185 results about "Image fusion" patented technology

The invention relates to the field of image analysis, in particular to a temporal bone disease classification method and system based on a multi-modal medical image fusion technology. The method comprises the following steps: acquiring a multi-modal image of a patient, performing adaptive distortion correction, and generating a standardized image set; performing layer-by-layer anatomical structure semantic segmentation and multi-modal image fusion on the standardized image set to construct an image fusion framework; according to the image fusion framework, performing intelligent recognition on the fine structure of the temporal bone, and constructing a personalized temporal bone anatomical structure chart; performing tissue function state analysis and digital pathology dynamic simulation based on the personalized temporal bone anatomical structure chart, and constructing a digital pathology model; and performing intelligent pathological feature classification based on the digital pathological model to obtain an intelligent classification report. According to the method, rapid, efficient and accurate temporal bone disease classification is realized.

The invention discloses a multi-modal medical image fusion diagnosis system based on artificial intelligence, and the system comprises the following steps: extracting shared features of CT and MRI images through a convolutional neural network, and mapping the shared features to the same feature space; a two-way step-by-step alignment strategy is adopted, a three-dimensional deformation field matrix is generated, and cross-modal image anatomical structure alignment is achieved; calculating modal feature weights and eliminating distribution differences through an attention mechanism and an adversarial domain adaptation layer; constructing a CT-MRI image block contrast learning task, and optimizing a shared feature encoder; a conditional generative adversarial network is used for generating a false image of a missing mode according to the semantic segmentation map, and data distribution is constrained through a Wasserstein distance; uniform feature extraction of multi-modal medical images is realized through a shared feature encoder, the cross-modal image alignment accuracy is improved in combination with a bidirectional deformation field prediction module, and the comprehensiveness and accuracy of fusion features are enhanced by using a multi-modal feature fusion module.

The invention discloses a bridge crack detection method and system combining an unmanned aerial vehicle and computer vision, and the method comprises the steps: employing the unmanned aerial vehicle to carry a laser radar to obtain bridge point cloud data, and constructing a bridge point cloud model; based on the point cloud model and the spatial constraint condition, planning an unmanned aerial vehicle flight route close to the bridge surface; a camera is carried by the unmanned aerial vehicle, and bridge images are collected along the flight route of the unmanned aerial vehicle; and fusing transfer learning and dynamic snakelike convolution to construct and train a crack recognition deep learning model, and performing crack recognition on a bridge image acquired by the unmanned aerial vehicle. According to the method, the crack recognition deep learning model based on transfer learning and dynamic snake-shaped convolution is constructed, so that full-process automation, intelligence and high precision of bridge crack detection are realized, the detection efficiency and quality are effectively improved, the labor cost and the safety risk are reduced, and the method has wide application value and popularization prospect.

The invention relates to the technical field of image analysis, in particular to a temporal bone and gallbladder sebaceous tumor detection method and system based on multi-modal medical image fusion. The method comprises the following steps: acquiring a multi-modal temporal bone and gallbladder sebaceous tumor image of a patient, carrying out multi-modal image time sequence space alignment, carrying out feature vector change rate fitting, and constructing a multi-time-point image feature vector map; performing multi-modal image voxel decomposition on the multi-time-point image feature vector map, and performing three-dimensional morphological analysis to generate a geometric morphological parameter set; carrying out potential pathological hierarchical structure mining based on the geometric morphology parameter set, carrying out semantic label labeling, and constructing a multi-modal lesion semantic labeling image; and carrying out multi-time-point image change analysis on the multi-modal lesion semantic marking image, and carrying out parallel lesion evolution state prediction so as to generate a cholangioma evolution state prediction map. According to the method, accurate positioning, effective segmentation and visualized display of the temporal bone and gallbladder sebaceous tumor focus are realized.

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 relates to a multi-modal medical image fusion navigation management system. The system comprises an image processing module which is used for acquiring a medical image data set and performing independent optimization processing on the medical image data set to generate a first image set; and the image fusion module is used for extracting multi-scale key points based on the first image set, generating a second feature set, performing cross-modal registration on the second feature set, calculating to obtain a third mapping set, and performing reconstruction to obtain an accurate image fusion image when the registration error of the third mapping set exceeds a preset threshold value. And the visual diagnosis and treatment module is used for extracting dynamic organization boundary features and a multi-modal fusion result based on the image fusion image, performing joint optimization by using a generative adversarial network and a reinforcement learning strategy, and finally generating a visual diagnosis and treatment path plan. The system provides comprehensive and clear comprehensive image information of diseased regions and surrounding tissues, assists in accurate diagnosis of illness states, and greatly improves scientificity and operability of diagnosis and treatment schemes.

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.

The invention discloses a multi-modal medical image fusion system based on deep learning, and relates to the technical field of medical images, and the system comprises an image collection module which obtains medical image data from different modalities; the data preprocessing module is used for carrying out denoising, standardization, image enhancement and registration on the acquired image data; the deep learning fusion module is used for carrying out feature extraction and information fusion on the image data of different modalities based on a convolutional neural network; the diagnosis reasoning module is used for evaluating potential disease information in the medical image data through a trained classification and regression model based on the fused image data; and the visualization module is used for visually displaying the processed image data and the diagnosis result and providing an image and a diagnosis report. According to the method, the limitation of a single mode is overcome by fusing the multi-mode images, more comprehensive and accurate diagnosis information is provided, feature extraction and fusion are performed by adopting the convolutional neural network, and the disease recognition precision is improved.

The invention relates to the field of medical image processing and pathological diagnosis, aims to solve the problems that brain glioma multi-modal image fusion precision is low, heterogeneity feature and pathological association is weak, and visualization readability is poor, and provides a visualization method and device. The method comprises the steps of multi-modal image preprocessing and registration, regional adaptive multi-modal fusion, pathology association type heterogeneity feature quantification, multi-dimensional pathology visualization, and result verification and optimization. Images are rigidly registered and aligned through mutual information, features are enhanced by adopting a regional differentiation attention fusion algorithm, features are quantified through an improved LSSVM, pathological gold standards are associated, and a three-dimensional visualization model is constructed to visually present heterogeneity information. The device comprises a memory and a processor and can execute the method. According to the method, the heterogeneity evaluation accuracy and clinical readability are improved, treatment scheme optimization is assisted, and the method is suitable for accurate diagnosis and treatment scenes of brain glioma.

The invention relates to the technical field of medical image processing, and discloses a neurovascular tumor recognition method and system based on multi-modal image fusion, and the system comprises a multi-dimensional collection module and an intelligent recognition module. According to the neural vascular tumor identification method and system based on multi-modal image fusion, PET / CT images and MRI images of a tumor part are obtained through a multi-dimensional acquisition module and are classified to form an image set, and an intelligent identification module carries out gray scale range standardization processing on the images of different modalities according to the same time point; according to the method, the difference between images collected by different devices or time points is eliminated, the images are registered and aligned to a unified three-dimensional coordinate system, the visual fusion registration capacity is high, and an intelligent recognition module analyzes the horizontal diameter and the vertical diameter of tumor tissue and the growth state of the tumor tissue according to the three-dimensional coordinate system, generates a tumor data set and a compression index and displays the tumor data set and the compression index. The compression degree of the tumor tissue on the blood vessel wall is evaluated, corresponding treatment suggestions are output, and the intelligent recognition and diagnosis effect is good.

The invention belongs to the technical field of remote sensing, and discloses a wolfberry planting distribution high-precision remote sensing monitoring method based on multi-temporal fusion and spectral feature optimization. According to the method, a multi-temporal and multi-spectral satellite image is used as a data source, and preprocessing and multi-temporal image fusion are firstly carried out; the method comprises the following core steps: establishing a time sequence characteristic curve according to a unique phenological period (such as bare soil characteristics in a dormancy period and high vegetation coverage in a rapid growth period) of wolfberry; in a spectral domain, screening out a characteristic spectrum dimension combination with the highest discrimination degree between the wolfberry and other crops through a characteristic wave band optimization algorithm (such as vegetation index difference degree and red edge characteristics); and in combination with an object-oriented classification or deep learning classification model, constructing a space-time coupling classifier, and performing high-precision extraction and distribution mapping on the Chinese wolfberry planting region. The method can effectively solve the problem of confusion classification of Chinese wolfberry and similar ground features (such as other shrubs and orchards), and realizes rapid and accurate monitoring of Chinese wolfberry planting area and spatial distribution.

The present invention relates to a spatio-spectral fusion method for non-paired hyperspectral and SAR images with dual-domain alignment, including: upsampling the hyperspectral image and performing feature extraction, performing Gaussian blur and feature extraction on the SAR image; approximating the radiation distribution of the SAR features to the radiation distribution features of the hyperspectral image; adjusting the spatial geometric information of the SAR features; extracting the change information between the image features, and predicting the low-spatial-resolution hyperspectral image through information injection; fusing the low-spatial-resolution hyperspectral image and the original SAR image after alignment to obtain the final high-spatial-resolution hyperspectral image. The beneficial effects of the present invention are: the present invention improves the spatio-spectral fusion accuracy of hyperspectral and SAR images, providing reliable support for subsequent applications.

The invention discloses a construction safety management method based on excavator visual angle image fusion, and belongs to the technical field of construction early warning, and the method specifically comprises the steps: obtaining the position and working condition of excavation equipment, and setting a dynamic alarm fence; triggering an equipment vision device to scan a personnel target in real time, identify a personnel bounding box and extract a personnel identity identification code; synchronously calling original coordinates of the corresponding ultra-wideband tag, capturing a posture visual frame, and generating a posture feature vector through skeleton joint point topology analysis; inputting the attitude vector into an attitude-error compensation model, and outputting a positioning offset predicted value to correct an original coordinate; according to the method, real-time compensation is carried out through dynamic accurate matching of the construction dangerous area and personnel positioning errors, misjudgment is avoided to the greatest extent, the safety risk active pre-judgment and alarm accuracy is improved, and the safety risk active pre-judgment and alarm accuracy is improved. And intelligent safety management of a construction site is realized.

The invention provides a vehicle-mounted instrument driving record panoramic image fusion three-dimensional projection method and system, and relates to the technical field of vehicle-mounted display, and the method comprises the steps: obtaining original panoramic image data collected by cameras around a vehicle and real-time driving state information of the vehicle; distortion correction and multi-view splicing processing are carried out on the original image to generate a spliced panoramic image; determining the type of a driving scene according to the real-time driving state information, setting corresponding three-dimensional viewpoint parameters including viewpoint height, viewpoint distance and pitch angle, performing three-dimensional space coordinate transformation on the spliced panoramic image based on the parameters, and generating a three-dimensional aerial view image adaptive to the current driving scene; an obstacle target is extracted, the boundary of an image area of the obstacle target is recognized, height stretching rendering is carried out at the corresponding position of the three-dimensional aerial view image, and a three-dimensional panoramic projection image containing an obstacle three-dimensional identifier is generated; and finally adaptively outputting to a vehicle-mounted instrument panel display screen for real-time display. The visual angle can be dynamically adjusted according to different driving scenes, the stereoscopic perception of the obstacle is enhanced, and the driving safety is improved.

The invention relates to the technical field of medical image diagnosis, and discloses a tooth and fracture line recognition treatment method based on the combination of an AI technology and a CBCT image, and the method comprises the steps: obtaining and preprocessing the CBCT image, and carrying out the multi-scale analysis and recognition of a tooth structure, a microcrack and a fracture line through a first AI model. And the second AI model combines the identification result and the patient characteristics, and generates a personalized treatment scheme through multi-objective optimization. Clinical feedback is used for continuously iteratively optimizing double models, and the diagnosis and treatment precision and effect are improved. The system comprises an image data acquisition unit, an image data preprocessing unit, a tooth and fracture line identification unit, a personalized treatment scheme generation unit and a feedback and optimization unit. Through AI and CBCT image fusion, accurate identification of teeth and fracture lines is realized, a personalized treatment scheme is recommended in combination with individual features of a patient and a multi-objective optimization algorithm, rapid response is realized, a closed-loop feedback mechanism continuous optimization model is established, and diagnosis and treatment precision, efficiency and individualization level are remarkably improved.

The invention discloses a focus intelligent detection and diagnosis system based on multi-modal medical image fusion, and relates to the technical field of medical image processing. A molybdenum target image, an ultrasonic image and a magnetic resonance image of the mammary gland of the patient are acquired through the data acquisition module; a data registration module is used for carrying out partition affine and local refinement registration on the multi-modal image by taking the molybdenum target image as a geometric anchor point and combining parameters such as compression force; extracting registered fusion feature data through a data fusion module; generating a candidate focus set by a focus detection module; finally, the risk assessment module calculates a comprehensive risk score through weighting, correction and consistency verification processes based on a plurality of interpretable feature indexes. And the output module generates a visual diagnosis result. According to the invention, intelligent detection and risk assessment of rechecking of breast lesions are realized, and the diagnosis accuracy and clinical reliability are effectively improved.

The invention belongs to the field of medical image processing, and relates to a three-dimensional reconstruction method and system based on multi-modal medical image fusion, and the method comprises the steps: obtaining an original multi-modal image sequence; the original multi-modal image sequence comprises a CT image and an MRI image; performing spatial alignment on the original multi-modal image sequence to obtain a spatial alignment image pair; the space alignment image pair comprises an aligned CT image and a corresponding aligned MRI image after alignment; performing multi-scale layered cognitive feature extraction on the spatial alignment image pair to obtain a corrected feature map; performing time-space frequency domain collaborative attention fusion on the corrected characteristic spectrum to obtain fusion characteristics; performing topological constraint three-dimensional diffusion reconstruction on the fusion features to obtain a medical image three-dimensional model; the three-dimensional reconstruction precision of multi-modal fusion is improved, and more reliable three-dimensional model support is provided for clinical diagnosis and treatment.

The invention relates to the technical field of medical image processing, in particular to an intraoperative risk assessment system based on multi-modal operation image fusion. The method comprises the following steps: acquiring a multi-modal operation grayscale image; determining difference images of the same type; acquiring a change area on the difference image, and performing image projection to obtain a modal change sequence; determining an abnormal coefficient according to the consistency performance of the modal change sequence; performing dynamic time warping matching on the modal change sequence, and determining a midpoint offset coefficient according to the position difference between a central point and a point matched with corresponding warping processing in the sequence; and in combination with the abnormal coefficient and the midpoint offset coefficient, determining an operation influence degree, and in combination with the operation influence degrees of all types of operation grayscale images in the sampling period, realizing risk assessment. According to the method, operation data of different modes are fused, operation scenes which are complex in operation and fine are compressed into simple multi-mode sequences for processing, the quantitative evaluation capability of risks in the operation is enhanced, and the risk evaluation accuracy and timeliness are improved.

The invention discloses a multi-modal image fusion assisted precise tumor needle biopsy guide system, and aims to solve the problem that the traditional needle biopsy guide technology is insufficient in precision. The system innovatively adopts a multi-scale feature fusion algorithm and an adaptive weight fusion strategy, deeply fuses ultrasonic, CT, MRI and other multi-modal images, and comprehensively improves the information amount of the images. Through real-time force feedback and path adjustment, in combination with VR / AR guide interaction, intelligent puncture guide is realized, and safe and accurate operation is guaranteed. Tumor features are extracted by using a deep learning algorithm, accurate tumor recognition and dynamic image analysis are realized, and the tumor growth trend is predicted. According to the system, the precision and success rate of needle biopsy are remarkably improved, the pain of a patient and the medical cost are reduced, powerful support is provided for personalized precise treatment of tumors, and the system has extremely high clinical application value.

The invention provides a target enhancement detection method and device for oil tank detection, and the method comprises the steps: inputting an obtained optical image and a synthetic aperture radar image of a target region into an image fusion model for image fusion enhancement, and obtaining a fusion enhanced image; inputting the fusion enhanced image into the trained YOLOv11 model for target detection to obtain an oil tank detection result of the target area; the image fusion model comprises a feature extractor and a DenseNet structure, the DenseNet structure is used for fusing an optical image and a synthetic aperture radar image to obtain a fused enhanced image, and the feature extractor is used for extracting shallow features and deep features of the optical image and the synthetic aperture radar image to obtain a fused enhanced image; the shallow-layer features and the deep-layer features are used for fusing information measurement between the enhanced image and the optical image and the synthetic aperture radar image; the target features can be expressed more comprehensively, so that the misjudgment rate and the omission ratio are effectively reduced, and the target detection precision is improved.

The invention discloses an AI foot shoe tree model construction system based on image reconstruction and parameterization, which comprises a multi-modal medical image fusion subsystem, a dynamic mechanical parameter adaptive subsystem, an AI feature enhancement subsystem, an intelligent parameterization driving subsystem and a real-time comfort simulation subsystem, the subsystems cooperatively work through data streams and interaction relations, and high-precision feature extraction of the extreme foot shape, individual specificity comfort simulation and rapid construction of a shoe tree model are achieved. By means of the multi-mode medical image fusion technology, low-dose CT images and optical scanning data are organically combined, the three-dimensional foot model containing skeleton and soft tissue layering is constructed, the problem that the robustness of extreme foot shape feature extraction is insufficient is solved, and the model construction precision is improved.

The invention relates to the technical field of photogrammetry, and discloses a photogrammetry space reconstruction method based on multi-source image fusion, which comprises the following steps: extracting an interior orientation element and an initial exterior orientation element, and establishing an image beam intersection constraint intensity model in an object space to calculate a constraint weight; monitoring a weight space change rate to identify a heterogeneous image connection area and extracting a high-weight image bundle as a geometric anchor point; a differential regularization item is applied to geometric anchor points to construct a stable geometric skeleton, a weight damping factor is utilized to restrain a low-weight image beam to execute conformal mapping correction, the image beam constraint strength is represented, the condition number of an adjustment method equation matrix is improved, numerical oscillation caused by difference of different-source sensors is restrained, geometric step of a fusion interface is eliminated, and the conformal mapping correction accuracy is improved. And geometric closing and seamless stitching of a multi-source image under a unified framework are realized.

The invention relates to the technical field of orthopedic surgery, and discloses an orthopedic surgery real-time navigation system based on multi-modal image fusion and artificial intelligence. The multi-modal image fusion module is used for generating a three-dimensional skeleton and soft tissue fusion model for surgical navigation; the artificial intelligence analysis module is used for determining a fracture line, a screw implantation angle and a cutting track; the intraoperative tracking module is used for generating a real-time correction instruction; the verification updating module is used for carrying out rationality verification on the real-time correction instruction; if the inspection is not passed, an alarm mechanism is triggered, instantaneous motion state parameters of the surgical instrument are collected, the instantaneous motion state parameters are compared with the cutting track, and the correction instruction is updated according to the comparison result; and the execution module is used for driving the surgical instrument to execute the surgical operation according to the updating correction instruction. According to the invention, manual operation errors can be effectively reduced.

The invention provides a multi-modal image fusion system and method for neurosurgery, and the method comprises the steps: carrying out the rigid transformation of a multi-modal magnetic resonance image and an ultrasonic image, and obtaining a preoperative magnetic resonance image and an intraoperative ultrasonic image after preliminary alignment; inputting the preoperative magnetic resonance image and the intraoperative ultrasonic image into a pre-trained lightweight deformation registration network for deformation modeling to obtain a voxel-level dense deformation field, and determining a confidence map of deformation field region registration based on the dense deformation field; according to the confidence map, multi-scale feature fusion based on confidence weighting is carried out on the deformed preoperative multi-modal labeling information and the corresponding features of the intra-operative ultrasonic image, and a three-dimensional anatomical structure chart for real-time navigation in the neurosurgery operation is obtained. Based on the scheme, brain tissue drift can be dynamically compensated in real time in a neurosurgery operation, and multi-modal image fusion is guided based on a compensation result.

The invention discloses a nasopharyngeal carcinoma necrosis prediction method and device based on feature fusion and a medium, and the method comprises the steps: obtaining multi-modal medical data of a target patient at a plurality of preset time points, and extracting medical features in different modals for each time point, so as to form a medical time sequence feature matrix in different modals; inputting the medical time sequence feature matrixes in the different modes into a gated twin-tower Transform network, and performing feature fusion on the medical time sequence feature matrixes in the different modes through a preset inter-channel-time step cross attention mechanism to obtain key image fusion features and nasopharyngeal carcinoma necrosis risk prediction results; and if the nasopharyngeal carcinoma necrosis risk prediction result is greater than a preset risk threshold, inputting the key image fusion features and the original three-dimensional image data into a 3D nnU-Net network to generate a nasopharyngeal carcinoma necrosis prediction map of the target patient. The prediction accuracy of nasopharyngeal carcinoma necrosis can be improved.

The invention discloses an AI auxiliary radiotherapy dose intelligent optimization device, which comprises a multi-modal image fusion module, a dose distribution prediction module, a dynamic adaptive optimization module and a prognosis model integration module, and is characterized in that the multi-modal image fusion module is used for aligning anatomical features of different images, eliminating respiratory motion artifacts and predicting the dose distribution of the different images; the dose distribution prediction module is used for rapidly predicting radiotherapy dose distribution based on anatomical features and historical data, the dynamic adaptive optimization module is used for monitoring anatomical changes in real time and dynamically adjusting a dose plan, and the prognosis model integration module is used for quantifying correlation between dose distribution and radioactive injury risks. High-precision alignment and respiratory motion artifact elimination of an anatomical structure are achieved through the multi-modal image fusion module, three-dimensional dose calculation is rapidly and accurately conducted through the dose distribution prediction module, organ displacement and deformation are effectively coped with through the dynamic self-adaptive optimization module through a real-time image monitoring and reinforcement learning algorithm, and the accuracy of the three-dimensional dose calculation is improved. And the prognosis model integration module constructs an individualized risk prediction model.

The invention discloses an engineering progress three-dimensional display system for building engineering management, relates to the technical field of engineering model display, solves the problem that the image fusion processing process is not comprehensive enough, and greatly reduces the data calculation dimension and simplifies the feature processing flow through image graying processing. While the calculation complexity is reduced, the data processing speed is effectively improved, and an efficient foundation is laid for subsequent three-dimensional modeling; in the feature point calibration link, a unique gray value difference judgment and feature polygon comparison mode is adopted, the influence caused by the difference of the shooting angles of the unmanned aerial vehicle is fully considered, the same feature points are accurately recognized through scaling processing, and compared with a traditional method, the accuracy of feature point calibration is remarkably improved; and the precision of subsequent image fusion and modeling is ensured.

The invention provides a sea target identification method and device based on AIS and SAR image fusion, and the method comprises the steps: obtaining the AIS data of a sea target through an AIS system, obtaining the SAR image of the sea target through an SAR satellite, and carrying out the preprocessing of the data; after time calibration and space calibration are carried out on the preprocessed data, the fusion weight of the AIS data and the SAR image in Kalman filtering is determined according to the confidence score of the AIS data and the SAR image; determining a dynamic measurement noise covariance matrix according to the fusion weight, calculating a Kalman gain according to the dynamic measurement noise covariance matrix and an error covariance matrix, and updating the state of the marine target and the error covariance matrix by using the Kalman gain; and inputting the SAR image and the final state output by the Kalman filtering into the deep learning model to obtain a sea target recognition result output by the deep learning model. According to the invention, the precision, robustness and real-time performance of sea target identification are improved.

The invention discloses a multi-modal remote sensing image segmentation method and system based on a cascade knowledge unification module, and belongs to the technical field of remote sensing image processing, and the method comprises the steps: carrying out the multi-modal initial feature extraction and fusion of a hyperspectral image and an SAR image; constructing a cascade knowledge unification module in combination with multi-level feature fusion, a cross attention mechanism and a CNN and SNN combined feature extraction strategy, and processing the initially extracted and fused features through the cascade knowledge unification module to obtain final fused features; and inputting the final fusion feature into the segmentation network to generate a remote sensing image segmentation result. Through multi-stage feature extraction, deep feature fusion and a cascade attention mechanism, the problem of heterogeneity existing in the fusion process of the hyperspectral image and the SAR image is effectively solved, and the precision and robustness of remote sensing image segmentation are improved.

The invention belongs to the technical field of image fusion, and discloses a hyperspectral and ranging image fusion method and device based on an enhanced network. The method comprises the following steps: constructing a hierarchical space-spectrum enhancement network architecture, wherein the hierarchical space-spectrum enhancement network architecture comprises a multi-scale space enhancement module, a global spectrum enhancement module and a space spectrum enhancement module; constructing a joint loss function including constraint loss and classification loss; and training a hierarchical space-spectrum enhancement network architecture by using a joint loss function, balancing constraint loss and classification loss, and completing collaborative optimization of feature reconstruction and classification tasks. According to the invention, by means of the LiDAR technology, the three-dimensional structure of the ground feature can be clearly presented, and the analysis capability of the ground feature form and spatial distribution is enhanced, so that the classification precision is improved.