2703 results about "Image registration" patented technology

A method for aligning a sample to an array is provided. An image of sample image of a sample can be received by a data processor. The sample image having a first resolution. An array image including an overlay of an array with the sample and an array fiducial can be received by the data processor. The array image having a second resolution lower than the first resolution of the sample image. The sample image can be registered to the array image by aligning the sample image and the array image. An aligned image can be generated based on the registering. The aligned image can include an overlay of the sample image with the array. The aligned image can be provided by the data processor. A method for detecting fiducials associated with an array is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

The invention relates to the technical field of geological monitoring, in particular to a method for intelligently monitoring geological disasters based on satellite remote sensing, which comprises the following steps of: obtaining corresponding earth surface coverage information through image registration and ortho-rectification processing; identifying terrain boundaries, geological structure features and geological attribute information through the land surface coverage information; recognizing surrounding hidden danger points through a topographic feature extraction method; an image fusion enhancement method is matched with a permanent scatterer interference method, a stable scatterer is extracted, a deformation monitoring network is constructed, and a phase difference value in a high-risk area is obtained through calculation; carrying out unwrapping processing through the phase difference value, and carrying out inversion to obtain earth surface three-dimensional deformation field information in the monitoring area; establishing a geological disaster risk assessment model through space-time clustering analysis; by establishing a risk assessment model, the geological disaster risk in the monitoring area is quantitatively assessed. The beneficial effect of the invention is that comprehensive monitoring and accurate early warning of geological hazard hidden dangers around the transformer substation are realized.

A method for aligning a sample to an array is provided. An image of sample image of a sample can be received by a data processor. The sample image having a first resolution. An array image including an overlay of an array with the sample and an array fiducial can be received by the data processor. The array image having a second resolution lower than the first resolution of the sample image. The sample image can be regisered to the array image by aligning the sample image and the array image. An aligned image can be generated based on the registering. The aligned image can can include an overlay of the sample image with the array. The aligned image can be provided by the data processor. A method for detecing fiducials associated with an array is provided. Systems and non-transitory computer readable mediums performing the method are also provided.

The invention provides a multi-modal image analysis-based robot-assisted wound treatment method and system, and relates to the technical field of robots, and the method comprises the steps: collecting a multi-modal image of a to-be-treated wound, determining an image registration result, obtaining a wound region image through a multi-scale fusion algorithm and wound region segmentation, and carrying out the type classification of wound tissues. A wound tissue type classification result is obtained; performing surrounding shooting on a wound area to obtain a wound multi-view image, registering subsequent depth maps frame by frame by taking a first frame of depth map as a reference, accumulating point cloud data, determining a wound three-dimensional point cloud, generating a wound three-dimensional reconstruction result by combining a curved surface evolution algorithm, and determining a wound parameter measurement result; based on the wound tissue type classification result and the wound parameter measurement result, a robot path planning algorithm is adopted, the optimal movement track of the robot is generated, the movement parameters of the robot are adjusted in real time through a robot control algorithm of vision and force feedback, and wound adjuvant therapy is completed.

The invention provides a remote sensing strip mine area detection method based on a double-branch structure and a feature fusion mechanism, and the method comprises the steps: obtaining multi-source high-resolution remote sensing image data of a strip mine mining area, carrying out the preprocessing of atmospheric correction, radiation calibration, color correction, image registration, cutting operation and the like, and obtaining time sequence remote sensing image data; a deep learning algorithm is adopted to construct a strip mine area detection model based on a double-branch structure and a feature fusion mechanism, training is carried out through the time sequence remote sensing image data, a remote sensing image detection model is obtained, the double-branch structure comprises a feature extraction branch and a feature generation branch, and the feature extraction branch comprises a feature extraction branch and a feature fusion branch; the feature fusion mechanism comprises a cross attention fusion module and a feature adaptive fusion module; inputting to-be-detected remote sensing image data into the remote sensing image detection model to obtain a detection result of the strip mine mining area. According to the method, the recognition precision of small target details and mining area boundaries of low-resolution images is improved, and the calculation efficiency and the detection precision are both considered.

The invention provides a chip welding method based on machine vision and control, and the method comprises the steps: obtaining a three-dimensional model of a chip and a substrate, obtaining an initial relative position of a chip welding spot and a substrate welding pad, and generating a mechanical arm initial motion instruction; images of the chip and the substrate are shot, pose deviation is calculated through image registration, if the deviation exceeds a threshold value, a compensation instruction is generated to be fused with an initial instruction, and the track of a mechanical arm is adjusted to be aligned with a bonding pad; a temperature sensor array is constructed to monitor the temperature of the welding area, the average temperature is calculated, and if a difference exists between the average temperature and the ideal welding temperature interval, the heater power is adjusted through a proportional integral differential algorithm; a piezoelectric sensor is used for obtaining a mechanical arm joint vibration signal, the dominant vibration frequency and amplitude are extracted through fast Fourier transform, and a piezoelectric actuator is used for generating an anti-phase driving signal to counteract vibration.

The invention discloses a mild traumatic brain injury image evaluation system based on deep learning, and relates to the technical field of medical imaging, and the system comprises a data source module which is used for obtaining head magnetic resonance imaging data as an input data source; the preprocessing module is used for carrying out de-noising processing and image registration on the skull magnetic resonance imaging data to realize standardized preprocessing; and the feature extraction module is used for extracting a local feature map from the multi-dimensional image data by constructing a multilayer convolutional neural network, and identifying a subcortical structure change and damage suspicious region. According to the mild traumatic brain injury image evaluation system based on deep learning provided by the invention, a standardized evaluation report automatically generated by the system not only integrates clinical information of a patient, but also provides detailed injury types, severity and follow-up visit suggestions, so that the efficiency and reliability of clinical decision making are greatly improved; and powerful support is provided for formulating an individualized diagnosis and treatment scheme.

The invention relates to the technical field of remote sensing monitoring, in particular to an SAR satellite data driven freeze-thaw landslide deformation monitoring system which comprises a multi-band SAR data acquisition module, a data preprocessing module, a deformation detection module, a dynamic modeling module, a risk assessment early warning module and a report comprehensive display module. Wherein the multi-band SAR data acquisition module is used for acquiring multi-band radar image data; the data preprocessing module is used for carrying out denoising, correction and image registration processing; the deformation detection module is used for generating a high-precision earth surface deformation graph; the dynamic modeling module is used for constructing a dynamic geologic model of the freeze-thaw landslide; and the risk assessment early warning module is used for carrying out landslide risk assessment. According to the invention, through integration of SAR satellite data and dynamic geological modeling, the precision and real-time performance of freeze-thaw landslide monitoring are significantly improved, the accuracy and timeliness of disaster early warning are enhanced, and efficient scientific and technological support is provided for geological disaster management.

The invention provides a burn area measurement method and system, electronic equipment and a storage medium, and relates to the field of area measurement. The method comprises the following steps: photographing a target part to obtain a multi-modal burn image set, and carrying out image registration; and constructing a global three-dimensional surface model based on the registered image set. Carrying out burn area identification segmentation on the multi-mode burn image, mapping a segmentation result to the global three-dimensional surface model, and obtaining a preliminary burn area three-dimensional surface model; and performing multi-scale feature extraction and fusion on the registered image set, and inputting a depth estimation model to obtain burn depth estimation data. And performing deep layering on the preliminary burn area three-dimensional surface model based on the burn depth estimation data to obtain burn area three-dimensional images of different depth levels. And calculating the actual burn area of each depth level based on the burn area three-dimensional images of different depth levels and the camera focal length information. Through the method, the accuracy of burn area measurement is improved.

The invention provides a large-scale satellite three-dimensional image data processing method, medium and system, and belongs to the technical field of satellite three-dimensional image data processing.The method comprises the steps that Gaussian filtering and wavelet noise reduction preprocessing is conducted on an image, and then cooperative computing of a GPU and a CPU is achieved based on a computing task distribution function. Through multi-scale feature extraction and matrix optimization, a stable feature matrix and a variable feature matrix are obtained. A quadtree spatial index is adopted to carry out data partitioning, and SIFT feature extraction and K-means clustering are combined to optimize feature representation. And establishing a stereoscopic image registration model, determining an optimal splicing sequence through a minimum spanning tree algorithm, realizing image splicing by adopting a progressive texture fusion method, and finally generating a high-precision three-dimensional earth surface model. According to the invention, the unification of processing efficiency and precision is realized, and the technical problem that the processing efficiency of large-scale stereoscopic image data is difficult to improve on the premise of ensuring the processing precision in the prior art is solved.

The invention provides an intelligent cleaning decision-making method and system for a photovoltaic power station based on machine learning, and relates to the technical field of photovoltaic power stations, and the method comprises the steps: collecting images through a visible light camera and an infrared thermal imaging camera carried by an unmanned plane, segmenting a pollution region based on color, texture and temperature features through a deep learning model after image registration and preprocessing, and obtaining a cleaning decision-making result; and calculating a pollution area proportion and establishing an evaluation index in combination with the temperature difference value so as to analyze the power generation efficiency loss and generate a pollution distribution map and an evaluation report. The pollution area can be accurately identified, the influence of the pollution degree on the power generation efficiency is quantitatively evaluated, and a decision basis is provided for power station cleaning maintenance.

The invention relates to the field of image comparison, and discloses a multi-layer PCB alignment deviation detection system and method based on image comparison, and the method comprises the steps: obtaining high-resolution image data before and after lamination of a multi-layer PCB, carrying out the unified registration preprocessing of an original image through the combination of a multi-mode image fusion algorithm and a geometric distortion correction technology, and obtaining a multi-layer PCB alignment deviation detection result; constructing a standardized image registration input set; key alignment feature extraction is carried out on the image registration input set, and a multilayer structure graph model is constructed based on a graph neural network; in combination with the alignment reference map, dynamically adjusting an image comparison window and a search region by adopting a region attention mechanism and a local adaptive matching algorithm, and constructing an alignment deviation mapping map; constructing a deviation evolution model by using a time sequence behavior recognition network according to the constructed alignment deviation mapping graph and historical process deviation data; and performing comprehensive evaluation on the image registration input set based on the deviation evolution model and the generated early warning information. The method has the advantage of improving the accurate detection level.

The invention relates to the technical field of multi-modal medical image registration optimization methods, in particular to a medical image registration optimization method and system, and the method comprises the following steps: inputting a medical image data set comprising an MRI image data set, a CT image data set and a PET image data set into a trained deformable registration network; obtaining a deformation field by using the deformable registration network; obtaining a final registration result of the two groups of medical image data based on the deformation field, obtaining an optimized final registration result through an iterative optimization algorithm, and obtaining a quality evaluation coefficient at the same time; constructing an optimized objective function by using the quality evaluation coefficient, and realizing the adjustment of the hyper-parameter of the deformable registration network through the back propagation of the optimized objective function; prediction registration is carried out by using the deformable registration network after hyper-parameter adjustment, so that an optimized registration result is obtained, the position and the shape of a convolution kernel are adaptively adjusted based on a feature extraction network of deformable convolution, and nonlinear deformation of a local anatomical structure is better captured.

The invention discloses an image registration and segmentation joint optimization method, system, device and medium, and relates to the technical field of image registration and segmentation, and the method comprises the steps: obtaining an original medical image, and employing a dynamic pairing strategy to obtain a floating image and a fixed image; the method comprises the following steps: constructing an image registration and segmentation joint optimization model, wherein the model comprises an encoder sharing weight, a segmentation decoder, a registration decoder and an uncertainty estimator; and inputting the floating image and the fixed image into an image registration and segmentation joint optimization model to obtain a registered image and a tag thereof, inputting the fixed image and the registered image into an uncertainty estimator to predict the uncertainty of registration, and minimizing a loss function to obtain an updated image registration and segmentation joint optimization model. According to the method, the problem of class imbalance of different anatomical structure labels can be relieved, and the forward action of the layered segmentation feature map in the deformation subfield generation process can be promoted.

The invention discloses a visible light-infrared dual-mode image registration method based on deep learning. The method comprises the following steps: acquiring a visible light image and an infrared image in the same scene; preprocessing the image; performing feature extraction on the preprocessed visible light image and the preprocessed infrared image, and performing feature enhancement on a feature extraction result by using a double attention mechanism; performing multi-scale feature fusion on the enhanced visible light image features and the enhanced infrared image features to generate cross-modal fusion features; inputting the cross-modal fusion features into a deformation field estimation network to generate an estimation result of a deformation field; and applying the estimation result of the deformation field to the infrared image to register the corresponding visible light image. According to the method, the problem of cross-modal registration between the visible light image and the infrared thermal imaging of the live pig body temperature non-contact prediction scene is effectively solved, high-precision pixel-level alignment is realized, and a technical basis is provided for non-contact pig body temperature monitoring.

The invention discloses an on-bridge pedestrian abnormal behavior identification and early warning method based on intelligent monitoring, and relates to the technical field of bridge engineering, and the method comprises the steps: carrying out the registration of a current visible light image and a current infrared image, and obtaining a registration dual-mode image based on a current image coordinate system; considering current bridge deformation to establish a current coordinate mapping conversion model for mapping pixel coordinate points in the current image coordinate system to physical coordinate points of a current bridge physical space coordinate system; obtaining the current three-dimensional actual coordinates of the pedestrian; recognizing abnormal behaviors of the pedestrians on the bridge based on the current three-dimensional actual coordinates of the pedestrians and acquiring current dynamic risk score values of the pedestrians; and determining the current risk level of the pedestrian according to the current dynamic risk score value of the pedestrian and a preset risk threshold value to realize multi-level linkage protection response. The method provided by the invention solves the technical problem in the prior art that the current actual coordinate of the pedestrian is difficult to accurately obtain due to environmental interference, and the abnormal recognition of the pedestrian on the bridge is inaccurate.

The invention relates to the technical field of geological exploration and three-dimensional modeling, in particular to a mining unmanned aerial vehicle high-precision three-dimensional geological modeling system which comprises a multispectral imaging acquisition module, a point cloud-image high-precision registration module, a geological feature fusion extraction module, a three-dimensional geological model construction module and a geological attribute visualization output module. Wherein the multispectral imaging acquisition module is used for acquiring registration image data and original point cloud data; the point cloud-image high-precision registration module is used for carrying out consistency matching and outputting a registration point cloud-image data set; the geological feature fusion extraction module is used for identifying geological boundaries and outputting geological feature vector data; and the three-dimensional geologic model construction module is used for generating a three-dimensional grid model with attribute topology. According to the method, through accurate three-dimensional modeling and lithology visualization, geological features and lithology information are efficiently combined, and reliable data support is provided for rock mass stability analysis.

Systems, methods, and computer-readable media for aerial localization in the absence of satellite (e.g., global navigation satellite system (GNSS)) signals while flying over heterogeneous environments. The present embodiments provide a training strategy for robust cross-seasonal image matching in complex, real-world environments. The challenge put forward is to successfully localize a UAV flying over both densely and sparsely populated areas by means of image registration. To this end, the focus of the present embodiments can be on devising loss components to a visual recognition network that adequately embeds the diverse feature types on the ground. A normalized error metric can be provided as well in order to render a conceptually different method comparable to the present approach. The present technical contributions can provide robust visual recognition algorithms in challenging natural environments.

The invention relates to the technical field of image processing, and discloses a virtual operation analogue simulation method and system based on a mixed reality technology. The method comprises the steps that three-dimensional space information is collected, feature extraction and registration are conducted on a real operation environment and video data, and mixed reality mapping and monitoring data are generated; performing medical image registration processing based on mixed reality mapping to obtain virtual scene data; processing instrument sensing data according to the mapping data and the monitoring parameters to obtain interaction data; performing surgical process simulation according to the virtual scene and the interaction data; performing operation trajectory analysis and identification based on the simulation data, and generating evaluation and early warning information; and analyzing the evaluation and early warning information, and outputting an analysis report. Through multi-source data fusion and a real-time evaluation feedback mechanism, the authenticity and accuracy of operation simulation training are effectively improved.

The invention relates to the technical field of path planning, in particular to a path planning method and system for an inspection robot, and provides the following scheme: executing an inspection task through an initial electronic map, collecting first image data and pose information, recognizing an inspection target, completing image registration, and generating second image data; inverting and calibrating the optimal observation direction based on the structural sensitivity distribution of the local visual features and the illumination variation trend, and determining the shooting position and angle to form a path node; a multi-objective optimization model is adopted, the path length, the view angle continuity and the illumination interference are comprehensively considered, and an optimal inspection path is generated; the method is suitable for outdoor inspection scenes with complex structures and non-uniform target distribution.

The invention relates to the technical field of industrial detection, and discloses a multi-channel self-adaptive glass component defect detection method which sequentially comprises the following steps: S1, multi-channel image acquisition; performing rapid switching and imaging of three illumination modes by adopting a time-sharing stroboscopic technology to obtain a multi-channel image of the glass component; s2, multi-channel image registration is carried out; s2, registering the multi-channel images obtained in the step S1 so as to align the features of the multi-channel images, and obtaining registered multi-channel images; s3, multi-channel feature extraction and fusion; performing single-channel feature extraction on the registered multi-channel image, and combining a channel attention mechanism and a space attention mechanism to obtain a multi-channel image fusion feature map; s4, segmenting a defect region; and S5, a post-processing scoring mechanism is used for preventing over-killing. Through a multi-channel image acquisition technology, multi-scale feature extraction in deep learning, a self-adaptive attention mechanism and a post-processing scoring mechanism are combined to prevent over-killing, and more efficient and more accurate plane glass component defect detection is carried out.

The invention discloses a multi-modal thermal imager intelligent monitoring method based on image processing. The method comprises the following steps: S1, performing dual-band synchronous imaging; s2, carrying out cross-modal image registration; s3, multispectral image fusion: performing smoothing processing on the infrared and visible light images through anisotropic diffusion to obtain a main structure layer image, calculating to obtain an infrared and visible light detail layer image, and linearly combining the detail layer image through PCA principal component weight, and finally, combining the detail layer image and the main structure layer image according to the self-defined weight to obtain a final fusion image. And S4, gas leakage detection is carried out, so that gas leakage detection and early warning can be realized. According to the invention, a gas enhancement algorithm is adopted to realize real-time detection of leaked gas, interference of most background noise can be eliminated, and the real-time performance and reliability of the system are improved.

The invention discloses an unsupervised deep learning image splicing method for portable fundus color photo equipment. The method comprises the following steps: constructing a multi-scale feature extraction module of an image registration network; the feature matching module is used for constructing an image registration network; the multi-scale feature extraction module and the feature matching module are sequentially connected to form the constructed image registration network; constructing a shallow feature extractor; constructing a feature reconstruction module; constructing a joint generator; sequentially connecting the shallow feature extractor, the feature reconstruction module and the seam generator to form a fusion network; constructing a fusion network; connecting the image registration network and the image fusion network to form an image splicing model; collecting data; preprocessing the data; obtaining a trained image registration network; obtaining a trained image fusion network; forming a fundus image splicing model by using the trained image registration network and the trained image fusion network; and splicing the images by using the eye fundus image splicing model. According to the method, a high-quality fundus spliced image can be efficiently generated.

The invention provides a prostate intervention guiding method and system based on PSMA PET, MRI and TRUS multi-mode fusion, a storage medium and electronic equipment, and the method comprises the steps: obtaining a preoperative MRI image and a PSMA PET / CT image, and carrying out the marking of a prostate focus; registering the PSMA PET image to a coordinate system corresponding to MRI (Magnetic Resonance Imaging); a TRUS image obtained in the operation is registered to a coordinate system corresponding to the MRI image, and an MRI-PSMA PET-TRUS fusion image is obtained; and based on the fused image, mapping the marking result of the prostate lesion to the TRUS image, and guiding the prostate interventional operation. Preoperative traditional MRI and PSMA PET are subjected to static fusion and then subjected to real-time fusion with intraoperative TRUS, multi-modal image features are combined, the reliability of an image focus area is improved, the positive effect on improving the accuracy of an interventional operation is achieved, and unnecessary injuries are reduced.

The present invention belongs to the field of medical image registration, and more specifically, relates to a building method of a multi-modal three-dimensional medical image segmentation and registration model, and an application thereof. The method includes: acquiring medical images of two modalities of each target, and respectively using the images as a reference image and a floating image to acquire a training sample; and using the training sample to simultaneously optimize three network parameters, so as to acquire a segmentation and registration model formed by a reference image segmentation model, a floating image segmentation model, and a registration model. The reference image and floating image segmentation models are respectively used to perform multi-scale segmentation on corresponding images to acquire multi-scale segmentation results having the same maximum scale as the original images. The registration model are used to acquire a multi-scale deformation field on the basis of the reference image, the floating image, a maximum scale reference image segmentation result, and a maximum scale floating image segmentation result. Each of a segmentation loss and a registration loss are the sum of segmentation and registration losses in each scale, and the segmentation loss includes a first-order gradient loss and / or a level set energy function loss. The present invention can improve registration accuracy.

The invention discloses a microscopic image seamless splicing and enhanced reconstruction method based on deep learning, and the method comprises the following steps: S1, collecting a plurality of original images with overlapped regions, and recording the spatial position information and imaging parameters of the original images; s2, preprocessing the original image to generate a standardized image sequence; s3, inputting the standardized image into a structure perception feature extraction network, and extracting a feature map fusing textures and structures; s4, inputting the feature image and the original image into an image registration module; s5, inputting the registration image into the boundary attention splicing network; s6, inputting the seamless image into the residual hierarchy reconstruction network, and enhancing image details through hole convolution and multi-scale branches; s7, image quality evaluation is executed, and the structural similarity, the signal-to-noise ratio and the edge retention rate are calculated; and S8, constructing a training set and carrying out end-to-end training optimization based on a joint loss function. According to the method, a multi-module deep network is fused, and seamless splicing and high-quality enhanced reconstruction of microscopic images are realized.

The invention provides a photovoltaic panel damage detection method, a medium and an electronic device, belongs to the technical field of photovoltaic panel damage detection, and aims to acquire a photovoltaic panel surface image and a fluorescence image through an unmanned aerial vehicle, perform image registration and semantic segmentation by using a deep learning algorithm, accurately separate out a photovoltaic panel area and improve the detection accuracy. And then inputting into a pre-trained defect detection model, and detecting and marking various defects and damages on the surface of the photovoltaic panel. The method mainly comprises the steps that 1) an unmanned aerial vehicle collects a surface image and a fluorescence image; 2) image preprocessing and registration; 3) performing semantic segmentation to accurately extract a photovoltaic panel area; (4) detecting and positioning defects; and 5) marking defect information on the image. According to the system, an advanced computer vision technology is utilized, and an intelligent and efficient solution is provided for operation and maintenance of the photovoltaic power station. The technical problem that the existing image analysis method is difficult to accurately capture and identify because some tiny defects often exist on the surface of the photovoltaic panel and the characteristics of the defects are not obvious is solved.

The invention discloses a two-stage infrared and visible light image registration method, system and device. The method comprises the following steps of image preprocessing, contour extraction, angular point detection, feature description and matching, affine transformation estimation, multi-scale optical flow estimation, optical flow constraint and loss function, reverse resampling and fusion and error evaluation. According to the invention, rough registration is carried out by using contour angular point features, so that a preliminary alignment result can be quickly obtained; refined alignment is carried out in combination with an unsupervised optical flow network, and sub-pixel-level registration precision is achieved. The contour angular points are based on shape information of an image target, are natural and are not influenced by spectral differences, and the matching stability is enhanced through main direction and angle features. The unsupervised depth optical flow model estimates a pixel displacement field by learning consistency characteristics of an input image, and does not need to depend on annotation data. The combination can effectively eliminate the difference between infrared light and visible light, and improves the robustness and adaptability of registration.

The invention discloses a small target recognition method and system for a multi-modal fusion image in a complex environment, and belongs to the technical field of computer vision and image recognition, and the method comprises the steps: obtaining a visible light image, an infrared image and environment sensor data; image registration is carried out on visible light and infrared images, and a multi-scale image feature pyramid is constructed. And respectively extracting visible light and infrared image features to obtain visible light and infrared imaging feature data. And performing multi-modal data fusion on the visible light and infrared imaging feature data based on a cross-modal attention mechanism, and adaptively adjusting a fusion weight based on environmental sensor data to generate fusion features. And performing space-time enhancement processing on the fusion feature to obtain an enhanced fusion feature. And performing target tracking detection on the small target, and outputting position and category information of the small target. According to the method, the small target recognition capability in a severe environment is remarkably improved, and high precision and robustness can still be kept in a foggy, low-visibility and dark scene.

The invention belongs to the field of geophysical monitoring, and discloses an earth crust deformation monitoring method and system based on satellite radar interference measurement, electronic equipment and a storage medium, and the method comprises the steps: obtaining a measurement image of a satellite radar for the earth surface, and carrying out the image registration of the measurement image, and obtaining a registered image; pre-processing the registered image to obtain a pre-processed image; performing interference processing on the preprocessed image to generate a differential interferogram containing a terrain phase and a deformation phase; phase unwrapping and terrain phase correction are carried out on the differential interferogram, a deformation phase is separated out, and earth surface deformation information is obtained; and performing time sequence analysis on the earth surface deformation information of the continuous time to obtain a change trend of earth crust deformation. According to the invention, based on satellite radar interference measurement, a large area can be covered in a short time, and effective monitoring can be carried out on wide plains, complex mountainous regions and remote regions.