18 results about "Radiation transfer" 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 present application relates to a kind of improved radiation transmission model mangrove chlorophyll estimation method, comprising: the measured data of the tree height, crown width and leaf area index of mangrove are collected;Mathematical relationship between leaf area index and tree height, crown width is constructed;Simulated spectral data is generated as training set using improved radiation transmission model;The sensitive feature screening of mangrove functional traits is carried out;Random forest inversion model based on physical constraint is constructed;The present application estimates the chlorophyll content of mangrove using the random forest inversion model based on physical constraint.The beneficial effects of the present application are: the present application can effectively improve the precision of chlorophyll inversion, provide scientific basis for the fine inversion of mangrove chlorophyll, provide data support and decision-making reference for the protection and scientific management of mangrove.

The application discloses an aurora brightness inversion method based on satellite remote sensing data, and comprises the following steps: constructing an atmospheric transmittance lookup table based on VIIRS night light data, inverting DNB band surface reflectivity and calculating moonlight radiation brightness; establishing a radiation transfer equation containing aurora uplink radiation, surface reflected aurora radiation and surface reflected moonlight radiation, and solving aurora real brightness pixel by pixel; and removing abnormal high brightness signals and urban artificial light interference, and obtaining spatially continuous and stable annual aurora brightness distribution through annual scale synthesis and spatial interpolation smoothing. The application can realize wide-range, quantitative and long-time sequence aurora brightness monitoring, effectively overcome the shortcomings of ground-based observation and numerical simulation, and provide reliable technical means for spatial weather research and polar environment monitoring.

The application focuses on the field of agricultural informatization and remote sensing application technology, and particularly relates to a kind of alfalfa yield estimation method based on multispectral and physical constraint sample enhancement. The method first acquires unmanned aerial vehicle multispectral image data corresponding to the alfalfa growth period, and lays out field quadrats with equal area in the image coverage area, collects measured data of alfalfa hay yield, leaf area index, chlorophyll content and leaf equivalent water thickness, and constructs an initial sample set. Then, input each parameter data within a reasonable range, generate predicted multispectral samples that meet the physical consistency constraint using the radiation transfer model, to expand the sample space. The measured samples and predicted samples are fused to construct an alfalfa hay yield estimation training data set. Based on the training data set, a mapping model between unmanned aerial vehicle multispectral features and alfalfa hay yield is established, and the alfalfa hay yield is estimated through machine learning.

The application discloses a kind of ocean polarization lidar multiple scattering correction methods based on machine learning, comprising: based on polarization Monte Carlo method, construct ocean polarization lidar radiation transfer numerical model, set multiple groups of lidar hardware and water body environment parameters, obtain a large number of simulation data, utilize machine learning method to construct the polarization multiple scattering correction model of different detection system, and use non-uniform water body to verify the effectiveness of model.According to the multiple scattering model of the effective attenuation coefficient of the measured signal and the lidar of different platforms, the signal depolarization ratio and water body parameter are calculated, and the polarization multiple scattering correction model is brought into the polarization multiple scattering correction model combined with the system parameters of lidar, to obtain seawater backscattering depolarization ratio.Compared with the direct seawater backscattering depolarization ratio of lidar signal depolarization ratio, the error between the seawater backscattering depolarization ratio corrected by the method and the in-situ value is significantly reduced.

The application discloses a point source emission carbon dioxide dry air column concentration remote sensing inversion error estimation method, system, device, medium and product, relates to the atmospheric gas monitoring technical field, and the method comprises the following steps: determining observation scene parameters affecting carbon dioxide remote sensing observation based on radiation transfer theory; constructing a point source carbon emission observation scene, generating radiance simulation data set by using a radiation transfer model; using the step-by-step Sobol analysis method, the iteration combination of global sensitivity analysis and local sensitivity analysis is used to analyze the influence of observation scene parameters on carbon dioxide radiance, and the parameter sensitivity degree is determined; according to the maximum posterior probability estimation algorithm and the benchmark scene parameters, the column average kernel and the posterior error are calculated, the posterior inversion error of the point source emission carbon dioxide dry air column concentration is calculated, the inversion error estimation of the point source emission carbon dioxide dry air column concentration is completed, and the effective determination of the point source remote sensing inversion influencing factor type and the quantitative estimation of the influence degree are realized.

The application discloses a structured grid radiation simulation method based on optical path ordering, and belongs to the field of radiation transmission simulation. The method reads and analyzes a structured grid file, constructs a structured data set, discretizes a solid angle, calculates the projection value of the geometric center of all units on the unit vector of each discrete direction, sorts the units according to the projection value, and obtains the unit calculation sequence along the actual propagation path of the light. The radiation transfer equation in each unit is solved according to the sequence, the calculation result of the upstream unit is transmitted to the downstream unit through the connecting surface as the incident boundary condition, and the global input radiation is converged through iterative calculation. The application ensures that the calculation sequence is consistent with the real propagation path of the radiation energy by optical path physical ordering, eliminates the non-physical reverse radiation transmission, improves the calculation accuracy and convergence, and can directly process general engineering grid data, thereby being convenient for integrated application.

The application provides a leaf area index inversion uncertainty quantification method and system, and belongs to the technical field of remote sensing data processing, and comprises the following steps: extracting target spectral features according to a remote sensing image; generating a simulation sample set based on a radiation transfer model; combining model sensitivity and parameter prior calculation to obtain a first uncertainty degree derived from physical parameter transmission; repeatedly training and constructing a machine learning model set by using the simulation sample set; calculating a second uncertainty degree derived from algorithm randomness according to the statistical dispersion degree of model set prediction values; and obtaining a total uncertainty degree according to the first and second uncertainty degrees. By quantifying physical modeling errors and algorithm randomness errors respectively, end-to-end decoupling and comprehensive of the uncertainty are realized, so that the accuracy of the confidence evaluation of the leaf area index inversion result is improved.

The application discloses a kind of soil-vegetation synergic interaction radiation transmission model construction method and system, belong to soil remote sensing monitoring technical field.The method includes: obtaining dynamic parameter, the parameter of target area crop is output soil state parameter and vegetation state parameter by running crop growth model;Parameter conversion is carried out by calling mechanism constraint transfer operator;Coupling radiation transmission model, and based on the sensor response function convolution method of radiation transmission mechanism, the hyperspectral reflectance data output by the canopy radiation transmission module is smoothed, and the equivalent reflectivity of each band is calculated by numerical integration;Output soil-crown complex mixed reflection characteristic data.The application breaks through the bottleneck problem of traditional radiation transmission model in soil organic carbon remote sensing monitoring under vegetation coverage scene, and finally achieves significant technical effect in spectral simulation accuracy, cross-platform applicability, scene generalization ability and application support value.

The application belongs to the technical field of agricultural remote sensing, and particularly relates to a method and system for inverting total photosynthetic area index of rape, comprising: constructing MLMSTM of rape based on vector radiation transfer theory to obtain HH, VV, VH and HV total backscattering coefficients and plant layer backscattering coefficient; determining the corrected MLMSTM according to Sobol parameter sensitivity analysis; determining the microwave scattering ratio of each pixel in the Radarsat-2 image region based on spatial interpolation method according to the corrected MLMSTM; and inverting the total photosynthetic area index TPAI of rape by using the trained machine learning model based on the microwave scattering ratio of each pixel. The method and system focus on the vertical layering scattering characteristics of rape at different phenological stages, realize high-precision inversion of rape canopy parameters, and provide technical support for rape growth monitoring and yield estimation.

The application discloses a kind of coupling optical and microwave remote sensing's daily scale canopy water content inversion method, belong to ecological environment remote sensing technical field, including: based on canopy radiation transfer model inversion optical image canopy water content data, based on zero-order microwave radiation transfer model and multi-channel collaborative inversion algorithm calculation vegetation optical thickness data;Obtain vegetation optical thickness data, vegetation type, vegetation index and meteorological data, after uniform resolution, construct sample data set;With optical image canopy water content data as true value label, combined with rainfall cumulative effect and vegetation optical thickness lag effect, construct extreme gradient boosting machine learning estimation model;The canopy water content of missing area of optical image is estimated using the model, and the global daily scale, spatiotemporal continuous vegetation canopy water content data set is obtained after filling up.The application uses the above method, realizes the complement of optical and microwave remote sensing advantage, provides reliable technical support for vegetation water condition monitoring.

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.

Embodiments of the present application provide a satellite remote sensing instrument radiation calibration method, device, equipment and storage medium. Including: obtaining a first radiation calibration coefficient set; the first radiation calibration coefficient set is a set of radiation calibration coefficients obtained based on a preset radiation source arranged at the front end of the optical path of the satellite remote sensing instrument; obtaining a second radiation calibration coefficient set and a non-radiation transfer component temperature value; the second radiation calibration coefficient set is a set of radiation calibration coefficients obtained based on a preset black body arranged between internal optical path components of the satellite remote sensing instrument; the non-radiation transfer component temperature value is the temperature value of a non-radiation transfer optical path component inside the satellite remote sensing instrument that does not participate in radiation transfer when observing the preset black body; using the second radiation calibration coefficient set and the non-radiation transfer component temperature value as training samples, and using the first radiation calibration coefficient set as a training label to perform model training, to obtain a calibration coefficient conversion model. The present scheme can improve the accuracy of radiation calibration.

The application provides a geological disaster full-spectrum remote sensing response simulation method coupling a surface process model and a three-dimensional radiation transfer model, and belongs to the technical field of remote sensing and disaster simulation. The method comprises the following steps: firstly, acquiring a digital elevation model, disaster elements and prior knowledge; secondly, constructing a terrain grid and registering, combining with basin analysis to calculate disaster frequency and erosion deposition, and generating a surface change DEM before and after disaster occurrence; then inputting the change DEM into a three-dimensional radiation transfer model, constructing a multi-temporal scene and inputting component spectral parameters, carrying out full-process simulation, and generating a remote sensing reflectivity image covering visible light to microwave full spectrum; finally, verifying the accuracy by using real data and outputting the result. The application realizes physical coupling of surface dynamics and radiation transfer processes, can effectively simulate the comprehensive remote sensing signals generated by the surface morphology and structure changes caused by disasters, and provides high-fidelity, multi-temporal data support for disaster mechanism research and monitoring and early warning.

This invention relates to the field of electromagnetic compatibility technology, and particularly to a method, device, electronic device, and storage medium for predicting far-field radiation from a communication power supply. The method includes: analyzing the common-mode noise propagation path of the communication power supply and establishing a dual-excitation-source radiation model containing both input port and output port common-mode excitation sources. This application achieves separate analysis of the radiation contributions from the input and output ports by establishing a dual-excitation-source radiation model; improves the accuracy of key parameters by obtaining the impedance parameters of each port separately and accurately converting the excitation source voltage using a sampling measurement circuit; further, it obtains the maximum radiation transfer function through full-angle simulation to ensure prediction coverage of the worst-case scenario; finally, it calculates the total radiation prediction value by integrating the above parameters, thus forming a complete and reliable prediction process that significantly improves the accuracy and practicality of far-field radiation prediction.

The present application provides a kind of sea surface foam thickness remote sensing inversion method and system, for the L-Ka dual-band of sea surface foam coverage, foam thickness is cooperatively inverted, from the multi-band microwave radiation transmission characteristics, utilize the radiation characteristic difference of foam layer under different frequencies, establish the foam brightness temperature prediction model based on microwave band, the model considers the emission and scattering characteristics of the medium in foam layer, the emissivity of different thickness foam layer is calculated using radiation transfer equation.Then, the quantitative relationship between foam thickness and sea surface wind speed is established, by coupling sea surface foam model and wave spectrum model, sea surface foam coverage, thickness inversion is realized.The method and system provided by the present application can break through the limitation of traditional hydrodynamic model, realize large-scale, high spatio-temporal resolution observation of sea surface foam thickness, provide a new observation method for in-depth understanding of sea-air interface dynamics process.

The present application relates to a kind of atmospheric parameter retrieval model training method and prediction method based on satellite radiation brightness temperature, training method includes: the multi-channel brightness temperature data of first stage, multi-channel attribute and observation geometric parameter are as model input, pressure layer parameter is as query item to obtain the first predicted atmospheric parameter of observation point in different pressure layer;According to the loss update model to be trained that is constructed by first predicted atmospheric parameter and label to obtain pre-training model;Based on the second stage of model input and query item to obtain second predicted atmospheric parameter and according to second predicted atmospheric parameter, the multi-channel attribute of second stage and observation geometric parameter, pass through the multi-channel simulated brightness temperature that is obtained by forward radiation transfer model;According to the consistency loss of brightness temperature that is structured according to multi-channel simulated brightness temperature and the multi-channel brightness temperature data of second stage and according to the loss update pre-training model that is constructed by second predicted atmospheric parameter and label to obtain atmospheric parameter retrieval model.The above method can realize accurate atmospheric parameter prediction.