80 results about "Backscatter coefficient" patented technology

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 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 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.

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 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 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.

This invention relates to a Bayesian estimation-based method for suppressing cross-polarization SAR noise, comprising: acquiring raw cross-polarization data and preprocessing it; acquiring noise vector information corresponding to the cross-polarization data and stripe boundary information of each sub-band; calculating the noise scaling factor of the sub-band based on the Bayesian method; calculating the power balance factor of the sub-band; and subtracting the reconstructed two-dimensional noise field from the backscattering coefficient of the raw cross-polarization data to achieve denoising. The beneficial effects of this invention are: it reads and calculates noise vector information from data products, obtains noise scaling factors through Bayesian estimation, and scales the noise vector to achieve the purpose of suppressing azimuth noise.

The application discloses a water body chlorophyll concentration profile inversion method and a laser radar detection method, wherein the inversion method comprises the following steps: obtaining laser radar detection echo signals at a specific depth in a target area, which comprises a fluorescence channel and a Raman channel; expressing a backscattering signal profile of the fluorescence channel as a function comprising a fluorescence backscattering coefficient; expressing a backscattering signal profile of the Raman channel as a function comprising a Raman backscattering coefficient; normalizing the backscattering signal profile function of the fluorescence channel with the backscattering signal profile function of the Raman channel to obtain a normalized signal; inverting the fluorescence backscattering coefficient based on a perturbation method; inverting a chlorophyll fluorescence absorption coefficient at a specific laser wavelength based on the inverted fluorescence backscattering coefficient; and inverting a chlorophyll concentration profile in the water body based on the inverted chlorophyll fluorescence absorption coefficient. The inversion method can invert the water body chlorophyll concentration profile from one measurement.

The present application belongs to the technical field of marine remote sensing, and particularly relates to a kind of full wind speed inversion methods based on spaceborne microwave active and passive combination physical statistics network, comprising the following steps: obtaining the brightness temperature data and backscattering coefficient of microwave radiometer and scatterometer, and performing data quality control;Construct sample data set;Load physical statistics constraint deep learning inversion algorithm model for training;Physical statistics constraint deep learning inversion algorithm model includes: input layer, network layer, physical layer, output layer;Network layer is composed of residual neural network, and physical layer is composed of Log formula of microwave radiometer and Log formula of microwave scatterometer to form microwave statistical matrix P matrix, which is a nonlinear matrix, and provides physical statistics constraint for the whole model;Output the wind speed of current full sea area.The present application effectively solves the problem of lack of physical support in the process of deep learning in marine parameter inversion, and provides high-quality and reliable wind speed products suitable for full wind speed range.

The invention provides a laser radar satellite inversion method and system for optical parameters of background aerosol, and the method comprises the steps: carrying out the preprocessing of satellite-borne laser radar calibration data, and obtaining a vertical channel signal, a parallel channel signal and a hyperspectral channel signal of the background aerosol; performing day and night backscattering coefficient inversion on the vertical channel signal, the parallel channel signal and the hyperspectral channel signal by adopting different denoising strategies to obtain a night backscattering coefficient and a final backscattering coefficient; performing noise reduction processing on the accumulated atmospheric optical thickness corresponding to the night signal to obtain a night extinction coefficient; constructing a laser radar ratio climate field according to the night backscattering coefficient and the night extinction coefficient; and multiplying the climate field by the final backscattering coefficient through the laser radar to obtain a final extinction coefficient. By introducing an innovative multi-scale denoising and systematic deviation correction technology, the low signal-to-noise ratio challenge is effectively overcome, and the inversion precision is remarkably improved.

The application discloses a global average sea clutter near real-time estimation method, system, device and storage medium, including the following steps: obtaining multi-source satellite-borne radar historical backscattering coefficient data, and performing time and space matching and binning on the ERA5 wind field; an average sea clutter estimation model is established according to a geophysical model function fitting; the satellite-borne microwave scatterometer wind field is corrected in deviation and fused with a numerical weather prediction wind field, so that a high-coverage, continuous near real-time global sea surface wind field is obtained; the relative azimuth is calculated in combination with radar observation parameters and fused wind direction, and global average sea clutter data is obtained by using the model; the application improves the precision and application range of sea clutter estimation under the condition of medium and small incident angles, provides a high-quality input wind field through a multi-source wind field fusion technology, realizes rapid, stable and near real-time estimation of global average sea clutter, has high timeliness and business feasibility, and can meet the needs of near real-time sea clutter monitoring and ocean remote sensing application.

The present application relates to the technical field of satellite calibration, and in particular to a SAR satellite absolute radiation calibration method, device, equipment and storage medium, wherein the SAR satellite absolute radiation calibration method comprises: determining a natural distribution target as a calibration field; imaging the calibration field respectively to obtain a calibrated SAR image and a medium-high orbit SAR image; establishing a corresponding relationship between noise power and an incident angle, and removing noise power from the calibrated SAR image and the medium-high orbit SAR image based on the corresponding relationship; performing geometric consistency processing; calculating the backscattering coefficient of the calibration field; and calculating the calibration constant of the medium-high orbit SAR satellite to be calibrated. The above method solves the technical bottleneck of difficulty in arranging a large-size corner reflector outside the field and inability to cover the full field of view of large-width imaging by introducing a calibrated SAR satellite as a radiation reference and combining it with the natural distribution target.

The invention relates to the technical field of agricultural remote sensing and information, and particularly discloses a method and system for simulating a radar backscattering coefficient of a rice field in a heading period, and the method comprises the steps: obtaining geometric and physiological parameters of the rice field, and synchronously and actually measuring radar data; dividing the canopy into a spike leaf mixing layer, a stem leaf mixing layer and an underlying surface layer; five scattering mechanisms of ears, leaves and stems are decomposed; calculating component complex permittivity based on the model; constructing a fine three-dimensional geometric model of the ears, the leaves and the stems; obtaining a scattering matrix of each component through electromagnetic simulation; calculating an interlayer attenuation coefficient and a propagation constant by using the forward scattering matrix; calculating phase deviations under different scattering paths; and finally, performing coherence superposition on the backscattering contributions of the components in combination with phase deviation to obtain a total backscattering electric field and a coefficient. Through combination of component-level fine electromagnetic simulation and a scene-level radiation transfer theory, high-precision and high-efficiency scattering simulation is realized, and remote sensing monitoring of rice is effectively supported.

This application provides a method for coarse and fine mode aerosol parameter inversion based on hyperspectral resolution lidar. The method simultaneously receives elastic scattering signals from the main channel and depolarization channel of aerosols, as well as molecular Rayleigh scattering signals, using a lidar system. Based on signal inversion, the total backscattering coefficient and total depolarization ratio of the aerosols are obtained. By clustering historical data, the characteristic depolarization ratios of coarse and fine modes are determined, along with the lidar ratio, to construct a multivariate hybrid model, achieving separation of the coarse and fine mode backscattering coefficients. The extinction coefficients of coarse and fine modes are calculated using the lidar ratio, and further integration yields the optical thickness of the coarse and fine modes. The coarse and fine mode aerosol parameter inversion method based on hyperspectral resolution lidar provided in this application does not require lidar ratio assumptions, possesses continuous all-day observation capability, and effectively overcomes the limitations of traditional passive remote sensing with day-night interruptions and the low signal-to-noise ratio of Raman lidar during the day.

This invention relates to the field of SAR image processing technology, specifically disclosing a SAR image topographic radiometric correction method that takes into account overlapping and shadowed areas. The method includes acquiring SAR images, DEM images, and source files within the study area; performing multi-view processing on the original SAR images, resampling the DEM images, and performing radiometric calibration; using an indirect positioning method to perform geometric positioning based on an RD model, generating lookup tables A and B, and registering the images; detecting pixels in overlapping and shadowed areas; calculating the effective scattering area corresponding to each SAR pixel; performing radiometric correction based on the effective scattering area, repairing the backscattering coefficient values ​​in shadowed areas, and finally performing geometric correction. This invention, to a certain extent, eliminates the topographic effects caused by the SAR imaging mechanism and terrain undulations, improving the radiometric correction effect in areas with large topographic undulations. The corrected product can be widely used in fields such as ground feature inversion and classification, deformation detection, and target detection.

The invention discloses a high-maneuvering low-speed target take-off and landing process parameterization detection method based on a common inductance signal. The method comprises the steps that a motion model of an aircraft in the take-off and landing stage is built, and a formula modeling result is obtained; obtaining distances and speeds of a plurality of suspicious targets; designing a band-pass filter in a Doppler domain to obtain an LVD result, calculating Doppler frequency information of the target, and obtaining accelerations of a plurality of suspicious targets; calculating retroreflection coefficients of the plurality of suspicious targets based on a radar antenna design principle; based on array antenna arrangement of a sensing system, obtaining space angles of a plurality of suspicious targets, and determining the space position of each suspicious target by combining the distances of the plurality of suspicious targets; and a detection module based on physical characteristics of the moving target is utilized to screen out an aircraft target according to respective threshold ranges of the speed, the acceleration, the retroreflection coefficient and the spatial position of the target. According to the invention, the detection capability of a low-speed and high-maneuverability moving target in a low signal-clutter-noise ratio environment is improved.

This application discloses a method, device, medium, and product for estimating quantitative parameters of ultrasound radio frequency signals, relating to the field of ultrasound imaging and signal processing technology. The method includes: acquiring and preprocessing the ultrasound radio frequency signal reflected from the tissue to be tested to obtain a normalized RF signal; constructing a joint objective function; integrating a data fitting term, an L2 norm regularization constraint term for the backscattering coefficient, an L2 norm regularization constraint term for the attenuation coefficient, and a joint TV norm spatial smoothing constraint term into the joint objective function; determining the constraint coefficients of the joint objective function based on the relative signal-to-noise ratio of the normalized RF signal and the tissue properties of the tissue to be tested; ensuring that all constraint coefficients satisfy a physical constraint condition greater than 0; estimating quantitative parameters of the tissue to be tested based on the joint objective function and all constraint coefficients; and generating a corresponding spatial distribution image based on the quantitative estimates. This application meets the high-precision requirements of clinical applications and has high versatility.

The application discloses a kind of based on remote sensing and ground cooperation's field irrigation water consumption estimation method and device, belong to remote sensing technical field.It includes: obtaining multi-source remote sensing data and extracting optical vegetation index, surface albedo, surface temperature and backscattering coefficient;Obtain meteorological data, soil volume water content measured value and daily actual evapotranspiration measured value;Based on optical vegetation index, surface albedo, surface temperature, meteorological data and daily actual evapotranspiration measured value, construct deep neural network and predict daily actual evapotranspiration estimate value;According to backscattering coefficient, optical vegetation index and soil volume water content measured value, estimate root zone soil water storage change amount;According to daily actual evapotranspiration estimate value, root zone soil water storage change amount and effective precipitation, calculate the total irrigation water consumption estimate value of complete irrigation cycle.The application realizes the irrigation water consumption estimation of high precision of field scale, large range, all-weather continuous without need of canal head metering facility.

A method for imaging an area of interest on the ground uses a georadar equipped with a plurality of transmitter antennas and a plurality of receiver antennas. A bistatic RCS matrix is calculated at each point of a grid based on the matrix representing the MIMO channel modelling the propagation and the reflection in the area of interest, a matrix representing the losses along the propagation paths of the channel, and a phase-shifter matrix representing the delays on these same propagation paths. The bistatic RCS matrices relating to discrete frequencies belonging to the same coherence sub-band are summed and the elements of the matrices thus obtained are then summed incoherently to provide an overall backscatter coefficient for each point of the grid. Afterwards, an image representing this backscatter coefficient at each point of the grid is generated. A method for detecting a ground target-uses the same principle.

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.

This invention discloses a layered inversion method and system for aerosol lidar based on polarization information. The method includes: acquiring parallel and vertical polarization channel echo signals from a polarized Mie scattering lidar; performing layered identification of aerosols based on the total echo signal to determine the altitude range of upper-air and near-surface aerosol layers; for upper-air aerosol layers, calculating the estimated aerosol depolarization ratio based on the polarization channel echo signals and establishing a quantitative inversion relationship between the volume depolarization ratio and the aerosol backscattering coefficient, thereby achieving the inversion of the upper-air aerosol backscattering coefficient; for near-surface aerosols, inverting the backscattering coefficient using the Fernald method; and stitching the inversion results from different altitude regions according to the layered information to form a backscattering coefficient profile across the entire altitude range. Using this invention, the stability and reliability of upper-air aerosol inversion can be effectively improved.

The invention relates to the field of operation state supervision, and discloses a multi-source visual remote sensing data fused grain machinery operation state monitoring method, which comprises the following steps of: firstly, acquiring high-resolution visible light, thermal infrared, synthetic aperture radar or hyperspectral data in the same time period, and carrying out preprocessing and accurate registration; and then, accurately judging whether the harvester is in a harvesting state or not by cooperatively analyzing a thermal abnormal signal in the thermal infrared image and combining attenuation of a backscattering coefficient of the synthetic aperture radar or mutation of a hyperspectral vegetation index. And for a non-harvesting state, a visible light image is further combined to distinguish shutdown, transfer or turning around, and fault analysis can be carried out on shutdown. In addition, the method further comprises the steps that authenticity verification and missing cutting detection are conducted on the harvesting state, and the operation quality is evaluated based on vegetation index uniformity and visible light image texture features. According to the invention, omnibearing, automatic and high-precision monitoring of the working state, authenticity and quality of the agricultural machine is realized.

The application discloses a kind of based on bilinear interpolation face target template SAR baseband echo fast generation method, belong to radar technical field.The application includes: by dividing face target into N×M rectangular grid, each rectangle represents a point target, according to the various parameters of airborne strip SAR radar to determine the number of rectangular grid in radar irradiation scene and the target template of radar irradiation scene, and establish the slant range difference storage table between radar irradiation scene center and other point targets, according to target template using bilinear interpolation algorithm adjustment face target's backscattering coefficient matrix format, finally on the basis of one-dimensional fast fourier transform echo generation method to the time delay caused by target slant range is approximately handled and the echo phase produced by slant range is accurately calculated, obtain SAR baseband echo.Compared with existing concentric circle algorithm, the present application method greatly reduces the SAR echo generation calculation amount, and the calculation efficiency is improved by more than 55%.