1252 results about "Source image" patented technology

The invention relates to the technical field of underwater fish school monitoring, and discloses an underwater fish school monitoring statistical system based on image fusion. According to the system, underwater video streams and sonar reflection intensity data of different spectral bands are acquired through an underwater multi-source image acquisition module, and time-space synchronous multi-modal image data streams are generated through timestamp alignment; a fish school contour reconstruction module is used for segmenting a fish school contour boundary and fusing visible light texture and sonar geometric features to generate an underwater three-dimensional fish school distribution set; the dynamic track mapping module tracks the mass center displacement, calculates the movement rate and the direction deviation angle, and correlates the water area depth to generate a dynamic track topological graph; the behavior anomaly analysis module extracts environment data based on the track mutation node, and detects aggregation density change and direction dispersion to mark an anomaly feature cluster; and the population statistics output module integrates the data, performs classified statistics on population distribution, a quantity threshold value and a migration path overlap ratio, and finally generates a fish school quantity distribution statistics thermodynamic map.

The invention belongs to the technical field of digital restoration of computer vision and cultural heritage, and particularly relates to a traditional picture restoration method fusing low-resolution prior and efficient visual selection, which comprises the following steps: constructing a multi-source image data set, taking images in the multi-source image data set as high-resolution images, preprocessing the high-resolution images to obtain low-resolution images, and carrying out high-resolution priori and high-efficiency visual selection on the low-resolution images. The high-resolution image and the low-resolution image are respectively masked to generate simulated damage mask images, and the simulated damage mask images comprise a regular damage mask image and an irregular damage mask image; taking the multi-source image data set and the preprocessed multi-source image data set as training data, and training a multi-source image model; the dual-stage repair network comprises a coarse repair network and a fine repair network; according to the method, the problems of structural semantic loss, high priori information dependency and insufficient global and local coordination when an existing image restoration method is used for processing a complex scene and a large-range missing region are solved.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention provides a tunnel crack identification method and system based on multi-source image processing, and relates to the technical field of tunnel engineering, and the method comprises the steps: obtaining multi-source data of a horizontal rock stratum tunnel; performing spatial registration on the multi-source data to generate a multi-modal image under the same reference system; extracting multi-modal features according to the multi-modal image, and constructing a multi-source feature map under the same space grid; performing crack initial detection on the multi-source feature map in combination with multi-scale filtering and structure tensor analysis to obtain a crack candidate region mask; accurate crack identification is carried out through the crack candidate area mask, a crack identification result is obtained by combining bedding direction constraint and a microconditional random field, and the crack identification result comprises a crack segmentation map and a crack category. The method solves the problem that existing crack identification does not consider the bedding characteristics of the horizontal rock stratum tunnel.

The invention relates to an infrared and visible light image fusion method based on a cross-domain Transform, and belongs to the field of computer image processing. The method comprises the following steps: respectively carrying out preprocessing operation on an infrared image and a visible light image to obtain a training data set; an end-to-end image generator network is designed, an encoder module is used for extracting deep semantic features of an infrared image and a visible light image, a fusion module introduces an axial attention mechanism to enhance the global modeling capability of the features, and feature fusion is carried out in combination with information of a spatial domain and a frequency domain; the fused features are gradually recovered to an image space through a decoder module, and a fused image is generated; constructing a fusion loss function module, and guiding the network to focus a significant feature difference between the source image and the fusion image based on a comparative learning idea; and finally, inputting the infrared and visible light image Y channel into the network model, generating a fusion image, completing a training process, and realizing unified optimization of fusion performance and visual quality.

The invention discloses a panoramic image real-time splicing algorithm and system based on multi-sensor fusion, and particularly relates to the technical field of panoramic image real-time splicing, and the algorithm comprises the following steps: constructing a structured fusion sequence based on multi-source images, postures and position information, optimizing a matching effect through high-density feature extraction and repeated texture recognition, and obtaining a multi-source image fusion sequence; a dynamic foreground and a static background are distinguished by using sparse optical flow so as to improve the visual angle estimation precision, pose fusion optimization is realized in combination with a multi-mode residual error, and the continuity and stability of a spliced image are improved through edge smoothing, brightness tuning and color correction; according to the method, the structured fusion sequence is constructed through multi-source data alignment, so that the data synchronization and splicing stability is improved; identifying repeated regions based on texture direction features, and optimizing feature matching accuracy; and through edge smoothing, brightness harmonizing and color consistency processing, the visual coherence and output quality of the panoramic image are enhanced.

The invention discloses an intelligent detection method for fault detection of an outdoor power line, and the method comprises the following steps: 1, carrying out the collection and preprocessing of multi-modal data, and carrying out the collection and preprocessing of the multi-modal data through an unmanned plane cluster, a distributed optical fiber sensor, a laser radar and meteorological monitoring equipment; visible light image data, infrared image data, laser point cloud data, vibration waveforms, temperature distribution and environmental parameters of the power line are synchronously obtained, and multi-source image data are processed, namely the visible light image data, the infrared image data and the laser point cloud data are processed; and 2, intelligent fault diagnosis: inputting the data acquired in the step 1 into a multi-task neural network model, and outputting a fault positioning and type identification result. According to the novel detection method based on multi-modal data fusion, an intelligent algorithm and closed-loop optimization, the fault identification precision, the dynamic decision-making capability and the comprehensive protection efficiency are improved, and the intelligent operation and maintenance requirements of a modern power grid are met.

The invention discloses a multi-source remote sensing image zero sample change detection method, and relates to the technical field of remote sensing image processing, and the method comprises the following steps: obtaining remote sensing images collected by two or more remote sensing sensors at different time points in the same geographic area, the image types including optical images and radar images; preprocessing each source image, unifying the spatial resolution and the registration precision, and denoising and standardizing the image; according to the method, the cross-modal shared semantic embedding space is constructed and unsupervised comparative learning is introduced, so that the semantic consistency of the multi-source remote sensing image is effectively improved, and the change recognition capability of the model under the zero sample condition is enhanced; and meanwhile, a difference fusion calculation and structure consistency constraint module is adopted, so that the boundary judgment precision of a change region and the overall structure consistency are improved, and the accuracy and stability of a detection result are remarkably improved.

The invention belongs to the technical field of super high-rise building three-dimensional reconstruction, and particularly relates to a multi-source data fusion super high-rise building group live-action three-dimensional model construction method. According to the method, an initial three-dimensional model is generated through a series of processing such as aerial triangulation encryption and triangulation network construction based on multi-source image data, in the process of recognizing a fuzzy region and performing data supplementary collection, regions with texture loss and structure distortion in the initial three-dimensional model can be positioned, supplementary collection requirements are determined according to characteristics of different regions and a preset threshold value, and the recognition accuracy of the initial three-dimensional model is improved. The method comprises the following steps of: performing oblique photography on an unmanned aerial vehicle to acquire data in a supplementary manner, fusing the data with original data, extracting a building structure contour, matching high-resolution texture data, performing texture binding and processing and the like to form a building monomer model, and performing spatial position and texture fusion on the building monomer model and a process three-dimensional model to generate a regional three-dimensional live-action model. And finally, splicing and fusing the three-dimensional live-action models of all the areas to form a complete super high-rise building group live-action three-dimensional model.

The invention discloses a multi-source image data fusion-based fine decoration air crack seepage quality problem detection method and device, and solves the technical problem of how to carry out comprehensive, high-precision and intelligent detection on the fine decoration surface air crack seepage quality problem. Comprising the following steps: 1) receiving visible light image data, thermal infrared image data and three-dimensional laser point cloud data of a target refined decoration surface acquired from visible light acquisition equipment, thermal infrared imaging equipment and three-dimensional laser scanning equipment respectively; 2) carrying out feature extraction on the visible light image data, the thermal infrared image data and the three-dimensional laser point cloud data; 3) obtaining point cloud projection coordinates, and then performing association fusion on the first two-dimensional feature and the second two-dimensional feature with corresponding three-dimensional features to generate fusion point cloud data containing multi-source features; and 4) based on the fused point cloud data, carrying out defect classification identification and spatial positioning to obtain a detection result. And comprehensive and high-precision detection of the quality problem of air crack seepage of the finely-decorated surface is realized.

The invention relates to the technical field of machine vision structure health monitoring, and discloses a steel structure weld fatigue cracking real-time monitoring system based on machine vision. The system comprises a space-time registration and fusion module, a multi-scale feature analysis module, a health monitoring module, a crack deduction calculation module and a regulation and control strategy generation module. Performing space-time registration and pixel-level fusion through the visual data of the plurality of image sensors to generate a synchronous multi-source image stream; a multi-level welding seam characteristic spectrum is constructed through multi-scale characteristic analysis, and a welding seam structure knowledge base is dynamically updated; the knowledge base and the real-time characteristic spectrum are used for monitoring the welding seam health state, and abnormity is recognized; deducing a crack initiation position and an evolution path in combination with historical damage data; and real-time load information is fused to pre-estimate the remaining service life, and a structural integrity regulation and control strategy is generated online. According to the invention, high-precision fusion of the multi-source visual data and active prediction of the crack trend are realized, and the monitoring accuracy and the early warning capability are improved.

The invention discloses a cross-modal eye fundus image generation method and system based on a generative adversarial network, relates to the technical field of medical image processing, and constructs an eye fundus focus perception and edge consistency generative adversarial network by taking a cyclic consistency generative adversarial network as a baseline. The core of the method is that a lesion perception mixed attention module is embedded in a bottleneck layer of a generator so as to strengthen the extraction capability of fine features of a lesion area; an edge information extraction module is designed, and key edge features are accurately extracted in combination with Roberts edge detection, wavelet transform and non-local mean denoising; and a joint loss function containing edge consistency loss is constructed, and the semantic consistency of a focus structure during cross-modal generation is ensured by minimizing the feature difference between the source image and the generated image. According to the method, the problems of disordered content, inconsistent structure and unstable training of the generated image in the prior art are effectively solved, and the simulation degree and clinical availability of the generated image are remarkably improved.

Disclosed in the present invention is a soft tissue photoacoustic / ultrasonic multi-modal image fusion method based on deep learning. The method comprises the following steps: an ultrasonic-photoacoustic imaging device collecting a human body soft tissue photoacoustic image and ultrasonic image, and performing size normalization processing; inputting the images into a spatial conversion module to convert same into a YCbCr space; inputting the images into a pre-convolutional module to change the number of data channels; inputting the images into a multi-scale feature extraction module to extract salient features of the source images; inputting the features into a filter prediction module to obtain a multi-scale filter; and inputting the filter into a filtering fusion and adaptive enhancement module and combining same with the input source images, so as to obtain a final fusion result. Compared with several traditional fusion methods and deep-learning-based fusion methods, the method provided in the present invention has a greater fusion effect, and more importantly, has an excellent real-time performance. Moreover, multiple modes of photoacoustic / ultrasonic fusion extension experiments are performed on a photoacoustic / ultrasonic multi-modal imaging system, verifying the effectiveness of the method in the present invention.

The invention discloses an industrial robot image processing method based on image fusion, and relates to the technical field of intelligent aquaculture, and the method comprises the steps: collecting an original image in real time through deploying an image collection device integrating visible light, polarization and multispectral imaging, and extracting suspended matter density, water body light transmittance and illumination intensity change information; generating a first image set; suppressing suspension interference through image filtering and enhancement processing to obtain a first corrected image; extracting an aquatic product individual region, performing multi-source image fusion, identifying color deviation, texture interruption and reflection feature anomaly regions, and constructing a lesion candidate set; gray scale reconstruction, edge gradient and brightness normalization correction of a multispectral channel are executed based on illumination and reflection changes, and a high-quality fusion image is generated; and calculating a health anomaly probability coefficient of the target individual by using the depth recognition model, comparing the health anomaly probability coefficient with a threshold value, and recording a recognition result and collecting information if the threshold value is exceeded. The method can significantly improve the accuracy of aquatic individual lesion recognition.

The invention discloses an ancient textile image restoration system based on artificial intelligence. The system comprises a multi-source image acquisition module, a damaged area detection module, a pattern generation module, a color restoration module, a texture synthesis module and a multi-scale fusion module. The system introduces a wavelet guidance-frequency domain attention mechanism and a rotation invariant Haar wavelet basis function to realize accurate identification and classification of a damaged area; a saliency-guided wavelet decomposition control and self-adaptive threshold denoising method is combined, so that the perception capability of slant textures and edge details is improved; the texture synthesis module constructs a hierarchical modeling strategy fusing Gram style loss, Wasserstein style loss and a total variation regular term, and realizes generation of high-quality textures with unified styles and smooth edges; the system can be widely applied to cultural relic digital repair and display scenes.

Systems and techniques are provided for processing image data. For example, a process can include processing a source image to generate a first features for the source image and a target image to generate a second features for the target image. The process can include generating a first cluster map for the source image based on prototypes and the first features for the source image, and generating a second cluster map for the target image based on the prototypes and the second features for the target image. The process can include determining a propagated cluster map for the source image based on the first cluster map and a correspondence between regions of the source image and regions of the target image. The process can include determining a loss based on a comparison of the propagated cluster map for the source image and the second cluster map for the target image.

The invention belongs to the technical field of image information processing, and discloses a scene guidance prompt representation enhanced infrared and visible light image fusion method, which is divided into two stages: a first stage, constructing a scene prompt generation network, and learning global visual semantic information covering a source image through a semantic segmentation task; in order to further enhance the prompt representation capability, a visual perception context prompt module is designed, interaction is performed by using a correlation matrix between modal specific features and text features, and the text features are refined in a dynamic weighting mode, so that scene prompt representation with richer semantics is obtained. In the second stage, a cross-modal alignment fusion network guided by prompt is provided, and infrared and visible light features are mapped to a unified shared embedding space by utilizing learned scene prompt. In the process, a pixel-text similarity matrix is established through a prompt driving feature alignment module, and accurate alignment of cross-modal features is realized, so that a fusion result of semantic consistency and detail fidelity is obtained.

The invention discloses a motor coil visual detection system and method based on multi-modal data fusion, and relates to the technical field of image recognition. The system solves the problems of single detection dimension, spatial structure information loss and the like in the existing motor coil visual detection, and comprises an image acquisition module, a data processing module, a defect identification module and a result output module. The image acquisition module comprises an RGB camera, a depth camera and a thermal imaging camera, and is used for respectively acquiring an appearance image, three-dimensional structure information and a temperature distribution diagram of the motor coil; the data processing module is used for performing registration, denoising and feature extraction on the multi-source image, and constructing uniform feature representation through a multi-modal fusion algorithm; the defect identification module analyzes the fusion features based on a deep learning model to realize appearance defect identification, three-dimensional deformation detection and thermal anomaly positioning of the coil; and the result output module classifies and evaluates the identification result and uploads the identification result to an upper system. The method is suitable for quality control and intelligent judgment of motor coil assembly.

Some implementations are directed to editing a source image based on a user request to edit the source image. The source image and the user request to edit the source image can be processed, using an image-editing system, to generate one or more image editing instructions. The one or more image editing instructions can indicate an image mask that edit (or preserves) one or more portions of the source image and / or can indicate a target object to be present in the edited image to replace a source object in the source image. Based on the one or more image editing instructions and source image, an edited image that shares the one or more portions with the source image and that differs from the source image by replacing the source object in the source image with the target object can be generated.

The invention discloses a deep learning-based multimodal image fusion method for soft tissue photoacoustic / ultrasound imaging. Steps: an ultrasound-photoacoustic imaging device acquires photoacoustic and ultrasound images of human soft tissue and performs size normalization processing; an input spatial transformation module converts the images to the YCbCr space; an input pre-convolution module modifies the number of data channels; an input multi-scale feature extraction module extracts salient features from the source images; an input filter prediction module derives multi-scale filters; and an input filter fusion and adaptive enhancement module combines the input source images to obtain the final fused result. The invention has superior fusion performance compared to several traditional fusion methods and deep learning-based fusion methods, and more importantly, it exhibits excellent real-time performance. Furthermore, various modes of photoacoustic / ultrasound fusion extension experiments have verified the effectiveness of the method proposed in the invention.

The invention relates to the technical field of rural image inspection and recognition, and discloses a multi-source image collaborative inspection and recognition analysis system and method for a digital rural area, and the method comprises the steps: collecting multi-source image data in real time; obtaining a plurality of characteristic parameters corresponding to each image data item in the image data set, and carrying out space-time registration and multi-scale fusion processing on the plurality of characteristic parameters of each image data item; performing target detection and identification analysis on the plurality of feature parameters in the fusion feature parameter set, and constructing an abnormal point identification model; setting a plurality of abnormal point change thresholds according to the inspection coordinate data set for classification processing to obtain a plurality of abnormal point categories; and setting a corresponding co-processing scheme according to the plurality of abnormal point categories, and setting early warning information corresponding to the change trends of the plurality of abnormal point categories based on the co-processing scheme. According to the invention, the intelligent degree and response efficiency of rural inspection are improved, and the safety and sustainable development of digital rural construction are effectively guaranteed.

The invention relates to a raised dust pollution source image enhancement processing method and system, and the method specifically comprises the following steps: collecting a raised dust pollution source image and environment parameters, and marking a raised dust region and a background to construct a data set; estimating image illumination and reflection components by using multi-scale Gaussian kernel convolution, fusing the image illumination and reflection components to obtain an enhanced image, enhancing the contour through adaptive morphological operation, and generating a morphological enhanced image; real raised dust texture is injected through random deformation field elastic transformation in combination with texture synthesis, and a synthetic enhanced image is obtained; constructing an image enhancement model, inputting a synthetic enhanced image, extracting multi-scale features by an encoder, enhancing the features by a decoder, reserving boundaries, optimizing the model in combination with a loss function, enhancing an output result through region self-adaptive fusion, and completing model training; and finally, inputting a newly acquired image into the trained model to generate a final enhanced flying dust pollution source image. The flying dust image quality is improved, features are accurately extracted, and pollution source identification and monitoring are assisted.

A computer system and a computer-implement method include obtaining a source image and a modification input that indicates a target edit to the source image and generating a modification encoding representing the target edit. An image generation model generates an output image that depicts the source image with the target edit based on the source image and the modification encoding. The image generation model is trained to perform a pose modification task and a part replacement task.

The embodiment of the invention relates to the technical field of image processing and positioning, in particular to a processing and positioning method and system for adaptive image recognition, and the method comprises the steps: obtaining a multi-source image data set of a target processing scene; performing image state adaptability analysis processing on the original image sequence to obtain an image recognition feature set of each image region in the original image sequence; performing positioning state mapping processing on the image recognition feature set based on the equipment positioning state data set, and generating a spatial positioning driving feature set corresponding to each image region; and generating a self-adaptive positioning control instruction according to the spatial positioning driving feature set, transmitting the self-adaptive positioning control instruction to an execution end of the processing equipment, and indicating the processing equipment to execute self-adaptive pose positioning control operation for the current image area. Therefore, the flexibility of processing equipment to cope with complex processing scenes is greatly improved, and it is ensured that target processing operation is accurately executed under various observation poses and environmental conditions.

The invention relates to the field of new energy, and discloses a photovoltaic system efficiency evaluation method and system based on unmanned aerial vehicle multi-source image fusion, and the method comprises the steps: collecting a visible light orthoimage, thermal imaging data and environment parameters of a photovoltaic system, and obtaining a registration result through employing an improved feature point matching algorithm; based on a registration result, combining spatial features of a visible light image and temperature features of thermal imaging, realizing accurate segmentation of the photovoltaic panel through a deep learning model, and identifying the type, the arrangement mode and the installation angle of the photovoltaic panel at the same time; establishing a mathematical model of the relation between the photovoltaic panel temperature distribution and the power generation efficiency, distinguishing the normal working temperature difference and the fault hot spot, and analyzing and determining the efficiency attenuation degree and the fault type of the photovoltaic system through a heat distribution mode. The method is accurate in image registration, good in data fusion effect, accurate in temperature anomaly detection and comprehensive in efficiency evaluation, and provides more efficient and accurate technical support for management and maintenance of the distributed photovoltaic system.

The invention discloses a parallel U-Net-based dual-domain collaborative infrared and visible light image fusion method. The method comprises the following steps: firstly, extracting initial features of a source image by using dense connection blocks; then, parallel frequency domain branches and spatial domain branches are constructed, the frequency domain branches are combined with discrete wavelet transform and fast Fourier convolution to decompose and enhance multi-scale global frequency domain features, and the spatial domain branches capture long-distance spatial dependence with linear calculation complexity by using a convolutional layer and Mama based on a selective state space model; dynamic interaction and weighted fusion of double-domain information are realized through an adaptive feature fusion module; and finally, generating a fused image through an image reconstruction module. According to the method, the problems of high calculation overhead and video domain information negligence in the prior art are solved, and infrared heat radiation maintenance and visible light texture enhancement are effectively considered.

The invention discloses an intelligent fire detection and quick response method based on image recognition. The method comprises the following steps: S1, synchronously acquiring and preprocessing multi-source image data; s2, carrying out multi-modal feature fusion and early fire identification; s3, intelligent grading response based on deep reinforcement learning: constructing a fire behavior grade dynamic evaluation network, inputting fused multi-modal features, outputting fire behavior grade probability distribution and a crisis index, judging a fire behavior grade according to a dynamic threshold value, and triggering a predefined grading response strategy; s4, performing cross-scene adaptive transfer learning; and S5, carrying out system self-diagnosis and dynamic optimization. According to the invention, through time-space synchronous acquisition and deep fusion of visible light, infrared and smoke multi-mode data, vision, thermal radiation and smoke characteristics of flames are integrated, and common interferences such as real fire behavior and lamplight, light reflection, a moving heat source, water mist and dust are effectively distinguished by using an improved lightweight CNN, a self-adaptive background temperature model and an environment airflow model.

The invention discloses a safety detection system for a high-speed rail transit corollary equipment structure. The safety detection system comprises an excitation response module, an infrared heat source image acquisition module, a parameter monitoring module, an image preprocessing module, a heat source feature extraction module and a structure anomaly recognition module. In the train operation process, the system excites trackside equipment structure response through train body aerodynamic disturbance, thermal response images are collected through high-frame-rate infrared imaging, and image standardization processing is carried out based on disturbance intensity and the relative distance. And the system further extracts thermal response characteristics of the equipment structure area, and compares the thermal response characteristics with a normal template to realize abnormal structure identification and positioning. Under the condition that train operation is not affected, non-contact, high-precision and intelligent detection of the structure state of the trackside equipment is achieved, and the method has the advantages of being flexible in deployment, accurate in recognition, high in real-time performance and the like and is suitable for rail transit operation and maintenance scenes.

The invention relates to the technical field of night monitoring, and discloses a night dynamic target tracking and image restoration method, which comprises the following steps of: establishing a heat-motion correlation model of a target through a space alignment algorithm in combination with heat source contour and intensity information acquired by a thermal imaging sensor and motion parameters detected by a millimeter wave radar; outputting an initial position and a motion state; inputting a target initial state and radar point cloud data based on an LSTM network, predicting a future N-frame trajectory, and dynamically correcting a prediction result by using real-time radar data; inputting the position information of the occlusion area and the trajectory prediction value into a GAN network to generate a complemented image, fusing the complemented image with the original heat source image, and recovering the complete contour of the target; and superposing and rendering the complemented image and the trajectory prediction into an AR picture, and when the target distance triggers a preset threshold value, realizing multi-channel safety early warning through vibration feedback, AR highlight warning and voice prompt. According to the invention, high-precision tracking and safety early warning of the night dynamic target can be improved.

The invention relates to a multi-modal image matching method and system based on learning features and epipolar geometric constraints. The method comprises the following steps: carrying out edge enhancement processing on an input image through wavelet transform; extracting a multi-scale dense feature map based on the transformed convolutional neural network, and generating a feature descriptor with rotation and scale invariance in combination with principal direction normalization; adopting an FLANN algorithm and dynamic distance constraint to realize preliminary feature matching; and introducing a basic matrix construction and epipolar geometric consistency verification mechanism, and eliminating mismatching point pairs in combination with an RANSAC affine constraint model. According to the method, image enhancement, deep learning and geometric verification strategies are fused, the problems of radiation nonlinearity and geometric distortion caused by imaging mechanism differences among multi-modal images are effectively solved, the matching precision and robustness are improved, and the method is suitable for remote sensing application scenes such as optical-SAR registration, multi-source image fusion and earth surface change detection.