126 results about "Backscatter coefficient" patented technology

The invention relates to the technical field of SAR (Synthetic Aperture Radar) image processing, and particularly discloses an SAR image terrain radiation correction method considering overlay and shadow regions, which comprises the following steps: acquiring an SAR image, a DEM (Digital Elevation Model) image and a source file in a research region; carrying out multi-view processing on the original SAR image, resampling the DEM image, and carrying out radiometric calibration; geometric positioning is carried out according to the RD model by adopting an indirect positioning method, lookup tables A and B are generated, and images are registered; detecting pixels of the overlay area and the shadow area; calculating an effective scattering area corresponding to each SAR pixel; and performing radiation correction according to the effective scattering area, repairing a back scattering coefficient value of the shadow area, and finally performing geometric correction. According to the method, the topographic effect caused by an SAR imaging mechanism and topographic relief is eliminated to a certain extent, the radiation correction effect of an area with large topographic relief is improved, and the corrected product can be widely applied to the fields of ground feature inversion and classification, deformation detection, target detection and the like.

The invention discloses a soil water content inversion method based on improved combination roughness, and relates to the field of remote sensing, and the method comprises the steps: synchronously obtaining a Sentinel-1 radar image and a Sentinel-2 optical image, and extracting different polarization backscattering coefficients, local incident angles and normalized water body indexes through preprocessing; the method comprises the following steps: generating a bare soil simulation backscattering coefficient data set, removing vegetation scattering contribution by using a water cloud model, obtaining a real bare soil backscattering coefficient, constructing a training set and a verification set containing actually measured soil water content, constructing a lookup table based on double models, and calculating the water content of the bare soil by minimizing a root-mean-square error between simulation and the real backscattering coefficient. Global search is carried out in a preset parameter space to determine an optimal earth surface root mean square height and a correlation length, a novel polynomial combination roughness is constructed, a physical correlation between the roughness and a backscattering coefficient is established, a dual-polarization empirical equation set is constructed, and simultaneous solution is carried out after parameters are optimized according to a criterion; according to the method, a more reasonable inversion result of the soil water content in a large range can be obtained.

The invention discloses a C-SAR satellite sea surface wind speed inversion method combining spatial features, and relates to the technical field of sea surface wind speed inversion, and the method comprises the steps: selecting C-SAR data and ERA-Interim wind field data; pre-processing the C-SAR data, and then carrying out time and space matching on the pre-processed C-SAR data and ERA-Interim wind field data; and inputting the matched C-SAR data and ERA-Interim wind field data into a double-branch feature fusion network model based on attention mechanism guidance, inverting the wind speed of the central pixel position of the sub-image, and then comparing the wind speed with the ASCAT wind field data and the ERA-Interim wind field data for verification. According to the method, a double-branch neural network structure is adopted, Branch1 is utilized to construct nonlinear features among a backscattering coefficient, an incident angle, a viewing angle, a sea surface wind speed and a wind direction, and Branch2 is utilized to excavate spatial features of C-SAR data and ERA-Interim wind field data; carrying out feature fusion on the nonlinear features and the spatial features by utilizing a channel attention mechanism module; the dynamic characteristics of C-SAR inversion of the sea surface wind speed are guaranteed, and the inversion precision is improved.

The invention discloses a soil humidity inversion method and device, a medium and equipment, and relates to the technical field of microwave remote sensing inversion. According to the method, the total backscattering coefficient, the normalized vegetation index, the leaf area index and the average leaf inclination angle of the electromagnetic waves of the target area pixel by pixel are obtained in combination with microwave remote sensing data and atmospheric apparent reflectance data of the target area; according to the normalized vegetation index, the leaf area index and the average leaf inclination angle, spatial clustering is carried out on vegetation, spatial heterogeneity of crop types is captured, and then based on the constraint that the surface roughness parameters and the vegetation parameters of the same vegetation type are the same and the soil humidity in the same surface space window is the same, the spatial heterogeneity of crop types is obtained. Through two-stage iteration inversion of the intra-class scale and the ground surface space window scale, the time dynamic state is adapted, the parameter calibration function of the in-situ soil humidity data is replaced, and the model parameters and the soil humidity can be determined without the in-situ soil humidity data. According to the invention, the accuracy and reliability of the soil humidity inversion method are improved.

The invention provides a winter wheat soil moisture inversion method and system based on an improved water cloud model, storage equipment and electronic equipment, and the method comprises the following steps: obtaining total backscattering characteristics VV and VH, an incident angle and a plurality of polarization characteristic parameters through employing a single-view complex image and a geocoding radiation correction image; calculating a vegetation index from the preprocessed optical image by applying a wave band calculation mode; an improved water cloud model method is applied to correct the radar backscattering coefficient affected by the winter wheat plants, the backscattering coefficient of the earth surface is obtained, and data redundancy is reduced; performing hyper-parameter optimization of the CNN network by applying a CPO algorithm; and constructing a CNN-CPO-RF model by using CNN and RF models to carry out soil moisture estimation. According to the invention, a more accurate soil moisture result can be obtained. According to the method, the robustness of farmland surface soil moisture inversion is remarkably improved, and a new technical means is provided for performing soil moisture inversion by using a satellite-borne SAR image.

The invention provides a distributed MIMO clutter echo simulation method based on GIS information, and the method comprises the steps: obtaining the GIS information of a geographic information system, and dividing a surface scatterer irradiated by an MIMO radar into a plurality of basic scattering units based on the GIS information; calculating slope distances, Doppler frequencies, terrain elevation values and land cover types of the plurality of basic scattering units to form a scene model element set; mapping a plurality of basic scattering units to a radar resolution unit by combining a backscattering coefficient empirical model and a resolution adaptive grid mapping method according to the scene model element set; dividing the radar resolution unit into a permanent scattering area and a dynamic scattering area according to GIS information; performing clutter statistical characteristic simulation processing on the permanent scattering region and the dynamic scattering region to obtain a clutter amplitude sequence; and performing echo analysis processing on the clutter amplitude sequence in a time-frequency domain to obtain clutter echo simulation data. And the simulation precision of clutter echo simulation data is integrally improved.

The invention discloses a spaceborne multi-wavelength laser radar detection cloud particle physical property inversion method. The method comprises the following steps: solving the altitude height and the surface height of each point of an echo signal; de-noising the echo signal; correcting the pre-processed signal to obtain an attenuated backscattering signal and identifying a cloud boundary; establishing a molecular transmittance lookup table, and calculating transmittance and atmospheric molecular parameters; the back scattering coefficient of the cloud is obtained through inversion according to the attenuation back scattering signal, the transmittance lookup table, the aerosol and cloud particle transmittance and the atmospheric molecule parameters; the optical thickness, the extinction coefficient and the like of the cloud are calculated by using the attenuation backscattering signal, the cloud particle and atmospheric molecule transmittance and the atmospheric molecule parameters; calculating the depolarization ratio of the cloud; calculating an integral backscattering coefficient and an integral depolarization ratio of the cloud by using the cloud boundary information; and identifying a cloud particle phase state. The method is characterized in that cloud boundary identification is carried out on spaceborne laser radar echo signals, and cloud optical parameters are solved accurately and efficiently.

The invention provides a method and a device for correcting a backscattering coefficient of a wide scanning synthetic aperture radar, and the method comprises the steps: determining a backscattering coefficient of each pixel in a to-be-processed image, and then determining a backscattering coefficient correction coefficient of the pixel through employing the data of a mode sea surface wind field, a geophysical model function and the like; and correcting the backscattering coefficient of the pixel by using the backscattering coefficient correction coefficient to generate a target backscattering coefficient corresponding to the pixel. According to the method and the device, the backscattering coefficients of the pixels are corrected, so that the amplitude band inconsistency between the SAR image strips in the wide-width scanning mode can be eliminated, the more uniform and higher-precision SAR image in the wide-width scanning mode can be obtained, and better data support is provided for subsequent processing.

The invention relates to a rice character parameter inversion method combining an optical-microwave collaborative model and a variational auto-encoder. The method comprises the following steps: acquiring multiband optical reflectivity, a microwave backscattering coefficient and observation geometric information of a research area; inputting the data into a multi-mode encoder, and encoding the data into probability distribution of a potential space; an optical and microwave physical model is used as a differentiable decoder, potential variables sampled from a potential space are received, and remote sensing data are simulated and generated; a self-supervised learning normal form is adopted, and end-to-end training is carried out on the whole frame by minimizing a composite loss function; a trained encoder is used as a rapid inversion model, vegetation biological physicochemical parameters are extracted from new remote sensing data, rapid inversion of the parameters is achieved, and uncertainty of the parameters is quantified. The rapid and accurate vegetation character parameter inversion method is established by fusing multi-modal data, a physical model and a deep learning technology, important reference is provided for rice growth monitoring and precision agriculture, and the method has popularization prospects.

The present invention provides a device deviation calculation method and an object measurement method capable of calculating a true extinction coefficient and a rear scattering coefficient of an object (object to be measured).　A device deviation calculation method according to the present invention includes a step (S12) for acquiring a Lidar signal, a step (S16) for calculating a straight line of an inclination -2α and an intercept ln (P0Cβ) fitting to the Lidar signal of the atmosphere in front of the object, a step (S18) for calculating Kα on the basis of the following Expression (1), and a step (S18) for calculating Kβ on the basis of the following Expression (2). (1) The inclination -2α = -2 × αt × Kα (where α is an extinction coefficient of the atmosphere at the time of the Lidar signal measurement, αt is an extinction coefficient of the standard atmosphere, and Kα represents a device deviation of an extinction coefficient of an observation device measuring the Lidar signal.) (2) The intercept ln(P0Cβ) = ln(βt × Kβ)

The invention relates to a forest biomass estimation method combining a polarization radar and a satellite-borne laser radar. The method comprises the following steps: preprocessing discrete footprint point data and polarimetric SAR data to obtain geographic position information and a polarimetric SAR backscattering coefficient; according to a random forest interpolation algorithm in combination with geographic position information and a polarized SAR backscattering coefficient, a nonlinear mapping relation among space coordinates, polarized SAR features and spaceborne laser radar parameters is automatically established through machine learning, and continuous spaceborne laser radar features are generated; and calculating the relative importance of all the satellite-borne laser radar features and the polarimetric SAR features with the forest biomass, sequentially selecting the features ranked in the top according to a relative importance ranking result to construct a forest biomass prediction model, and realizing forest biomass prediction by using the forest biomass prediction model. By adopting the method, high-precision forest biomass estimation can be realized.

The invention discloses a clutter generation method and device, and relates to the technical field of radar signal processing, and the specific technical scheme comprises the steps: obtaining the position information of a radar antenna, the position information of a clutter scattering block, and the antenna beam information; calculating phase information corresponding to the clutter scattering block according to the position information of the radar antenna and the position information of the clutter scattering block; calculating a backscattering coefficient of the clutter scattering block by using the antenna beam information and the position information of the clutter scattering block; calculating the amplitude of a clutter scattering block by using the backscattering coefficient and the phase information; constructing a transfer function of a clutter scattering block by using the amplitude, the backscattering coefficient and the phase information; performing convolution processing on the transfer function and the radar transmitting signal to obtain a clutter echo signal corresponding to the clutter scattering block; and accumulating the clutter echo signal corresponding to each clutter scattering block to obtain a clutter echo signal corresponding to the radar. Therefore, the accuracy of clutter simulation is improved.

The invention discloses a method and system for inverting the concentration of suspended solids in a complex water body through remote sensing, and belongs to the technical field of ecological environments. Comprising the following steps: integrating a remote sensing image and measured data to obtain a synchronous sampling point pair; analyzing the backscattering coefficient of the particulate matters, and determining the backscattering coefficient of the optimal wavelength position for reflecting the total suspended matter concentration through an iteration method; analyzing the particulate matter absorption coefficient, and determining the absorption coefficient of the optimal wavelength position for reflecting the total suspended matter concentration through an iteration method; according to the proportional relation between inorganic particles and organic particles in the water body, determining the weight of the absorption and scattering coefficient for estimating the total suspended matter concentration; and constructing a remote sensing estimation model of the total suspended matter concentration in the complex water body and applying the model to a remote sensing image. According to the method, the difference of the organic particulate matters and the inorganic particulate matters in the aspects of absorption and scattering characteristics is considered, and the method is suitable for a water body mainly containing algae, a water body mainly containing non-algae and a complex water body environment in which the water bodies and the non-algae are mixed, and has relatively high adaptability and accuracy.

The invention discloses a radio frequency tag backscattering modulation method and device based on impedance switching, which are applied to the technical field of radio frequency identification, and the method comprises the following steps: determining a backscattering coefficient of a radio frequency tag to be adjusted; performing frequency scanning on the working area of the radio frequency tag to be adjusted to obtain a multi-frequency reflection signal in the working area; performing multipath analysis and feature extraction processing on the multi-frequency reflection signals in sequence to obtain multipath reflection feature data; performing fusion processing on the multi-path reflection characteristic data and the backscattering coefficient to obtain a backscattering value; inputting the deviation angle of the radio frequency tag to be adjusted and the plurality of backscattering values into the constructed directional impedance optimization model to obtain a directional compensation coefficient; performing backscattering modulation processing on the to-be-adjusted radio frequency tag based on the directivity compensation coefficient to obtain an impedance switching parameter; and executing an impedance switching strategy corresponding to the impedance switching parameter. According to the method provided by the embodiment of the invention, reliable transmission of the radio frequency temperature measurement data can be ensured.

The invention relates to a rapid calculation method for a lake water surface roughness parameter, which comprises the following steps: acquiring a lake water surface height standard deviation and a surface slope initial value by using a sea wave spectrum model, and calculating an initial value of a related length parameter; and constructing a backscattering coefficient simulation model by taking the initial values of the height standard deviation and the related length, the lake temperature and the salinity as inputs, actually measuring a backscattering coefficient value by combining a radar remote sensing image, and solving the related length and the height standard deviation, namely a surface roughness parameter, by utilizing a least square method. By adopting the method disclosed by the invention, the lake water body surface roughness can be quickly calculated by utilizing radar remote sensing image data, and the algorithm has universality and stability in an inland lake; besides, large-scale and long-time-sequence lake water body surface roughness parameters can be obtained, data support is provided for lake water body physical property monitoring based on radar remote sensing image data, and the method has important significance on development of inland lake water body research based on microwave data.

The invention discloses a method for inverting a cloud condensation nucleus number concentration profile based on a laser radar and a neural network. The method comprises the following steps: collecting multi-wavelength laser radar data including a backscattering coefficient vertical profile, an extinction coefficient vertical profile, a laser radar ratio and a depolarization ratio; the method comprises the following steps: acquiring an atmospheric temperature profile and a water vapor mixing ratio profile which are in time-space synchronization with multi-wavelength laser radar data, performing grid alignment on a vertical height, and constructing a multi-channel input feature set for each preset target supersaturation ratio; and constructing a convolutional neural network profile inversion model, taking the multi-channel input feature set as input and the cloud condensation nucleus number concentration profile data as output, training the model, and obtaining the trained convolutional neural network profile inversion model. According to the method, the limitation of continuous, high-precision and high-temporal-spatial-resolution detection of CCN number concentration vertical distribution is overcome, and key data support is provided for the fields of weather forecast, climate models, artificial influence weather and the like.

The invention relates to a direct assimilation method and system for an aerosol attenuation backscattering coefficient, and relates to the technical field of data assimilation of atmospheric aerosol. The method comprises the following steps: based on a laser radar equation, constructing a nonlinear observation operator of a direct assimilation aerosol attenuation backscattering coefficient by adopting a Mie scattering theory; according to a three-dimensional variational assimilation theory and a nonlinear observation operator, constructing an assimilation model for solving an aerosol attenuation backscattering coefficient of an objective function minimum solution; acquiring assimilated laser radar aerosol observation data for preprocessing, and inputting the preprocessed observation data into an assimilation model; and operating the assimilation model, taking the minimum solution obtained by solving as an aerosol increment field, and superposing the aerosol increment field to a background field to obtain an analysis field of the aerosol. According to the method, direct assimilation of the first-level observation data of the laser radar aerosol is realized, more accurate aerosol initial distribution is provided for the mode after assimilation, and a more accurate aerosol analysis and prediction result can be obtained.

The invention provides a method and device for establishing a backscattering coefficient correction model, and the method comprises the steps: obtaining multi-source data comprising an observation value corresponding to a backscattering coefficient; performing space-time matching on the multi-source data, generating a data unit corresponding to each satellite observation pixel, and filtering the data units through a preset rule to obtain a reserved data set; outputting a simulation value through a physical model based on the reserved data set not including the observation value, constructing a training set based on the simulation value and the reserved data set, and using the observation value as a target variable of the training set; and training a machine learning model by using the training set, and performing grid search optimization on preset hyper-parameters in the machine learning model to obtain a final backscattering coefficient correction model. A random forest algorithm is adopted to effectively learn and fit a complex and high-order nonlinear relationship between a physical model simulation value and a satellite observation value.

The application discloses an impedance-switching-based radio frequency tag backscattering modulation method and device, and is applied to the field of radio frequency identification technology, and the method comprises the following steps: determining a backscattering coefficient of a radio frequency tag to be adjusted; performing frequency scanning on a working area of the radio frequency tag to be adjusted to obtain multi-frequency reflection signals in the working area; sequentially performing multi-path analysis and feature extraction processing on the multi-frequency reflection signals to obtain multi-path reflection feature data; performing fusion processing on the multi-path reflection feature data and the backscattering coefficient to obtain a backscattering value; inputting an offset angle of the radio frequency tag to be adjusted and the backscattering value into a constructed directional impedance optimization model to obtain a directional compensation coefficient; based on the directional compensation coefficient, performing backscattering modulation processing on the radio frequency tag to be adjusted to obtain an impedance switching parameter; and executing an impedance switching strategy corresponding to the impedance switching parameter. The method provided in the application can ensure reliable transmission of radio frequency temperature measurement data.

The invention discloses a space-borne polarization SMAP-R data gap filling method based on multi-polarization SAR data. The method comprises the following steps: acquiring SMAP-R, SMAP, GF-3, Sentinel-1, MODIS and SR TM 30m DEM data; preprocessing the data to obtain a total intensity reflectivity, a normalized Stokes parameter and a backscattering coefficient; unifying data spatial resolution and dividing data sets; building a data supplement method model of the multi-polarization SAR back scattering modeling SMAP-R polarization parameters based on integrated learning; verifying the parameter inversion performance by using an ANN model; and finally filling the missing grid data. According to the method, modeling is carried out on SMAP-R polarization data through SAR back scattering data, secondary inversion verification is carried out, SMAP-R spatial data missing is effectively supplemented, and the spatial resolution of inversion earth surface parameters is improved.

This invention discloses a chlorophyll concentration profile inversion method based on a physical information neural network, belonging to the field of physical information machine learning technology. It is used for chlorophyll concentration profile inversion, including acquiring marine optical profile observation data to form observation sample data, and performing fractional standardization on the observation sample data; constructing and training a physical information neural network model, destandardizing the predicted data, and constructing a physical constraint loss function; inputting the data to be inverted into the trained physical information neural network model, and calculating the chlorophyll concentration profile using the model's predicted values. This invention constructs a physical information neural network model and introduces a physical constraint loss function to ensure that the inverted absorption coefficient, backscattering coefficient, and irradiance profile conform to the light transmission law in water, avoiding physical anomalies generated by purely data-driven methods, and achieving cross-domain inversion from apparent optical quantities to intrinsic optical quantities without the need for step-by-step calculations or intermediate parameter estimation.

The invention belongs to the technical field of airport meteorological monitoring, and relates to an airport dominant visibility measurement method and system based on multi-source data fusion, and the method comprises the steps: 1) employing the backscattering coefficient, PM2.5 concentration and relative humidity of a laser radar as feature vectors, and carrying out the weather classification through a support vector machine classifier; 2) under different weather types, using a laser radar to obtain echo signals of the whole airport area, and using a point-type visibility meter to obtain point-type visibility; 3) preprocessing the echo signal and obtaining laser visibility based on the echo signal; 4) performing dynamic weight iterative optimization on the point-mode visibility, the laser visibility and the near visibility under different weather types through a dynamic weight multilayer sensor to obtain final visibility; and 5) performing dominant visibility judgment based on the final visibility, and determining the dominant visibility. Through multi-source data fusion and an intelligent algorithm, the problems of insufficient precision, poor environmental adaptability and low automation degree in the prior art can be solved.

The invention relates to the technical field of pipe network leakage identification, and particularly provides a water supply pipe network leakage identification method and system based on satellite monitoring and a medium, and the method comprises the steps: obtaining SAR satellite time sequence data of a target region; processing SAR satellite time sequence data to obtain backscattering coefficient time sequence variation and earth surface time sequence deformation information; humidity abnormal points are extracted based on the time sequence variation, and deformation abnormal points are extracted based on the deformation information; the humidity abnormal points and the deformation abnormal points are subjected to spatial superposition screening with a water supply pipe network buffer area, time sequence correlation analysis is carried out after screening, and suspected leakage points are obtained; and according to a multi-parameter decision rule, screening out points which simultaneously meet conditions of spatial proximity, humidity anomaly, deformation anomaly and time sequence synchronization, and taking the points as high-confidence suspected leakage points. According to the method, the change of the surface humidity is reflected by using the change quantity of the backscattering coefficient, and the humidity abnormity and the surface deformation are taken as a joint criterion, so that the accuracy of leakage point identification is greatly improved.

This invention discloses a method for retrieving global ocean optical attenuation coefficients based on remote sensing reflectance. It obtains the remote sensing reflectance at any wavelength below the sea surface and the ratios related to inherent optical quantities using satellite-provided remote sensing data. For clear seawater and relatively clear coastal seawater, the QAA_v5 algorithm is used to obtain the absorption and backscattering coefficients at any wavelength; for turbid coastal seawater, the QAA-RGR algorithm is used. Different models for solving the scattering coefficients are used for different sea areas. The global ocean optical attenuation coefficient is obtained by adding the absorption and scattering coefficients. This invention divides the global ocean into regions and provides models for solving the absorption and scattering coefficients for different regions. It can directly calculate the optical attenuation characteristics of different sea areas using satellite-provided data, improving the reliability of solving the optical attenuation coefficient and enabling dynamic monitoring of the variation of the global ocean optical attenuation coefficient.

The present invention discloses an aerosol lidar inversion method based on a dual-wavelength Mie scattering lidar, which belongs to the field of lidar technology and includes the following steps: SA1, forward modeling the first effective radius R eff A relationship table of the first extinction coefficient ratio, first backscattering coefficient ratio, and first lidar ratio of the dual wavelengths within the range; SB1, assuming the second lidar ratio of the dual wavelength, to obtain the second extinction coefficient ratio and the second backscattering coefficient ratio; S2, assigning the second extinction coefficient ratio to the first extinction coefficient ratio of SA1, substituting it into the relationship in step SA1 to invert and obtain the second effective radius; S3, obtaining a new dual-wavelength lidar ratio; S4, comparing the new dual-wavelength lidar ratio obtained in step S3 with the second lidar ratio assumed in step SB1. When it exceeds the expected error, replace the assumed second lidar ratio in step SB1, and repeat steps SB1, S2, S3, and S4; otherwise, the dual-wavelength lidar ratio obtained in step S3 is considered to be the most accurate value.

The invention discloses a glacier surface accumulated snow identification method using time sequence optics and SAR data. The method comprises the following steps: generating a monthly-scale cloudless NDSI composite image; constructing a wet snow-non-wet snow binary training sample set of spatial-temporal distribution, and constructing a monthly-scale wet snow distribution prediction model based on a convolutional neural network to perform spatial-temporal feature learning; obtaining a month-by-month wet snow distribution diagram of a corresponding year, and calculating and generating a glacier surface annual wet snow coverage frequency distribution diagram; in combination with backscattering coefficient characteristics of the time sequence SAR image, establishing an SAR image wet snow discrimination threshold dynamic calculation model by counting backscattering coefficient distribution of the time sequence SAR image, and extracting wet snow distribution of the multi-temporal SAR image; and for the obtained residual area outside the wet snow distribution area, terrain slope direction parameters are extracted through a digital elevation model, classification constraints are constructed in combination with accumulated snow seasonal characteristics, and fine distinguishing of dry snow and glacier ice is realized. According to the invention, the automation degree and classification precision of high mountain area accumulated snow classification are improved.

The invention provides a method for selecting calibration height layers of satellite-borne laser radars with different wavelengths, and relates to the field of atmospheric remote sensing. On the basis of SAGE III / ISS and reanalysis data, optical parameters of a stratosphere blue light wave band are accurately calculated by combining wavelength conversion and data fusion; comprising the following implementation steps: step 1), selecting SAGE III / ISS data and preprocessing the data; 2) correcting the refractive index of the aerosol, and carrying out stratosphere scattering calculation; 3) establishing a stratosphere aerosol optical parameter wavelength conversion model; step 4), calculating a stratosphere blue light aerosol backscattering coefficient and an atmospheric molecule backscattering coefficient; step 5), data space-time matching fusion; and step 6), calculating an aerosol scattering ratio and selecting a calibration height.

The invention discloses an SAR-based electrical equipment geological disaster monitoring and early warning method and system, and relates to the technical field of electrical equipment monitoring, and the method comprises the steps: obtaining multi-temporal SAR image data covering an electrical equipment geological region, and carrying out the preprocessing, and obtaining a backscattering coefficient image; for the processed SAR image data, carrying out edge detection by adopting an adaptive parameter improved IROEWA combined with an NMS algorithm, and extracting an edge feature map; according to the backscattering coefficient image, an earth surface dielectric constant image is obtained through inversion based on an SPM or IEM model, and a dielectric constant variation image is obtained through calculation; fusing the edge feature map and the dielectric constant variation image, extracting geologic body fracture features, and generating a fracture-equipment association map in combination with spatial distribution of power equipment; and generating early warning information according to the crack-equipment association map. According to the invention, the edge detection precision of the peripheral terrain of the power equipment and the early warning timeliness and precision are improved.

This invention discloses a machine learning-based method and system for identifying exhaust gas particles, relating to the field of image processing technology. By acquiring exhaust gas particle images, feature extraction is performed to obtain color cast features. Based on these color cast features, the exhaust gas particle images are color-corrected to obtain a color-corrected image. Particle distribution features are extracted from the color-corrected image using a target model. These particle distribution features are then fused with the color-corrected image to obtain an adaptive transmission map. The backscattering coefficient is calculated based on the adaptive transmission map, and the adaptive transmission map is enhanced based on the backscattering coefficient to obtain a final enhanced image. Exhaust gas particles are identified based on the final enhanced image. Through color correction, adaptive transmission map construction, and backscattering enhancement, the color accuracy and particle distribution visualization of exhaust gas particle images are improved, along with image clarity and detail, thereby enhancing the accuracy and reliability of particle identification.

The invention belongs to the technical field of synthetic aperture radar (SAR) image processing, and discloses an underwater vehicle wake SAR image data generation method and system, and the method comprises the steps: respectively constructing an underwater vehicle wake model based on a computational fluid dynamics (CFD) method and a random sea surface model based on a sea wave spectrum model; performing linear superposition on the underwater vehicle wake model and the random sea surface model by adopting a VOF-spectrum coupling interpolation superposition method to obtain a composite sea surface simulation model; based on a composite sea surface simulation model, a dual-scale electromagnetic scattering model is adopted to calculate a backscattering coefficient, tilt modulation, fluid modulation and speed bunching modulation are introduced in sequence, and finally speckle noise is added to generate a wake SAR image. According to the method, the physical characteristics of the wave wake in the underwater vehicle can be simulated more accurately, and the physical authenticity and accuracy of wake generation are remarkably improved; a simulation result which is high in accuracy and has real SAR image statistical characteristics can be obtained, and the engineering application value is remarkably improved.