAR, LiDAR, etc.)
  • Image restoration and enhancement
  • atmospheric correction, sensor calibration, destriping, correction of the smile effect in optical and hypspectral imaging, shadow removal, dehazing
  • advanced signal and image processing strategies for noise reduction
  • inpainting for the correction of missing/corrupted data

We look forward to hearing from you and receiving your contribution.

Kind regards,

Prof. Jocelyn Chanussot
Dr. Giorgio Licciardi
Dr. Samuel Foucher
Prof. KunShan Chen
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 papers will be 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 1800 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

  • noise reduction
  • despeckling
  • subspace identification 
  • atmospheric correction
  • calibration
  • smile effect 
  • destriping 
  • inpainting
  • filtering

Published Papers (13 papers)

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Research

Open AccessArticle
Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration
Remote Sens. 2018, 10(12), 1893; https://doi.org/10.3390/rs10121893
Received: 17 October 2018 / Revised: 15 November 2018 / Accepted: 21 November 2018 / Published: 27 November 2018
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Abstract
The spatial resolution and clarity of remote sensing images are crucial for many applications such as target detection and image classification. In the last several decades, tremendous image restoration tasks have shown great success in ordinary images. However, since remote sensing images are [...] Read more.
The spatial resolution and clarity of remote sensing images are crucial for many applications such as target detection and image classification. In the last several decades, tremendous image restoration tasks have shown great success in ordinary images. However, since remote sensing images are more complex and more blurry than ordinary images, most of the existing methods are not good enough for remote sensing image restoration. To address such problem, we propose a novel method named deep memory connected network (DMCN) based on the convolutional neural network to reconstruct high-quality images. We build local and global memory connections to combine image detail with global information. To further reduce parameters and ease time consumption, we propose Downsampling Units, shrinking the spatial size of feature maps. We verify its capability on two representative applications, Gaussian image denoising and single image super-resolution (SR). DMCN is tested on three remote sensing datasets with various spatial resolution. Experimental results indicate that our method yields promising improvements and better visual performance over the current state-of-the-art. The PSNR and SSIM improvements over the second best method are up to 0.3 dB. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle
Ground Reflectance Retrieval on Horizontal and Inclined Terrains Using the Software Package REFLECT
Remote Sens. 2018, 10(10), 1638; https://doi.org/10.3390/rs10101638
Received: 31 August 2018 / Revised: 7 October 2018 / Accepted: 12 October 2018 / Published: 15 October 2018
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Abstract
This paper presents the software package REFLECT for the retrieval of ground reflectance from high and very-high resolution multispectral satellite images. The computation of atmospheric parameters is based on the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) routines. Aerosol [...] Read more.
This paper presents the software package REFLECT for the retrieval of ground reflectance from high and very-high resolution multispectral satellite images. The computation of atmospheric parameters is based on the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) routines. Aerosol optical properties are calculated using the OPAC (Optical Properties of Aerosols and Clouds) model, while aerosol optical depth is estimated using the dark target method. A new approach is proposed for adjacency effect correction. Topographic effects were also taken into account, and a new model was developed for forest canopies. Validation has shown that ground reflectance estimation with REFLECT is performed with an accuracy of approximately ±0.01 in reflectance units (for the visible, near-infrared, and mid-infrared spectral bands), even for surfaces with varying topography. The validation of the software was performed through many tests. These tests involve the correction of the effects that are associated with sensor calibration, irradiance, and viewing conditions, atmospheric conditions (aerosol optical depth AOD and water vapour), adjacency, and topographic conditions. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle
A Variational Model for Sea Image Enhancement
Remote Sens. 2018, 10(8), 1313; https://doi.org/10.3390/rs10081313
Received: 26 June 2018 / Revised: 2 August 2018 / Accepted: 6 August 2018 / Published: 20 August 2018
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Abstract
The purpose of sea image enhancement is to enhance the information of the waves, whose contrast is generally weak. Enhancement effect is often affected by impulse-type noise and non-uniform illumination. In this paper, we propose a variational model for sea image enhancement using [...] Read more.
The purpose of sea image enhancement is to enhance the information of the waves, whose contrast is generally weak. Enhancement effect is often affected by impulse-type noise and non-uniform illumination. In this paper, we propose a variational model for sea image enhancement using a solar halo model and a Retinex model. This paper mainly makes the following three contributions: 1. Establishing a Retinex model with noise suppression ability in sea images; 2. Establishing a solar-scattering halo model through sea image bitplane analysis; 3. Proposing a variational enhancement model combining the Retinex and halo models. The experimental results show that our method has a significant enhancement effect on sea surface images in different illumination environments compared with typical methods. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle
Millimeter-Wave InSAR Image Reconstruction Approach by Total Variation Regularized Matrix Completion
Remote Sens. 2018, 10(7), 1053; https://doi.org/10.3390/rs10071053
Received: 17 May 2018 / Revised: 23 June 2018 / Accepted: 30 June 2018 / Published: 3 July 2018
Cited by 1 | PDF Full-text (728 KB) | HTML Full-text | XML Full-text
Abstract
Millimeter-wave interferometric synthetic aperture radiometer (InSAR) can provide high-resolution observations for many applications by using small antennas to achieve very large synthetic aperture. However, reconstruction of a millimeter-wave InSAR image has been proven to be an ill-posed inverse problem that degrades the performance [...] Read more.
Millimeter-wave interferometric synthetic aperture radiometer (InSAR) can provide high-resolution observations for many applications by using small antennas to achieve very large synthetic aperture. However, reconstruction of a millimeter-wave InSAR image has been proven to be an ill-posed inverse problem that degrades the performance of InSAR imaging. In this paper, a novel millimeter-wave InSAR image reconstruction approach, referred to as InSAR-TVMC, by total variation (TV) regularized matrix completion (MC) in two-dimensional data space, is proposed. Based on the a priori knowledge that natural millimeter-wave images statistically hold the low-rank property, the proposed approach represents the object images as low-rank matrices and formulates the data acquisition of InSAR in two-dimensional data space directly to undersample visibility function samples. Subsequently, using the undersampled visibility function samples, the optimal solution of the InSAR image reconstruction problem is obtained by simultaneously adopting MC techniques and TV regularization. Experimental results on simulated and real millimeter-wave InSAR image data demonstrate the effectiveness and the significant improvement of the reconstruction performance of the proposed InSAR-TVMC approach over conventional and one-dimensional sparse InSAR image reconstruction approaches. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle
System Noise Removal for Gaofen-4 Area-Array Camera
Remote Sens. 2018, 10(5), 759; https://doi.org/10.3390/rs10050759
Received: 23 April 2018 / Revised: 8 May 2018 / Accepted: 12 May 2018 / Published: 15 May 2018
Cited by 1 | PDF Full-text (4449 KB) | HTML Full-text | XML Full-text
Abstract
Gaofen-4 is a geostationary orbit area array imaging satellite. Due to the difficulty of the on-orbit radiometric calibration of area array cameras, there is system noise in the images. This paper analyzes the source of the system noise, constructs a noise model of [...] Read more.
Gaofen-4 is a geostationary orbit area array imaging satellite. Due to the difficulty of the on-orbit radiometric calibration of area array cameras, there is system noise in the images. This paper analyzes the source of the system noise, constructs a noise model of Gaofen-4, and proposes a practical method to remove the system noise using multiple images. Gaussian filtering is used to remove radiometric characteristics, and the Grubbs criterion is used to remove gradient characteristics, thereby transforming the images into noise images. System noise can be removed using correction coefficients obtained by superimposing multiple noise images. Using a variety of denoising methods to perform contrast experiments, the results show that the proposed method can effectively maintain image edge details and texture information while removing image noise. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle
A Practical Approach to Landsat 8 TIRS Stray Light Correction Using Multi-Sensor Measurements
Remote Sens. 2018, 10(4), 589; https://doi.org/10.3390/rs10040589
Received: 13 February 2018 / Revised: 26 March 2018 / Accepted: 5 April 2018 / Published: 10 April 2018
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Abstract
It has been noticed that the Landsat 8 Thermal Infrared Sensor (TIRS) had an issue with stray light since its launch in 2013. This artifact is due to out-of-field radiance that scatters onto the TIRS focal plane. Much effort has been taken to [...] Read more.
It has been noticed that the Landsat 8 Thermal Infrared Sensor (TIRS) had an issue with stray light since its launch in 2013. This artifact is due to out-of-field radiance that scatters onto the TIRS focal plane. Much effort has been taken to develop an algorithm to remove this artifact. One proposed approach involves using TIRS data itself (referred to as TIRS-on-TIRS) to retrieve the true sensor-reaching radiance. This approach has been proven to be operational and supports the TIRS Collection-1 product. A methodology of calibrating the TIRS sensor with information from the Geostationary Operational Environmental Satellite (GOES) instrument may optimally reduce the stray light effect for special cases where there is a large temperature contrast between the edge of the TIRS image and out-of-field radiance (referred to as GOES-on-TIRS). This paper illustrates a GOES to TIRS conversion (GTTC) algorithm with the North American Regional Reanalysis (NARR) data to support the GOES-on-TIRS method. Results show this GOES_TIRS correction method performs similarly to the TIRS Collection-1 product. Additionally, a simplified methodology is proposed to improve the GOES data processing which can operationalize the GOES-on-TIRS algorithm. Results also show that, using the proposed algorithm with these special cases, the maximum difference between the Collection-1 product and the GOES-on-TIRS correction results in a temperature difference from 0.5% to 0.7%. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle
Color Enhancement for Four-Component Decomposed Polarimetric SAR Image Based on a CIE-Lab Encoding
Remote Sens. 2018, 10(4), 545; https://doi.org/10.3390/rs10040545
Received: 31 January 2018 / Revised: 14 March 2018 / Accepted: 14 March 2018 / Published: 2 April 2018
Cited by 3 | PDF Full-text (28723 KB) | HTML Full-text | XML Full-text
Abstract
Color enhancement of decomposed fully polarimetric synthetic aperture radar (PolSAR) image is vital for visual understanding and interpretation of the polarimetric information about the target. It is common practice to use RGB or HIS color space to display the chromatic information for polarization-encoded, [...] Read more.
Color enhancement of decomposed fully polarimetric synthetic aperture radar (PolSAR) image is vital for visual understanding and interpretation of the polarimetric information about the target. It is common practice to use RGB or HIS color space to display the chromatic information for polarization-encoded, Pauli-basis images, or model-based target decomposition of PolSAR images. However, to represent the chroma for multi-polarization SAR data, the region of basic RGB color space does not fully cover the human perceptual system, leading to information loss. In this paper, we propose a color-encoding framework based on the CIE-Lab, a perceptually uniform color space, aiming at a better visual perception and information exploration. The effective interpretability in increasing chromatic, and thus visual enhancement, is presented using extensive datasets. In particular, the four decomposed components—volume scattering, surface scattering, double bounce, and helix scattering—along with total return power, are simultaneously mapped into the color space to improve the discernibility among the scattering components. The five channels derived from the four-component decomposition method can be simultaneously mapped to CIE-Lab color space intuitively. Results show that the proposed color enhancement not only preserves the color tone of the polarization signatures, but also magnifies the target information embedded in the total returned power. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessFeature PaperArticle
Noise Reduction in Hyperspectral Imagery: Overview and Application
Remote Sens. 2018, 10(3), 482; https://doi.org/10.3390/rs10030482
Received: 1 March 2018 / Revised: 12 March 2018 / Accepted: 16 March 2018 / Published: 20 March 2018
Cited by 6 | PDF Full-text (7048 KB) | HTML Full-text | XML Full-text
Abstract
Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth’s surface emitted by the Sun. The received radiance at the sensor is usually degraded by atmospheric effects and instrumental (sensor) noises which include thermal (Johnson) noise, quantization [...] Read more.
Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth’s surface emitted by the Sun. The received radiance at the sensor is usually degraded by atmospheric effects and instrumental (sensor) noises which include thermal (Johnson) noise, quantization noise, and shot (photon) noise. Noise reduction is often considered as a preprocessing step for hyperspectral imagery. In the past decade, hyperspectral noise reduction techniques have evolved substantially from two dimensional bandwise techniques to three dimensional ones, and varieties of low-rank methods have been forwarded to improve the signal to noise ratio of the observed data. Despite all the developments and advances, there is a lack of a comprehensive overview of these techniques and their impact on hyperspectral imagery applications. In this paper, we address the following two main issues; (1) Providing an overview of the techniques developed in the past decade for hyperspectral image noise reduction; (2) Discussing the performance of these techniques by applying them as a preprocessing step to improve a hyperspectral image analysis task, i.e., classification. Additionally, this paper discusses about the hyperspectral image modeling and denoising challenges. Furthermore, different noise types that exist in hyperspectral images have been described. The denoising experiments have confirmed the advantages of the use of low-rank denoising techniques compared to the other denoising techniques in terms of signal to noise ratio and spectral angle distance. In the classification experiments, classification accuracies have improved when denoising techniques have been applied as a preprocessing step. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle
Speckle Suppression Based on Sparse Representation with Non-Local Priors
Remote Sens. 2018, 10(3), 439; https://doi.org/10.3390/rs10030439
Received: 16 January 2018 / Revised: 28 February 2018 / Accepted: 9 March 2018 / Published: 11 March 2018
Cited by 3 | PDF Full-text (7987 KB) | HTML Full-text | XML Full-text
Abstract
As speckle seriously restricts the applications of remote sensing images in many fields, the ability to efficiently and effectively suppress speckle in a coherent imaging system is indispensable. In order to overcome the over-smoothing problem caused by the speckle suppression algorithm based on [...] Read more.
As speckle seriously restricts the applications of remote sensing images in many fields, the ability to efficiently and effectively suppress speckle in a coherent imaging system is indispensable. In order to overcome the over-smoothing problem caused by the speckle suppression algorithm based on classical sparse representation, we propose a non-local speckle suppression algorithm that combines the non-local prior knowledge of the image into the sparse representation. The proposed algorithm first applies shearlet to sparsely represent the input image. We then incorporate the non-local priors as constraints into the image sparse representation de-noising problem. The denoised image is obtained by utilizing an alternating minimization algorithm to solve the corresponding constrained de-noising problem. The experimental results show that the proposed algorithm can not only significantly remove speckle noise, but also improve the visual effect and retain the texture information of the image better. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle
A Relative Radiometric Calibration Method Based on the Histogram of Side-Slither Data for High-Resolution Optical Satellite Imagery
Remote Sens. 2018, 10(3), 381; https://doi.org/10.3390/rs10030381
Received: 4 January 2018 / Revised: 25 February 2018 / Accepted: 27 February 2018 / Published: 1 March 2018
Cited by 1 | PDF Full-text (11408 KB) | HTML Full-text | XML Full-text
Abstract
Relative radiometric calibration, or flat fielding, is indispensable for obtaining high-quality optical satellite imagery for sensors that have more than one detector per band. High-resolution optical push-broom sensors with thousands of detectors per band are now common. Multiple techniques have been employed for [...] Read more.
Relative radiometric calibration, or flat fielding, is indispensable for obtaining high-quality optical satellite imagery for sensors that have more than one detector per band. High-resolution optical push-broom sensors with thousands of detectors per band are now common. Multiple techniques have been employed for relative radiometric calibration. One technique, often called side-slither, where the sensor axis is rotated 90° in yaw relative to normal acquisitions, has been gaining popularity, being applied to Landsat 8, QuickBird, RapidEye, and other satellites. Side-slither can be more time efficient than some of the traditional methods, as only one acquisition may be required. In addition, the side-slither does not require any onboard calibration hardware, only a satellite capability to yaw and maintain a stable yawed attitude. A relative radiometric calibration method based on histograms of side-slither data is developed. This method has three steps: pre-processing, extraction of key points, and calculation of coefficients. Histogram matching and Otsu’s method are used to extract key points. Three datasets from the Chinese GaoFen-9 satellite were used: one to obtain the relative radiometric coefficients, and the others to verify the coefficients. Root-mean-square deviations of the corrected imagery were better than 0.1%. The maximum streaking metrics was less than 1. This method produced significantly better relative radiometric calibration than the traditional method used for GaoFen-9. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle
Directional 0 Sparse Modeling for Image Stripe Noise Removal
Remote Sens. 2018, 10(3), 361; https://doi.org/10.3390/rs10030361
Received: 29 December 2017 / Revised: 11 February 2018 / Accepted: 22 February 2018 / Published: 26 February 2018
Cited by 5 | PDF Full-text (4868 KB) | HTML Full-text | XML Full-text
Abstract
Remote sensing images are often polluted by stripe noise, which leads to negative impact on visual performance. Thus, it is necessary to remove stripe noise for the subsequent applications, e.g., classification and target recognition. This paper commits to remove the stripe noise to [...] Read more.
Remote sensing images are often polluted by stripe noise, which leads to negative impact on visual performance. Thus, it is necessary to remove stripe noise for the subsequent applications, e.g., classification and target recognition. This paper commits to remove the stripe noise to enhance the visual quality of images, while preserving image details of stripe-free regions. Instead of solving the underlying image by variety of algorithms, we first estimate the stripe noise from the degraded images, then compute the final destriping image by the difference of the known stripe image and the estimated stripe noise. In this paper, we propose a non-convex 0 sparse model for remote sensing image destriping by taking full consideration of the intrinsically directional and structural priors of stripe noise, and the locally continuous property of the underlying image as well. Moreover, the proposed non-convex model is solved by a proximal alternating direction method of multipliers (PADMM) based algorithm. In addition, we also give the corresponding theoretical analysis of the proposed algorithm. Extensive experimental results on simulated and real data demonstrate that the proposed method outperforms recent competitive destriping methods, both visually and quantitatively. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle
Variational Destriping in Remote Sensing Imagery: Total Variation with L1 Fidelity
Remote Sens. 2018, 10(2), 300; https://doi.org/10.3390/rs10020300
Received: 3 January 2018 / Revised: 28 January 2018 / Accepted: 12 February 2018 / Published: 15 February 2018
Cited by 2 | PDF Full-text (21922 KB) | HTML Full-text | XML Full-text
Abstract
This paper introduces a variational method for destriping data acquired by pushbroom-type satellite imaging systems. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. It is based on the basic principles of regularization and [...] Read more.
This paper introduces a variational method for destriping data acquired by pushbroom-type satellite imaging systems. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. It is based on the basic principles of regularization and data fidelity with certain constraints using modern methods in variational optimization, namely, total variation (TV), L 1 fidelity, and the alternating direction method of multipliers (ADMM). The proposed algorithm, TV– L 1 , uses sparsity-promoting energy functionals to achieve two important imaging effects. The TV term maintains boundary sharpness of the content in the underlying clean image, while the L 1 fidelity allows for the equitable removal of stripes without over- or under-penalization, providing a more accurate model of presumably independent sensors with an unspecified and unrestricted bias distribution. A comparison is made between the TV– L 2 model and the proposed TV– L 1 model to exemplify the qualitative efficacy of an L 1 striping penalty. The model makes use of novel minimization splittings and proximal mapping operators, successfully yielding more realistic destriped images in very few iterations. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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Open AccessArticle
A Novel Method to Remove Fringes for Dispersive Hyperspectral VNIR Imagers Using Back-Illuminated CCDs
Remote Sens. 2018, 10(1), 79; https://doi.org/10.3390/rs10010079
Received: 28 November 2017 / Revised: 2 January 2018 / Accepted: 6 January 2018 / Published: 9 January 2018
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Abstract
Dispersive hyperspectral VNIR (visible and near-infrared) imagers using back-illuminated CCDs will suffer from interference fringes in near-infrared bands, which can cause a sensitivity modulation as high as 40% or more when the spectral resolution gets higher than 5 nm. In addition to the [...] Read more.
Dispersive hyperspectral VNIR (visible and near-infrared) imagers using back-illuminated CCDs will suffer from interference fringes in near-infrared bands, which can cause a sensitivity modulation as high as 40% or more when the spectral resolution gets higher than 5 nm. In addition to the interference fringes that will change with time, there is fixed-pattern non-uniformity between pixels in the spatial dimension due to the small-scale roughness of the imager’s entrance slit, creating a much more complicated problem. A two-step method to remove fringes for dispersive hyperspectral VNIR imagers is proposed and evaluated. It first uses a ridge regression model to suppress the spectral fringes, and then computes spatial correction coefficients from the object data to correct the spatial fringes. In order to evaluate its effectiveness, the method was used to remove fringes for both the calibration data and object data collected from two VNIR grating-based hyperspectral imagers. Results show that the proposed method can preserve the original spectral shape, improve the image quality, and reduce the fringe amplitude in the 700–1000 nm region from about ±23% (10.7% RMSE) to about ±4% (1.9% RMSE). This method is particularly useful for spectra taken through a slit with a grating and shows flexible adaptability to object data, which suffer from time-varying interference fringes. Full article
(This article belongs to the Special Issue Data Restoration and Denoising of Remote Sensing Data)
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