scholarly journals A Novel Approach for Cloud Detection in Scenes with Snow/Ice Using High Resolution Sentinel-2 Images

Atmosphere ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 44 ◽  
Author(s):  
Ling Han ◽  
Tingting Wu ◽  
Qing Liu ◽  
Zhiheng Liu
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Matched Filter ◽  
Spectral Characteristics ◽  
Cloud Detection ◽  
Novel Approach ◽  
Visible Wavelength Range ◽  
Number Frequency ◽  
Detection Effect ◽  
Sentinel 2

The recognition of snow versus clouds causes difficulties in cloud detection because of the similarity between cloud and snow spectral characteristics in the visible wavelength range. This paper presents a novel approach to distinguish clouds from snow to improve the accuracy of cloud detection and allow an efficient use of satellite images. Firstly, we selected thick and thin clouds from high resolution Sentinel-2 images and applied a matched filter. Secondly, the fractal digital number-frequency (DN-N) algorithm was applied to detect clouds associated with anomalies. Thirdly, spatial analyses, particularly spatial overlaying and hotspot analyses, were conducted to eliminate false anomalies. The results indicate that the method is effective for detecting clouds with various cloud covers over different areas. The resulting cloud detection effect possesses specific advantages compared to classic methods, especially for satellite images of snow and brightly colored ground objects with spectral characteristics similar to those of clouds.

scholarly journals Cloud Detection Based on High Resolution Stereo Pairs of the Geostationary Meteosat Images

2020 ◽  
Vol 12 (3) ◽  
pp. 371 ◽  
Author(s):  
Sahar Dehnavi ◽  
Yasser Maghsoudi ◽  
Klemen Zakšek ◽  
Mohammad Javad Valadan Zoej ◽  
Gunther Seckmeyer ◽  
...  
Keyword(s):  
High Resolution ◽  
Matched Filter ◽  
Stereo Pair ◽  
Cloud Detection ◽  
Operating Characteristics ◽  
Detection Techniques ◽  
Infrared Imager ◽  
Detection Approach ◽  
Considerable Impact ◽  
Image Pairs

Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future.

scholarly journals A GENERATIVE ADVERSARIAL NETWORK APPROACH FOR SUPER-RESOLUTION OF SENTINEL-2 SATELLITE IMAGES

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 9-14
Author(s):  
F. Pineda ◽  
V. Ayma ◽  
C. Beltran
Keyword(s):  
High Resolution ◽  
Satellite Images ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Structural Similarity ◽  
Generative Adversarial Network ◽  
Acceptable Result ◽  
Adversarial Network ◽  
High Resolution Satellite Images ◽  
Sentinel 2

Abstract. High-resolution satellite images have always been in high demand due to the greater detail and precision they offer, as well as the wide scope of the fields in which they could be applied; however, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Recent models of convolutional neural networks (CNN) are very suitable for applications with image processing, like resolution enhancement of images; but in order to obtain an acceptable result, it is important, not only to define the kind of CNN architecture but the reference set of images to train the model. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a peruvian satellite, which serve as the reference for the super-resolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR) and the Structural Similarity (SSIM). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.

scholarly journals Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network

2021 ◽  
Vol 13 (16) ◽  
pp. 3319
Author(s):  
Nan Ma ◽  
Lin Sun ◽  
Chenghu Zhou ◽  
Yawen He
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Spectral Library ◽  
Remote Sensing Images ◽  
Different Types ◽  
Sentinel 2

Automatic cloud detection in remote sensing images is of great significance. Deep-learning-based methods can achieve cloud detection with high accuracy; however, network training heavily relies on a large number of labels. Manually labelling pixel-wise level cloud and non-cloud annotations for many remote sensing images is laborious and requires expert-level knowledge. Different types of satellite images cannot share a set of training data, due to the difference in spectral range and spatial resolution between them. Hence, labelled samples in each upcoming satellite image are required to train a new deep-learning-based model. In order to overcome such a limitation, a novel cloud detection algorithm based on a spectral library and convolutional neural network (CD-SLCNN) was proposed in this paper. In this method, the residual learning and one-dimensional CNN (Res-1D-CNN) was used to accurately capture the spectral information of the pixels based on the prior spectral library, effectively preventing errors due to the uncertainties in thin clouds, broken clouds, and clear-sky pixels during remote sensing interpretation. Benefiting from data simulation, the method is suitable for the cloud detection of different types of multispectral data. A total of 62 Landsat-8 Operational Land Imagers (OLI), 25 Moderate Resolution Imaging Spectroradiometers (MODIS), and 20 Sentinel-2 satellite images acquired at different times and over different types of underlying surfaces, such as a high vegetation coverage, urban area, bare soil, water, and mountains, were used for cloud detection validation and quantitative analysis, and the cloud detection results were compared with the results from the function of the mask, MODIS cloud mask, support vector machine, and random forest. The comparison revealed that the CD-SLCNN method achieved the best performance, with a higher overall accuracy (95.6%, 95.36%, 94.27%) and mean intersection over union (77.82%, 77.94%, 77.23%) on the Landsat-8 OLI, MODIS, and Sentinel-2 data, respectively. The CD-SLCNN algorithm produced consistent results with a more accurate cloud contour on thick, thin, and broken clouds over a diverse underlying surface, and had a stable performance regarding bright surfaces, such as buildings, ice, and snow.

scholarly journals Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion

2020 ◽  
Vol 12 (9) ◽  
pp. 1433 ◽  
Author(s):  
Radoslaw Guzinski ◽  
Hector Nieto ◽  
Inge Sandholt ◽  
Georgios Karamitilios
Keyword(s):  
High Resolution ◽  
Spectral Characteristics ◽  
Heat Fluxes ◽  
Actual Evapotranspiration ◽  
Field Scale ◽  
Thermal Data ◽  
Spatio Temporal ◽  
Data Gap ◽  
The One ◽  
Sentinel 2

The Sentinel-2 and Sentinel-3 satellite constellation contains most of the spatial, temporal and spectral characteristics required for accurate, field-scale actual evapotranspiration (ET) estimation. The one remaining major challenge is the spatial scale mismatch between the thermal-infrared observations acquired by the Sentinel-3 satellites at around 1 km resolution and the multispectral shortwave observations acquired by the Sentinel-2 satellite at around 20 m resolution. In this study we evaluate a number of approaches for bridging this gap by improving the spatial resolution of the thermal images. The resulting data is then used as input into three ET models, working under different assumptions: TSEB, METRIC and ESVEP. Latent, sensible and ground heat fluxes as well as net radiation produced by the models at 20 m resolution are validated against observations coming from 11 flux towers located in various land covers and climatological conditions. The results show that using the sharpened high-resolution thermal data as input for the TSEB model is a sound approach with relative root mean square error of instantaneous latent heat flux of around 30% in agricultural areas. The proposed methodology is a promising solution to the lack of thermal data with high spatio-temporal resolution required for field-scale ET modelling and can fill this data gap until next generation of thermal satellites are launched.

scholarly journals A Novel Approach for Mapping Wheat Areas Using High Resolution Sentinel-2 Images

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2089 ◽  
Author(s):  
Ali Nasrallah ◽  
Nicolas Baghdadi ◽  
Mario Mhawej ◽  
Ghaleb Faour ◽  
Talal Darwish ◽  
...  
Keyword(s):  
High Resolution ◽  
Novel Approach ◽  
Sentinel 2

scholarly journals Spectral characteristics of some agricultural crops in different phenological phases of vegetation

2020 ◽  
pp. 56-70
Author(s):  
V. A. Tabunschik ◽  
Т. M. Chekmareva ◽  
R. V. Gorbunov
Keyword(s):  
Software Package ◽  
Satellite Images ◽  
Spectral Characteristics ◽  
Spectral Brightness ◽  
Space Images ◽  
Brightness Coefficient ◽  
Spectral Reflectivity ◽  
Spectral Brightness Coefficient ◽  
Sentinel 2 ◽  
Phenological Phases

For deciphering crops from satellite images at different time periods, it is necessary to have information about the spectral reflectivity of plants during their passage through the phenological phases of vegetation. An attempt was made to evaluate the spectral reflectivity of the main fruit crops and grapes in different phenological phases of the growing season using Sentinel-2 satellite images and the ENVI software package. Field research methods, plots were selected on which peach, grapes, cherries, apple trees, plums, and apricots grow are used. It was established that planting crops was carried out by mixing cultivars in order to reduce the risk of additional costs as a result of possible adverse natural processes and phenomena. For each section, the maximum, minimum, and average values of the spectral brightness coefficient were obtained and analyzed within 13 bands of Sentinel-2 satellite images. Space images were selected for 04/07/2019, 04/27/2019 and 05/12/2019, as the most suitable for the periods of the beginning of flowering (04/07/2019), the end of flowering (04/27/2019) and the beginning of fruit ripening (12/05/2019), with minimal cloud overlap values. To eliminate the external influence of the soil within each pixel of the image, the linear spectral separation module of the ENVI software package was used, a reference soil fragment was selected and its spectral characteristics were obtained, which made it possible to depict graphs of the spectral curves of the crops under study within each section. It was not possible to obtain a distinction of the spectral brightness coefficient for all sections, which is associated with the presence of additional external elements.

A Framework of Generating 10-m Spectral Surface Albedo Products from Sentinel-2 and MODIS data

2021 ◽  
Author(s):  
Rui Song ◽  
Jan-Peter Muller ◽  
Alistair Francis
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Resolution ◽  
Surface Albedo ◽  
Atmospheric Correction ◽  
Cloud Detection ◽  
Endmember Extraction ◽  
The Earth ◽  
Sentinel 2

<p><strong>Abstract: </strong>Surface albedo is a fundamental radiative parameter as it controls the Earth’s surface energy budget and directly affects the Earth’s climate. A new method is proposed of generating 10-m high-resolution spectral surface albedo from Sentinel-2 L1C top-of-atmosphere (TOA) reflectance and MODIS bi-directional reflectance distribution function (BRDF) data. This high-resolution spectral surface albedo generation system will be described and consists of 5 parts: 1) retrieval of Sentinel-2 spectral surface reflectance using the Sensor Invariant Atmospheric Correction (SIAC) algorithm; 2) generation of Sentinel-2 cloud mask using machine learning; 3) extraction of pure pixels and their corresponding abundance values from 20-m Sentinel-2 data using an Endmember Extraction Algorithm; 4) inversion of high-resolution albedo from MODIS_albedo/Sentinel2_BRF ratio matrix; and 5) downscaling retrieved 20-m spectral and broadband albedo to 10-m. The SIAC algorithm is developed by [1], and has demonstrated to vastly improve the accuracy of Sentinel-2 atmospheric correction when compared against the use of in situ AERONET data. The machine learning cloud detection approach CloudFCN [2] is based on a Fully Convolutional Network architecture, and has become a standard Deep Learning approach to image segmentation. The CloudFCN exhibits state-of-the-art performance in picking up cloud pixels which is comparable to other methods in terms of performance, high speed, and robustness to many different terrains and sensor types. The endmember extraction uses N-FINDR along with Automatic Target Generation Process to identify the pure pixels from Sentinel-2 spectral data. The extracted pure pixels are used to relate the albedo-to-reflectance matrix with the abundance values of different pure pixels. The high-resolution albedo values are finally retrieved by solving this over-parameterised matrix. This framework also produces a MODIS BRDF prior based on 20-years of MCD43A1 and VNP43A1 daily BRDF data. This BRDF prior is produced on a daily basis, and will be used to temporally interpolate the high-resolution albedo values over pixels that are covered by clouds. The produced high-resolution albedo data will be validated over different tower sites where long-time series of in situ albedo products have been produced [3].</p><p>Keywords: high-resolution, surface albedo, Sentinel-2, SIAC, machine learning, endmember</p><p>[1] Yin, F.; Lewis, P.E.; Gomez-Dans, J.; Wu, Q. A sensor-invariant atmospheric correction method: Application to Sentinel-2/MSI and Landsat 8/OLI. EarthArXiv, 21 Feb. 2019 web, doi:10.31223/osf.io/ps957.</p><p>[2] Francis, A.; Sidiropoulos, P.; Muller, J.-P. CloudFCN: Accurate and Robust Cloud Detection for Satellite Imagery with Deep Learning. Remote Sens. 2019, 11, 2312. https://doi.org/10.3390/rs11192312.</p><p>[3] Song, R.; Muller, J.-P.; Kharbouche, S.; Yin, F.; Woodgate, W.; Kitchen, M.; Roland, M.; Arriga, N.; Meyer, W.; Koerber, G.; Bonal, D.; Burban, B.; Knohl, A.; Siebicke, L.; Buysse, P.; Loubet, B.; Leonardo, M.; Lerebourg, C.; Gobron, N. Validation of Space-Based Albedo Products from Upscaled Tower-Based Measurements Over Heterogeneous and Homogeneous Landscapes. Remote Sens. 2020, 12, 833. https://doi.org/10.3390/rs12050833.</p>

scholarly journals POSSIBILITIES OF USING SATELLITE IMAGES AND GIS TECHNOLOGIES FOR MONITORING INUNDATION OF PIPELINE SYSTEMS

2020 ◽  
Vol 4 (1) ◽  
pp. 21-28
Author(s):  
Vyacheslav A. Melkiy ◽  
Daniil V. Dolgopolov ◽  
Alexey A. Verkhoturov
Keyword(s):  
Satellite Images ◽  
Spectral Characteristics ◽  
Remote Sensing Data ◽  
Space Systems ◽  
Water Index ◽  
Geotechnical Monitoring ◽  
Pipeline Systems ◽  
Normalized Difference Water Index ◽  
Automated Interpretation ◽  
Sentinel 2

The purpose of this research is the study of possibilities of practical use of multi-zone satellite images for implementation of geotechnical monitoring of pipeline transport facilities during floodings. Modern methods and approaches are required for monitoring extended objects and analyzing large amount of remote sensing data. Such methods can be applied for studying of spectral characteristics of the Earth's surface obtained using space systems, collected in databases using geoinformation technologies (GIS). Use of special indexes and technologies for automated interpretation of multi-zone satellite images allows obtaining and analyzing information about state of pipeline systems at time of flooding. Research showed that Sentinel-2 satellite data makes it possible for fairly correctly determine of flood situation by image indexed with using of Normalized Difference Water Index (NDWI) and highlight areas and objects flooded of water.

scholarly journals Elimination of cloud shadows on materials of aviation shooting in the visible range

2020 ◽  
Vol 26 (2) ◽  
pp. 286-297
Author(s):  
Ilya Rylskiу ◽  
Evgeniy Eremchenko ◽  
Tatiana Kotova
Keyword(s):  
High Resolution ◽  
Aerial Photography ◽  
Satellite Images ◽  
Spectral Characteristics ◽  
Significant Proportion ◽  
Aerial Photographs ◽  
Visible Range ◽  
Reference Images ◽  
Prototype Software ◽  
High Clouds

Aerial photography is often impossible due to the presence of high clouds with contrasting shadows that do not allow to obtain materials suitable for decryption. At the same time, in a significant proportion of projects in Russia, the snowless season suitable for surveying is very short. The inability to perform aerial photography while flying below the clouds leads to cost increasing. In some cases, projects cannot be completed. Existing software does not allow to solve the problem of equalizing the brightness in the shadows for several reasons. The main reason is the inability to identify the boundaries of the shadows using only the spectral characteristics of the images, the inability to determine the amount of correction for shaded areas. To solve this problem, it is proposed to use reference images of the worse resolution obtained from the satellites. Reference images are used to localize and determine the magnitude of the spectral correction of aerial photographs. The work is performed with single orthophotographs or orthophotomosaics in the same coordinate system. To determine the boundaries of the shaded zones and the values of the corrections in brightness, methods of cartographic algebra on regular data arrays are used. Further, the obtained correction matrices are subject to filtering and are used to correct high-resolution aerial photographs. The paper gives an example of the use of free (or cheap) satellite images to eliminate or reduce the contrast of shadows on aerial photographs with a detail of 20 cm. The created prototype software allows to perform additive or multiplicative correction of an array of individual aerial photographs. The proposed approach requires more time for data processing, but gives much more acceptable results for visual (manual) decryption. The method is not recommended for use when working with images in more than 10 cm, when solving monitoring tasks with frequent repeated surveys, and also, if necessary, to carry out automated decoding using spectral standards.

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