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Correction of Remotely Sensed Imagery

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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 July 2021) | Viewed by 44159

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Dr. Benoit Vozel

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Institute of Electronics and Telecommunications IETR UMR CNRS 6164, University of Rennes, 22305 Lannion, France
Interests: blind estimation of degradation characteristics (noise, PSF); blind restoration of multicomponent images; multimodal image correction; multicomponent image compression; multi-channel adaptive processing of signals and images; unsupervised machine learning and deep learning; multi-mode remote sensing data processing; remote sensing
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Dr. Adrianos Retalis

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Institute for Environmental Research & Sustainable Development (IERSD), National Observatory of Athens (NOA), GR 15236 Athens, Greece
Interests: environmental applications of remote sensing; atmospheric correction; air quality assessment/monitoring; aerosols; natural hazards; land cover/use change; GIS; spatial data analysis; climate change; natural disasters and extremes; desertification; precision farming; soil erosion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of highly efficient correction methods for the fast and accurate retrieval of bio- and geo-physical key variables for a wide variety of atmospheric conditions and land or ocean surface types remains a challenging task for providing the end-users with methods suitable for operational processing flow.

Producing high precision standard surface reflectance products both in terms of recovering quality and implementation efficiency is even more challenging for the multisensor exploitation of remotely sensed scenes possibly captured in different inclement conditions.

These methods should adapt to a wide variety of operational conditions, involving a wide range of spatial and spectral resolutions, including coarse, medium and high resolution cases, different temporal scales (frequent or long-term surveys) as well as to different sensor suites (multi-, hyper- spectral, radar). In every case, the ultimate goal is to provide high quality sensor data that meet the users' requirements and expectations for interdisciplinary applications.

This Special Issue is thus intended to cover the last advances (but not limited to) related to the accurate correction of data remotely sensed from emerging technologies and platforms (spaceborne, airborne and from Unmanned Aerial Vehicles). This typically includes the conventional stages of atmospheric, radiometric, and geometric corrections as well as all methods for internal calibration, external re-calibration and post-processing methods developed to mitigate or properly compensate for a strong variation of disturbances or correct attenuation, artefacts, measurement and residual errors from any error source (calibration anomaly, geolocation, misalignment, topographic relief effect, shadow area, data inconsistencies between sensor(s),...). Validation methods for comparing retrieved parameters to either forward bio- or geo- physical or in situ measurements are also focused.

A wide spectrum of recent and latest emerging applications highlighting remotely sensed data corrections are obviously targeted including biodiversity assessment, vegetation and environmental monitoring (identification of diversity in grassland species, invasive plants, biomass estimation, wetlands), precision agriculture in agricultural ecosystems and crop management, water resource and quality management in nearshore coastal (mapping near-surface water constituents, benthic habitats) and inland waters (analysis and surveying of rivers and lakes), sustainable forestry and agroforestry (forest preservation and mapping of forest species, wildfire detection), mapping archaeological areas, urban development and management, land data assimilation system over mountainous terrain and hazard monitoring.

Dr. Benoit Vozel
Dr. Adrianos Retalis
Guest Editors

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Keywords

atmospheric correction
radiometric correction
geolocation
calibration
Unmanned Aerial Vehicles
airborne data
satellite remote sensing
bio- and geo-physical variables

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Published Papers (11 papers)

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15 pages, 2212 KiB

Open AccessArticle

Effect of Atmospheric Corrections on NDVI: Intercomparability of Landsat 8, Sentinel-2, and UAV Sensors

by David Moravec, Jan Komárek, Serafín López-Cuervo Medina and Iñigo Molina

Remote Sens. 2021, 13(18), 3550; https://doi.org/10.3390/rs13183550 - 7 Sep 2021

Cited by 41 | Viewed by 6189

Abstract

Sentinel-2 and Landsat 8 satellites constitute an unprecedented source of freely accessible satellite imagery. To produce precise outputs from the satellite data, however, proper use of atmospheric correction methods is crucial. In this work, we tested the performance of six different atmospheric correction methods (QUAC, FLAASH, DOS, ACOLITE, 6S, and Sen2Cor), together with atmospheric correction given by providers, non-corrected image, and images acquired using an unmanned aerial vehicle while working with the normalised difference vegetation index (NDVI) as the most widely used index. We tested their performance across urban, rural, and vegetated land cover types. Our results show a substantial impact from the choice of the atmospheric correction method on the resulting NDVI. Moreover, we demonstrate that proper use of atmospheric correction methods can increase the intercomparability between data from Landsat 8 and Sentinel-2 satellite imagery. Full article

(This article belongs to the Special Issue Correction of Remotely Sensed Imagery)

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28 pages, 12932 KiB

Open AccessArticle

Modified Linear Scaling and Quantile Mapping Mean Bias Correction of MODIS Land Surface Temperature for Surface Air Temperature Estimation for the Lowland Areas of Peninsular Malaysia

by Nurul Iman Saiful Bahari, Farrah Melissa Muharam, Zed Zulkafli, Norida Mazlan and Nor Azura Husin

Remote Sens. 2021, 13(13), 2589; https://doi.org/10.3390/rs13132589 - 2 Jul 2021

Cited by 4 | Viewed by 3280

Abstract

MODIS land surface temperature data (MODIS T_s) products are quantified from the earth surface’s reflected thermal infrared signal via sensors onboard the Terra and Aqua satellites. MODIS T_s products are a great value to many environmental applications but often subject to discrepancies when compared to the air temperature (T_a) data that represent the temperature measured at 2 m above the ground surface. Although they are different in their nature, the relationship between T_s and T_a has been established by many researchers. Further validation and correction on the relationship between these two has enabled the estimation of T_a from MODIS T_s products in order to overcome the limitation of T_a that can only provide data in a point form with a very limited area coverage. Therefore, this study was conducted with the objective to assess the accuracy of MODIS T_s products, i.e., MOD11A1, MOD11A2, MYD11A1, and MYD11A2 against T_a and to identify the performance of a modified Linear Scaling using a constant and monthly correction factor (LS-MBC), and Quantile Mapping Mean Bias Correction (QM-MBC) methods for lowland area of Peninsular Malaysia. Furthermore, the correction factor (CF) values for each MBC were adjusted according to the condition set depending on the different bias levels. Then, the performance of the pre- and post-MBC correction for by stations and regions analysis were evaluated through root mean square error (RMSE), percentage bias (PBIAS), mean absolute error (MAE), and correlation coefficient (r). The region dataset is obtained by stacking the air temperature (T_{a_r}) and surface temperature (T_{s_r}) data corresponding to the number of stations within the identified regions. The assessment of pre-MBC data for both 36 stations and 5 regions demonstrated poor correspondence with high average errors and percentage biases, i.e., RMSE = 3.33–5.42 °C, PBIAS = 1.36–12.07%, MAE = 2.88–4.89 °C, and r = 0.16–0.29. The application of the MBCs has successfully reduced the errors and bias percentages, and slightly increased the r values for all MODIS T_s products. All post-MBC depicted good average accuracies (RMSE and MAE < 3 °C and PBIAS between ±5%) and r between 0.18 and 0.31. In detail, for the station analysis, the LS-MBC using monthly CF recorded better performance than the LS-MBC using constant CF or the QM-MBC. For the regional study, the QM-MBC outperformed the others. This study illustrated that the proposed LS-MBC, in spite of its simplicity, managed to perform well in reducing the error and bias terms of MODIS T_s as much as the performance of the more complex QM-MBC method. Full article

(This article belongs to the Special Issue Correction of Remotely Sensed Imagery)

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18 pages, 4201 KiB

Open AccessArticle

Automatic Sub-Pixel Co-Registration of Remote Sensing Images Using Phase Correlation and Harris Detector

by Laila Rasmy, Imane Sebari and Mohamed Ettarid

Remote Sens. 2021, 13(12), 2314; https://doi.org/10.3390/rs13122314 - 12 Jun 2021

Cited by 18 | Viewed by 3771

Abstract

In this paper, we propose a new approach for sub-pixel co-registration based on Fourier phase correlation combined with the Harris detector. Due to the limitation of the standard phase correlation method to achieve only pixel-level accuracy, another approach is required to reach sub-pixel matching precision. We first applied the Harris corner detector to extract corners from both references and sensed images. Then, we identified their corresponding points using phase correlation between the image pairs. To achieve sub-pixel registration accuracy, two optimization algorithms were used. The effectiveness of the proposed method was tested with very high-resolution (VHR) remote sensing images, including Pleiades satellite images and aerial imagery. Compared with the speeded-up robust features (SURF)-based method, phase correlation with the Blackman window function produced 91% more matches with high reliability. Moreover, the results of the optimization analysis have revealed that Nelder–Mead algorithm performs better than the two-point step size gradient algorithm regarding localization accuracy and computation time. The proposed approach achieves better accuracy than 0.5 pixels and outperforms the speeded-up robust features (SURF)-based method. It can achieve sub-pixel accuracy in the presence of noise and produces large numbers of correct matching points. Full article

(This article belongs to the Special Issue Correction of Remotely Sensed Imagery)

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17 pages, 4868 KiB

Open AccessArticle

Tropospheric Correction of Sentinel-1 Synthetic Aperture Radar Interferograms Using a High-Resolution Weather Model Validated by GNSS Measurements

by Nikolaos Roukounakis, Panagiotis Elias, Pierre Briole, Dimitris Katsanos, Ioannis Kioutsioukis, Athanassios A. Argiriou and Adrianos Retalis

Remote Sens. 2021, 13(12), 2258; https://doi.org/10.3390/rs13122258 - 9 Jun 2021

Cited by 6 | Viewed by 2830

Abstract

Synthetic Aperture Radar Interferometry (InSAR) is a space geodetic technique used for mapping deformations of the Earth’s surface. It has been developed and used increasingly during the last thirty years to measure displacements produced by earthquakes, volcanic activity and other crustal deformations. A limiting factor to this technique is the effect of the troposphere, as spatial and temporal variations in temperature, pressure, and relative humidity introduce significant phase delays in the microwave imagery, thus “masking” surface displacements due to tectonic or other geophysical processes. The use of Numerical Weather Prediction (NWP) models as a tropospheric correction method in InSAR can tackle several of the problems faced with other correction techniques (such as timing, spatial coverage and data availability issues). High-resolution tropospheric modelling is particularly useful in the case of single interferograms, where the removal of the atmospheric phase screen (and especially the highly variable turbulent component) can reveal large-amplitude deformation signals (as in the case of an earthquake). In the western Gulf of Corinth, prominent topography makes the removal of both the stratified and turbulent atmospheric phase screens a challenging task. Here, we investigate the extent to which a high-resolution WRF 1-km re-analysis can produce detailed tropospheric delay maps of the required accuracy by coupling its output (in terms of Zenith Total Delay or ZTD) with the vertical delay component in GNSS measurements. The model is operated with varying physical parameterization in order to identify the best configuration, and validated with GNSS zenithal tropospheric delays, providing a benchmark of real atmospheric conditions. We correct sixteen Sentinel-1A interferograms with differential delay maps at the line-of-sight (LOS) produced by WRF re-analysis. In most cases, corrections lead to a decrease in the phase gradient, with average root-mean-square (RMS) and standard deviation (SD) reductions in the wrapped phase of 6.0% and 19.3%, respectively. Results suggest a high potential of the model to reproduce both the long-wavelength stratified atmospheric signal and the short-wave turbulent atmospheric component which are evident in the interferograms. Full article

(This article belongs to the Special Issue Correction of Remotely Sensed Imagery)

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19 pages, 5936 KiB

Open AccessArticle

Calibration of Satellite Low Radiance by AERONET-OC Products and 6SV Model

by Cristiana Bassani and Sindy Sterckx

Remote Sens. 2021, 13(4), 781; https://doi.org/10.3390/rs13040781 - 20 Feb 2021

Cited by 2 | Viewed by 2965

Abstract

For water quality monitoring using satellite data, it is often required to optimize the low radiance signal through the application of radiometric gains. This work describes a procedure for the retrieval of radiometric gains to be applied to OLI/L8 and MSI/S2A data over coastal waters. The gains are defined by the ratio of the top of atmosphere (TOA) reflectance simulated using the Second Simulation of a Satellite Signal in the Solar Spectrum—vector (6SV) radiative transfer model, REF, and the TOA reflectance acquired by the sensor, MEAS, over AERONET-OC stations. The REF is simulated considering quasi-synchronous atmospheric and aquatic AERONET-OC products and the image acquisition geometry. Both for OLI/L8 and MSI/S2A the measured TOA reflectance was higher than the modeled signal in almost all bands resulting in radiometric gains less than 1. The use of retrieved gains showed an improvement of reflectance remote sensing, Rrs, when with ACOLITE atmospheric correction software. When the gains are applied an accuracy improvement of the Rrs in the 400–700 nm domain was observed except for the first blue band of both sensors. Furthermore, the developed procedure is quick, user-friendly, and easily transferable to other optical sensors. Full article

(This article belongs to the Special Issue Correction of Remotely Sensed Imagery)

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25 pages, 15087 KiB

Open AccessArticle

Lookup Table Approach for Radiometric Calibration of Miniaturized Multispectral Camera Mounted on an Unmanned Aerial Vehicle

by Hongtao Cao, Xingfa Gu, Xiangqin Wei, Tao Yu and Haifeng Zhang

Remote Sens. 2020, 12(24), 4012; https://doi.org/10.3390/rs12244012 - 8 Dec 2020

Cited by 23 | Viewed by 3920

Abstract

Over recent years, miniaturized multispectral cameras mounted on an unmanned aerial vehicle (UAV) have been widely used in remote sensing. Most of these cameras are integrated with low-cost, image-frame complementary metal-oxide semiconductor (CMOS) sensors. Compared to the typical charged coupled device (CCD) sensors or linear array sensors, consumer-grade CMOS sensors have the disadvantages of low responsivity, higher noise, and non-uniformity of pixels, which make it difficult to accurately detect optical radiation. Therefore, comprehensive radiometric calibration is crucial for quantitative remote sensing and comparison of temporal data using such sensors. In this study, we examine three procedures of radiometric calibration: relative radiometric calibration, normalization, and absolute radiometric calibration. The complex features of dark current noise, vignetting effect, and non-uniformity of detector response are analyzed. Further, appropriate procedures are used to derive the lookup table (LUT) of correction factors for these features. Subsequently, an absolute calibration coefficient based on an empirical model is used to convert the digital number (DN) of images to radiance unit. Due to the radiometric calibration, the DNs of targets observed in the image are more consistent than before calibration. Compared to the method provided by the manufacturer of the sensor, LUTs facilitate much better radiometric calibration. The root mean square error (RMSE) of measured reflectance in each band (475, 560, 668, 717, and 840 nm) are 2.30%, 2.87%, 3.66%, 3.98%, and 4.70% respectively. Full article

(This article belongs to the Special Issue Correction of Remotely Sensed Imagery)

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28 pages, 48431 KiB

Open AccessArticle

Fourier Domain Anomaly Detection and Spectral Fusion for Stripe Noise Removal of TIR Imagery

by Qingjie Zeng, Hanlin Qin, Xiang Yan and Tingwu Yang

Remote Sens. 2020, 12(22), 3714; https://doi.org/10.3390/rs12223714 - 12 Nov 2020

Cited by 16 | Viewed by 3285

Abstract

Stripe noise is a common and unwelcome noise pattern in various thermal infrared (TIR) image data including conventional TIR images and remote sensing TIR spectral images. Most existing stripe noise removal (destriping) methods are often difficult to keep a good and robust efficacy in dealing with the real-life complex noise cases. In this paper, based on the intrinsic spectral properties of TIR images and stripe noise, we propose a novel two-stage transform domain destriping method called Fourier domain anomaly detection and spectral fusion (ADSF). Considering the principal frequencies polluted by stripe noise as outliers in the statistical spectrum of TIR images, our naive idea is first to detect the potential anomalies and then correct them effectively in the Fourier domain to reconstruct a desired destriping result. More specifically, anomaly detection for stripe frequencies is achieved through a regional comparison between the original spectrum and the expected spectrum that statistically follows a generalized Laplacian regression model, and then an anomaly weight map is generated accordingly. In the correction stage, we propose a guidance-image-based spectrum fusion strategy, which integrates the original spectrum and the spectrum of a guidance image via the anomaly weight map. The final reconstruction result not only has no stripe noise but also maintains image structures and details well. Extensive real experiments are performed on conventional TIR images and remote sensing spectral images, respectively. The qualitative and quantitative assessment results demonstrate the superior effectiveness and strong robustness of the proposed method. Full article

(This article belongs to the Special Issue Correction of Remotely Sensed Imagery)

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24 pages, 10954 KiB

Open AccessArticle

The Impact of SAR Parameter Errors on the Ionospheric Correction Based on the Range-Doppler Model and the Split-Spectrum Method

by Fangjia Dou, Xiaolei Lv, Qi Chen, Guangcai Sun, Ye Yun and Xiao Zhou

Remote Sens. 2020, 12(10), 1607; https://doi.org/10.3390/rs12101607 - 18 May 2020

Cited by 4 | Viewed by 2598

Abstract

Interferometric synthetic aperture radar (InSAR) products may be significantly distorted by microwave signals traveling through the ionosphere, especially with long wavelengths. The split-spectrum method (SSM) is used to separate the ionospheric and the nondispersive phase terms with lower and higher spectral sub-band interferogram images. However, the ionospheric path delay phase is very delicate to the synthetic aperture radar (SAR) parameters including orbit vectors, slant range, and target height. In this paper, we get the impact of SAR parameter errors on the ionospheric phase by two steps. The first step is getting the derivates of geolocation with reference to SAR parameters based on the range-Doppler (RD) imaging model and the second step is calculating the derivates of the ionospheric phase delay with respect to geometric positioning. Through the numerical simulation, we demonstrate that the deviation of ionospheric phase has a linear relationship with SAR parameter errors. The experimental results show that the estimation of SAR parameters should be accurate enough since the parameter errors significantly affect the performance of ionospheric correction. The root mean square error (RMSE) between the corrected differential interferometric SAR (DInSAR) phase with SAR parameter errors and the corrected DInSAR phase without parameter errors varies from centimeter to decimeter level with the L-band data acquired by the Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR) over Antofagasta, Chile. Furthermore, the effectiveness of SSM can be improved when SAR parameters are accurately estimated. Full article

(This article belongs to the Special Issue Correction of Remotely Sensed Imagery)

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31 pages, 10236 KiB

Open AccessArticle

Radiometric and Atmospheric Corrections of Multispectral μMCA Camera for UAV Spectroscopy

by Robert Minařík, Jakub Langhammer and Jan Hanuš

Remote Sens. 2019, 11(20), 2428; https://doi.org/10.3390/rs11202428 - 19 Oct 2019

Cited by 33 | Viewed by 4854

Abstract

This study presents a complex empirical image-based radiometric calibration method for a Tetracam μMCA multispectral frame camera. The workflow is based on a laboratory investigation of the camera’s radiometric properties combined with vicarious atmospheric correction using an empirical line. The effect of the correction is demonstrated on out-of-laboratory field campaign data. The dark signal noise behaviour was investigated based on the exposure time and ambient temperature. The vignette effect coupled with nonuniform quantum efficiency was studied with respect to changing exposure times and illuminations to simulate field campaign conditions. The efficiency of the proposed correction workflow was validated by comparing the reflectance values that were extracted from a fully corrected image and the raw data of the reference spectroscopy measurement using three control targets. The Normalized Root Mean Square Errors (NRMSE) of all separate bands ranged from 0.24 to 2.10%, resulting in a significant improvement of the NRMSE compared to the raw data. The results of a field experiment demonstrated that the proposed correction workflow significantly improves the quality of multispectral imagery. The workflow was designed to be applicable to the out-of-laboratory conditions of UAV imaging campaigns in variable natural conditions and other types of multiarray imaging systems. Full article

(This article belongs to the Special Issue Correction of Remotely Sensed Imagery)

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19 pages, 9448 KiB

Open AccessArticle

Physically Based Approach for Combined Atmospheric and Topographic Corrections

by Federico Santini and Angelo Palombo

Remote Sens. 2019, 11(10), 1218; https://doi.org/10.3390/rs11101218 - 23 May 2019

Cited by 17 | Viewed by 4256

Abstract

The enhanced spectral and spatial resolutions of the remote sensors have increased the need for highly performing preprocessing procedures. In this paper, a comprehensive approach, which simultaneously performs atmospheric and topographic corrections and includes second order corrections such as adjacency effects, was presented. The method, developed under the assumption of Lambertian surfaces, is physically based and uses MODTRAN 4 radiative transfer model. The use of MODTRAN 4 for the estimates of the radiative quantities was widely discussed in the paper and the impact on remote sensing applications was shown through a series of test cases. Full article

(This article belongs to the Special Issue Correction of Remotely Sensed Imagery)

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15 pages, 7343 KiB

Open AccessLetter

Numerical Focusing of a Wide-Field-Angle Earth Radiation Budget Imager Using an Artificial Neural Network

by Mehran Yarahmadi, J. Robert Mahan, Kevin McFall and Anum Barki Ashraf

Remote Sens. 2020, 12(1), 176; https://doi.org/10.3390/rs12010176 - 3 Jan 2020

Cited by 3 | Viewed by 4263

Abstract

Narrow field-of-view scanning thermistor bolometer radiometers have traditionally been used to monitor the earth’s radiant energy budget from low earth orbit (LEO). Such instruments use a combination of cross-path scanning and along-path spacecraft motion to obtain a patchwork of punctual observations which are ultimately assembled into a mosaic. Monitoring has also been achieved using non-scanning instruments operating in a push-broom mode in LOE and imagers operating in geostationary orbit. The current contribution considers a fourth possibility, that of an imager operating in LEO. The system under consideration consists of a Ritchey-Chrétien telescope illuminating a plane two-dimensional microbolometer array. At large field angles, the focal length of the candidate instrument is field-angle dependent, resulting in a blurred image in the readout plane. Presented is a full-field focusing algorithm based on an artificial neural network (ANN). Absorbed power distributions on the microbolometer array produced by discretized scenes are obtained using a high-fidelity Monte Carlo ray-trace (MCRT) model of the imager. The resulting readout array/scene pairs are then used to train an ANN. We demonstrate that a properly trained ANN can be used to convert the readout power distribution into an accurate image of the corresponding discretized scene. This opens the possibility of using an ANN based on a high-fidelity imager model for numerical focusing of an actual imager. Full article

(This article belongs to the Special Issue Correction of Remotely Sensed Imagery)

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