19 results about "Visibility function" patented technology

The invention discloses an efficient three-dimensional scene anti-rendering method and system based on a multi-view image and a storage medium, and the method comprises the steps: employing a neural symbol distance function network as a geometric model to represent a scene geometric structure of the multi-view image, decomposing a neural radiation field into a diffuse reflection field and a specular reflection field, in combination with the self-shielding probability of the mirror reflection field, the diffuse reflection field, the mirror reflection field and the self-shielding probability of the mirror reflection field, calculating color information of the spatial points and optimizing the geometric model; a material network is adopted to represent albedo and roughness and obtain material information, color information cached in a neural radiation field, an illumination visibility function and direct illumination are introduced, global illumination of each light ray is calculated, importance sampling and illumination Monte Carlo integral are combined to obtain a rendering result, and the material network and a direct illumination network are optimized; and on the basis of the geometric structure, the material information and the illumination information obtained by the decoupled geometric model, the material network and the direct illumination network, a high-quality image is generated by adopting rendering pipeline calculation.

The invention provides a synthetic aperture radiometer error correction method and system, terminal equipment and a medium, and the method comprises the steps: obtaining an original brightness temperature image of a target near-field imaging scene, and obtaining a corresponding initial near-field visibility function; inputting the initial near-field visibility function into a pre-constructed near-field error compensation network model, extracting frequency domain feature information, and compensating a near-field error to obtain a new near-field visibility function; calculating an error between the new near field visibility function and the target far field visibility function, and if the error is less than or equal to a preset error threshold, performing Fourier transform inversion on the new near field visibility function to obtain a corrected reconstructed brightness temperature image; otherwise, constructing a loss function according to the error, carrying out the back propagation of the near-field error compensation network model, obtaining a trained near-field error compensation network model, and obtaining a corrected reconstructed brightness temperature image based on the trained near-field error compensation network model. The near-field error can be corrected, and the quality of the reconstructed brightness temperature image is improved.

The invention discloses a differential measurement-based satellite-borne synthetic aperture microwave radiometer imaging correction method, which comprises the following steps of: respectively selecting a reference area and a correction area in a global range, and respectively intercepting scientific data and auxiliary data in a specific time period in the reference area and the correction area based on load normal earth observation data; averaging the visibility functions intercepted in the reference area to obtain a reference visibility function; generating a reference brightness temperature and a correction brightness temperature through a simulation model by using the auxiliary data intercepted in the reference area and the correction area respectively; performing difference and inversion processing on the normal earth observation visibility function to be subjected to imaging processing by using the reference visibility function to obtain a difference inversion brightness temperature; calculating a correction coefficient by using the reference visibility function, the reference brightness temperature, the visibility function intercepted in the correction area and the correction brightness temperature; and correcting and compensating the differential inversion brightness temperature by using the correction coefficient and the reference brightness temperature to obtain the final inversion brightness temperature.

The present invention provides a method for predicting the visibility relationship and duration between a satellite and a ground node. First, according to the motion models of the satellite and the ground node and relevant satellite-ground visibility constraints, a visibility function between the satellite and the ground node is constructed; secondly, according to the periodic law of the satellite's on-orbit motion and relevant visibility constraints, a solution interval for all zeros of the constructed visibility function is constructed and the zeros are solved. The starting or ending zero of each positive interval of the constructed visibility function is the starting or ending moment of the visibility interval between the satellite and the ground node. Based on this, the set of visibility intervals within a given time can be quickly calculated, thereby realizing the efficient and accurate solution of the visibility intervals between the satellite and the ground node.

The invention belongs to the field of synthetic aperture radio array imaging detection, and relates to a deep learning-based synthetic aperture radio array rapid imaging method, which comprises the following steps of: processing observation data based on optimization parameter combination and matrix mapping to generate a brightness temperature image training set with frequency domain relevance and high image quality; an imaging neural network model composed of a pre-imaging network and an image recovery network is constructed, and high-dimensional nonlinear mapping among the visibility function, the projection baseline and the brightness temperature image is realized; through preprocessing, data preprocessing and real-time imaging operation, rapid and high-quality imaging of observation data is realized. According to the method, a sun brightness temperature image data set consistent with an actual observation scene is constructed based on the observation data of a circular array solar radio imaging telescope of meridian engineering; an imaging neural network model adapted to different sun brightness temperature characteristics and projection baselines is designed; the imaging speed of the synthetic aperture radio telescope can be obviously improved while high-quality imaging is realized.

The invention discloses an autonomous planning method and system for a flight path of an intelligent patrol unmanned aerial vehicle in a forest region, and belongs to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: firstly constructing a forest region three-dimensional environment and a transient visibility function, extracting a transient visibility collapse non-stationary index and a visual angle anisotropic shielding gradient, and forming a transient observation risk field; further combining communication signal attenuation and energy constraint to construct an asymmetric evaluation function, and introducing a time sensitive weight to realize alignment of a path and a visibility peak value; candidate paths are generated in the path feasible space, and time axis re-calibration is carried out on the paths through dynamic environment perception; meanwhile, identifying an uncovered high-frequency collapse area based on transient window dislocation residual errors, performing local path topology reconstruction, and generating a compensation path; and finally, model parameters are reversely corrected through patrol results, and adaptive optimization of path planning is realized. According to the method, the capturing capability and patrol precision of the transient target in the complex environment of the forest region can be remarkably improved.

The invention discloses a neural rendering-based remote sensing image three-dimensional reconstruction method, which comprises the following steps of: independently modeling sunlight and sky diffuse reflection light by using an irradiance model, accurately learning a solar visibility function by using sun correction rays, and finally adding an attention mechanism to optimize a network architecture. And the network can dynamically distribute corresponding weights according to the importance of the features. By improving the sampling method, the problem that sampling and calculation are complex is solved; an irradiance model is adopted, and dynamic characteristics of a transient object and a shadow region can be captured more accurately; and an attention mechanism is added in the network, and key feature information in the remote sensing image is fully utilized, so that the reconstruction precision is improved, and the detail representation is optimized. The method can be applied to scenes such as urban planning and digital twinning, ecological environment monitoring, disaster emergency response and cultural relic protection, and more vivid visual experience and more accurate light and shadow effects are provided for users.

The 3D point cloud simplification method combining human visual perception characteristics belongs to the field of 3D point cloud data processing, and aims to solve the problem that the sharp increase of the amount of dense 3D point cloud data aggravates the burden of data processing, storage and transmission in the later stage, the present application provides a kind of 3D point cloud simplification method combining geometric features and human visual perception characteristics;Combined with the geometric features of point cloud, the algorithm establishes a one-way perception sharpness function and a local visibility function to complete the importance evaluation of points, and then formulates different simplification rules according to the importance of points to realize hierarchical simplification of point cloud.In addition, in order to improve the universality of the mixed feature evaluation model, a dynamic optimization strategy for the weight of each evaluation function is established, and the real-time updating of the weight value is realized based on the feature evaluation result.The experiment verifies the effectiveness of the algorithm, compared with the traditional point cloud simplification algorithm, the algorithm can maximize the retention of local details of point cloud while maintaining the overall uniformity of data.

The invention discloses a multi-view-based three-dimensional reconstruction model quality evaluation method and system and a medium, and belongs to the field of multi-view three-dimensional reconstruction model quality evaluation. Comprising the following steps that a model generated through multi-view three-dimensional reconstruction is obtained, and the surface of the model is a Lambert body surface; the rendering equation of the Lambert body surface is converted into a point multiplication form of an incident light spherical harmonic coefficient and a surface transmission characteristic spherical harmonic coefficient, and the surface transmission characteristic is a product of a binary visible function and a geometric cosine term; the real irradiance of the surface of the reconstruction model is reversely deduced through the pixel intensity of the multi-view image; based on the incident light spherical harmonic coefficient, calculating the reconstruction irradiance of the reconstruction model by using a spherical harmonic coefficient linear combination equation; if the difference value between the reconstruction irradiance gradient and the real irradiance gradient between any two points in the model is greater than a preset threshold value, determining that a geometric error exists in a three-dimensional reconstruction result between the two points; according to the method, which parts of the reconstruction model have errors can be accurately judged.

The invention discloses a comprehensive aperture interference radiometer amplitude-phase error self-correction method, system and device based on adaptive filtering. The method comprises the following steps: S1, constructing a visibility function model under the amplitude-phase error action of the synthetic aperture interference radiometer; s2, decomposing the visibility function model into an amplitude error matrix and a phase error matrix, respectively establishing an amplitude error self-adaptive correction model and a phase error self-adaptive correction model, and finally constructing an amplitude-phase error correction model of the synthetic aperture interference radiometer; and S3, taking a signal obtained after the correction model processes the multi-block visibility data as a feedforward signal, inputting the feedforward signal into an adaptive filter, and generating an amplitude-phase error estimation signal by the filter and correcting the amplitude-phase error estimation signal. According to the method, the amplitude-phase error of the visibility function can be effectively suppressed without additional hardware or an external reference source, and the robustness and the imaging quality of a comprehensive aperture imaging system are remarkably improved.

The invention belongs to the technical field of radiation detection, and particularly relates to a synthetic aperture radiometer target detection method and device under a high dynamic condition based on continuous frame image processing. Utilizing a synthetic aperture radiometer to continuously obtain millimeter wave signals of a target scene, and obtaining visibility functions of a plurality of continuous frames according to preset integral time; performing background subtraction, Gaussian low-pass filtering and Fourier transform on the visibility function sequence to obtain a plurality of frames of brightness temperature maps; solving a rotation matrix of the current frame image according to the attitude angle at the current moment, and performing registration fusion on the brightness temperature image according to the rotation matrix to obtain a preliminary result image; a convolution kernel is set according to antenna array arrangement and platform motion imaging properties, the preliminary result image is enhanced, then the image is further enhanced by adopting multi-scale white top-hat transform, and a final result image is obtained; and setting a target detection window, screening candidate target points according to pixels in the window, and finally setting a threshold value according to a full-image pixel mean value and variance to determine the positions of the target points.

The invention discloses a method for correcting mutual coupling errors of antennas of a satellite-borne synthetic aperture radiometer. The method comprises the following steps of: 1, observing a target scene by the satellite-borne synthetic aperture radiometer to obtain a measured target scene visibility function; step 2, observing the flat cold air by the satellite-borne synthetic aperture radiometer to obtain a measured cold air visibility function; step 3, calculating system 1K response by using the measured cold air visibility function and the brightness temperature of the back lobe model when the cold air is observed; 4, correcting a target scene visibility function by using the 1K response calculated by the cold air visibility and combining the physical temperature of the receiver during scene observation; and 5, performing brightness temperature reconstruction on the corrected target scene visibility function to obtain a corrected reconstructed brightness temperature. According to the method, the error influence of antenna mutual coupling on the non-zero baseline of the synthetic aperture radiometer can be effectively corrected, the imaging quality of the satellite-borne synthetic aperture radiometer is improved, and the method has a wide market application prospect.

The invention discloses a small radio interference array visibility simulation and FITS generation method and system, and relates to the technical field of radio astronomy. The method comprises the following steps: firstly, constructing a small interference array consisting of at least four antennas, generating a time domain voltage signal of each antenna by simulating a target celestial body signal and background noise, and performing accurate time shifting according to geometric delay; then, carrying out segmented Fourier transform on the time domain signal to generate a dynamic frequency spectrum, and calculating a complex visibility function through cross-correlation operation between the antenna pairs; finally, the visibility data, the antenna position, the observation frequency, the calculated UVW coordinates and other information are packaged and output according to a standard FITS file format. According to the invention, a complete process from signal simulation to standard data format generation is realized, a standardized solution is provided for data processing, archiving and imaging of a small radio interference array, and the method is especially suitable for research on rapid interference measurement and mapping positioning of a temporary source.

The application discloses a one-dimensional synthetic aperture depth convolutional neural network and an image reconstruction method, the method is characterized in that: a deep convolutional neural network structure for a one-dimensional synthetic aperture radiometer is designed, a visibility function measured by a one-dimensional synthetic aperture system is taken as an input of the convolutional neural network, a microwave brightness temperature image is taken as an output, and one-dimensional synthetic aperture microwave brightness temperature image reconstruction is realized. Compared with a traditional brightness temperature reconstruction method, the method can obviously inhibit Gibbs oscillation without reducing spatial resolution, and improves the quality of the reconstructed brightness temperature image; the application is simple and reliable, can effectively improve the imaging quality of the synthetic aperture radiometer, improves the effective utilization rate of satellite remote sensing data, and has a wide market application prospect.

The invention relates to a method for reconstructing an image by improving a two-dimensional mirror image synthetic aperture through a metamaterial and a medium, and the method comprises the steps: configuring a two-dimensional mirror image synthetic aperture system which comprises a two-dimensional antenna array and two groups of reflection structures which are perpendicular to each other; two groups of mutually perpendicular reflection structures are replaced by magnetic conductor structures, and the magnetic conductor structures have in-phase reflection characteristics in a target frequency band; according to the position coordinates of the antennas in the two-dimensional antenna array and the in-phase reflection characteristic of the magnetic conductor structure, correlation values between the antenna pairs are calculated; constructing a transfer matrix according to the calculated correlation values; establishing a system of linear equations through the transfer matrix and the cosine visibility function, and solving the system of linear equations to obtain cosine visibility; and performing anti-cosine transform on the cosine visibility, and reconstructing a brightness temperature image of the target scene. According to the method, the negative interference of the transfer matrix is effectively eliminated through the electromagnetic regulation and control characteristics of the artificial magnetic conductor, and the optimization of the rank defect error is realized.

A kind of comprehensive aperture radiometer error correction method based on deep learning, in order to correct the amplitude and phase error of antenna and channel and gibbs oscillation, improve the imaging quality, generate original scene brightness temperature image according to the dynamic range of actual application scene;Generate visibility function containing system error;Original scene brightness temperature image and visibility function jointly construct training data set and test data set;The deep learning network is trained using the training data set;The network correction effect is verified using the test data set.

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

This invention belongs to the field of synthetic aperture radio array imaging detection, and relates to a rapid imaging method for synthetic aperture radio arrays based on deep learning. The method includes: processing observation data based on optimized parameter combinations and matrix mapping to generate a brightness temperature image training set with frequency domain correlation and high image quality; constructing an imaging neural network model composed of a pre-imaging network and an image restoration network to achieve a high-dimensional nonlinear mapping between the visibility function, the projected baseline, and the brightness temperature image; and achieving rapid, high-quality imaging of the observation data through preprocessing, data preprocessing, and real-time imaging operations. This invention is based on observation data from the Meridian Project's circular array solar radio imaging telescope, constructing a solar brightness temperature image dataset consistent with the actual observation scenario; designing an imaging neural network model adapted to different solar brightness temperature characteristics and projected baselines; and significantly improving the imaging speed of synthetic aperture radio telescopes while achieving high-quality imaging.

The invention provides a sea-land pollution suppression method based on modified Fourier transform, and the method comprises the steps: receiving a microwave radiation signal containing a sea-land boundary scene as a snapshot signal of a microwave remote sensing image, and calculating a uniform equal view field xi and a spatial frequency vector u of a radiometer according to the array arrangement of the radiometer; calculating a guide vector A and an autocorrelation matrix R according to the snapshot signal; performing redundant average processing on the autocorrelation matrix R to obtain a visibility function vector of an observation scene, writing an inversion matrix Q in a Fourier transform form, and adjusting a field of view of a radiometer at a sea-land junction according to a sea-land scene position to enable the field of view to only contain the sea or the land; and calculating a correction coefficient according to the adjusted view field to obtain a final inversion matrix G, and inverting the visibility function vector into brightness temperature.