scholarly journals Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model

2021 ◽  
Vol 14 (6) ◽  
pp. 4083-4110
Author(s):  
Meng Gao ◽  
Bryan A. Franz ◽  
Kirk Knobelspiesse ◽  
Peng-Wang Zhai ◽  
Vanderlei Martins ◽  
...  
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Deep Neural Network ◽  
Ocean Color ◽  
Synthetic Data ◽  
Forward Model ◽  
High Spectral Resolution ◽  
Acceptable Error ◽  
Microphysical Properties ◽  
Aerosol Properties

Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral scanning radiometer named the Ocean Color Instrument (OCI) and two multi-angle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems for applications to the HARP2 instrument and its predecessors. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 s in a single CPU core or 1 ms in a GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water-leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water-leaving signals can be retrieved efficiently and within acceptable error. Comparing to the retrieval speed using a conventional radiative transfer forward model, the computational acceleration is 103 times faster with CPU or 104 times with GPU processors. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth-observing satellite missions with similar capabilities.

scholarly journals Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model

2021 ◽  
Author(s):  
Meng Gao ◽  
Bryan A. Franz ◽  
Kirk Knobelspiesse ◽  
Peng-Wang Zhai ◽  
Vanderlei Martins ◽  
...  
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Deep Neural Network ◽  
Ocean Color ◽  
Synthetic Data ◽  
Forward Model ◽  
High Spectral Resolution ◽  
Acceptable Error ◽  
Microphysical Properties ◽  
Aerosol Properties

Abstract. NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in the timeframe of 2023, will carry a hyperspectral Ocean Color Instrument (OCI) and two Multi-Angle Polarimeters (MAP): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and the SRON Spectro-Polarimeter for Planetary EXploration one (SPEXone). The MAP measurements contain rich information on the microphysical properties of aerosols and hydrosols, and therefore can be used to retrieve accurate aerosol properties for complex atmosphere and ocean systems. Most polarimetric aerosol retrieval algorithms utilize vector radiative transfer models iteratively in an optimization approach, which leads to high computational costs that limit their usage in the operational processing of large data volumes acquired by the MAP imagers. In this work, we propose a deep neural network (NN) model to represent the radiative transfer simulation of coupled atmosphere and ocean systems, for applications to the HARP instrument. Through the evaluation of synthetic datasets for AirHARP (airborne version of HARP2), the NN model achieves a numerical accuracy smaller than the instrument uncertainties, with a running time of 0.01 s in a single CPU core or 1 ms in GPU. Using the NN as a forward model, we built an efficient joint aerosol and ocean color retrieval algorithm called FastMAPOL, evolved from the well-validated Multi-Angular Polarimetric Ocean coLor (MAPOL) algorithm. Retrievals of aerosol properties and water leaving signals were conducted on both the synthetic data and the AirHARP field measurements from the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign in 2017. From the validation with the synthetic data and the collocated High Spectral Resolution Lidar (HSRL) aerosol products, we demonstrated that the aerosol microphysical properties and water leaving signals can be retrieved efficiently and within acceptable error. The FastMAPOL algorithm can be used to operationally process the large volume of polarimetric data acquired by PACE and other future Earth observing satellite missions with similar capabilities.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

scholarly journals Optimal estimation retrieval of aerosol microphysical properties from SAGE II satellite observations in the lower stratosphere

2009 ◽  
Vol 9 (6) ◽  
pp. 23719-23753
Author(s):  
D. Wurl ◽  
R. G. Grainger ◽  
A. J. McDonald ◽  
T. Deshler
Keyword(s):  
Particle Size ◽  
Near Infrared ◽  
Lower Stratosphere ◽  
Synthetic Data ◽  
Optimal Estimation ◽  
Retrieval Algorithm ◽  
Extinction Data ◽  
Data Set ◽  
Microphysical Properties ◽  
Aerosol Properties

Abstract. A new retrieval algorithm is presented, which is based on the Optimal Estimation (OE) approach and aimed to improve current estimates of aerosol microphysical properties under non-volcanic conditions. The new OE algorithm retrieves log-normal particle size distribution parameters and associated uncertainties from multi-wavelength aerosol extinction data at visible to near infrared wavelengths. The algorithm was tested on synthetic data and then applied to SAGE (Stratospheric Aerosol and Gas Experiment) II data measured in 1999 in the lower stratosphere between 10 and 35 km. Model validation based on synthetic data shows that the new algorithm is able to retrieve the particle size of typical background aerosols accurately and that the retrieved uncertainties are a good estimate of the true errors. Aerosol properties retrieved from measured SAGE II extinction data (recorded in 1999) using the OE approach were compared to Principal Component Analysis (PCA) results retrieved from the same SAGE II data set. The OE surface area and volume densities are observed to be larger than the PCA values by 20–50% and 10–40% whereas the OE effective radii tend to be smaller by about 10–40%. An examination of the OE algorithm biases with in situ data indicates that the new OE estimates are likely to be more realistic than the PCA results. Based on the results of this study we suggest that the new OE retrieval algorithm provides improved estimates of aerosol properties in the lower stratosphere under low aerosol loading conditions.

scholarly journals A neural network radiative transfer model approach applied to the Tropospheric Monitoring Instrument aerosol height algorithm

2019 ◽  
Vol 12 (12) ◽  
pp. 6619-6634 ◽  
Author(s):  
Swadhin Nanda ◽  
Martin de Graaf ◽  
J. Pepijn Veefkind ◽  
Mark ter Linden ◽  
Maarten Sneep ◽  
...  
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
European Space Agency ◽  
Forward Model ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Aerosol Layer ◽  
Layer Height ◽  
Monitoring Instrument

Abstract. To retrieve aerosol properties from satellite measurements of the oxygen A-band in the near-infrared, a line-by-line radiative transfer model implementation requires a large number of calculations. These calculations severely restrict a retrieval algorithm's operational capability as it can take several minutes to retrieve the aerosol layer height for a single ground pixel. This paper proposes a forward modelling approach using artificial neural networks to speed up the retrieval algorithm. The forward model outputs are trained into a set of neural network models to completely replace line-by-line calculations in the operational processor. Results comparing the forward model to the neural network alternative show an encouraging outcome with good agreement between the two when they are applied to retrieval scenarios using both synthetic and real measured spectra from TROPOMI (TROPOspheric Monitoring Instrument) on board the European Space Agency (ESA) Sentinel-5 Precursor mission. With an enhancement of the computational speed by 3 orders of magnitude, TROPOMI's operational aerosol layer height processor is now able to retrieve aerosol layer heights well within operational capacity.

scholarly journals Complex imaging of phase domains by deep neural networks

IUCrJ ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 12-21
Author(s):  
Longlong Wu ◽  
Pavol Juhas ◽  
Shinjae Yoo ◽  
Ian Robinson
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Single Particle ◽  
Deep Neural Network ◽  
Phase Retrieval ◽  
Particle Image ◽  
Synthetic Data ◽  
Accurate Estimate ◽  
Initial Guess

The reconstruction of a single-particle image from the modulus of its Fourier transform, by phase-retrieval methods, has been extensively applied in X-ray structural science. Particularly for strong-phase objects, such as the phase domains found inside crystals by Bragg coherent diffraction imaging (BCDI), conventional iteration methods are time consuming and sensitive to their initial guess because of their iterative nature. Here, a deep-neural-network model is presented which gives a fast and accurate estimate of the complex single-particle image in the form of a universal approximator learned from synthetic data. A way to combine the deep-neural-network model with conventional iterative methods is then presented to refine the accuracy of the reconstructed results from the proposed deep-neural-network model. Improved convergence is also demonstrated with experimental BCDI data.

scholarly journals A neural network radiative transfer model approach applied to TROPOMI’s aerosol height algorithm

2019 ◽  
Author(s):  
Swadhin Nanda ◽  
Martin de Graaf ◽  
J. Pepijn Veefkind ◽  
Mark ter Linden ◽  
Maarten Sneep ◽  
...  
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Near Infrared ◽  
Forward Model ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Aerosol Layer ◽  
Neural Network Models ◽  
Layer Height

Abstract. To retrieve aerosol properties from satellite measurements of the oxygen A-band in the near infrared, a line-by-line radiative transfer model implementation requires a large number of calculations. These calculations severely restrict a retrieval algorithm's operational capability as it can take several minutes to retrieve aerosol layer height for a single ground pixel. This paper proposes a forward modeling approach using artificial neural networks to speed up the retrieval algorithm. The forward model outputs are trained into a set of neural network models to completely replace line-by-line calculations in the operational processor. Results of comparing the forward model to the neural network alternative show encouraging results with good agreements between the two when applied to retrieval scenarios using both synthetic and real measured spectra from TROPOMI (TROPOspheric Monitoring Instrument) on board the ESA Sentinel-5 Precursor mission. With an enhancement of the computational speed by three orders of magnitude, TROPOMI's operational aerosol layer height processor is now able to retrieve aerosol layer heights well within operational capacity.

scholarly journals Adaptive Data Screening for Multi-Angle Polarimetric Aerosol and Ocean Color Remote Sensing Accelerated by Deep Learning

2021 ◽  
Vol 2 ◽  
Author(s):  
Meng Gao ◽  
Kirk Knobelspiesse ◽  
Bryan A. Franz ◽  
Peng-Wang Zhai ◽  
Vanderlei Martins ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Spectral Band ◽  
Ocean Color ◽  
Screening Method ◽  
Synthetic Data ◽  
High Spectral Resolution ◽  
Retrieval Algorithm ◽  
Retrieval Performance ◽  
Data Screening

Remote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90–120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sunglint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20–30 angles across all bands (i.e., five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected.

scholarly journals Modeling extra-deep EM logs using a deep neural network

Geophysics ◽  
2021 ◽  
pp. 1-47
Author(s):  
Sergey Alyaev ◽  
Mostafa Shahriari ◽  
David Pardo ◽  
Ángel Javier Omella ◽  
David Selvåg Larsen ◽  
...  
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Deep Neural Network ◽  
Forward Model ◽  
Training Dataset ◽  
Training Set ◽  
Proprietary Information ◽  
Set Size ◽  
Evaluation Time ◽  
Dataset Size

Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We present a methodology to construct a deep neural network (DNN) model trained to reproduce a full set of extra-deep EM logs consisting of 22 measurements per logging position. The model is trained in a 1D layered environment consisting of up to seven layers with different resistivity values.A commercial simulator provided by a tool vendor is utilized to generate a training dataset.The dataset size is limited because the simulator provided by the vendor is optimized for sequential execution.Therefore,we design a training dataset that embracesthe geological rules and geosteering specifics supported by the forward model. We use this dataset to produce an EM simulator based on a DNN without access to the proprietary information about the EM tool configuration or the original simulator source code.Despite employing a relatively small training set size, the resulting DNN forward model is quite accurate for the considered examples: a multi-layer synthetic case and a section of a published historical operation from the Goliat Field.The observed average evaluation time of 0.15 milliseconds per logging position makes it also suitable for future use as part of evaluation-hungry statistical and/or Monte-Carlo inversion algorithms within geosteering workflows.

A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases using X-ray Images

2020 ◽  
Vol 16 ◽  
Author(s):  
Saleh Albahli
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Intelligent System ◽  
Synthetic Data ◽  
Disease Detection ◽  
X Ray ◽  
Chest Disease ◽  
And Gender ◽  
Rare Classes

Background: Scanning patient’s lungs to detect a Coronavirus 2019 (COVID-19) may lead to similar imaging with other chest diseases that strongly requires a multidisciplinary approach to confirm the diagnosis. There are only few works targeted pathological x-ray images. Most of the works targeted only single disease detection which is not good enough. Some works have provided for all classes however the results suffer due to lack of data for rare classes and data unbalancing problem. Methods: Due to arise of COVID-19 virus medical facilities of many countries are overwhelmed and there is a need of intelligent system to detect it. There have been few works regarding detection of the coronavirus but there are many cases where it can be misclassified as some techniques do not provide any goodness if it can only identify type of diseases and ignore the rest. This work is a deep learning-based model to distinguish between cases of COVID-19 from other chest diseases which is need of today. Results: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provide effective analysis of chest related diseases with respect to age and gender. Our model achieves 87% accuracy in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results. Conclusion: If the gap in identifying of all viral pneumonias is not filled with effective automation of chest disease detection the healthcare industry may have to bear unfavorable circumstances.

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