19 results about "Microwave remote sensing" patented technology

The present application relates to the technical field of agricultural drought information monitoring, and more particularly to an agricultural drought monitoring and prediction system based on unmanned aerial vehicle microwave remote sensing and deep learning, which comprises: a microwave remote sensing data fusion subsystem that carries multi-frequency microwave sensors on an unmanned aerial vehicle; dynamic adjustment of feature weights based on crop growth stages; elimination of geometric deviations between unmanned aerial vehicle flight strips through time series registration, and construction of a three-dimensional drought feature field by fusing multi-temporal data; a deep drought diagnosis subsystem that inputs the three-dimensional drought feature field into a space-time convolution network, extracts field patch-level anomalies in the spatial domain, and captures drought evolution patterns in the time domain; a drought response subsystem that divides drought levels based on the diagnosis results of the space-time convolution network; generation of differentiated irrigation schemes in combination with farmland Internet of Things data; and comparison of actual irrigation effects with prediction results. The present application realizes an upgrade of the agricultural drought prevention and control mode from passive monitoring to active prediction, and from extensive management to precise regulation.

This disclosure provides an optimization method for microwave remote sensing target classification. In one specific embodiment, the method includes acquiring multiple target sample images of a region to be detected via a ground-based center; extracting the boundary point coordinates of the multiple target sample images and calculating the centroid coordinates of the multiple target sample images; constructing a target sample dataset; receiving and storing the target sample dataset via a satellite; observing the region to be detected, acquiring satellite observation intervals and observation images, reading the target sample dataset, and filtering out multiple target sample images located within the observation interval; classifying the observation images to obtain at least one classification result; calculating the matching degree between each classification result and the multiple target sample images located within the observation interval, and obtaining the matching result. This implementation further optimizes microwave remote sensing target classification under low memory and computing power conditions, improves classification accuracy, and solves the false alarm problem.

The application discloses a physical information neural network optimized soil carbon storage precise evaluation system, relates to the cross field of artificial intelligence and geoscience, and comprises a multi-source heterogeneous data intelligent fusion module which is used for realizing multi-source heterogeneous data fusion from satellite hyperspectral images, unmanned aerial vehicle laser radar point clouds, microwave remote sensing, ground sensors, electromagnetic and thermal sensing, optical and acoustic monitoring, metagenomic sequencing, XRD mineralogy, hydrology and DEM through multi-modal space-time alignment, multi-physical field coupling perception, biological-mineral map construction and dynamic hydrological topology construction, and outputting consistency feature matrix, hydrological driving factor matrix and carbon stability biological index. In the application, key physical, chemical and biological kinetic equations of soil carbon cycle are embedded in a deep learning framework, so that the interpretability, generalization ability and prediction precision of the model in a complex environment are improved, and unified modeling and layered consistency prediction of surface and deep soil carbon cycle processes are realized.

The application relates to a kind of MEO satellite machine BiSAR imaging methods based on improved equivalent single base, and relates to the field of microwave remote sensing imaging.The present application is to solve the problem of low precision and low efficiency of the existing satellite machine BiSAR imaging method.The present application comprises: establishing a non-stop MEO satellite machine BiSAR improved equivalent single base model based on the ground rectangular coordinate system;Using the non-stop MEO satellite machine BiSAR improved equivalent single base model to obtain the reflection echo of the MEO SAR transmitting linear frequency modulation signal, and then obtaining the closed expression of the echo signal two-dimensional spectrum;Using the compensation term in the closed expression of the echo signal two-dimensional spectrum to design a distance high-order compensation function, and correcting the distance error of the target;Using the compensation term in the closed expression of the echo signal two-dimensional spectrum to design a range-dependent compensation function, using the range-dependent compensation function and the echo corrected in step four to correct the phase error difference of the target at different azimuth positions, and obtaining a focused SAR image.The present application is used for satellite machine BiSAR imaging.

This invention relates to a soil complex dielectric hybrid model that integrates interfacial polarization and temperature competition mechanisms, comprising the following steps: (1) measuring clay content, temperature, and humidity in the soil, while simultaneously determining the microwave frequency of the desired remote sensing product, and calculating the maximum bound water content and free water conductivity; (2) obtaining the real part of the free water dielectric considering the MW interfacial polarization mechanism; (3) obtaining the imaginary part of the free water dielectric considering the bound water effect, the MW interfacial polarization mechanism, and the temperature competition mechanism; (4) obtaining the complex dielectric constant of bound water; (5) based on the complex dielectric constants of free water, bound water, soil parent material, and air, as well as the maximum bound water content, soil porosity, volume fraction of soil parent material, soil humidity, and the wet soil complex dielectric hybrid model, calculating the real and imaginary parts of the complex dielectric constant of wet soil. This invention provides a soil complex dielectric hybrid model with higher accuracy and a wider frequency band applicability, offering a new parameterization scheme for passive microwave remote sensing and in-situ soil moisture inversion.

This invention discloses a method and device for geological disaster prevention and control of power transmission and transformation tower foundations, belonging to the field of landslide detection technology. The method involves: real-time acquisition of optical remote sensing images, radar remote sensing images, microwave remote sensing images, and thermal infrared remote sensing images; overlaying optical and radar remote sensing images to generate slope topographic spatial distribution data; analyzing vegetation growth status in the optical remote sensing images to obtain vegetation spatial distribution data; overlaying the slope topographic spatial distribution data and vegetation spatial distribution data to obtain topographic-vegetation coupled spatial distribution data, thereby determining the landslide sensitivity level of each preset grid unit and generating landslide sensitivity level spatial distribution data; and retrieving water storage spatial distribution data based on microwave and thermal infrared remote sensing images to assess the stability of the tower foundation area where the power transmission and transformation equipment is located, obtaining landslide level distribution data to determine control measures. This invention solves the problem of low accuracy in landslide detection in existing technologies.

This invention discloses an intelligent identification method for ice avalanche hazards based on multi-source remote sensing and ground-based collaboration. The method includes: fusing optical remote sensing data, microwave remote sensing data, and lidar data as space-based observation data; combining ground meteorological station observation data and UAV-based measured data to form multi-source collaborative monitoring data; using an adaptive spatiotemporal fusion algorithm to reconstruct data gaps caused by cloudy snow cover in the multi-source collaborative monitoring data, generating high spatiotemporal resolution continuous snow cover dynamic monitoring data; constructing an ice avalanche hazard identification feature vector based on the snowline dynamic change index; standardizing the ice avalanche hazard identification feature vector and inputting it into a pre-trained ice avalanche hazard classification model to obtain intelligent identification results for ice avalanche hazard areas, forming a closed loop from data fusion and feature mining to intelligent early warning, significantly improving the accuracy and timeliness of ice avalanche hazard identification.

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 present application belongs to the technical field of data processing, and particularly relates to a forest microwave remote sensing ground surface dead combustible moisture content monitoring method and system, which comprises the following steps: constructing a soil background scattering prediction model by using historical time series data, and obtaining the soil background scattering coefficient under the current environment; constructing a dielectric and energy coupling balance equation considering the two-way attenuation effect, and introducing a water impedance attenuation function and a water gain scattering function; constructing a residual target function according to the total backscattering coefficient observed by a satellite and the soil background scattering coefficient, and using a constrained interval search algorithm to invert the ground surface dead combustible moisture content. The present application decouples the soil background and the dead combustible signal, solves the problem that the traditional linear additive model is invalid in a humid environment, and realizes the accurate monitoring of the ground surface dead combustible moisture content under complex water and heat conditions.

This invention relates to the technical field of passive microwave remote sensing of the atmosphere, and in particular to a method for passively inverting atmospheric wind fields based on regional network observations of microwave radiometers. The method includes: acquiring historical brightness temperature data and corresponding wind field reference data at the same time scale from multiple spatially distributed ground-based microwave radiometer stations within the same region; constructing a sample dataset after time matching, removing rainfall samples, and preprocessing; using a machine learning model, combining multi-channel brightness temperature data from multiple stations in a preset order as input features, and using wind field reference data as output, training to establish a mapping relationship between spatial differences in brightness temperature and atmospheric wind fields, thus obtaining a wind field inversion model; finally, inputting real-time observed brightness temperature data from multiple stations into the model to invert and obtain the regional atmospheric wind field profile data at the current time; realizing passive inversion of atmospheric wind fields based on microwave radiometer network observations, suitable for real-time monitoring of regional wind fields and meteorological operational applications.

The application discloses a kind of high-frequency microwave radiometer optical systems and its design method, belong to microwave remote sensing technical field.The system includes by servo motor driven main rotating parabolic mirror, two fixed parabolic mirrors respectively fixed at cold load and hot load interface, and cone wave corrugated horn antenna placed at the focal point of main mirror;Main rotating parabolic mirror can be scanned from 0 to 180 °, receive atmospheric radiation signal and the calibration parallel signal reflected by fixed mirror, converge to antenna.The application also provides a kind of general design method, through parallel selection, far-field integration method accurately calibrates antenna phase center, and ensure that it coincides with the focal point of main mirror.The application adopts combined parabolic mirror structure to realize the efficient coupling of multiple signal sources, has wide-range beam scanning capability, and replaces artificial experience alignment through systematic phase center calibration, significantly improves signal coupling efficiency and transmission stability, suitable for high-frequency radiometer development.

This invention belongs to the field of microwave radar image processing technology, and relates to a method for inverting significant wave height based on wave period. First, a significant wave height calculation model is established with the wave period as input. Then, microwave remote sensing images of the sea surface are acquired and preprocessed, such as coordinate transformation (e.g., images from shore-based sea observation radar and shipborne navigation radar) and noise suppression. Next, the wave period is inverted from the preprocessed images using spectral analysis techniques. Finally, the inverted wave period is substituted into the pre-established model to calculate the significant wave height. This invention, by introducing the wave period as a key intermediate variable, closely correlates image features with ocean wave physical parameters, overcoming the shortcomings of insufficient accuracy and stability in existing direct inversion methods. It provides a physically meaningful, highly accurate, and easily operational method for obtaining significant wave height.

The application relates to a mobile target inverse synthetic aperture radar imaging method, and relates to the technical field of microwave remote sensing. The application is used to solve the problem of poor quality of the existing ISAR imaging method. The mobile target inverse synthetic aperture radar imaging method disclosed by the application randomly generates the positions of a particle population, and updates the positions of each particle on the basis of a particle swarm algorithm, wherein the particles are radar scattering points; the particle population after the position update is divided on the basis of a hierarchical clustering method to obtain sub-populations; the particles in each sub-population are subjected to particle swarm optimization based on generalized Radon-Fourier transformation to obtain the global optimal positions in each sub-population; the echo signals of the radar scattering points are reconstructed by using all the global optimal positions in the population; and two-dimensional FFT processing is performed on the reconstructed echo signals to obtain the mobile target inverse synthetic aperture radar imaging result.

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.

The application provides a remote sensing load channel inter-registration device and a method thereof, which comprises: a metal bucket with an opening facing upwards; a one-dimensional rotating mechanism accommodated in the metal bucket; and a remote sensing load accommodated in the metal bucket and arranged on a rotating end of the one-dimensional rotating mechanism. After data collection is completed, data processing is performed to obtain a beam pointing based on a semi-physical simulation antenna pattern, compare each channel beam pointing obtained by the remote sensing load collected data, and correct the channel inter-beam pointing of the remote sensing load according to the data comparison deviation. The application establishes a remote sensing load channel inter-registration test device under real working conditions such as full aperture and full link. The moon is used as a reference source to actually measure the microwave beam pointing of the remote sensing load, correct the channel inter-deviation, ensure the effectiveness of the microwave remote sensing data, and verify the feasibility of the channel inter-registration scheme of the on-orbit remote sensing load. The application has the effect of realizing the channel inter-registration test of the remote sensing load.

This invention discloses a method for retrieving diurnal canopy water content by coupling optical and microwave remote sensing, belonging to the field of ecological and environmental remote sensing technology. The method includes: retrieving canopy water content data from optical images based on a canopy radiative transfer model; calculating vegetation optical thickness data based on a zero-order microwave radiative transfer model and a multi-channel collaborative inversion algorithm; acquiring vegetation optical thickness data, vegetation type, vegetation index, and meteorological data, and constructing a sample dataset after unifying the resolution; using the canopy water content data from optical images as ground truth labels, and combining the cumulative effect of rainfall and the lag effect of vegetation optical thickness, constructing an extreme gradient boosting machine learning estimation model; using this model to estimate the canopy water content in areas missing from the optical images, and filling in the missing areas to obtain a global diurnal, spatiotemporally continuous vegetation canopy water content dataset. This invention, employing the above method, achieves the complementary advantages of optical and microwave remote sensing, providing reliable technical support for monitoring vegetation moisture status.

This invention discloses a method for sea surface wind vector inversion based on optimal fitting of the wave spectrum of a shore-based GNSS-R system. Relating to the field of microwave remote sensing technology, it addresses the technical problems of existing nearshore GNSS-R technologies, such as ineffective wind direction inversion, difficulty in signal separation, and lack of a synchronous inversion mechanism. The method includes: acquiring direct and reflected signals from a shore-based receiver; separating specular reflection and multi-order Bragg scattering spectral components using the MUSIC algorithm combined with iterative Gaussian fitting; extracting spectral width features from the specular reflection spectral components and extracting the positive-to-negative peak ratio and normalized Doppler frequency ratio features from the Bragg scattering spectral components; constructing a wind speed cost function based on the spectral width features and minimizing this function to invert wind speed; and constructing a wind direction cost function based on the two features and minimizing this function to invert wind direction. This invention enables synchronous wind speed and direction inversion on a shore-based platform, requires no external auxiliary data, effectively extracts weak Bragg scattering components, and exhibits high nearshore inversion accuracy and stability.

The present application belongs to the field of active microwave remote sensing and microwave scatterometer, and particularly relates to a kind of two-dimensional look-up table acquisition methods of spaceborne microwave scatterometer slice segmentation, the method comprises: obtaining the three-dimensional distribution of Doppler frequency offset with satellite platform subsatellite point latitude, azimuth and pitch;And based on the three-dimensional distribution, obtain Doppler frequency offset;Again, using the relationship between echo frequency and Doppler frequency offset of spaceborne sector-beam conical scanning microwave scatterometer, obtain the three-dimensional distribution of echo frequency of spaceborne sector-beam conical scanning microwave scatterometer with satellite platform subsatellite point latitude, azimuth and pitch;With equidistant ground slice as target, obtain the position of equidistant ground slice on the echo frequency corresponding to pulse sampling point, obtain the three-dimensional look-up table of slice segmentation;Dimension reduction optimization is carried out to three-dimensional segmentation look-up table, and two-dimensional look-up table of spaceborne sector-beam conical scanning microwave scatterometer slice segmentation is obtained.

The application discloses a kind of evaluation methods, systems, electronic equipment and storage medium of spaceborne SAR uncontrolled positioning accuracy, it is related to microwave remote sensing SAR satellite technical field, alleviateed the problem that existing spaceborne SAR uncontrolled positioning accuracy evaluation technology cannot be accurately evaluated and decomposed inaccuracy.A kind of evaluation method, the position and speed of the satellite to be evaluated at initial time and the imaging position of target point are obtained;The position and speed of the satellite to be evaluated at beam crossing time are obtained, the slant range between the satellite to be evaluated and target point and doppler center frequency;The predicted imaging position of the target point is obtained by SAR geometric model;Based on imaging position and predicted imaging position, obtain a plurality of error proportion factors;Further, the total positioning error is obtained as the evaluation of spaceborne SAR uncontrolled positioning accuracy.The application is suitable for the field of spaceborne SAR overall design.