Clear Pixels Research Articles

A deep learning based relative clarity classification method for infrared and visible image fusion

Infrared and visible image fused(IVIF) results normally suffer from detail loss, noise occurrence, low contrast, and blurred edges. In this paper, a new method is proposed to address the detail loss, low contrast, and blurring issue in IVIF. Specifically, visible images are enhanced by guided filter and high dynamic range compression. Infrared images are normalized by a linear transformation. Then we use blur and clear discrimination to detect salient pixels between infrared and visible images. A fully weight shared multi-path residual neural network is proposed for blur discrimination between infrared and visible image pixels in the same position. Clear pixels are treated as salient pixels, which contribute more to fused images than blur pixels. The output of our proposed network is a binary classification map for blur and clear discrimination, which is treated as our fusion weight map in the fusion stage. To deal with the discontinuous problem, we compute the two distance-transformed maps of the binary classification map and its complementary map. The two distance-transformed maps are used as weight maps to fuse the enhanced infrared and visible images. Finally, we use single scale retinex (SSR) to further enhance our fused images. The experimental results in public IVIF datasets demonstrate the superior performance of our proposed approach over other state-of-the-art methods in terms of both subjective visual quality and objective metrics. The source code is available in https://github.com/eyob12/Multi_path_residual_neural_network_based_IVIF

Cloud detection from multi-angular polarimetric satellite measurements using a neural network ensemble approach

Abstract. This paper describes a neural network cloud masking scheme from PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar) multi-angle polarimetric measurements. The algorithm has been trained on synthetic measurements and has been applied to the processing of 1 year of PARASOL data. Comparisons of the retrieved cloud fraction with MODIS (Moderate Resolution Imaging Spectroradiometer) products show overall agreement in spatial and temporal patterns, but the PARASOL neural network (PARASOL-NN) retrieves lower cloud fractions. Comparisons with a goodness-of-fit mask from aerosol retrievals suggest that the NN cloud mask flags fewer clear pixels as cloudy than MODIS (∼ 3 % of the clear pixels versus ∼ 15 % by MODIS). On the other hand the NN classifies more pixels incorrectly as clear than MODIS (∼ 20 % by NN, versus ∼ 15 % by MODIS). Additionally, the NN and MODIS cloud mask have been applied to the aerosol retrievals from PARASOL using the Remote Sensing of Trace Gas and Aerosol Products (RemoTAP) algorithm. Validation with AERONET shows that the NN cloud mask performs comparably with MODIS in screening residual cloud contamination in retrieved aerosol properties. Our study demonstrates that cloud masking from multi-angle polarimeter (MAP) aerosol retrievals can be performed based on the MAP measurements themselves, making the retrievals independent of the availability of a cloud imager.

Asymmetric Loss Functions for Noise-Tolerant Learning: Theory and Applications.

Supervised deep learning has achieved tremendous success in many computer vision tasks, which however is prone to overfit noisy labels. To mitigate the undesirable influence of noisy labels, robust loss functions offer a feasible approach to achieve noise-tolerant learning. In this work, we systematically study the problem of noise-tolerant learning with respect to both classification and regression. Specifically, we propose a new class of loss function, namely asymmetric loss functions (ALFs), which are tailored to satisfy the Bayes-optimal condition and thus are robust to noisy labels. For classification, we investigate general theoretical properties of ALFs on categorical noisy labels, and introduce the asymmetry ratio to measure the asymmetry of a loss function. We extend several commonly-used loss functions, and establish the necessary and sufficient conditions to make them asymmetric and thus noise-tolerant. For regression, we extend the concept of noise-tolerant learning for image restoration with continuous noisy labels. We theoretically prove that lp loss ( ) is noise-tolerant for targets with the additive white Gaussian noise. For targets with general noise, we introduce two losses as surrogates of l0 loss that seeks the mode when clean pixels keep dominant. Experimental results demonstrate that ALFs can achieve better or comparative performance compared with the state-of-the-arts.

A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region’s boundary in a manner that is more substantially consistent with the true color image. Using the PM2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM2.5 ≥ 35 μg/cm3 is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM2.5 ≥ 50 μg/cm3 is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite.

Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand.

Critical applications of satellite data products include monitoring vegetation dynamics and assessing vegetation health conditions. Some indicators like normalized difference vegetation index (NDVI) and land surface temperature (LST) are used to assess the status of vegetation growth and health. But one of the major problems with passive remote sensing satellite data products is cloud and shadow cover that leads to data gaps in the images. The present study proposes temporal aggregation of images over a short time span and developing short span harmonic analysis of time series (SS-HANTS) and pixel-wise multiple linear regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI datasets. The calculated root mean square (RMSE) values were ranging from 0.03 to 0.11 and 1.50 to 2.98°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) computed from cloud retrieved continuous NDVI and LST images at province level shows that most of the western provinces have healthy vegetation condition than other provinces in the northeast of Thailand.

Spatio-temporal subpixel mapping with cloudy images

Spatio-temporal subpixel mapping (STSPM) has shown great potential for monitoring land surfaces, by generating land cover maps with both fine spatial and temporal resolutions. Selecting cloud-free fine spatial resolution images as ancillary data for STSPM can ensure that the temporal dependence term is measured for all subpixels, as in all current STSPM methods. However, such images are generally limited by cloud contamination, thereby resulting in great land cover changes between the available clear image and the desired fine spatial resolution land cover map. This research proposes a cloud-independent STSPM (C-STSPM) method to reconstruct the fine spatial resolution land cover maps by using cloudy images directly, which are assumed to have fewer land cover changes than temporally distant clear images. Cloud-independent spatio-temporal dependence was proposed in the presence of cloudy pixels. Experiments were performed under various cloud conditions involving 21 × 21 pairs of simulated cloudy images. The results demonstrate that by utilizing land cover information of clear pixels in cloudy images, more accurate prediction can be produced by C-STSPM compared to directly discarding those cloudy images, even if the number of cloud pixels increases to 95%. The advantage of C-STSPM is more evident when the clouds are distributed sparsely, which benefits from the increased number of clear pixels at the edge of the cloudy areas. Furthermore, a negative linear correlation was detected between the prediction accuracy and the ratio of overlapping cloudy pixels in the cloudy images. Moreover, the C-STSPM method helps to deal with abrupt changes occurred in the temporally distant cloud-free images by utilizing the temporally adjacent cloudy images with gradual land cover changes. Overall, the C-STSPM method provides a completely new solution to make fuller use of the widely existing cloudy images in multi-scale time-series images.

Impact of 3D cloud structures on the atmospheric trace gas products from UV–Vis sounders – Part 1: Synthetic dataset for validation of trace gas retrieval algorithms

Abstract. Retrievals of trace gas concentrations from satellite observations are mostly performed for clear regions or regions with low cloud coverage. However, even fully clear pixels can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. Quantifying the error of retrieved trace gas concentrations due to cloud scattering is a difficult task. One possibility is to generate synthetic data by three-dimensional (3D) radiative transfer simulations using realistic 3D atmospheric input data, including 3D cloud structures. Retrieval algorithms may be applied on the synthetic data, and comparison to the known input trace gas concentrations yields the retrieval error due to cloud scattering. In this paper we present a comprehensive synthetic dataset which has been generated using the Monte Carlo radiative transfer model MYSTIC (Monte Carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres). The dataset includes simulated spectra in two spectral ranges (400–500 nm and the O2A-band from 755–775 nm). Moreover it includes layer air mass factors (layer-AMFs) calculated at 460 nm. All simulations are performed for a fixed background atmosphere for various sun positions, viewing directions and surface albedos. Two cloud setups are considered: the first includes simple box clouds with various geometrical and optical thicknesses. This can be used to systematically investigate the sensitivity of the retrieval error on solar zenith angle, surface albedo and cloud parameters. Corresponding 1D simulations are also provided. The second includes realistic three-dimensional clouds from an ICON large eddy simulation (LES) for a region covering Germany and parts of surrounding countries. The scene includes cloud types typical of central Europe such as shallow cumulus, convective cloud cells, cirrus and stratocumulus. This large dataset can be used to quantify the trace gas concentration retrieval error statistically. Along with the dataset, the impact of horizontal photon transport on reflectance spectra and layer-AMFs is analysed for the box-cloud scenarios. Moreover, the impact of 3D cloud scattering on the NO2 vertical column density (VCD) retrieval is presented for a specific LES case. We find that the retrieval error is largest in cloud shadow regions, where the NO2 VCD is underestimated by more than 20 %. The dataset is available for the scientific community to assess the behaviour of trace gas retrieval algorithms and cloud correction schemes in cloud conditions with 3D structure.

The Map Data of BRDF-Adjusted Surface Reflectance from GOCI Geostationary Satellite Imagery over Korean Peninsula

In this study, the spatial maps of the BRDF (Bidirectional Reflectance Distribution Function) adjusted surface reflectance (SR) were estimated by using the Geostationary Ocean Color Imager (GOCI) mounted on the Communication, Ocean and Meteorological Satellite (COMS) over the Korean Peninsula. The BRDF-Adjusted surface Reflectance (BAR) is a more effective indicator that not only quantitatively identifies the growth characteristics of vegetation, but also corrects the bidirectional error in the time series observation characteristics, because the surface reflectance is changed according to the solar altitude during the daytime period. Therefore, this BAR products have high data utilization in various fields such as agriculture, environment, land surface information, and atmosphere. In this study, BRDF-adjusted surface reflectance maps were calculated for the Korean peninsula from April 2011 to December 2012 with hourly temporal resolution from 9 a.m. to 4 p.m. For the BAR surface reflectance, the spatial observation range is from latitude 34° N to 39 °N and longitude 125 ° E to 130 ° E, and the spatial resolution is 500 m. The semi-empirical BRDF model was used to calculate the BRDF-adjusted surface reflectance, and the radiometric characteristics of surface reflectance were decomposed into isotropic scattering, geometric scattering, and volumetric scattering. For this model simulation, at least 7 clear pixels are required to fit BRDF model. In this study, unlike the Nadir BRDF-Adjusted surface Reflectance (NBAR) calculation method which was calculated from the existing polar orbiting satellites, semi-empirical BRDF modeling was performed with a value fixed to the satellite viewing angle for each pixel since geostationary satellites of GOCI are difficult to observe in the nadir direction unlike polar satellites. It is more effective to perform BRDF correction by fixing them at the viewing angle in the case of GOCI geostationary satellite.

Effect of Scattering Angle on Earth Reflectance

After March 2020 the range of scattering angle for DSCOVR EPIC and NISTAR has been substantially increased with its upper bound reaching 178°. This provides a unique opportunity to observe bi-directional effects of reflectance near backscattering directions. The dependence of the top-of-atmosphere (TOA) reflectance on scattering angle is shown separately for ocean and land areas, for cloudy and clear pixels, while cloudy pixels are also separated into liquid and ice clouds. A strong increase of TOA reflectance towards backscattering direction is reported for all components (except cloudless areas over ocean). The observed increase of reflectance is confirmed by cloud and vegetation models. The strongest correlation between TOA reflectance and scattering angle was found near IR where contribution from vegetation dominates. Surface Bidirectional Reflectance Factor (BRF) acquired by DSCOVR EPIC and Terra MISR sensors over the Amazon basin is used to demonstrate the bi-directional effects of solar zenith and scattering angles on variation of reflected radiation from rainforest.

MODIS-Based AVHRR Cloud and Snow Separation Algorithm

The long-term data record (LTDR) has the goal of developing a quality and consistent Advanced Very High Resolution Radiometer (AVHRR) surface reflectance and albedo products dating back to 1982 at 0.05° spatial resolution. Distinguishing between cloud and snow is of critical importance when analyzing global albedo trends, for they influence the Earth’s energy balance. However, this task is specially challenging when working with AVHRR given its limited spectral bands. Therefore, the current version of the LTDR does not distinguish between snow and clouds. To this end, we propose the Moderate Resolution Imaging Spectroradiometer (MODIS)-based AVHRR Class Separation Algorithm (MACSSA), whose goal is to identify clear land and snow pixels using AVHRR data. We make use of a combination of optical and thermal information from satellite and reanalysis data, along with monthly climatology information. These are used as inputs for two different support vector machine (SVM) models, which are then applied to AVHRR data to retrieve the MACSSA predicted tags. These are compared first against reference tags retrieved from the MYD10C1 product over pixels with less than 2-min overpass time difference between MODIS Aqua and NOAA16–19, distributed all around the world, and second against the Climate Change Initiative Cloud (Cloud_cci AVHRR) project. We found the product to be highly accurate in identifying clear land pixels, with a probability of detection of clear pixels (PODclear) of 97%. The discrimination of snow and clouds shows a PODsnow of 89%, which is encouraging given the spectral limitations of the AVHRR sensor.

Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery

Small-scale placer mining in Colombia takes place in rural areas and involves excavations resulting in large footprints of bare soil and water ponds. Such excavated areas comprise a mosaic of challenging terrains for cloud and cloud-shadow detection of Sentinel-2 (S2A and S2B) data used to identify, map, and monitor these highly dynamic activities. This paper uses an efficient two-step machine-learning approach using freely available tools to detect clouds and shadows in the context of mapping small-scale mining areas, one which places an emphasis on the reduction of misclassification of mining sites as clouds or shadows. The first step is comprised of a supervised support-vector-machine classification identifying clouds, cloud shadows, and clear pixels. The second step is a geometry-based improvement of cloud-shadow detection where solar-cloud-shadow-sensor geometry is used to exclude commission errors in cloud shadows. The geometry-based approach makes use of sun angles and sensor view angles available in Sentinel-2 metadata to identify potential directions of cloud shadow for each cloud projection. The approach does not require supplementary data on cloud-top or bottom heights nor cloud-top ruggedness. It assumes that the location of dense clouds is mainly impacted by meteorological conditions and that cloud-top and cloud-base heights vary in a predefined manner. The methodology has been tested over an intensively excavated and well-studied pilot site and shows 50% more detection of clouds and shadows than Sen2Cor. Furthermore, it has reached a Specificity of 1 in the correct detection of mining sites and water ponds, proving itself to be a reliable approach for further related studies on the mapping of small-scale mining in the area. Although the methodology was tailored to the context of small-scale mining in the region of Antioquia, it is a scalable approach and can be adapted to other areas and conditions.

Comparison of Masking Algorithms for Sentinel-2 Imagery

Masking of clouds, cloud shadow, water and snow/ice in optical satellite imagery is an important step in automated processing chains. We compare the performance of the masking provided by Fmask (“Function of mask” implemented in FORCE), ATCOR (“Atmospheric Correction”) and Sen2Cor (“Sentinel-2 Correction”) on a set of 20 Sentinel-2 scenes distributed over the globe covering a wide variety of environments and climates. All three methods use rules based on physical properties (Top of Atmosphere Reflectance, TOA) to separate clear pixels from potential cloud pixels, but they use different rules and class-specific thresholds. The methods can yield different results because of different definitions of the dilation buffer size for the classes cloud, cloud shadow and snow. Classification results are compared to the assessment of an expert human interpreter using at least 50 polygons per class randomly selected for each image. The class assignment of the human interpreter is considered as reference or “truth”. The interpreter carefully assigned a class label based on the visual assessment of the true color and infrared false color images and additionally on the bottom of atmosphere (BOA) reflectance spectra. The most important part of the comparison is done for the difference area of the three classifications considered. This is the part of the classification images where the results of Fmask, ATCOR and Sen2Cor disagree. Results on difference area have the advantage to show more clearly the strengths and weaknesses of a classification than results on the complete image. The overall accuracy of Fmask, ATCOR, and Sen2Cor for difference areas of the selected scenes is 45%, 56%, and 62%, respectively. User and producer accuracies are strongly class- and scene-dependent, typically varying between 30% and 90%. Comparison of the difference area is complemented by looking for the results in the area where all three classifications give the same result. Overall accuracy for that “same area” is 97% resulting in the complete classification in overall accuracy of 89%, 91% and 92% for Fmask, ATCOR and Sen2Cor respectively.

Tracking lake surface elevations with proportional hypsometric relationships, Landsat imagery, and multiple DEMs

AbstractMultidecadal inland surface water dynamics are of increasing interest due to their importance to climate, ecology, and society, yet several key challenges impede long‐term monitoring of inland surface waters globally. This research investigates two novel methods, one addressing subhydroflattened surface estimate uncertainty issues, and a second addressing temporal resolution issues, using 46 water bodies across the western United States. First, low water level estimate uncertainty was reduced using multiple digital elevation models (ALOS, SRTM, and NED) to derive the hypsometric relationship for each lake from the digital elevation model with the lowest hydroflattened water surface. This technique reduced the number of images with subhydroflattened water surfaces by at least 549 over the best individual DEM resulting in higher water surface elevation estimate accuracy. Second, this paper introduces proportional hypsometry which dynamically generates surface area/elevation relationships for every image using clear pixels only by removing contamination from both the image and DEM. Proportional hypsometry was found to be ill‐suited for subhydroflattened water surface levels but produced comparable accuracy to clear images for above hydroflattened water levels. Overall, using the lowest hydroflattened surface along with proportional hypsometry improved temporal resolution enabling analysis of nearly 10,000 additional images while maintaining similar accuracy levels as images with <1% contamination (2.35 m RMSE vs. 2.17 m RMSE). This research increases lower water elevation estimate accuracy and temporal resolution and is scalable enabling regional and global water dynamic analysis.

Improved cloud detection over sea ice and snow during Arctic summer using MERIS data

Abstract. The historic MERIS (Medium Resolution Imaging Spectrometer) sensor on board Envisat (Environmental Satellite, operation 2002–2012) provides valuable remote sensing data for the retrievals of summer sea ice in the Arctic. MERIS data together with the data of recently launched successor OLCI (Ocean and Land Colour Instrument) on board Sentinel 3A and 3B (2016 onwards) can be used to assess the long-term change of the Arctic summer sea ice. An important prerequisite to a high-quality remote sensing dataset is an accurate separation of cloudy and clear pixels to ensure lowest cloud contamination of the resulting product. The presence of 15 visible and near-infrared spectral channels of MERIS allows high-quality retrievals of sea ice albedo and melt pond fraction, but it makes cloud screening a challenge as snow, sea ice and clouds have similar optical features in the available spectral range of 412.5–900 nm. In this paper, we present a new cloud screening method MECOSI (MERIS Cloud Screening Over Sea Ice) for the retrievals of spectral albedo and melt pond fraction (MPF) from MERIS. The method utilizes all 15 MERIS channels, including the oxygen A absorption band. For the latter, a smile effect correction has been developed to ensure high-quality screening throughout the whole swath. A total of 3 years of reference cloud mask from AATSR (Advanced Along-Track Scanning Radiometer) (Istomina et al., 2010) have been used to train the Bayesian cloud screening for the available limited MERIS spectral range. Whiteness and brightness criteria as well as normalized difference thresholds have been used as well. The comparison of the developed cloud mask to the operational AATSR and MODIS (Moderate Resolution Imaging Spectroradiometer) cloud masks shows a considerable improvement in the detection of clouds over snow and sea ice, with about 10 % false clear detections during May–July and less than 5 % false clear detections in the rest of the melting season. This seasonal behavior is expected as the sea ice surface is generally brighter and more challenging for cloud detection in the beginning of the melting season. The effect of the improved cloud screening on the MPF–albedo datasets is demonstrated on both temporal and spatial scales. In the absence of cloud contamination, the time sequence of MPFs displays a greater range of values throughout the whole summer. The daily maps of the MPF now show spatially uniform values without cloud artifacts, which were clearly visible in the previous version of the dataset. The developed cloud screening routine can be applied to address cloud contamination in remote sensing data over sea ice. The resulting cloud mask for the MERIS operating time, as well as the improved MPF–albedo datasets for the Arctic region, is available at https://www.seaice.uni-bremen.de/start/ (Istomina et al., 2017).

Gated Convolutional Networks for Cloud Removal From Bi-Temporal Remote Sensing Images

Pixels of clouds and cloud shadows in a remote sensing image impact image quality, image interpretation, and subsequent applications. In this paper, we propose a novel cloud removal method based on deep learning that automatically reconstructs the invalid pixels with the auxiliary information from multi-temporal images. Our method’s innovation lies in its feature extraction and loss functions, which reside in a novel gated convolutional network (GCN) instead of a series of common convolutions. It takes the current cloudy image, a recent cloudless image, and the mask of clouds as input, without any requirements of external training samples, to realize a self-training process with clean pixels in the bi-temporal images as natural training samples. In our feature extraction, gated convolutional layers, for the first time, are introduced to discriminate cloudy pixels from clean pixels, which make up for a common convolution layer’s lack of the ability to discriminate. Our multi-level constrained joint loss function, which consists of an image-level loss, a feature-level loss, and a total variation loss, can achieve local and global consistency both in shallow and deep levels of features. The total variation loss is introduced into the deep-learning-based cloud removal task for the first time to eliminate the color and texture discontinuity around cloud outlines needing repair. On the WHU cloud dataset with diverse land cover scenes and different imaging conditions, our experimental results demonstrated that our method consistently reconstructed the cloud and cloud shadow pixels in various remote sensing images and outperformed several mainstream deep-learning-based methods and a conventional method for every indicator by a large margin.

Fusion of External and Internal Prior Information for the Removal of Gaussian Noise in Images.

In this paper, a new method for the removal of Gaussian noise based on two types of prior information is described. The first type of prior information is internal, based on the similarities between the pixels in the noisy image, and the other is external, based on the index or pixel location in the image. The proposed method focuses on leveraging these two types of prior information to obtain tangible results. To this end, very similar patches are collected from the noisy image. This is done by sorting the image pixels in ascending order and then placing them in consecutive rows in a new two-dimensional image. Henceforth, a principal component analysis is applied on the patch matrix to help remove the small noisy components. Since the restored pixels are similar or close in values to those in the clean image, it is preferable to arrange them using indices similar to those of the clean pixels. Simulation experiments show that outstanding results are achieved, compared to other known methods, either in terms of image visual quality or peak signal to noise ratio. Specifically, once the proper indices are used, the proposed method achieves PSNR value better than the other well-known methods by >1.5 dB in all the simulation experiments.

AC-coupled n-in-p pixel detectors on MCz silicon with atomic layer deposition (ALD) grown thin film

We report initial characterization of our novel sensor process solutions with AC-coupled n+/p−/p+ pixel detectors made on 150 mm diameter p-type Magnetic Czochralski silicon (MCz-Si) wafers. The pixels were segmented in a 52 × 80 dual column array and designed to be AC capacitive coupled. The resistive coupling between pixels, allowing quality assurance probing prior the flip chip bonding, was realized with thin film metal-nitride resistors fabricated by sputtering deposition. This approach allows us to omit punch-through resistor structures, which reduces the overall process complexity. Moreover, our previous studies have emphasized that applying ALD Aluminum Oxide (Al2O3) field insulator and passivation layer results in negative net oxide charge and thus additional p-spray or p-stop surface current termination structures are not necessary. Our focused application is a radiation-hard ALD AC-coupled pixel detector to be used in future particle physics experiments, such as the High-Luminosity Large Hadron Collider (HL-LHC), as well as photon counting applications. The pixel detectors were tested at Helsinki Institute of Physics (HIP) Detector laboratory and Ruđer Bošković Institute (RBI). We show measurement data of pixel detectors and other test structures. For the TiN resistors surrounding pixels, the resistance values were measured to be about 15kΩ. Data of electrical properties, full depletion voltage and leakage current are shown as well. Our Transient Current Technique (TCT) measurements indicated clear pixel segmentation with excellent homogeneity. For further study, AC-coupled sensors were hybridized to PSI46dig read out chips (ROC) by flip-chip interconnection technique and tested with a radioactive source.

Cloud<i>_</i>cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties

Abstract. We present version 3 (V3) of the Cloud_cci Along-Track Scanning Radiometer (ATSR) and Advanced ATSR (AATSR) data set. The data set was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) programme. The cloud properties were retrieved from the second ATSR (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the AATSR on board Envisat, which spanned 2002–2012. The data are comprised of a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius, and optical thickness, alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top- and bottom-of-atmosphere shortwave and longwave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with co-located CALIPSO data. The cloud masking has improved significantly, particularly in its ability to detect clear pixels. The Kuiper Skill score has increased from 0.49 to 0.66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulus cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments. The correlation with MAC-LWP increased from 0.4 to over 0.8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth's Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new data set is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol–cloud interactions, and providing contextual information for co-located ATSR-2/AATSR surface temperature and aerosol products. The following new digital identifier has been issued for the Cloud_cci ATSR-2/AATSRv3 data set: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 (Poulsen et al., 2019).

Thick cloud removal in Landsat images based on autoregression of Landsat time-series data

Thick-cloud contamination causes serious missing data in Landsat images, which substantially limits applications of these images. To remove thick clouds from Landsat data, the most popular methods employ auxiliary data such as a cloud-free image of the same area acquired on another date (referred to as the “reference image”). However, the performance of most previous methods strongly depends on the usefulness of the specific reference image, but in some cases high-quality cloud-free reference images are rarely available. In addition, some of these methods ignore the use of partially cloud-contaminated reference images, but clear pixels in these images can be very useful. To address these issues, a new cloud-removal method (AutoRegression to Remove Clouds (ARRC)) has been developed in this study. The most important improvement of ARRC is that it considers autocorrelation of Landsat time-series data and employs multi-year Landsat images including partially cloud-contaminated images in the cloud-removing process. ARRC also addresses the cases in which autocorrelation of Landsat time series might be adversely affected by abrupt land cover changes over multiple years. We compared ARRC with the widely-used MNSPI (modified neighborhood similar pixel interpolator) method at four challenging sites, including an urban area in Beijing and three croplands, in the North China Plain, northeastern Vietnam, and Iowa, USA. Results from the cloud-simulated images showed that ARRC performed better than MNSPI and achieved lower RMSE values (e.g., 0.02129 vs. 0.03005, 0.03293 vs. 0.04725, 0.02740 vs. 0.03556, and 0.03303 vs. 0.03973 in the near-infrared band at the four testing sites, respectively). Moreover, the experiments demonstrated improved performance when clear pixels in partially cloud-contaminated images were used by ARRC. Furthermore, cloud-free images reconstructed by ARRC were visually better than those reconstructed by MNSPI when both approaches were applied to actual cloud-contaminated Landsat images.

Evaluation and comparison of a machine learning cloud identification algorithm for the SLSTR in polar regions

A Feed Forward Neural Net (NN) approach to distinguish between clouds and the surface has been applied to the Sea and Land Surface Temperature Radiometer in polar regions. The masking algorithm covers the Arctic, Antarctic and regions typically classified as the cryosphere such as northern hemisphere permafrost. The mask has been trained using collocations with the CALIOP active lidar, which in narrow strips provide more accurate detection of cloud, and was subsequently evaluated as a function of cloud type and surface type. The mask was compared with the existing operational Bayesian and Empirical cloud masks by eye and also statistically using CALIOP data. It was found to perform exceptionally well in the polar regions. The Kuiper skill score improved from 0.28, for the operational Bayesian and 0.17 for the Empirical masks to 0.77 for the NN. The NN algorithm also has a much more homogeneous performance over all surface types. The key improvement came from better identification of clear scenes; for the NN mask, the same performance in terms of contamination of cloudy pixels in the sample of identified clear pixels can be achieved while retaining 40% of the clear pixels compared with 10% for the operational cloud identification. The algorithm performed with almost the same skill over sea and land. The best performance was achieved for opaque clouds while transparent and broken clouds showed slightly reduced accuracy.