116 results about "Image geometry" patented technology

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a transformer fault diagnosis method and system based on image recognition, and the method comprises the steps: collecting a transformer multi-mode image sequence in real time, and carrying out the definition and part integrity evaluation and screening to form an initial image set; performing multi-scale space registration on the initial image set and a transformer normal state standard template to generate a reference image, and reversely deriving a displacement vector field based on pixel-level difference; carrying out smooth optimization and geometric reconstruction on the displacement vector field under the geometric constraint of the transformer structure, and generating a correction image with a real structure; fault feature enhancement is carried out in a gradient domain of the corrected image, a fault area is identified through matching of multichannel feature extraction and a transformer typical fault feature library, and a diagnosis report integrating fault types, confidence coefficients and geometric parameters is generated; according to the method, the problem of image geometric deformation caused by shooting condition differences is effectively solved, and the accuracy and reliability of fault identification are improved.

The invention relates to the technical field of image processing, and discloses a fluorescent lifetime microscopic image large-view-field splicing method and system and electronic equipment, and the method comprises the steps: constructing a tissue region mask for a to-be-spliced image, and constructing a vignetting model in a tissue region to complete vignetting correction; counting brightness indexes in an organization area to determine a global brightness reference, and realizing image group brightness unification through global zooming and single image brightness adaptive correction; further realizing alignment of adjacent images through feature point matching, and finishing image block splicing in combination with a minimum color difference suture fusion method to obtain an image band; an overlapping area is extracted from an image belt to generate an effective content mask, relative displacement is estimated by using a phase correlation method after low-frequency suppression, image belt splicing is completed by multiplexing a minimum color difference suture fusion method, a large-view-field spliced image is output, the automation degree and robustness of splicing are improved, the spliced image is geometrically consistent and visually seamless, and the splicing efficiency is improved. And the requirements of medical research on high-resolution and large-field-of-view fluorescence lifetime microscopic images are met.

The invention relates to a GIS (Gas Insulated Switchgear) equipment mechanical defect diagnosis method based on a Gramb angle field and a dual-channel PCNN-Attention neural network, and belongs to the technical field of gas insulated switchgear mechanical vibration defect diagnosis. The method solves the problems that traditional diagnosis depends on artificial feature extraction, so that subjectivity is high, information mining is insufficient, and defect severity evaluation is missing. According to the technical scheme, the method comprises the steps that a one-dimensional vibration signal is converted into a GASF two-dimensional image and a GADF two-dimensional image through a GASF field so as to completely reserve time sequence topological features; carrying out data enhancement by adopting an image geometric transformation technology so as to improve the generalization ability of the model; and a dual-channel PCNN-Attention model is constructed, and synchronous intelligent identification of defect types and severity is realized through parallel feature extraction and dynamic weight optimization of an attention mechanism. According to the method, the diagnosis accuracy, reliability and adaptive capacity are improved, and support is provided for equipment state operation and maintenance.

The invention relates to the technical field of glass bottle detection, and discloses a glass bottle bottom defect detection method which comprises the following steps: building a detection platform, and adopting a multi-view imaging module of an annular LED and oblique light supplement combined light source, a vertical camera and 3-4 oblique cameras; after the to-be-detected glass bottle is positioned, synchronously collecting a bottle bottom full-view image set; graying, adaptive median filtering, CLAHE histogram equalization and Otsu binarization preprocessing are carried out on the image; extracting geometric and textural features of the image, inputting the geometric and textural features into an improved YOLOv5 model for identification and classification, and judging defects by combining with multi-view result fusion; and sorting the glass bottles according to a detection result and generating a traceable report. The identification rate of tiny defects is larger than or equal to 99%, the detection time of a single bottle is smaller than or equal to 0.5 second, and the method is compatible with glass bottles with the diameter of 30-100 mm, is suitable for large-scale quality control in the fields of food and beverage, medicine packaging and the like, and has extremely high practical value.

The invention relates to the field of image recognition, discloses an image recognition method and an image recognition system, and systematically solves the core pain point of traditional document recognition through optical-topology fusion processing and a dynamic resource allocation mechanism. The image recognition method is composed of an acquisition module, a grid module, a phase module, a setting module and a distribution module, pixel brightness is calculated and coded based on document RGB data, and a two-dimensional coding matrix is generated; constructing a geometric correction grid, forming a nonlinear constraint field, and enhancing the anti-deformation capability of the image; generating spiral phase light waves in the constraint field by using a spatial light modulator, and generating a time-varying phase map; determining a local topology index by detecting the number of phase jump times; and extracting the closed boundary region as a character block, and generating an analysis result file. The system breaks through traditional limitation, improves image geometric correction precision, feature extraction sensitivity and boundary judgment accuracy, efficiently completes document analysis, and is suitable for scenes such as document digitization and information retrieval.

The invention discloses a lawn coverage evaluation platform based on image recognition, which belongs to the technical field of lawn measurement and comprises an image acquisition module, an image preprocessing module, a lawn area recognition module, a coverage calculation module, a scoring and analysis module and a result output module which are connected in sequence. The method comprises the following steps: performing standardized image acquisition on a lawn through an image acquisition module, and preprocessing an image to realize geometric standardization of the image; an artificial intelligence image segmentation model is adopted to improve the accuracy and adaptability of lawn area identification; the sample areas are scored and analyzed by calculating the coverage, and the method is used for comparative analysis of multiple varieties. By adopting the method, the acquisition efficiency, the identification accuracy and the evaluation standardization level of the coverage data in the lawn grass seed regional test can be improved, and the problems of non-uniform acquisition conditions, fragmentation of an identification method, incomparable results and the like in the prior art are solved.

The invention relates to the technical field of industrial visual inspection, and discloses an asynchronous exposure double-view-field imaging system and method, and the system can achieve the merging of independent view field images collected by two parallel lenses into a single linear array camera through a reflector and a non-polarization beam splitting cube based on the asynchronous exposure double-view-field imaging system. Meanwhile, in cooperation with precise time sequence control, the system drives two independent stroboscopic light sources to be synchronous with line trigger signals of the camera, different view fields are alternately illuminated in odd-even line periods, the camera is made to perform asynchronous exposure, and two paths of image information are collected in a time-sharing mode; and finally, reconstructing two complete, independent and seamlessly spliced detection images from the interleaved data streams through an odd-even line separation algorithm. According to the invention, with the hardware cost of a single camera, the detection visual field is obviously expanded, the detection efficiency and the image geometric precision under the high-line scanning frequency are ensured, and the excellent balance of the performance, the cost and the efficiency is realized.

The invention discloses a target positioning method and system based on target height correction monocular distance measurement, and the method comprises the steps: carrying out the calibration of a ship-borne camera through a data set, employing a YOLO target detection model, carrying out the detection of video data collected by the ship-borne camera, and obtaining the bounding box information of a target; and secondly, according to bounding box information, calculating a yaw angle of the target relative to a main point of the shipborne camera, and obtaining an azimuth angle by combining an orientation angle of the main point of the shipborne camera. And then height correction is carried out to measure the distance between the target ship and the shipborne camera. And finally, according to the azimuth angle and the distance, combining the latitude and longitude coordinates of the shipborne camera to obtain the actual geographic latitude and longitude coordinates of the target ship. According to the method, the goodness of fit between distance measurement and actual image geometry is improved, the actual object distance is reflected more accurately, and accurate and efficient target positioning is carried out.

The invention discloses a multi-scale feature fusion-based atmospheric turbulence video correction method and system, and the method comprises the steps: extracting shallow texture, middle structure and high semantic information through a multi-scale feature pyramid, and carrying out the channel weighting processing of different frequency components through the combination of frequency adaptive enhancement and a deformable attention alignment mechanism; adaptive optimization is realized for turbulence sensitivity difference, feature level alignment is completed under the guidance of optical flow, tilt distortion is eliminated, and reference features are dynamically maintained and turbulence noise accumulation is inhibited by adopting a gating reference updating module. In addition, cross-frame space-time fusion is completed through a multi-head time channel self-attention mechanism, time consistency is improved, and image geometry and high-frequency details are gradually recovered by using a two-stage decoder. According to the method, the definition of the turbulence degraded image can be remarkably improved, and technical support is provided for high-precision imaging.

The invention discloses a short-focus projection image correction optimization system based on artificial intelligence, and the system comprises an image collection and structure modeling module which is used for generating a structure perception feature map; the structure guide matching module is used for constructing a stable matching subset and executing matching point screening; the dense matching and geometric correction module is used for outputting a coarse correction image; the structure residual error fine tuning module is used for constructing a structure residual error image and inputting the structure residual error image into the local fine tuning sub-network, guiding the model to carry out fine-grained correction on a structure error region, and generating a final correction image; the structure consistency evaluation module is used for evaluating key point deviation, edge direction similarity and geometric alignment degree; the correction convergence judgment module is used for tracking the correction score change; and the correction result output module is used for outputting a final correction image. The method has the advantages of high precision, strong robustness and iterative optimization, and is suitable for image geometric alignment and enhancement requirements in complex optical projection scenes such as smart classrooms, interactive display, immersive projection and the like.

The invention relates to the technical field of automobile detection, in particular to a wheel base difference vision measurement method based on a rim circle common tangent, which comprises the following steps: S1, image acquisition: acquiring projection images of two rims on one side of a vehicle through a monocular camera fixed on a bracket; s2, determining internal parameters of the camera and the circle center of the rim: carrying out edge detection and ellipse fitting on the rim image, and calculating the internal parameters of the camera by combining a circular point and absolute conic constraint; s3, determining external parameters of the camera: resolving a rotation matrix and a translation vector of the camera based on the geometrical relationship between the inner and outer common tangent of the ellipse and the image; s4, solving a wheelbase difference: obtaining the positions of the circle centers of the left and right rims in the world coordinate system, and calculating the spatial distance difference as the wheelbase difference; through image processing and geometric modeling, the method is low in cost and suitable for accurate measurement of the wheelbase difference of multiple vehicle types, and has the advantages of being easy and convenient to operate, small in error and low in equipment requirement.

The invention belongs to the technical field of wireless communication, and particularly relates to a machine vision integrated communication enhancement integration method and system, a communication part and a machine vision part are equipped, and target equipment can be identified by the communication part and the machine vision part; when the machine vision-fused communication enhancement integrated system deals with massive equipment access, accurate identification and management of equipment are realized through a multi-mode multi-level identification system, a dynamic association algorithm and a collaborative management mechanism; the equipment identified by the communication part is matched with the equipment identified by the machine vision part to ensure that the target equipment identified by the communication part and the machine vision part is the same in the processing flow; perceptual data obtained by the communication part and including parameters such as distance, position / angle, speed and the like of the target equipment are fused with perceptual data obtained by the machine vision part and including parameters such as images, geometric features, position / angle, speed, distance and the like of the target equipment.

The application discloses a method for intelligent component identification and segmentation of an aircraft CAD model based on three-mode fusion, comprising: obtaining an aircraft CAD model with a boundary representation structure; constructing a multi-modal training data set that fuses surface images, geometric parameters and component semantic labels; training a multi-modal identification model; using the model for preliminary identification; for low confidence or small area surfaces, starting a geometric rule auxiliary module based on geometric topology and parameterization rules for semantic completion and correction to form a final segmentation result; automatically extracting key geometric feature lines; and converting and outputting into a structured engineering semantic format that can be used by grid generation software. The application realizes full automation of component identification, solves the problems of low efficiency and consistency caused by manual dependence, improves the identification integrity and engineering practicability of complex geometry, and the output result can directly drive subsequent grid generation, thereby significantly improving the automation level of the digital simulation pre-processing process.

The application provides a task-driven defogging perception method and system, and belongs to the technical field of cooperative perception of Internet of Vehicles. The method comprises the following steps: collecting a first foggy image stream and a second foggy image stream; obtaining a first processed image by intercepting and processing the first foggy image stream; obtaining a second processed image by intercepting and processing the second foggy image stream; respectively performing geometric alignment on the first processed image and the second processed image to obtain a first aligned image and a second aligned image; performing defogging and image enhancement processing on the first aligned image and extracting a first feature; performing defogging and image enhancement processing on the second aligned image and extracting a second feature; respectively repairing the first feature and the second feature by using a diffusion model; fusing the repaired first feature and the second feature, and determining a final perception result based on the fused feature. The application improves the robustness of automatic driving and balances the low power consumption of AI intelligent glasses and the high performance computing demand of a vehicle-mounted system.

The invention relates to the field of remote sensing image processing and terrain correction, and particularly discloses a radar geometric field-driven SAR distortion information adaptive neighborhood propagation identification method, which comprises the following steps of: S1, acquiring a Golden digital elevation model of a research area and a pixel-by-pixel ellipsoid incident angle graph generated by SAR satellite precision orbit parameters and imaging geometry, and carrying out geometric unified processing on the Golden digital elevation model and the pixel-by-pixel ellipsoid incident angle graph; s2, calculating a terrain parameter, an effective slope angle, an R geometric response field and an R field directivity gradient based on the unified raster data; s3, main distortion region classification is carried out according to the effective slope angle and the pixel-by-pixel ellipsoid incident angle; s4, defining an adjacent gradient and constructing a self-adaptive threshold value and a shadow self-adaptive threshold value; and S5, performing passive region propagation from the boundary of the main distortion region, and generating a final classification grid map after space overlapping correction. According to the method, the defect of blind propagation of the existing method is overcome, and high-precision automatic identification of SAR geometric distortion under complex terrains is realized.

The application discloses a kind of underwater concrete crack width automation measurement system and method based on double laser, it is related to underwater structure health monitoring and optical measurement technical field.The application includes: double laser projection module, for projecting a pair of circular laser points with fixed physical spacing to underwater concrete surface to be measured as physical scale reference;Image acquisition module, including underwater camera, for collecting image containing laser point and crack area;Data processing module, for performing underwater camera calibration, laser point detection, crack image segmentation and crack physical width calculation.The method corrects the image geometric distortion caused by "water-glass-air" refraction by establishing an accurate underwater multi-interface refraction imaging model;Nonlinear mapping relationship from image pixels to physical space is established using double laser points;Based on unsupervised crack segmentation model, crack area is automatically segmented, the physical width of each position of crack is calculated and width distribution cloud chart is generated.

This invention relates to a method for stitching and adjusting the grayscale of multispectral images using a filter array, belonging to the field of aerial image processing technology. The purpose of this invention is to utilize the SURF algorithm to register projected images, using the mean of the grayscale average ratio of overlapping regions in single-band images as the grayscale adjustment coefficient, and adjusting the grayscale of the registered images sequentially based on the grayscale of a reference image. This invention comprises three steps: determining image geometric relationships, adjusting image grayscale, and stitching images. This invention solves the problem of grayscale differences in single-band multispectral stitched images and minimizes the loss of spectral information of ground features. It not only effectively solves the problem of inconsistent grayscale of ground features in images but also maximizes the preservation of spectral information characteristics of ground features in different bands.

The invention relates to an urban green land health situation monitoring method and device based on a mobile phone camera visible light image. The method comprises the steps that the visible light image of an urban green land canopy is collected through a mobile phone camera; distortion correction and vertical calibration are carried out on the acquired image; compressing and re-sampling the image; noise reduction processing is performed on the image, and image details are reserved; converting the image from an RGB color space to an HSI color space, and eliminating the influence of an illumination condition on the image; calculating an EXG index, a leaf area index and a vegetation coverage; performing pseudo-color enhancement display on the vegetation index result; and outputting a monitoring result. According to the invention, through the square preview frame and the gyroscope vertical calibration system, the geometric error of the image is significantly reduced, and the area measurement and calculation precision is improved; matrix parallel operation and self-adaptive compression factors are adopted, so that the computing power bottleneck of a mobile terminal is broken through, and the image processing efficiency and the equipment compatibility are remarkably improved; through the whole-process anti-interference design, stable monitoring in a field complex scene is realized.

The application is suitable for the technical field of power equipment state monitoring, and provides a flow blocking valve state identification method, system, device and program product. The method comprises: acquiring a reference image and a to-be-inspected image of a flow blocking valve; identifying first structure key points from the reference image, and identifying second structure key points from the to-be-inspected image; performing geometric correction on the to-be-inspected image based on the first structure key points and the second structure key points, to obtain a corrected image consistent with the reference image in geometric space; calculating a reference angle in the reference image and a current angle in the corrected image respectively, and determining the state of the flow blocking valve according to the angle difference between the current angle and the reference angle. The accuracy and universality of flow blocking valve state identification are improved.

The application discloses an image processing method and an electronic device, and relates to the technical field of image processing, wherein the method comprises the following steps: extracting a first geometric edge line set of an initial depth image according to the initial depth image corresponding to a target monocular image; screening geometric edge lines with gradient scores greater than a target score threshold from a depth gradient image generated according to the initial depth image and an initial edge line set of the target monocular image to determine a second geometric edge line set; and fusing the first geometric edge line set and the second geometric edge line set to determine image geometric edge lines of the target monocular image and construct a final geometric edge line set. The method solves the problems of serious texture interference, insufficient utilization of depth information, difficulty in balancing the number and purity of edge lines, and insufficient real-time performance in the prior art, and achieves the technical effects of filtering texture noise while ensuring the number and robustness of edge lines and meeting the needs of multi-sensor fusion tasks.

The application discloses a tunnel water mist environment under inspection image de-scattering enhancement and reconstruction method, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a polarized image sequence and pose information collected synchronously; decoupling a scene by using a fusion neural network to output a fog-free image, a geometric gradient map and a water mist concentration map; calculating a frequency domain fusion weight map by using a frequency domain attention generation network; performing multi-scale frequency domain decomposition on the fog-free image to obtain high-frequency components and low-frequency components; performing pixel-level weighted fusion and inverse transformation according to the weight map to generate a preliminary enhanced image and perform color correction. The application adopts a physical constraint deep learning and a frequency domain adaptive fusion mechanism, can effectively eliminate the interference of tunnel water mist scattering, and improves the definition, detail performance and color authenticity of the inspection image, thereby providing high-quality visual support for tunnel safety monitoring.

The present invention relates to image distortion correction technology and provides an image distortion correction method for multi-pose photography using unmanned aerial vehicles (UAVs), comprising the following steps: calibrating camera intrinsic parameter coefficients to obtain a camera intrinsic parameter matrix in a camera coordinate system; calculating a rotation matrix from the camera coordinate system to a carrier coordinate system using an inertial sensor (IMU); constructing a pinhole camera imaging model to project points in the camera coordinate system to an image coordinate system; calculating a rotation matrix from the carrier coordinate system to a geographic coordinate system using the inertial sensor (IMU); and obtaining a transformation formula and a scaling factor based on the relationship between perspective projection and weak perspective projection to perform image distortion correction. Based on the coordinate transformation relationship and the weak perspective transformation relationship, the present invention achieves scale and rotation consistency between an aerial image and a reference image, improves the accuracy of image geometric distortion correction, and effectively improves the accuracy and reliability of scene matching navigation systems in practical applications.

The invention provides a weaving printing method and system based on image processing and related equipment, and relates to the technical field of weaving printing. The method comprises the steps that before printing is executed, printing motion parameters are collected; the printing motion parameters comprise the scanning motion speed of a printer nozzle and the continuous conveying speed of a printing medium; according to the scanning movement speed and the continuous conveying speed, a staggered cutting angle formed by relative movement between the printer nozzle and the printing medium is calculated; calculating an affine transformation matrix according to the miscut angle; and according to the affine transformation matrix, geometric correction is carried out on the to-be-printed image, and printing is executed. The method aims at solving the problem of image geometric distortion caused by compound motion of reciprocating scanning of a spray head and continuous conveying of a printing medium in a weaving printing method, the printed image is effectively corrected, and higher printing quality is achieved.

System (SYS) and delated methods for supporting an imaging operation of an imaging apparatus (IA) capable of assuming different imaging geometries. The system comprises an input interface (IN) for receiving a current image acquired by the imaging apparatus (IA) of a current region of interest (ROI_1) at a current imaging geometry (p). A pre-trained machine learning component (MLC) computes output data that represents an imaging geometry change (Δp) for a next imaging geometry (p′) in relation to a next region of interest (ROI_2). An output interface (OUT) outputs a specification of the imaging geometry change.

The application discloses a motor insert sheet defect detection method based on multi-sensor fusion, relates to the technical field of motor insert sheet defect detection, and comprises the following steps: in the presence of boundary penetration, calculating edge matching mismatch degree, edge jump rate, edge inversion proportion and edge closure deviation to form a geometric conflict feature set of an insert sheet edge, which is used for representing image geometric expression differences caused by boundary penetration; inputting the geometric conflict feature set into a fusion confidence evaluation function to obtain a fusion confidence evaluation factor, which is used for identifying whether the insert sheet edge has a fusion distortion risk. The application solves the structural expression conflict problem caused by boundary penetration in the multi-view imaging fusion process of the motor insert sheet, constructs a fusion confidence evaluation mechanism to identify the fusion distortion risk, dynamically limits the image participation fusion condition, and thus improves the recognition accuracy of the edge structure and the stability of the fusion result in the insert sheet defect detection.

This invention relates to a method and medium for detecting building changes based on UAV imagery. By using UAV images of the same target area acquired at different times, the method incorporates UAV ground control points and aerial POS information into an aerial triangulation system to jointly calculate the camera's exterior orientation elements and imaging geometry. Orthophotos of the preceding and following times are generated under a unified coordinate reference, and building probability maps of the preceding and following times are obtained through processing. The merged orthophoto obtained after combining the preceding and following times UAV images is used to construct multiple object units based on cross-temporal consistency. Furthermore, the average first and second semantic confidence scores corresponding to the preceding and following times orthophotos are obtained. Based on whether the two average confidence scores meet three preset judgment conditions, each object unit is determined and labeled as belonging to a specific type of building change area. Thus, stable and accurate detection of building changes based on UAV imagery is achieved.

The present application relates to the technical field of image optimization, in particular to a UAV aerial image optimization method and system based on artificial intelligence, comprising the following steps: calling flight state information to analyze attitude deviation, calculating the sun irradiation angle to adjust exposure, evaluating noise indicators to adjust filtering, identifying scene labels to match processing parameters, generating gradient heat maps to optimize texture, and obtaining aerial image optimization records. In the present application, by calling real-time UAV flight attitude data and light change trend, the attitude and light state deviation in the shooting process are finely evaluated, the image geometry and light parameters are precisely adjusted, the filtering strength is dynamically optimized by using the regional noise deviation amplitude, the scene features are constructed according to the local semantic density, the texture reconstruction strength is dynamically classified and optimized according to the local gradient change of the image, the problems of uneven illumination and regional detail loss of aerial images are effectively reduced, and the overall visual clarity and content recognition accuracy of the image are improved.

The application relates to the technical field of industrial vision detection, and discloses an asynchronous exposure double-view field imaging system and an imaging method. Based on the asynchronous exposure double-view field imaging system, independent view field images collected by two parallel lenses can be combined into a single linear array camera through a mirror and a non-polarization beam splitting cube. Meanwhile, cooperating with precise timing control, the system drives two independent stroboscopic light sources and a camera line trigger signal to be synchronous, alternately illuminates different view fields in an odd-even line cycle, makes the camera asynchronously expose and collect two-way image information at different times, and finally reconstructs two complete, independent and seamlessly spliced detection images from the interwoven data stream through an odd-even line separation algorithm. The application realizes significant expansion of a detection view field with a single camera hardware cost, guarantees detection efficiency and image geometric accuracy under a high line scanning frequency, and realizes excellent balance of performance, cost and efficiency.

The application discloses a kind of hierarchical light field image encoding methods, comprising: enhancement layer coding network utilizes the enhancement layer coding strategy based on domain-specific reference to carry out interlayer prediction and residual compression to spatial feature, and angle feature is generated at decoding end, and the light field image is encoded;Interlayer spatial prediction module is constructed, the co-location and local self-similarity within reference spatial feature are modeled simultaneously, to implicitly reconstruct high-frequency details in high-resolution light field representation, predict spatial latent representation;Spatial-angle context-aware reconstruction module is constructed, long-distance and short-distance spatial-angle correlation is explored in long-range and short-range context-aware branch respectively to model light field image geometry and preserve local details, and the light field image of enhancement layer is reconstructed.The application encodes light field image of different spatial resolution in an end-to-end manner, and improves the encoding performance of spatial hierarchical light field image.

The present invention discloses a two-dimensional deformation decomposition method for landslides based on a local surface parallel flow model, and relates to the field of landslide deformation monitoring. First, ascending and descending SAR data are processed in time series using the StaMPS technology to obtain the one-dimensional deformation of the landslide along the LOS direction. Second, the imaging geometry information and LOS deformation information of the ascending and descending SAR data are combined to invert the optimal sliding direction and sliding inclination of the landslide movement based on the local surface parallel flow model. Finally, the LOS deformation information of the landslide, the imaging geometry information of the ascending and descending SAR data, and the inverted optimal sliding direction and sliding inclination are combined to calculate the two-dimensional deformation of the landslide along the sliding direction and the normal direction of the sliding surface. The new two-dimensional deformation decomposition method based on the local surface parallel flow model is applicable to translational loess landslides, and helps prevent and mitigate landslide disasters.