17 results about "Brightness temperature" patented technology

The present application relates to a kind of sea ice surface snow depth combined estimation method, based on the characteristics that microwave radiometer brightness temperature decreases with the increase of sea ice surface snow depth, FY brightness temperature data is used to calculate the brightness temperature of open water at different frequencies, then the brightness temperature of sea ice surface of 10 sub-channels and 20 polarization gradient rate characteristics are calculated using open water brightness temperature and FY brightness temperature data.The OIB airborne data is gridded, and the daily average OIB snow depth of 12.5 km resolution is obtained.The correlation between 30 brightness temperature characteristics and OIB snow depth is calculated, and the linear regression model and machine learning model of one-year ice and multi-year ice surface snow depth estimation are established by selecting 5 sub-features with high correlation, thereby developing the combined estimation model of snow depth.The above is the sea ice surface snow depth combined estimation method proposed by the present application, which improves the estimation accuracy of polar sea ice surface snow depth.

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 application provides a sub-band brightness temperature-based synthetic aperture radiometer radio frequency interference detection method and system, which comprises the following steps: sequentially ordering all brightness temperature data along sub-bands; traversing all sub-bands, performing fixed threshold detection on each sub-band brightness temperature data, and marking the detected radio frequency interference as abnormal; traversing all sub-bands, performing two-dimensional spatial radio frequency interference detection on each sub-band brightness temperature, and marking the detected radio frequency interference as abnormal; centering each sub-band brightness temperature, and then performing threshold detection on the absolute value of each sub-band centered brightness temperature data to obtain sub-band brightness temperature after radio frequency interference suppression. The application has the advantages that it can be embedded in a real-time radio frequency interference detection module of a satellite-borne synthetic aperture radiometer, the brightness temperature data of multiple sub-bands are jointly processed, it has the advantages of simple steps and strong detection capability, and it can effectively detect most radio frequency interferences presented by sub-band brightness temperature.

This invention discloses a method for enhancing the brightness temperature resolution of a microwave radiometer, along with related devices, equipment, and storage media. It relates to the field of spaceborne microwave radiometer technology and aims to solve the problems of insufficient brightness temperature resolution, sidelobe-induced reconstruction distortion, and the difficulty in balancing resolution and stability in spaceborne real-aperture microwave radiometers. The method includes: acquiring relevant data; constructing a convex optimization model with sidelobe suppression constraints and solving for initial weights; iterating with the initial weights, adaptively updating until a termination condition is met, and outputting the optimal weights; using the optimal weights to reconstruct the original brightness temperature data, outputting a result with enhanced resolution and no obvious oscillation artifacts. This invention requires no hardware modification, balances sidelobe suppression and resolution through algorithmic optimization, takes into account both accuracy and stability, adapts to the real-time requirements of spaceborne systems, provides support for meteorological and oceanographic monitoring, and discloses corresponding devices, equipment, and storage media.

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 radiometer multi-sensor data fusion preprocessing method for an embedded platform and relates to the technical field of embedded measurement. The method comprises the following steps: S1, controlling a scanning mechanism to perform a scanning motion; S2, in a back-scan stroke, calculating absolute gain estimation of a first frequency band receiving channel based on cold space brightness temperature radiation, and in an observation stroke, obtaining brightness temperature observation values of the first frequency band receiving channel and a second frequency band receiving channel based on scene brightness temperature radiation; S3, in the observation stroke, taking the absolute gain estimation of the first frequency band receiving channel as real-time gain of the first frequency band receiving channel, and setting real-time gain of the second frequency band receiving channel as a product of the real-time gain of the first frequency band receiving channel and a pre-calibrated inter-frequency transfer coefficient; and S4, periodically extending the back-scan stroke to calculate absolute gain estimation of the second frequency band receiving channel, and correcting the inter-frequency transfer coefficient based on a deviation of the absolute gain estimation of the second frequency band receiving channel and the product.

The present application belongs to the technical field of satellite application, and particularly relates to a space-based synthetic aperture radiometer external calibration method, system, device and medium based on stable external targets, which comprises the following steps: first, constructing a reference brightness temperature dataset based on a reference satellite; then, constructing an average deviation set of all sampling points in a non-aliasing field of view based on an observation brightness temperature sample set of a satellite to be calibrated and the reference brightness temperature dataset; and finally, linearly interpolating the average deviation set to the gridded brightness temperature data of the satellite to be calibrated to complete the external calibration of the space-based synthetic aperture radiometer in the antenna coordinate system; the system, device and medium are used to realize the method; the present application has the effect of reducing the observation error of the space-based synthetic aperture radiometer by only relying on data processing, and has a certain flexibility on the premise of adapting to the synthetic aperture radiometer architecture.

The application discloses a position adaptive method for microwave brightness temperature fast simulation, and relates to the technical field of remote sensing radiation transmission and artificial intelligence. The application adopts a position adaptive U-Net structure, while keeping the advantages of cross-scale feature fusion and skip connection of an encoder-decoder, introduces an LC non-shared convolution layer before the decoding end output, establishes a position-radiation response relationship in units of pixels, and thus significantly enhances the adaptation capability to complex land surface spatial heterogeneity and non-stationarity without explicitly introducing static priors such as surface emissivity and vegetation type. The method of the application takes meteorological variables and observation viewing angle parameters as inputs, takes satellite microwave brightness temperature as a supervision signal, is trained and verified based on the adopted L1 index, and obtains optimal weights by using a learning rate adaptation and an early stopping strategy. The application significantly reduces the processing complexity, improves the spatial structure consistency, and especially performs outstandingly in scenes such as abrupt slope direction, dry-wet transition zone and scanning edge angle.

The application belongs to the technical field of processing of satellite-borne microwave radiometer remote sensing brightness temperature, and discloses a deep learning satellite-borne microwave radiometer remote sensing brightness temperature super-resolution reconstruction method, which comprises the following steps: constructing a microwave brightness temperature super-resolution data set; constructing a microwave brightness temperature super-resolution model MW-SRUNet; the encoder part performs up-sampling on the original brightness temperature through deconvolution, extracts the earth surface feature brightness temperature information through convolution and pooling, and generates a feature matrix; the decoder part restores the brightness temperature to the size of the original brightness temperature through multiple deconvolutions; a self-defined loss function is used for training the model in the area where the earth surface brightness temperature changes sharply, the influence of abnormal values on the model training is reduced, a self-defined evaluation function is used for evaluating the model training process, and the super-resolution performance of the model is intuitively understood; and the output of the model is the reconstructed high-resolution microwave brightness temperature. The application solves the problem of low microwave brightness temperature resolution, and realizes 4 times super-resolution reconstruction of the microwave brightness temperature.

The application discloses a kind of based on Himawari-8 time series data multi-band joint brightness temperature prediction method, it is related to the field of forest fire monitoring, including cutting Himawari-8 satellite remote sensing data;Read the brightness temperature data of normal image element in Himawari-8 satellite remote sensing data;The brightness temperature data of the same image element before time t certain time sequence length is as input sequence;Brightness temperature data at time t is as label, as true value and prediction value are compared;After feature normalization processing is carried out to the extracted time series brightness temperature data, as the input of model, the defined label is as the output of model, these data are as training data, and the prediction model is trained;When actually predicting, first, clear sky image element is screened out, the time series brightness temperature data of these clear sky image element is as the input of prediction model, then the trained time series prediction model is used to predict brightness temperature.The application compared with simple average method, brightness temperature prediction result error is smaller, can reach the purpose of accurately predicting brightness temperature data.

This application relates to the field of microwave radiation reference technology, and particularly to a method and system for selecting uniform and stable scenes for spaceborne microwave radiation reference transfer. The method includes: based on brightness temperature observation data from a reference spaceborne microwave radiometer, performing spatial projection, temporal grouping, and atmospheric condition screening on candidate surface scenes; selecting a surface scene from the screened candidate surface scenes that meets preset conditions as the target surface scene for spaceborne microwave radiation reference transfer; wherein, the preset conditions characterize the spatial uniformity and temporal stability of the brightness temperature. The method and system for selecting uniform and stable scenes for spaceborne microwave radiation reference transfer provided by this invention can more clearly screen the reference scenes used for transfer, ensuring that the transferred scenes have spatial uniformity and temporal stability.

This invention discloses a microwave radiometer radiofrequency interference suppression method based on a combination of the data domain and brightness temperature domain. It primarily addresses the problem of degraded imaging performance caused by abnormal brightness temperature points during suppression in the original visibility data domain in existing technologies. The implementation scheme is as follows: Visibility data is obtained through complex correlation, and an optimized weight vector is calculated based on a selected optimization model; the visibility data and the optimized weight vector are weighted and summed to obtain a brightness temperature estimation image after one suppression step; peak points are found in the brightness temperature estimation image, and their status as brightness temperature anomalies is determined by an image threshold. If a peak point is found, its brightness temperature is suppressed until it falls below the brightness temperature anomaly threshold, and the brightness temperature estimation image is updated; this process of finding peak points is repeated until the entire brightness temperature estimation image falls below the image threshold, thus completing the suppression of microwave radiofrequency interference. This invention eliminates abnormal brightness temperature points near null regions, improves imaging quality, and can be used for integrated aperture passive microwave radiation measurements.

This invention discloses a method for correcting the inversion of surface temperature and humidity from remote sensing satellites by incorporating surface roughness parameters. The method includes the following steps: acquiring multi-channel observed brightness temperature data, a set of surface roughness parameters, and auxiliary meteorological data from a passive microwave remote sensing satellite; calculating initial values ​​of surface temperature and soil moisture based on the observed brightness temperature data using a benchmark inversion model that does not consider surface roughness details; constructing a radiative transfer forward model incorporating the set of surface roughness parameters, and using the forward model to simulate and generate simulated brightness temperature data corresponding to the current estimated values ​​of surface temperature and soil moisture; performing iterative optimization with the goal of minimizing the difference between the observed brightness temperature data and the simulated brightness temperature data until a preset convergence condition is met, thereby obtaining the corrected surface temperature and soil moisture.

This invention discloses a method for constructing a space brightness temperature radiation model for on-orbit satellite antennas, specifically in the field of remote sensing measurement. It completes multi-coordinate system transformation of antenna radiation patterns; preprocesses cold-space brightness temperature mapping data, and constructs a cold-space radiation background database by combining piecewise binary linear interpolation and linear fitting. Based on brightness temperature thresholds, the Milky Way region is identified, and the upper and lower boundaries are solved by fitting the center line of the Milky Way strip using weighted least squares, thus establishing a Milky Way brightness temperature strip model. A land-sea segmentation model is used to classify land and sea using latitude and longitude coordinates, generating surface brightness temperature data. A standardized surface brightness temperature background database is constructed through interpolation and surface fitting. The spatial pointing of each radiometer beam is determined; surface-pointing beams are matched with surface brightness temperature data, while non-surface-pointing beams use cold-space background data to solve for the spatial brightness temperature of each beam, generating a satellite antenna background radiation value vector. The antenna brightness temperature integral effect is numerically calculated using the brightness temperature integral formula, completing the overall construction of the space brightness temperature radiation background model.

The application discloses a kind of methods for soil moisture inversion of active and passive microwave combination, according to Mironov model, Zhao model, ω-τ model, Oh model, water cloud model forward model jointly simulating passive microwave brightness temperature and active microwave backscattering, by constructing active microwave backscattering and passive microwave brightness temperature observation and its forward model simulation value between active and passive joint cost function, utilize the meta-heuristic optimization algorithm based on Newton-Raphson method minimizes the cost function to give the best 1km scale soil moisture estimation value under the heterogeneous underlying surface;By introducing adaptive weight mechanism, according to the normalized minimum simulation error of active and passive channels, dynamic weight allocation is realized, the effective characterization of the spatiotemporal dynamic change of soil moisture is realized, and the precision and robustness of soil moisture inversion under complex surface, vegetation state and seasonal change environment are improved.

This invention provides a method and system for generating products for a circular array solar radio imaging telescope, comprising: generating raw complex visibility function data and raw solar radio spectrum data based on the output signals of each antenna element of the telescope; obtaining calibrated data through multi-dimensional calibration of channels, phase, the entire system, and the spectrum; generating a solar radio brightness temperature image based on the calibrated complex visibility function data, and generating two types of fast-view products by combining the calibrated spectrum data; performing quality inspection on all data products, and classifying and storing qualified products after encapsulation according to standardized naming rules and metadata specifications. This invention achieves end-to-end processing from raw signals to standardized products, balancing data accuracy, real-time performance, and quality controllability, fully complying with relevant technical specifications, and adapting to the high-resolution observation requirements of the telescope.

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.