15 results about "Radar backscatter" patented technology

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 discloses an NRCS determination method for a Ku wave band scatterometer under a rainfall condition, and relates to the field of satellite ocean remote sensing and meteorological detection.The method comprises the steps that the action mechanism of rainfall on radar signals in the atmosphere is considered, and a sea surface radar back scattering theoretical model is constructed based on a wind-generated sea surface wave spectrum and a dual-scale Bragg scattering theory; obtaining a rainfall attenuation coefficient, a normalized radar backscattering cross section contributed by raindrop scattering and dual-polarization normalized radar backscattering cross sections of a C wave band and a Ku wave band based on a sea surface radar backscattering theoretical model; according to the rainfall attenuation coefficient, the normalized radar backscattering cross section contributed by raindrop scattering and the dual-polarization normalized radar backscattering cross sections of the C wave band and the Ku wave band, the normalized radar backscattering cross section of the Ku wave band scatterometer under the rainfall condition is obtained; according to the method, the accurate normalized radar back scattering cross section of the Ku wave band scatterometer is obtained under the rainfall condition.

The invention relates to the technical field of tree height statistics, in particular to a mangrove forest tree height estimation method and device, equipment and a medium. According to the method, the spectral band, the topographic feature and the radar backscattering feature of the boundary area where the mangrove forest is located are collected, and the tree height mean value of the mangrove forest in the boundary area is estimated through the spectral band, the topographic feature and the radar backscattering feature. As the topographic features and radar back scattering are satellite remote sensing data acquired by the radar, the spectral band is data acquired by the spectral imager sensor, and the radar can acquire the satellite remote sensing data of any boundary area where the mangrove forest is located, even if the boundary area is located in a shallow sea area, the data can be accurately acquired. Therefore, the method for estimating the height of the mangrove forest is not limited by the growing environment of the mangrove forest. And the tree height is estimated by adopting the multi-source satellite remote sensing data, so that the accuracy of the tree height estimation value can be improved.

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.

The present invention discloses a landslide disaster early warning method, which belongs to the field of image processing technology. The present invention collects synthetic aperture radar images of a mountain monitoring area under HV and HH polarization modes to obtain HV images and HH images; finds the HV and HH images corresponding to the starting time of the landslide event, and based on the difference in radar backscattering amplitude and phase between the starting time and the subsequent HV images, extracts dual-polarization amplitude change values ​​and phase change values ​​respectively, and constructs a dual-polarization amplitude and phase change image; counts the number of times a pixel point is marked as a landslide point, and constructs an amplitude and phase position confidence distribution matrix; finally, uses a landslide disaster early warning neural network to process the dual-polarization amplitude and phase change image, and combines the confidence applied by the amplitude and phase position confidence distribution matrix to obtain a landslide disaster risk value, effectively solving the problem of low accuracy of landslide disaster early warning in the existing technology.

A method and system for deterministic sea wave profiling and / or prediction. Mixed space-time sampling data of the sea is obtained, where the sampling data is helical in time and space and comprises a plurality of beam waveforms. The sampling data is separated into a plurality of sub-sets of the sampling data, where each sub-set of the sampling data corresponds to a region of the sea. For each sub-set of the sampling data, a sub-set sea model is determined using a subsampled set of wavenumbers. Determining the sub-set sea model is based on the beam waveforms in the corresponding sub-set of the sampling data. The sub-set sea models are up-sampled and then combined to form a complete sea model.

The application discloses a water area extraction method fusing radar backscattering and interference coherence coefficients, and belongs to the technical field of remote sensing water area extraction, and comprises the following steps: S1, acquiring a polarimetric SAR image, and pre-processing the polarimetric SAR image to obtain a backscattering coefficient image and an interference coherence coefficient image; S2, calculating an SDWI water body index according to the backscattering coefficient image; S3, determining a water body preliminary extraction result according to the SDWI water body index; S4, performing fusion processing on the water body preliminary extraction result based on the interference coherence coefficient image to generate a fused water body extraction result; and S5, performing refinement processing on the fused water body extraction result to obtain a final water body range. The application can not only accurately calculate a segmentation threshold, but also can avoid the influence of submerged plants and silt on the extraction result, and improves the accuracy of water area extraction.

The present invention relates to the field of flood disaster monitoring technology, and in particular to a flood disaster monitoring method based on threshold interval and neighborhood analysis. The method comprises the following steps: obtaining polarization radar images of the flood-stricken area and preprocessing them to obtain radar backscatter coefficient band data; secondly, plotting a radar backscatter coefficient histogram and obtaining the peak backscatter coefficient values ​​of land and water bodies through histogram bimodal identification; then, calculating the threshold segmentation interval based on the peak points, segmenting the image into water bodies, land, and mixed water and land pixels, and obtaining preliminary water body extraction results; finally, eliminating pseudo-water bodies in the water body, and performing neighborhood water and land attribute discrimination on the mixed water and land pixels, ultimately obtaining flood submerged water body extraction results. The present invention improves the monitoring accuracy of submerged water bodies.

The present application relates to the technical field of marine remote sensing, in particular to a SAR sea surface high wind speed inversion method fusing a double attention mechanism: SAR remote sensing image data under a typhoon is acquired; the SAR remote sensing image data is preprocessed to obtain a plurality of SAR slice data; the plurality of SAR slice data is input into a trained SAR sea surface high wind speed inversion model, the SAR sea surface high wind speed inversion model extracts features from the plurality of SAR slice data through a weight file; the extracted features are subjected to channel compression regression to obtain an inversion wind speed result. The present application is based on the complex texture features and high-dimensional frequency domain features of the radar backscattering intensity in SAR data, constructs a complex nonlinear mapping model weight on the mechanism of nonlinear response of sea surface wind speed, and realizes sea surface wind speed inversion under a typhoon. The present application significantly improves the problem of sea surface wind speed inversion distortion caused by SAR signal saturation under a typhoon, and greatly improves the inversion accuracy.

The invention discloses a planting plot space structure reconstruction method and system based on multi-source data fusion, and relates to the technical field of remote sensing application, and the method comprises the steps: obtaining a high-resolution optical image, a synthetic aperture radar image and digital elevation model data of a target region, carrying out the radiometric calibration, atmospheric correction, terrain correction and registration processing, and carrying out the reconstruction of the spatial structure of the planting plot; generating a multi-source data cube; spectral features, texture features, scattering features and topographic features are extracted from the multi-source data cube, channel dimension stacking is carried out, and a high-dimensional multi-channel feature image is generated; a back scattering coefficient graph of the analog synthetic aperture radar is calculated by using a surface microwave scattering physical model and a high-dimensional multi-channel feature image, and a scattering residual image is obtained by calculating a residual with a real synthetic aperture radar image. According to the method, feature aggregation and semantic similarity calculation are carried out through a graph convolutional neural network, graph spots are optimized and merged according to a node incidence matrix, and a high-precision plot space structure reconstruction vector graph is output after topology check and geometric correction.

The application provides a method and system for inverting marine vortex dynamic parameters in a SAR image, which is based on a vortex SAR imaging theory model to invert and extract dynamic characteristics of the vortex, and a quantitative relationship between a SAR image radar backscattering coefficient and a vortex physical model is constructed, which is different from previous methods of digital image processing and machine learning, which can only extract the position and radius characteristics of the vortex. The vortex dynamic parameters obtained by the method include vortex center position, suction intensity, viscosity coefficient, velocity circulation, maximum rotation speed radius of the vortex, maximum angular velocity, maximum vortex intensity and other dynamic parameters.

The invention discloses a radar vertical detection sand and dust weather monitoring method, system and device and a medium, and belongs to the technical field of sand and dust weather monitoring, and the method comprises the steps: collecting a laser back scattering echo signal; the method comprises the following steps: carrying out polarization separation processing on a laser back scattering echo signal to obtain a parallel polarization signal component and a vertical polarization signal component; calculating a signal intensity ratio through the parallel polarization signal component and the vertical polarization signal component to obtain a depolarization ratio parameter; comparing and judging the depolarization ratio parameter with a preset sand and dust threshold value, and obtaining sand and dust particle identification information according to a judgment result; performing spatial height analysis and vertical height statistics through the sand and dust particle identification information to obtain a sand and dust weather detection result and sand and dust vertical profile data; and comprehensively outputting the sand and dust weather detection result and the sand and dust vertical profile data as sand and dust weather monitoring information. According to the invention, a laser radar backscattering polarization separation technology is adopted, and accurate identification of sand and dust particles is realized by analyzing a depolarization ratio parameter.

The present invention relates to the field of marine engineering technology, and in particular to a high-resolution sea ice detection method based on a rotating fan-beam scatterometer. The sea ice monitoring method can distinguish between ice and water within a 10km grid in the Arctic and Antarctic regions. By utilizing scatterometer level 1 data and a polar stereographic projection conversion method, the spatial resolution of existing scatterometer sea ice monitoring methods is improved. In addition, a sliding time window is used to collect training data and dynamically train and update the model, which to a certain extent solves the problem that the ice-water characteristics of the radar backscatter coefficient vary greatly with the seasons.

A method of performing vibrational radar backscatter communications includes transmitting radar waveforms at millimeter wave frequencies, receiving, from a vibrating transponder, vibrational backscatter of radar waveforms, performing synchronization on the vibrational backscatter to determine a start of an encoded communication message sequence, calculating a predicted symbol sequence based on the encoded communication message sequence, identifying, from one or more predetermined messages, an actionable message from the predicted symbol sequence, and communicating the actionable message to a display of a vehicle or causing the vehicle to take an action. This method can also be performed on a computing system that includes a processor, memory, and instructions stored in the memory and the method can be stored on computer readable storage media.

The application discloses a method for determining the NRCS of a Ku-band scatterometer under rainfall conditions, and relates to the field of satellite ocean remote sensing and meteorological detection, which comprises the following steps: considering the action mechanism of rainfall in the atmosphere on a radar signal, and constructing a sea surface radar backscattering theory model based on a wind-generated sea surface spectrum and a double-scale Bragg scattering theory; obtaining a rainfall attenuation coefficient, a normalized radar backscattering cross section of raindrop body scattering contribution, and dual-polarization normalized radar backscattering cross sections of C-band and Ku-band based on the sea surface radar backscattering theory model; and obtaining the normalized radar backscattering cross section of the Ku-band scatterometer under rainfall conditions according to the rainfall attenuation coefficient, the normalized radar backscattering cross section of raindrop body scattering contribution, and the dual-polarization normalized radar backscattering cross sections of C-band and Ku-band, so that the application can obtain an accurate normalized radar backscattering cross section of the Ku-band scatterometer under rainfall conditions.