25 results about "Atmospheric profile" patented technology

The application discloses an infrared image adaptive atmospheric correction method and device based on a radiation transmission model, and belongs to the technical field of remote sensing image radiation correction. The method first extracts the spatial range, imaging time and observation geometry parameters of an image scene, constructs a scene-based atmospheric profile grid by distance inverse ratio weighted interpolation based on a reanalysis atmospheric profile, and combines a digital elevation model and a view zenith angle to correct and apply terrain and observation geometry constraints to generate control node input parameters. Then, the atmospheric radiation transmission model is called in batches to obtain the hyperspectral results of each control node, and the transmittance and path radiance parameters of the infrared target band are extracted by spectral response function convolution. Finally, the control node parameters are reprojected to the original image coordinates, the four-neighbor-domain bilinear interpolation and nine-neighbor-domain weighted interpolation results are adaptively fused for each pixel, the full-resolution atmospheric parameter field is recovered, and the observed radiance at the ground is inversed.

The application discloses a kind of based on deep learning's full-waveband microwave radiation transmission simulation method and system, method includes: based on global reanalysis data and surface type product, obtain atmospheric profile and surface parameter, adopt near neighbor search and de-redundancy, construct representative "surface parameter-atmospheric profile" database;Using high-precision microwave radiation transmission mode, simulate and calculate the atmospheric top layer brightness temperature covering full-waveband microwave, construct training sample pair;Calculate the correlation coefficient between all output channels, and select the representative channel subset with reduced number from threshold value;Build a fully connected neural network, with the brightness temperature of the representative channel subset as the intermediate feature layer, and use a phased composite loss function strategy for training;Surface parameters and atmospheric profile are input into the trained model to obtain full-waveband brightness temperature prediction results, and / or using automatic differentiation mechanism, the trained model is used as a differentiable operator to calculate the Jacobian matrix of the output brightness temperature relative to the input parameters.

The invention discloses a shipborne marine atmospheric boundary layer refractive index profile detection system and method, and relates to the technical field of marine atmospheric environment monitoring and electromagnetic wave propagation safeguard.The method comprises the steps that sea-air downlink infrared radiation spectrum data and sea surface air temperature, relative humidity and air pressure data are obtained; constructing a background atmosphere profile by using the reanalysis data, and correcting an initial profile bottom layer by using sea surface observation air temperature, relative humidity and air pressure data to form a prior state vector; constructing an inversion cost function including an observation residual term, a background prior term and a sea surface boundary constraint term, and performing inversion to obtain an atmospheric temperature profile and a relative humidity profile; and calculating an atmospheric refractive index profile according to the temperature profile, the relative humidity profile and the air pressure profile. According to the method, the marine atmospheric refractive index profile can be continuously obtained under the shipborne dynamic observation condition, and reliable data support is provided for marine electromagnetic propagation environment analysis and waveguide diagnosis.

The application discloses a micro-light target device and an absolute radiation calibration method for a spaceborne micro-light load, and adopts a light source which is industrialized and customized and meets corresponding conditions in terms of spectral energy stability, waveband response, light source emission energy stability, light source emission energy uniformity in each direction and light source power as a calibration light source of the target, so that the problem that the light source in the prior art has great uncertainty due to the influence of lamp temperature and the narrow spectral range cannot meet the full spectral range calibration requirement can be overcome. In addition, the application arranges multiple targets with different openings to form an absolute radiation calibration target array of the spaceborne micro-light load, so that multi-point and full waveband radiation calibration can be realized, and through the combination of the micro-light targets with different openings, the measured target radiance, the atmospheric profile and the aerosol content and other parameters, and the simulation of the entrance pupil radiance of the spaceborne micro-light load at the top of the atmosphere by means of a radiation transmission model, high-precision absolute radiation calibration of the spaceborne micro-light load with different spatial resolutions can be realized.

This application provides a method and system for correcting the brightness temperature of a spaceborne one-dimensional integrated aperture microwave radiometer. The method includes: acquiring two-dimensional observed brightness temperature data O along the vertical satellite orbit and along the satellite orbit for different ascent and descent orbits of the microwave radiometer; acquiring external data such as atmospheric profiles, surface data, and sea surface salinity along the radiation transmission path to obtain simulated Earth radiation brightness temperature data B, and removing outlier data; subtracting the observed brightness temperature data from the simulated brightness temperature data to obtain O-B data for multiple single orbits, and removing outlier data; taking the median of the O-B data to obtain the average difference in O-B brightness temperature and the two-dimensional corrected brightness temperature; dividing the average difference in O-B brightness temperature by the observed brightness temperatures in the corresponding two vertical directions to obtain two-dimensional relative brightness temperature correction coefficients based on different ascent and descent orbits; and correcting the observed brightness temperature using the two-dimensional relative brightness temperature correction coefficients or the two-dimensional corrected brightness temperature. The advantage of this application is that the established two-dimensional correction coefficients have a better correction effect.

The application discloses a kind of based on two-dimensional radiation transmission constraint's edge atmosphere profile inversion method, system, belongs to atmospheric remote sensing inversion technical field.The present application is with edge radiation data, auxiliary profile, observation geometric parameter and along track observation sequence as input, after pre-processing, it is sent into deep neural network;Network uses time series convolution network to extract along track feature, through multi-head attention mechanism fusion multi-source information, profile estimate value is decoded and output by full connection layer;Introduce differentiable two-dimensional radiation transmission model as physical constraint, the radiation value generated by forward simulation of inversion profile is compared with observation, and constraint loss is constructed;Training uses composite loss function, fuses mean square error, gradient loss, smooth regularization and two-dimensional radiation transmission constraint loss, and the network is optimized by end-to-end gradient backpropagation.The present application significantly improves the inversion accuracy of polar vortex edge, stratospheric jet and other high gradient regions, with the advantages of strong physical consistency and good generalization ability.

The application provides a method for constructing an air-based occultation atmospheric inversion system fusing a ground-based GNSS horizontal gradient, which can construct a high-precision atmospheric horizontal gradient inversion model, establish an air-based occultation atmospheric inversion system fusing the horizontal gradient model, and eliminate the spherical symmetry assumption in air-based occultation low-layer atmospheric inversion. The method for constructing a high-precision low-layer atmospheric profile inversion system based on the fusion of the two is first proposed, so as to maximize the complementarity between them. Meanwhile, the measured air-based experimental data are used to complete the occultation atmospheric inversion based on the newly proposed method, and the results of the sounding balloon during the experiment are compared to complete the analysis and demonstration of the effectiveness of the inversion system and improvement, and finally a set of air-based occultation detection system fusing the ground-based GNSS atmospheric inversion result is constructed.

The invention discloses a microwave communication loss modeling method, system and device under an atmospheric waveguide condition and a storage medium, and the method comprises the steps: obtaining atmospheric profile data, and selecting nodes based on the atmospheric profile data to construct a graph structure; constructing an initial feature vector for each node in the graph structure, and constructing an improved graph isomorphic network according to the initial feature vectors; performing node embedding learning on the graph structure by using the improved graph isomorphic network to generate a node embedding representation set; obtaining a low-order propagation graph structure in sequence according to the node embedding representation set, and extracting node embedding representations of corresponding hierarchies; fusing the graph-level features of the graph structure and the low-order propagation graph structure, and splicing to generate a structured embedding set; based on the structured embedding set, training and prediction are carried out through a regression prediction network, a microwave communication loss value under the atmospheric waveguide condition is output, and a high-precision and high-adaptability modeling scheme is provided for microwave communication path loss estimation.

The application discloses a microwave fast radiative transfer calculation method and equipment based on a CDF / EOF atmospheric profile library, and the method comprises the following steps: constructing a CDF / EOF standardized atmospheric profile library; constructing a microwave line-by-line transmittance database, and obtaining channel equivalent transmittance by combining an instrument spectral response function; constructing a prediction factor set based on atmospheric profile parameters in the CDF / EOF standardized atmospheric profile library, and obtaining microwave fast transmittance coefficients through regression fitting; performing microwave radiative transfer calculation based on profile matching results of the CDF / EOF standardized atmospheric profile library and the microwave fast transmittance coefficients, and correcting and verifying the calculation results. The application aims to construct a CDF / EOF standardized atmospheric profile library, fit high-universality fast transmittance coefficients, realize high-precision and fast calculation of microwave radiative transfer, and thus solve the technical problems that existing profile libraries are single in coverage, poor in coefficient universality, and difficult to balance calculation precision and speed.

The application discloses an infrared image adaptive atmospheric correction method and device based on a radiation transmission model, and belongs to the technical field of remote sensing image radiation correction. The method first extracts the spatial range, imaging time and observation geometry parameters of an image scene, constructs a scene-based atmospheric profile grid by distance inverse ratio weighted interpolation based on a reanalysis atmospheric profile, and combines a digital elevation model and a view zenith angle to correct and apply terrain and observation geometry constraints to generate control node input parameters. Then, the atmospheric radiation transmission model is called in batches to obtain the hyperspectral results of each control node, and the transmittance and path radiance parameters of the infrared target band are extracted by spectral response function convolution. Finally, the control node parameters are reprojected to the original image coordinates, the four-neighbor-domain bilinear interpolation and nine-neighbor-domain weighted interpolation results are adaptively fused for each pixel, the full-resolution atmospheric parameter field is recovered, and the observed radiance at the ground is inversed.

The invention discloses a surface temperature inversion method and system based on an urban complex geometric structure, and the method comprises the steps: obtaining a thermal infrared remote sensing image, an atmosphere contour line, WVC, DSM, land utilization data, and a spectrum library of a certain city; the thermal infrared reflection wave band data, the atmospheric profile bottom layer temperature, the atmospheric parameters, the sky visual factor and the urban surface material emissivity are obtained by preprocessing the thermal infrared reflection wave band data, the atmospheric profile bottom layer temperature, the atmospheric parameters and the urban surface material emissivity; introducing a sky visual factor to quantify an urban geometric structure, and providing an urban pixel thermal radiation model; calculating a surface temperature data set, an effective emissivity average value data set, an effective emissivity difference value data set and an on-satellite brightness temperature data set, constructing a simulation data set together with a sky visual factor, and dividing the simulation data set into a training data set, a test data set and a verification data set; fitting a local regression coefficient of the GWRSW model by using the training data set and the test data set; and inputting the verification data set into the GWRSW model to estimate the surface temperature of the urban pixel. According to the method, the land surface temperature inversion precision in a complex urban environment is effectively improved.

The application discloses a shipborne marine atmospheric boundary layer refractive index profile detection system and method, relates to the technical field of marine atmospheric environment monitoring and electromagnetic wave propagation guarantee, and comprises the following steps: acquiring sea-air downward infrared radiation spectrum data and sea surface air temperature, relative humidity and air pressure data; constructing a background atmospheric profile by using reanalysis data, and correcting the initial profile bottom layer by using the sea surface observation air temperature, relative humidity and air pressure data to form a prior state vector; constructing an inversion cost function containing an observation residual term, a background prior term and a sea surface boundary constraint term, and inverting to obtain an atmospheric temperature profile and a relative humidity profile; and calculating an atmospheric refractive index profile according to the temperature profile, the relative humidity profile and the air pressure profile. The application can continuously acquire the marine atmospheric refractive index profile under the shipborne dynamic observation condition, and provides reliable data support for marine electromagnetic propagation environment analysis and waveguide diagnosis.

The invention provides an atmospheric laser radar data denoising method based on self-supervised learning, and relates to the technical field of laser radar signal processing. The method comprises the following steps: acquiring and preprocessing original laser radar echo data, and constructing a random blind spot sample based on the preprocessed original laser radar echo data; constructing a blind spot network based on a Unet architecture, training the blind spot network by using a random blind spot sample, and generating a target blind spot network; and inputting the to-be-processed laser radar echo data into the target blind spot network, and performing denoising processing on the to-be-processed laser radar echo data by using the target blind spot network. A self-supervised learning framework based on a blind spot mechanism is adopted, random blind spot samples are constructed for original laser radar echo data, and a blind spot network structure with neighborhood prediction capability is trained by using the random blind spot samples, so that the model can retain detail features in atmospheric profiles such as cloud layer boundaries and aerosol gradients; and the balance between noise suppression and signal fidelity is realized.

The invention provides an atmospheric correction method and device based on a JAVA language, and relates to the technical field of atmospheric sciences, and the method comprises the steps: analyzing metadata of a hyperspectral image file to obtain input parameters, and employing object-oriented design to package an atmospheric profile model, a gas absorption model, an aerosol model and a surface reflectance model into independent objects; inputting the input parameters into the models corresponding to the independent objects to obtain model outputs, synthesizing optical attributes based on the model outputs, and solving by combining a surface reflectance model to obtain a radiation transmission result; and generating an atmospheric correction coefficient based on the radiation transfer result, performing atmospheric correction on the image file based on the atmospheric correction coefficient, and performing inversion to obtain the surface reflectance. According to the method, accurate modeling of a classical physical process is realized, the maintainability, the cross-platform capability and the calculation performance in a distributed environment of the model are remarkably improved through object-oriented design, and accurate atmospheric correction of a remote sensing image is effectively realized.

The application discloses a time-series temperature anomaly remote sensing extraction method for coal fire area identification and belongs to the field of remote sensing algorithm development. Optical image data with a thermal infrared band are acquired, radiation calibration and atmospheric correction are performed on the optical image data, NDVI, vegetation coverage VFC and surface emissivity epsilon are calculated, atmospheric profile parameters are acquired, a temperature inversion method is selected to invert the surface temperature, an adaptive window-based temperature anomaly extraction algorithm is used to extract temperature anomaly pixels from the inverted surface temperature data, meanwhile, the time continuity of coal fire combustion is considered, a time parameter is introduced, time-series temperature anomaly pixels are extracted through time-series analysis, and a time-series temperature anomaly image is composed of the time-series temperature anomaly pixels. The algorithm based on unbiased estimation is simple to operate, fast in operation, avoids introduction of artificial subjective errors and can obviously improve the accuracy of extraction of temperature anomaly regions.

The invention discloses a microwave hyperspectral atmospheric profile inversion method and system based on variable channel bandwidth, and the method comprises the following steps: frequency band division and candidate bandwidth generation: dividing a frequency spectrum into an absorption peak region and a plurality of wing regions, and constructing a plurality of candidate bandwidth configuration sets; establishing an observation error covariance matrix for each candidate bandwidth configuration; under each candidate bandwidth configuration, a priori and a covariance are utilized, an optimal estimation method based on Levenberg-Marquardt is adopted, and physical feasibility penalty is introduced, so that an inversion profile is obtained; carrying out layer-by-layer calculation on the inversion result under each candidate bandwidth configuration to evaluate indexes; and selecting an optimal bandwidth configuration and a corresponding inversion profile according to an evaluation criterion. The method has the technical effects that the resolution and the signal-to-noise ratio of the peak region / wing region differentiated bandwidth are considered under the condition that the number of channels is not increased; error covariance calibers are unified, and cross-scheme comparison is fair; quality control and layering indexes are combined, and inversion is stable and robust; and the method has engineering implementability and expandability.

The present application relates to the technical field of atmospheric science and machine learning, and in particular to a vertical atmospheric profile fine-grained processing method, system and equipment based on a KAN network, to solve the problems of insufficient vertical resolution, poor physical consistency and mismatch of high-resolution observations of the existing NWP model; it comprises obtaining input data of the KAN network; the input data is nonlinearly mapped by a B-spline function in the KAN network to obtain control point parameters and bias terms of the B-spline function; physical knowledge constraints are introduced into the KAN network, the atmospheric physical law is converted into a constraint term of the loss function, and the output of the KAN network is constrained; the control point parameters and bias terms of the B-spline function are defined as training parameters of the KAN network, a pre-defined staged training strategy is adopted to train the training parameters of the KAN network in stages; the trained KAN network outputs atmospheric variable functions in a continuous height range, to realize the conversion of NWP coarse-grained profiles to high-resolution continuous vertical profiles.

The application discloses a wide-range fast optical satellite sensor on-satellite observation radiance simulation method and belongs to the technical field of satellite sensing data processing.The application is a method for quickly obtaining wide-range optical satellite sensor on-satellite observation radiance by combining an atmospheric radiation transmission model with a machine learning method.First, a large number of high-precision and representative atmospheric profiles are used as input parameters of atmospheric radiation transmission model simulation to obtain a large number of simulated on-satellite radiance values and corresponding ground state, observation angle and atmospheric state parameter data sets as training data sets for constructing a machine learning algorithm, a machine learning algorithm is used to fit the nonlinear relationship between the on-satellite radiance and the influence factors in the simulation results, and a machine learning model capable of quickly predicting the on-satellite observation radiance of the optical satellite sensor is obtained.The application solves the problem that the sensor has no corresponding global channel reflectivity, and greatly shortens the simulation time and computing resources required.

This invention discloses an on-orbit radiometric calibration method for the thermal infrared channel of a meteorological satellite, comprising the following steps: selecting a suitable working time for the field calibration test based on satellite transit information and weather conditions at the calibration site; using an unmanned surface vessel (USV) carrying an infrared radiometer CE312, releasing the USV via a dock approximately two hours before satellite transit, and navigating on a pre-set route on the lake surface. This invention significantly saves manpower, material resources, and financial investment by utilizing the USV to automatically observe and acquire water surface radiation brightness temperature; by combining this with the acquisition of encrypted atmospheric profile data from radiosondes, the calibration frequency can be greatly increased, and calibration accuracy can be further improved; by comparing the entrance pupil brightness temperature information obtained from the thermal infrared channels of various satellites, the spatial distribution of brightness temperature during the synchronous test period at the calibration site and the degree of influence of cloud and weather conditions on the lake surface can be analyzed, providing significant reference for the selection of the synchronous testing area.

This invention discloses a method and system for retrieving limb atmospheric profiles based on Bayesian deep learning, belonging to the field of atmospheric remote sensing inversion technology. The invention includes: constructing a Bayesian neural network comprising a shared feature extraction backbone and multiple task branches; the backbone network models the weight distribution based on variational Bayesian inference; the average kernel prediction branch outputs a low-rank representation of the average kernel matrix; designing a multi-task loss function that integrates negative log-likelihood, KL divergence, average kernel consistency, and physical constraints; training multiple models using deep ensemble learning; quantifying randomness and cognitive uncertainty through Monte Carlo sampling; and finally outputting a complete inversion product containing the profile, hierarchical uncertainty, and average kernel matrix. This invention achieves, for the first time, end-to-end joint learning of average kernel information and deep learning inversion, improving inference speed by approximately 100 times while maintaining the same accuracy as traditional methods, meeting the needs of near real-time operational applications.

The application discloses a kind of GNSS single station real-time atmospheric profile monitoring method, system and equipment, it is related to GNSS atmospheric detection field.The present application includes: real-time processing the observation data of single GNSS station, obtains the water vapor information carried by GNSS signal;The space region where GNSS station is located is discretized, and the slant-path water vapor observation value of GNSS side edge signal is calculated using water vapor height factor model;Establish the observation equation set of water vapor pressure parameter, and the atmospheric water vapor pressure parameter information is solved using optimization algorithm, and the precision of water vapor pressure parameter is evaluated;Based on meteorological physics function relationship, construct atmospheric specific humidity, atmospheric wet refractive index, atmospheric water vapor density and other meteorological parameter inversion model;Finally, using the meteorological data of GNSS station and collocated sounding station, the external precision of a variety of atmospheric water vapor profile is tested.The present application uses single GNSS station, realizes the all-weather, high-precision inversion of a variety of atmospheric parameter vertical profile.

The invention relates to the technical field of aerosol remote sensing, and discloses an aerosol and earth surface multi-parameter comprehensive inversion method and device and a program product, pixels containing interference factors such as cloud, ice and snow are eliminated through a pixel screening method, interference of non-clear sky pixels on subsequent inversion is eliminated, and the accuracy of a final inversion result is indirectly improved. Furthermore, by performing auxiliary information matching on the initial clear sky pixel data set, key information such as atmospheric gas optical parameters, atmospheric profile and earth surface reflection model parameters is supplemented for the initial clear sky pixels, and inversion deviation caused by information loss is reduced. Furthermore, a priori estimation parameter set of the aerosol phase matrix, the aerosol and the ground surface multiple parameters is determined, a preset cost function is used as an inversion target, comprehensive inversion is performed on the aerosol and the ground surface multiple parameters by using a preset radiation transmission mode, direct comprehensive inversion of the aerosol and the ground surface multiple parameters is realized, the inversion precision is improved, and a DPC sensor is adapted.

This invention discloses a brightness temperature simulation method for ice and snow environment observation, belonging to the field of image processing technology. The method includes: Step 1, constructing a physical model of ice and snow layers using the SMRT model, and setting the electromagnetic theory model and ice and snow microstructure of the SMRT model; Step 2, calculating the surface emissivity ε corresponding to the uplink radiation; Step 3, acquiring mixed pixels, correcting the surface emissivity of the mixed pixels to obtain the emissivity of the mixed pixels; Step 4, using the RTTOV model combined with ERA5 atmospheric profile data and near-surface parameters to generate the simulated brightness temperature for Arctic ice and snow environment observation. This ice and snow environment observation brightness temperature simulation method utilizes the SMRT model to simulate the transmission process of microwave radiation in multiple layers of snow, sea ice, and other media, allowing for a better understanding of the physical characteristics of snow and its impact on microwave signals. By effectively coupling the SMRT and RTTOV models, more accurate model support can be provided for remote sensing inversion of Arctic ice and snow environment parameters.

The invention relates to the technical field of wireless communication, in particular to an optimized low-altitude condition radio wave propagation prediction method and device and a storage medium, and the method comprises the steps: firstly constructing a three-dimensional collaborative database fusing high-resolution terrain elevation, atmospheric profile and earth surface material information; based on a three-dimensional graphic obstacle recognition algorithm, a low-altitude propagation loss model or a ground propagation model is adaptively selected as a dominant prediction model by analyzing the shielding proportion of an obstacle on a propagation path to a first Fresnel region. And if the low-altitude model is selected, intelligently matching the corresponding diffraction loss sub-model according to the topographic features, and calculating the topographic diffraction loss. Meanwhile, the atmospheric attenuation loss is calculated in a layered manner, and the earth surface reflection loss is calculated by combining earth surface material information. And finally, carrying out superposition calculation. According to the invention, through multi-source data fusion, propagation mechanism intelligent discrimination and multi-physical effect cooperative calculation, the precision and adaptive ability of radio wave propagation prediction in a low-altitude complex environment are significantly improved.