201 results about "Interferometric phase" patented technology

The invention discloses an interferometric phase unwrapping method based on a double-branch decoding residual network, and the method comprises the following steps: S1, constructing an interferometric phase data set which comprises an interferometric phase diagram of pixel phase wrapping and a corresponding real interferometric phase diagram; s2, constructing a double-branch decoding residual network, wherein the double-branch decoding residual network comprises an input layer, an encoder network, a double-branch decoder network comprising a first branch decoder module and a second branch decoder module, and an output layer which are connected in sequence; training the double-branch decoding residual network by using the interferometric phase data set to obtain a double-branch decoding residual network interferometric phase unwrapping model and the weight of the model; and S3, inputting the interferometric phase diagram subjected to pixel phase winding into a double-branch decoding residual network interferometric phase unwrapping model to obtain a predicted interferometric unwrapping phase diagram. According to the method, the interferometric phase unwrapping under the condition of low signal-to-noise ratio is realized, and the interferometric measurement precision of the synthetic aperture radar is improved.

The invention relates to the technical field of image processing, in particular to a slope deformation monitoring method based on image recognition, and the method comprises the steps: obtaining a slope interferometric phase diagram; segmenting the slope interferometric phase diagram to obtain a plurality of sub-regions, and obtaining local differences of phase points in the sub-regions; determining the difference expression degree of the phase values corresponding to the phase points and the phase difference of the sub-regions; carrying out region segmentation; determining distribution similarity and overall similarity of adjacent sub-regions when segmentation is finished; calculating the comprehensive similarity of the sub-regions at the end of segmentation; carrying out region merging; and filtering is carried out according to the standard deviation of the final sub-region so as to realize slope deformation monitoring. By dynamically judging whether region segmentation and merging are carried out or not, compared with a fixed threshold mode, the method is more accurately adapted to the spatial variability of a deformation mode in a slope interferometric phase diagram, the quadtree filtering effect is effectively improved, and slope deformation monitoring is more accurate.

The invention discloses an intelligent data processing method and system for geological disaster early warning, and relates to geological disaster early warning, and the method comprises the steps: collecting multi-source heterogeneous data; extracting topographic features of the geological disaster area according to the optical satellite image data and the laser radar data; performing differential interference processing on the synthetic aperture radar data to obtain differential interference phase data of the geological disaster area; performing phase unwrapping by using an improved minimum cost flow algorithm to obtain a continuous absolute phase value # imgabs0 #; performing nonlinear estimation on the estimation result of the surface deformation quantity to obtain state parameter estimation of the geological disaster area; and performing early warning detection by using a support vector machine (SVM). In order to solve the problem of low geological disaster prediction precision caused by difficult phase unwrapping in the prior art, the unwrapping problem is converted into a space-time network flow graph, space-time continuity constraints are introduced, the incidence relation between data in different periods is considered, a multi-scale strategy is adopted for solving and the like, and the prediction precision is improved.

The invention discloses a white light interference phase unwrapping method, device and equipment and a medium, and relates to the technical field of interference light measurement. The method comprises the following steps: acquiring a plurality of white light interference images of a white light interference vertical measurement method; calculating light intensity difference images of the adjacent white light interference images, and calculating a plurality of difference images by adopting a centroid method to obtain a rough contour surface of the sample piece; calculating the original light intensity data by adopting a spatial frequency domain method to obtain ununwrapped phase data; obtaining a phase jump position based on the ununwrapped phase data, and segmenting the difference data of the rough contour surface and the ununwrapped phase data according to the jump position; and the phase is corrected by comparing adjacent segments, so that phase unwrapping is realized. The phase unwrapping method for correcting the phase jump region of the phase data on the basis of the rough contour surface has the advantages of being simple in algorithm implementation, high in precision, high in processing speed and good in robustness.

The invention discloses a full-field-domain structure high-precision deformation and vibration measurement system based on a microwave interference radar, and the system comprises an observation configuration module which is used for obtaining radar observation parameters and completing the signal transmission and reception; the signal processing module is used for preprocessing the echo signal and constructing an interference phase matrix; the phase unwrapping module is used for generating a continuous phase sequence and calculating the displacement of a reflection point; the frequency spectrum extraction module is used for extracting the frequency component and the vibration amplitude of each reflection point; the response clustering module is used for extracting reflection point clusters with similar frequency responses and constructing a vibration distribution diagram; the modal identification module is used for generating a vibration modal feature vector and marking a difference region; and the track generation module is used for generating a dynamic deformation track diagram of the full field domain structure. According to the invention, high-precision deformation and vibration mode synchronous measurement of a complex structure under full-field-domain and non-contact conditions is realized, and the precision, efficiency and intelligent level of structure monitoring are effectively improved.

The invention discloses a mountainous area large-scale landslide InSAR deformation rate monitoring method and system, relates to the technical field of synthetic aperture radar interferometry, and aims to solve the problem that an existing landslide InSAR deformation rate calculation method is low in accuracy. The method comprises the following steps: generating a first interferometric phase diagram and a second interferometric phase diagram by adopting a hybrid baseline strategy; optimizing the first interferometric phase diagram by adopting a time sequence phase optimization algorithm to obtain a first target interferometric phase diagram; optimizing the second interferometric phase diagram by adopting a Gaussian filtering algorithm to obtain a second target interferometric phase diagram; combining the first target interferometric phase diagram and the second target interferometric phase diagram to obtain a mixed interferometric phase diagram; and performing phase unwrapping processing and time sequence analysis on the mixed interferometric phase diagram to obtain the surface deformation rate of the target mountainous area. According to the method for monitoring the deformation rate of the large-scale landslide in the mountainous area, the accuracy and the coverage range of monitoring the InSAR deformation rate of the landslide in the mountainous area are remarkably improved.

According to the single-frame high-quality shearing speckle interference phase extraction method and device, the quality of the obtained phase is comparable to that of a traditional multi-frame phase solution algorithm, and single-frame high-quality phase extraction of shearing speckle interference is completed. The method comprises the following steps: (1) making a data set; (2) constructing a neural network; (3) neural network training; (4) detecting the detected piece: when the detected piece is static, carrying out N-step phase shift method shooting to obtain N frames of speckle interferograms when the detected piece is static; applying an external force to the measured piece, and recording light intensity information by using a camera to obtain a frame of interferogram when the measured piece is deformed; (5) single-frame phase extraction: phase extraction is carried out according to an N + 1 time phase shift method, and on the basis of N frames of phase shift interferograms when the measured piece is static, phase extraction is carried out by using one frame of interferograms after the measured piece is deformed, so that low-quality phase distribution is obtained; and (6) phase repairing.

The invention discloses a method and system for constructing an interferometric phase unwrapping model based on discrete wavelet transform, and relates to the field of image phase unwrapping, and the method comprises the specific steps: constructing an InSAR synthetic data set based on an SRTM DEM, a two-dimensional Gaussian curved surface and an InSAR measurement error source; an interferometric phase unwrapping model based on discrete wavelet transform is constructed, the interferometric phase unwrapping model comprises an encoder network, a bottleneck layer, a decoder network and an output layer which are connected in sequence, and the encoder network comprises a plurality of cross-stacked dual-channel data down-sampling modules and pyramid pooling modules; and training the interferometric phase unwrapping model based on the InSAR synthetic data set, and optimizing model parameters by minimizing a loss function to obtain a final interferometric phase unwrapping model. According to the method, the interferometric phase unwrapping of the low-coherence region is realized under the condition of lightweight parameters, the phase continuity hypothesis is not depended on, and the interferometric measurement precision of the synthetic aperture radar is improved.

The invention discloses a construction period landslide disaster advanced prediction and rapid networking monitoring method, and the method comprises the steps: collecting synthetic aperture radar data, digital elevation model data and satellite precision orbit data of a monitoring region, carrying out the data preprocessing, and obtaining a single-view complex data image set; constructing an interference image pair connection graph, performing interference processing, and generating a differential interference graph and a coherence coefficient graph; performing interferometric phase unwrapping processing on the differential interferogram and the coherence coefficient graph according to a minimum cost flow algorithm to obtain earth surface deformation information; ground control points are selected, and the surface accumulated deformation quantity and the surface deformation rate are obtained; interpreting and delineating a potential deformation area, establishing an interpretation mark, selecting a typical deformation area, and extracting a time sequence deformation data set; and analyzing the time sequence deformation data set according to the neural network model to obtain a deformation prediction result and a disaster risk assessment result. According to the invention, the monitoring accuracy and prediction capability of landslide disasters can be improved.

The present invention relates to a method for the computer-aided generation of a data-driven model for the computer-aided processing of digital SAR data from a repeat-pass SAR system configured to determine surface deformations. The digital SAR data consist of a plurality of interferograms (Γ), each of which is obtained from a first SLC acquisition (s1) of the SAR system at a first time point and a second SLC acquisition (s2) of the SAR system at a second time point later than the first, and a Doppler center frequency (fdc), an interferometric phase (φ̂), and a coherence (P), all of which are obtained from a respective pair of first and second SLC acquisitions (s1, s2). The data-driven model is configured to generate a zero-Doppler phase (φzd), which represents the phase contribution associated with the deformation causing a zero-Doppler LOS change.to determine an along-track phase (φat), representing the phase contribution associated with deformation in an along-track direction, and an estimate of an associated along-track surface motion (uat). The generation of the data-driven model comprises the following steps: - Generating a synthetic training dataset (TDS) comprising a variety of interferograms (Γ), each formed by a pair of a first and a second SLC capture (s1, s2), by inducing a model-based and external 3D deformation into a given first SLC capture (s1) to obtain the associated second SLC capture (s2), which relates to a time point after a deformation event, and by applying a reverse approach,which is specified by a given coherence (ρ) and a noise-free interferometric phase (φtheo) after the deformation event; - Training a neural network (NN) with the training dataset (TDS), wherein the neural network (NN) comprises an input for inputting the plurality of interferograms (Γ) and an output for outputting the along-track phase (Φat), the zero-Doppler phase (φzd) and the associated along-track surface motion (uat) for the plurality of interferograms (Γ), wherein the trained neural network (NN) represents the generated data-driven model.

The invention discloses a phase feedback all-optical non-magnetic temperature control device and method based on an FPGA, the device comprises an interferometer module, an FPGA system module and a temperature control module, and through the coordination work of the interferometer module, the FPGA system module and the temperature control module, magnetic noise can be reduced and high-precision stable temperature control can be completed on the basis that a traditional temperature sensor is not used. According to the method, a corresponding state-space equation is constructed by using a corresponding relation between an interferometric phase and temperature in an interferometer, a segmented Kalman filtering algorithm is executed through an FPGA, temperature information hidden in an interferometer signal is extracted, the temperature information is converted into a voltage signal, and a stable temperature control effect is achieved through interaction with a temperature control chip.

The invention relates to the technical field of landslide geological recognition, in particular to a reservoir area landslide geological disaster recognition method and system, and the method comprises the steps: collecting SAR images and DEM data of a reservoir area at each time point; forming the SAR images of the adjacent time points into master and slave SAR images, obtaining an interferogram of the master and slave SAR images, and dividing the interferogram of the master and slave SAR images into an incoherent water area, a terrain transition area and a high-coherence stable area; determining a terrain phase gradient of each pixel point in the interferogram, obtaining a terrain gradient amplitude of each pixel point, correcting an objective function of a least square phase unwrapping algorithm, and obtaining an interferometric phase after phase unwrapping; elevation information corresponding to the interferogram is determined, the average deformation rate of each pixel point is calculated, and the potential landslide risk of the reservoir area corresponding to each pixel point is identified. Therefore, the accuracy of reservoir area landslide risk identification is improved.

The invention relates to a single-rail InSAR-based mining area large-gradient deformation three-dimensional calculation method, which comprises the following steps of: performing interference processing on InSAR data to obtain an interference pattern, acquiring mining area earth surface deformation by a prediction or actual measurement method, and converting the mining area earth surface deformation into a deformation phase as a reference phase; removing a reference phase from an InSAR interferometric phase to obtain an interferogram residual wrapping phase with reduced fringe density and reduced unwrapping difficulty; unwrapping the residual winding phase to obtain a residual phase, and superposing the residual phase with the reference phase to realize the repair of the InSAR deformation phase; deducing a surface movement vector depression angle function; based on the earth surface point movement vector depression angle function, the earth surface point horizontal movement vector direction and the radar beam LOS direction space parameters, the expression of the included angle between the LOS direction and the earth surface movement vector is solved, and earth surface three-dimensional deformation is solved through monorail InSAR deformation.

The invention discloses a single-bit interference synthetic aperture radar data processing method based on a joint channel variable threshold, which relates to the technical field of signal processing, and comprises the following steps: firstly, setting a quantization threshold, and separating noise and useful signals in a frequency spectrum; then designing a band-pass filter to suppress quantization noise; finally, according to the strong correlation of adjacent pixel points of the radar image and the weak correlation of residual quantization noise, coherent accumulation is carried out on pixel energy, and the signal-to-noise ratio is improved; according to the single-bit interferometric synthetic aperture radar data processing method based on the joint channel variable threshold, the estimation precision of the interferometric phase can be improved, so that a high-quality ground object digital elevation image is obtained.

The invention relates to the technical field, and discloses a ground-based synthetic aperture radar atmospheric phase correction method and system, and the method comprises the steps: sequentially carrying out the permanent scattering point selection and phase unwrapping of collected time sequence single-view complex image data, and obtaining the first time sequence interference phase data of a permanent scattering point in the time sequence single-view complex image data; performing time sequence clustering analysis on the first time sequence interferometric phase data to obtain a plurality of permanent scattering point homogeneous regions, and performing atmospheric phase local correction on the first time sequence interferometric phase data of the permanent scattering points in each permanent scattering point homogeneous region by adopting a pre-constructed atmospheric phase correction linear model to obtain a plurality of permanent scattering point homogeneous regions; and performing time sequence low-pass filtering processing on the obtained second time sequence interference phase data corresponding to the permanent scattering points in each permanent scattering point homogeneous region to obtain corrected deformation image data corresponding to each permanent scattering point homogeneous region. According to the method, the precision of atmospheric phase correction is effectively improved.

The invention discloses a phase unwrapping method for an SAR (Synthetic Aperture Radar) interferometric phase diagram. The phase unwrapping method comprises the following steps: acquiring prior information of the interferometric phase diagram; based on prior information, unwrapping the interferometric phase diagram to obtain an absolute phase; elevation information is extracted according to the absolute phase; according to the elevation information, the prior information is reversely optimized. The acquisition of the prior information is based on image segmentation of the interferometric phase diagram. According to the phase unwrapping method for the SAR interferometric phase diagram and the related product, all segmented areas are executed one by one, all reconstruction results are further geocoded and integrated, fine three-dimensional reconstruction of a building can be achieved, target extraction is conducted by fusing structural features in the two-dimensional imaging direction, and the construction efficiency is improved. And the accuracy of building identification is obviously improved. The structure auxiliary information of image segmentation effectively reduces the risk of phase discontinuity in the phase unwrapping process, significantly reduces the unwrapping error, and achieves the high-precision and low-cost three-dimensional reconstruction of a building under the condition of a double-track long baseline.

The invention discloses a feature fusion type permanent scatterer recognition technology based on deep learning. The method comprises the following steps: firstly, constructing a residual attention U-shaped network to extract SAR image spatial features, and constructing a C-LSTM network to extract differential interferometric phase time sequence features; a feature fusion module is constructed to couple spatial features and time sequence features, and a prediction module is constructed to output a dual-channel permanent scatterer recognition result; and finally, obtaining a single-channel permanent scatterer identification result through channel maximum value screening, and further obtaining the spatial distribution of the permanent scatterer in the research area by using image block splicing and geocoding technologies. According to the method, a data driving mode is adopted, and rapid recognition of the permanent scatterer can be realized through network training; according to the permanent scatterer identification technology provided by the invention, mixed loss of weighted summation of focus loss and residual point loss is adopted, the problem of imbalance of positive and negative difficult samples is solved, and effectiveness evaluation of an identification result acting on a downstream task is realized; according to the method, permanent scatterer identification is carried out by using the constructed feature fusion type network and the mixed loss, and a relatively small number of residual points can be kept while a relatively large number of permanent scatterers are identified, so that the identification result has relatively large possibility when acting on a downstream task, and an accurate earth surface deformation result can be obtained.

The invention discloses a glucose metabolism dynamic monitoring method and system based on multispectral imaging, and relates to the technical field of glucose metabolism dynamic monitoring, and the method comprises the following steps: obtaining polarization state distribution data and interferometric phase data of a target tissue, and constructing a Jones matrix; performing inverse polarization transformation compensation on the Jones matrix to obtain a polarization interference signal and extract a phase distribution diagram; dividing the phase distribution diagram into a plurality of sub-regions, extracting a phase gradient feature of each sub-region, obtaining a plurality of phase resolving threshold candidate sets, and performing ambiguity suppression to obtain a plurality of first phase mapping features; constructing a three-dimensional glucose metabolism activity model by adopting a feature fusion algorithm, and carrying out glucose metabolism abnormality identification; and data analysis is performed on an identification result to obtain an optimal phase solution threshold value, and the optimal phase solution threshold value is applied to the glucose metabolism monitoring process, so that the problem that the accuracy of noninvasive, dynamic and spatial resolution and high-precision monitoring of the tissue glucose metabolism process is reduced due to the fact that the precision of phase solution ambiguity of a traditional method is unstable is solved.

The invention discloses a winding interference phase-based creep type landslide hidden danger intelligent identification method and system, and the method comprises the steps: firstly obtaining multi-scene time continuous winding interference pattern data, and carrying out the up-sampling of the data to a fixed input size of 128 * 128 through bilinear interpolation; then, sine and cosine functions are utilized to map the wrapping interferogram, a sine phase and a cosine phase are generated, and phase information is further enriched. And through a set mapping function, converting the phase data into an RGB color space so as to realize multi-dimensional expression of the data. In the feature extraction and fusion stage, a multi-branch network architecture sharing weight is adopted, independent feature extraction is performed on each RGB channel, and the same-layer features are integrated through a feature fusion mechanism to extract high-dimensional semantic information. And then, feature information is recovered step by step through a decoder, classification mapping is completed, and finally a prediction result of the single-scene interferogram is obtained.

The invention discloses an MIMO ATI SAR two-dimensional ocean current measurement system parameter design method based on a genetic algorithm, and the method comprises the steps: building the observation geometry of an MIMO ATI SAR system, and determining a sea surface flow field measurement system index, an interferometric phase expression, and a sea surface two-dimensional velocity vector expression; according to the sea surface two-dimensional velocity vector expression, determining geometric configuration parameters influencing velocity vector errors, and establishing a mathematical relationship between the geometric configuration parameters and the velocity vector errors; determining MIMO ATI SAR system parameters influencing the velocity vector error, and establishing a mathematical relationship between the MIMO ATI SAR system parameters and the velocity vector error; establishing a sea surface two-dimensional velocity vector error objective function based on the mathematical relationship between the geometric configuration parameters and the velocity vector error and the mathematical relationship between the MIMO ATI SAR system parameters and the velocity vector error; and solving the sea surface two-dimensional velocity vector error objective function based on a genetic algorithm to obtain the optimal geometric configuration parameters of the MIMO ATI SAR system and the optimal parameters of the MIMO ATI SAR system.

The invention provides a multi-baseline InSAR residual phase error correction method and system based on flat ground prior information, and the method comprises the steps: selecting a large flat ground region and a small flat ground region for the terrain prior knowledge of a research region, constructing a residual linear phase model, removing the residual linear error phase of a multi-baseline InSAR interferometric phase, and carrying out the correction of the multi-baseline InSAR residual phase error. The function of improving the phase error accuracy and the correction speed of the multi-baseline InSAR is achieved. According to the method, the correction precision of the interference phase of the multi-baseline InSAR after flat ground removal is improved, the phase error correction precision under the condition of small baselines is high, and the terrain reconstruction precision of the multi-baseline InSAR is high; and a reliable data basis is provided for accurate reconstruction of the multi-baseline InSAR terrain. According to the method, the speed of the error phase correction process is high, and the consumption of computing resources and the hardware cost of the system are reduced.

The invention relates to the technical field of radar remote sensing, and discloses a high-precision deformation monitoring and target imaging method based on MIMO-SAR. The method comprises the following steps: receiving an original MIMO-SAR echo data stream and a Beidou navigation system positioning data stream acquired by an image sensor; performing standardized preprocessing on the original echo data to generate standard radar data blocks; aligning radar data blocks and Beidou positioning data time and space, and establishing a mapping relation between data and a spatial position; performing sub-aperture division on the aligned data blocks to generate a multi-sub-aperture data set; performing beam forming processing on each sub-aperture data set to obtain a multi-view complex image; extracting an interference phase of a plurality of images and carrying out geometric correction by using Beidou data; unwrapping the corrected phase to obtain an absolute phase field; inverting a millimeter-level deformation sequence based on the absolute phase field; dynamically updating imaging focusing parameters according to the deformation sequence time evolution characteristics; and performing high-resolution imaging on subsequent data blocks by using the updated parameters, and outputting a deformation monitoring result and a target image.

The invention provides a forest vertical structure information inversion method and system based on radar interferometry, and the method comprises the steps: carrying out the small-view processing of high-resolution SAR interferometry data with a space baseline, calculating the small-view InSAR phase height, extracting the statistical distribution of the small-view interferometry phase height in a set space window, and carrying out the calculation of the small-view interferometry phase height. Generating a small-looks interferometric phase height histogram; establishing an under-forest ground elevation regression estimation model based on the statistical characteristics of the small-looks interferometric phase height histogram, and determining model parameters to invert the terrain; and two canopy top indexes are extracted, two index results are fused by setting a threshold value, and final forest vertical structure information is generated. The method has the advantages that more canopy internal and under-forest information can be captured only by using single-baseline and single-polarization radar interference data, and the sensitivity to a forest vertical structure is improved; and the forest vertical section, the tree height and the terrain can be synchronously inverted.

An inertial sensor includes a dual atom interferometer. A moving standing light wave generation device in the dual atom interferometer generates M (3≤M) moving standing light waves. Each of the M moving standing light waves is generated by a pair of counter-propagating laser beams. The moving standing light wave generation device adjusts two-photon detuning of a pair of counter-propagating laser beams corresponding to each of N (2≤N<M) moving standing light waves out of the M moving standing light waves such that the difference between an interferometric phase corresponding to one atomic beam obtained by interfering with the M moving standing light waves and an interferometric phase corresponding to the other atomic beam obtained by interfering with the M moving standing light waves stays constant.

The invention discloses an iron tower PS pixel point simulation and differential interference deformation monitoring method and system based on three-dimensional point cloud data, and the method comprises the steps: obtaining the point cloud data of an iron tower body structure under a radar coordinate system through a radar distance Doppler positioning method through the three-dimensional point cloud data of the iron tower body structure; identifying pixels containing the iron tower point cloud data in the SAR image as iron tower PS pixel points; constructing a radar scattering weight model based on elevation information of the iron tower point cloud data, and obtaining an iron tower PS pixel point scattering intensity value through weighted summation in combination with a weight adjustment parameter; constructing a complex value of the point cloud data of the iron tower according to the interferogram parameters and a radar scattering weight model, and obtaining a simulation interference elevation phase of the PS pixel point of the iron tower by using complex field summation; differential interferometric phases of iron tower PS pixel points in the interferogram are obtained through difference; and performing statistical analysis on the differential phase map of the iron tower PS pixel point set region in the time sequence interferogram to obtain a deformation result.

The invention relates to the technical field of geodetic surveying instruments and geological disaster monitoring, and discloses a geological environment displacement surveying system based on a GNSS multipath effect, and the system comprises a signal collection unit which is used for receiving a satellite signal and extracting a signal-to-noise ratio sequence; the interference feature separation unit is used for stripping a direct signal power component and generating a residual oscillation sequence; the geometric inversion calculation unit is used for generating a reference interferometric phase template in a height search domain, and generating a height-correlation map to lock the current vertical height by calculating and superposing correlation coefficients between the residual oscillation sequence and the reference interferometric phase template; according to the invention, through a spatial domain geometric matching mechanism, high-precision distance measurement is realized in a complex environment in which a satellite visual segmental arc is broken, dependence of a traditional spectral analysis method on observation duration is broken through, and signal-to-noise ratio data endogenesis is utilized to decouple observation station attitudes and environmental medium attributes. And the stability and the multi-dimensional perception capability are improved.

The invention relates to a speckle interferometric phase diagram filtering method based on a conditional diffusion model, and belongs to the technical field of optical measurement. The method comprises the following steps: S1, obtaining training data: generating or collecting multiple groups of paired speckle interferometric phase diagrams including noisy speckle interferometric phase diagrams and corresponding denoising reference phase diagrams; s2, constructing a conditional diffusion model; S3, training the conditional diffusion model: training the diffusion model in a supervision mode; and S4, taking the to-be-processed phase diagram as a conditional image, and generating a denoised phase diagram through a back diffusion process. According to the invention, the noise processing accuracy of the phase diagram in laser speckle interference measurement can be improved.

The invention discloses an adaptive multi-loop network InSAR phase unwrapping method and device, and relates to the technical field of interferometric synthetic aperture radar measurement. The method comprises the following steps: acquiring an interferometric phase diagram and a topographic map of a complex topographic region; calculating coherence, a topographic gradient map, a phase gradient standard deviation and residual density based on the interferometric phase map and the topographic map; based on the coherence, the terrain gradient map, the phase gradient standard deviation and the residual density, dynamically selecting the size of a ring through a decision function, and constructing a multi-ring network; determining the connectivity of the multi-ring network according to a bridging strategy and a connectivity detection method, and outputting a global connected network; on the basis of the global connected network, layered residual optimization is carried out by adopting a local absorption and global planning double-layer strategy, and an optimized residual is output; and based on the optimized residual error, unwrapping all rings in the global connected network through an in-ring local unwrapping and global optimization strategy, and outputting an unwrapping result. According to the invention, the problem of network connectivity in a complex area can be solved.

The application provides a complex terrain area elevation inversion method based on small unmanned aerial vehicle borne interferometric SAR. N-track small unmanned aerial vehicle flight tracks are set, the X-axis and Y-axis coordinates of the starting point and ending point of each track small unmanned aerial vehicle are unchanged, and the value of the Z-axis is gradually increased along the vertical direction. N-track echo data are obtained by the small unmanned aerial vehicle borne SAR according to the set tracks, and BP algorithm is used for imaging processing to obtain N SAR images. The offset of the SAR images of adjacent short baseline small unmanned aerial vehicles is obtained. The offset of the SAR images of long baseline small unmanned aerial vehicles is calculated based on the offset of the SAR images of small unmanned aerial vehicles, and the SAR images of long baseline are image matched. The SAR images after image matching are processed to obtain corresponding interference phase images, and the interference phase images are filtered by using Goldstein algorithm. Multi-baseline phase unwrapping is performed based on the interference phase of the interference phase images. The complex terrain area elevation inversion method of SAR is realized.

The invention discloses a high-precision InSAR (Interferometric Synthetic Aperture Radar) deformation monitoring method combining polarization information and deep learning, and particularly relates to the crossing field of radar remote sensing and deep learning technologies. The method comprises the following steps: acquiring multi-temporal complete polarization or dual polarization SAR original data, generating single-view complex data through radiometric calibration, registration and multi-view processing, and extracting a polarization coherence matrix; constructing a sample set containing polarization features and interferometric phase labels; designing a double-branch deep learning network model composed of a polarization feature extraction branch and a phase optimization branch, and processing a polarization matrix and a phase sequence by adopting a three-dimensional convolution structure and a long and short term memory-convolution hybrid structure; the deep polarization features and the optimized phase information are fused through the trained model, and the deformation quantity is output through regression calculation; and finally, generating a deformation graph under a geodetic coordinate system through geocoding. The polarization scattering characteristic is effectively utilized, and the monitoring precision and reliability in a complex scene are improved.