40 results about "Satellite image processing" patented technology

The invention relates to the technical field of intelligent operation inspection of power transmission lines, in particular to an intelligent detection method of a power grid frame and a satellite image processing system.The method comprises the steps that the power grid frame is detected according to collected multi-source monitoring information, information of a plurality of power grid structures is obtained, and a power grid topology model is established; performing connection analysis on the power grid topology model to obtain a power grid topology analysis result, and performing spatial distribution analysis on the power grid frame by collecting coordinate information of the power grid frame and the power grid topology analysis result to obtain power grid coverage data. And performing data visualization on the plurality of pieces of power grid structure information, the power grid topology analysis result and the power grid coverage data to generate a power grid frame detection result. According to the invention, the method effectively solves a problem that the weak edge region is liable to leak detection in the recognition of the power grid frame in a large-range and high-complexity scene, remarkably improves the recognition efficiency and accuracy of the power grid frame, and reduces the cost and time of manual inspection.

The invention relates to the technical field of intelligent operation inspection of power transmission lines, in particular to an intelligent tower detection method and a satellite image processing system, and the method comprises the steps: obtaining tower space information through constructing a tower information database, carrying out the complexity judgment of the tower space information, and carrying out the labeling processing of the tower space information according to a judgment result, the method comprises the following steps: acquiring tower marking data, training a tower identification model according to the acquired tower marking data, then performing correction fusion on tower detection information, acquiring correction detection information, identifying the correction detection information according to the tower identification model, and outputting a tower identification result; through the method, the problems of low identification precision, low efficiency and high marking cost of a traditional tower identification method in a large-scale and high-complexity scene are effectively solved, the tower detection precision is improved, the adaptability of a model in a complex environment is enhanced, and the intelligent level and the operation and maintenance efficiency of electric power inspection are improved.

The invention discloses an inter-ice water channel inversion method based on multi-channel satellite data, and belongs to the technical field of satellite image processing, and the method comprises the steps: obtaining a reflectivity abnormal value of a visible light radiation brightness value and an initial value of a reflectivity abnormal threshold value, and obtaining a brightness temperature abnormal value BTA of a thermal infrared radiation brightness value and an initial value of a brightness temperature abnormal threshold value; step 2, acquiring a pixel credibility matrix; and step 3, adjusting a brightness temperature abnormal threshold value and a reflectivity abnormal threshold value by adopting an iterative dual-threshold adaptive optimization algorithm based on fuzzy classification, calculating a brightness temperature abnormal value membership degree and a reflectivity abnormal value membership degree, and judging the comprehensive probability that the pixel belongs to the inter-ice water channel according to each membership degree and the pixel credibility matrix. According to the method, the dynamic complexity of the polar region environment can be better adapted by utilizing the advantages of the visible light data in the aspect of identifying the boundary and texture of the ground features and the advantages of the thermal infrared data in the aspect of distinguishing the ground features at different temperatures, and the inversion precision of the inter-ice water channel is improved.

The invention relates to the technical field of satellite image processing, and discloses an improved CycleGAN SAR satellite image simulation generation method and system, and the method comprises the steps: obtaining a real optical satellite image data set and a real SAR image data set, placing the real optical satellite image data set and the real SAR image data set in a source domain and a target domain respectively, carrying out the preprocessing of the two domain images, and dividing the two domain images into a training set, a verification set and a test set; constructing an improved CycleGAN model of a forward and reverse mapping relation between the optical image and the synthetic aperture radar image, and integrating the improved adversarial loss and cyclic consistency loss to generate a total loss function of the model; performing unsupervised adversarial training on the model based on the improved loss function, and alternately updating parameters of a generator and a discriminator until the model converges; and inputting an optical image to be converted into the trained improved cyclic generative adversarial network CycleGAN model, outputting a simulated SAR image and performing post-processing enhancement, thereby solving the contradiction between the limitation of SAR satellite image acquisition and abundant optical image resources, and providing support for the application depending on the SAR image.

The invention relates to the technical field of electric power facility monitoring, in particular to an electric power inspection system and a satellite image processing method, and the system comprises a front-end sensing module which collects state data and establishes an inspection path after receiving an inspection instruction; the edge processing module is used for receiving the state data, performing real-time processing and feature extraction according to the inspection path and generating key parameter information; the central analysis platform receives the key parameter information, performs diagnosis analysis based on the key parameter information, and generates an inspection diagnosis report; the feedback optimization module is used for updating a feature extraction rule of the edge processing module and an intelligent diagnosis analysis model of the central analysis platform based on the inspection diagnosis report and the key parameter information; and the inspection result module sends the inspection diagnosis report to the operation and maintenance terminal. According to the invention, the problems of low inspection efficiency, high safety risk and response delay caused by incapability of realizing efficient, real-time and automatic fault detection and state monitoring in a remote or dangerous area in a traditional inspection mode are effectively solved.

The invention relates to the technical field of satellite image processing, and particularly discloses a different-orbit stereo image pair automatic construction and selection method for a large-area target, and the method comprises the steps: obtaining a satellite stereo image data set, assigning elements, constructing a different-orbit stereo image pair, and selecting the different-orbit stereo image pair. Wherein three rounds of stereo image pair selection are carried out on the basis of a strategy that the overlapping area is preferential and the overlapping area proportion between image pairs is limited, and finally a stereo image pair automatic construction and selection result which can cover a large-area target range and uses the least number of stereo image pairs is obtained. The production efficiency is improved, and meanwhile it is guaranteed that the low-quality phenomenon of the production result is avoided.

The application discloses a road centerline determination method and system based on a deep neural network satellite map, and relates to the technical field of satellite image processing; the method comprises the following steps: preprocessing time-phase satellite images of a collected to-be-processed region, vehicle GPS track data and street view image sequences to obtain a dynamic region in the to-be-processed region, a track density map and a street view feature mapping table; inputting the time-phase satellite images into a pre-trained road segmentation model to obtain multiple road probability maps; based on the multiple road probability maps, performing dynamic region weighted fusion, non-dynamic region logical or operation and threshold determination to obtain an initial road mask; vectorizing the initial road mask into a centerline network and extracting cross nodes to obtain an initial road network topology; and the application has the advantages of improving the accuracy of the road network topology and guaranteeing the rationality of path planning.

The present invention relates to the technical field of intelligent operation and inspection of power transmission lines, and in particular to an intelligent detection method for towers and a satellite image processing system. The method comprises: acquiring tower spatial information by constructing a tower information database, performing complexity determination on the tower spatial information, and labeling the tower spatial information according to the determination result to acquire tower labeling data; training a tower recognition model according to the acquired tower labeling data; then correcting and fusing the tower detection information to acquire corrected detection information; identifying the corrected detection information according to the tower recognition model, and outputting a tower recognition result. The present invention effectively solves the problems of low recognition accuracy, low efficiency, and high labeling cost of traditional tower recognition methods in large-scale and high-complexity scenarios, thereby improving the accuracy of tower detection, enhancing the adaptability of the model in complex environments, and improving the intelligence level and operation and maintenance efficiency of power inspection.

The invention discloses a road center line determination method and system based on a deep neural network satellite map, and relates to the technical field of satellite image processing. The method comprises the following steps: preprocessing a collected time phase satellite image, vehicle GPS track data and a streetscape image sequence of a to-be-processed area to obtain a dynamic area, a track density map and a streetscape feature mapping table in the to-be-processed area; inputting the time-phase satellite image into a pre-trained road segmentation model to obtain a plurality of road probability graphs; based on the multiple road probability maps, after dynamic region weighted fusion and non-dynamic region logic or operation and threshold judgment are carried out, an initial road mask is obtained; vectorizing the initial road mask into a center line network and extracting cross nodes to obtain an initial road network topology; the method has the advantages that the road network topology accuracy is improved, and the path planning reasonability is guaranteed.

The present invention relates to the technical field of intelligent operation and inspection of power transmission lines, and in particular to an intelligent detection method for substations and a satellite image processing system. The method comprises: collecting target data information, and constructing a power station detection data set by acquiring a historical multidimensional feature data set, establishing a multi-level feature extraction network to perform multi-target feature extraction on the target data information, performing feature fusion on the multi-target feature mapping by setting a void convolution module, constructing a power station comprehensive detection model based on the multi-level feature extraction network and the void convolution module, and performing power station detection on the target data information through the power station comprehensive detection model to obtain power station detection results. The present invention effectively solves the problem that traditional detection methods are easily interfered with by factors such as weather and terrain in complex environments, resulting in unstable recognition effect and low accuracy, effectively improves the fusion capability of multi-level features, and thus ensures high-precision recognition of substations and their equipment.

The present disclosure relates to an image processing method, apparatus, electronic device, and medium, and relates to the fields of image processing, satellite image processing technology, etc. The method includes: obtaining an image to be processed, where the image to be processed is an image that needs to be color-adjusted; dividing the image to be processed into blocks according to N rows and M columns to obtain respective block images, determining the color deviation values of the respective block images to obtain respective color deviation values, where N and M are positive integers; based on the color deviation values corresponding to the respective block images, performing color adjustment on the image to be processed by means of histogram matching, and performing an elimination operation on the splicing edges of the color-adjusted image to obtain a grayscale image; adjusting the size of the grayscale image, and inputting the grayscale image with the adjusted size into a style transfer neural network to perform style transfer to obtain a target image. The present disclosure realizes efficient and automated image color adjustment and improves the efficiency.

The application discloses a distributed InSAR system atmospheric phase correction and deformation extraction method, belongs to the field of satellite image processing, and is characterized in that the method comprises the following steps: acquiring multi-satellite near-synchronous observation data of a distributed InSAR system to perform image registration; performing conjugate multiplication on the registered images to obtain interference phases; performing difference on the two interference phases to perform atmospheric phase correction; performing phase filtering processing on the interference phases after the atmospheric phase correction; performing phase unwrapping on the interference phases after the filtering processing; and performing geocoding processing to complete deformation extraction. Through near-synchronous imaging of multiple platforms on the same region, the generation path of time dimension interference is cut off from the physical layer, the influence of short-term atmospheric fluctuation on the phase is greatly weakened, noise and ambiguity in the phase unwrapping process are further removed in combination with the redundancy of multi-view observation of the distributed system, and high-precision extraction of the surface deformation field is realized.

The present invention discloses a method for inverting polynyas based on multi-channel satellite data, belonging to the field of satellite image processing technology. The method comprises the following steps: obtaining reflectivity anomalies and initial values ​​of reflectivity anomaly thresholds for visible light radiation brightness values, and obtaining brightness temperature anomaly values ​​(BTA) and initial values ​​of brightness temperature anomaly thresholds for thermal infrared radiation brightness values; secondly, obtaining a pixel credibility matrix; and thirdly, using an iterative dual-threshold adaptive optimization algorithm based on fuzzy classification to adjust the brightness temperature anomaly thresholds and reflectivity anomaly thresholds, calculate the brightness temperature anomaly membership and reflectivity anomaly membership, and determine the comprehensive probability of a pixel belonging to a polynya based on the memberships and the pixel credibility matrix. The method of the present invention utilizes the advantages of visible light data in identifying object boundaries and textures and the advantages of thermal infrared data in distinguishing objects of different temperatures. It can better adapt to the dynamic complexity of polar environments and improve the accuracy of polynya inversion.

The application discloses a kind of star map registration method and system based on triangle, which comprises: star map is preprocessed, and star map foreground layer is obtained;Star selection algorithm is used to star selection to star map foreground layer in sub-region, and star target point is obtained;Based on triangle distance constraint, same name star point registration is carried out to star target point, and same name star point registration matrix is obtained;Based on same name star point registration matrix, star map motion background compensation is carried out using affine transformation, and star map registration is completed.The system comprises: preprocessing module, selection module, registration module, compensation module.The scheme greatly improves the accuracy of star map registration on the basis of reducing the complexity of calculation, and is suitable for satellite image processing field.

The invention discloses a remote sensing image parcel extraction method and device based on a diffusion model, and belongs to the technical field of deep learning, remote sensing satellites, image processing and the like, the method can accurately capture boundary information of parcels of fields through the strong feature learning and generation capability of the diffusion model, effectively avoids adhesion between adjacent parcels of fields, and improves the efficiency of remote sensing image parcel extraction. According to the method, independent and accurate extraction of each field is realized, clearness and accuracy of field boundaries can be ensured even in a field scene with similar or complex field features, and the requirements of large-scale and refined cultivated land resource management are met; according to the method, by introducing a cross attention mechanism and other technologies, original image information is directly fused in the denoising process, so that the generated field boundary is purer and finer, a high-quality extraction result can be obtained without depending on complex post-processing operation, and the whole field extraction process is greatly simplified; the method has higher robustness for different illumination conditions, seasonal changes, image noise and the like.

The application discloses a kind of satellite image water resource dynamic monitoring method and system based on deep learning.The water resource dynamic monitoring system of the application includes satellite image processing module, deep learning feature extraction module, differential geometry water body boundary representation module, curvature flow dynamic monitoring module, multi-scale evolution prediction module, multi-source data fusion module, decision support module and water body capacity estimation module, system uses differential geometry theory to model water body boundary as continuous manifold, the dynamic change of boundary is represented by curvature flow, and the evolution trend is predicted by using Ricci flow to construct multi-scale representation.The system can accurately represent the characteristics of water body boundary, achieve high-precision change detection, and the boundary positioning accuracy reaches sub-pixel level.It can be applied to watershed water resource assessment, urban water resource management, reservoir monitoring and disaster warning and other scenarios.

The present invention provides a land parcel segmentation method based on a SAM model, and relates to the technical field of land parcel segmentation. The land parcel segmentation method based on the SAM model specifically comprises the following steps: S1. classifier training, S2. medium-resolution remote sensing image processing, S3. high-resolution satellite image processing, S4. segmentation result processing, and S5. result output. By adopting the spatiotemporal features of medium-resolution remote sensing images to generate prompt points and guide the SAM model to perform segmentation, the method avoids the problem that the traditional land parcel segmentation method has a strong dependence on training data during supervised learning during use, resulting in the current algorithm being more customized for a certain type of cultivated land in a certain area, requiring the user to collect data and design and train the model according to a specific application scenario. This method enables the method to perform effective land parcel segmentation work over a large range, and makes the workflow have the characteristics of low cost and zero sample, ensuring that the method has low cost and good generalization performance.

The present invention relates to the field of image restoration technology based on artificial intelligence and machine vision, and specifically relates to an image restoration method based on high-order gradient constraint and diffusion generation model. This method integrates an improved anisotropic diffusion equation into a deep learning framework, constructs a restoration module with high-order gradient constraint, and can effectively improve the detail and contour information in the image. At the same time, a modified LogCosh loss function is proposed, aiming to better handle scenarios with more outliers, thereby enhancing the robustness of the model to image outliers. Finally, through a pre-trained diffusion generation model, high-quality restored images are generated. The image restoration method proposed by the present invention is applicable to the field of natural image processing, and also applicable to fields such as remote sensing and satellite image processing. It can effectively alleviate image degradation caused by factors such as noise, significantly improve image clarity, and thus improve the image restoration effect.

The application discloses a kind of mud-rock flow disaster proneness dynamic evaluation method based on InSAR, belong to satellite image processing field.For how from large-scale area screening out with mud-rock flow disaster risk gully area, the application proposes a kind of method for screening mud-rock flow disaster risk area based on interferometry synthetic aperture radar and digital elevation model technology.The processing method is that the sentinel-1 remote sensing satellite image data (SAR image) of monitoring area is obtained periodically, the satellite image data of monitoring area is calculated by InSAR technical method, the ground surface deformation rate of monitoring area is obtained, the gully topography of monitoring area is identified by DEM technical method, the area that meets ground surface deformation rate faster and gully topography simultaneously is screened out, as mud-rock flow disaster risk area is monitored.

The invention relates to the technical field of satellite image processing, and discloses a satellite remote sensing image data processing method and system, and the method comprises the steps: obtaining the satellite remote sensing image data of a target region, carrying out the geometric distortion correction of the satellite remote sensing image data, and obtaining the corrected satellite remote sensing image data; performing radiometric calibration on the corrected satellite remote sensing image data to obtain initial image data; identifying ground feature features in the initial image data, and generating a ground feature feature distribution map of the initial image data according to the ground feature features; extracting the surface reflectance of the initial image data, and performing feature inversion index analysis on the surface feature features according to the surface reflectance to obtain feature quantitative information corresponding to the surface feature features; and carrying out graph information fusion on the ground feature distribution graph and the feature quantitative information to generate target satellite remote sensing image data. The processing accuracy and efficiency of the satellite remote sensing image data can be improved.

The application relates to a satellite image change detection post-processing method based on invariance analysis, and relates to the technical field of high-resolution satellite image processing. The method comprises the following steps: for each change detection result spot, corresponding spot images are cut from the front and rear images; feature point extraction and matching are performed on each pair of spot images; the position deviation between the feature point pairs is calculated to determine the geometric position difference between the front and rear images; local position correction is performed on the rear spot image; dense matching is performed on the corrected front and rear spot images; and the invariance of the current spot is analyzed according to the feature point matching result and the dense matching result to determine whether the spot is still changed. The application eliminates the false changes caused by geometric displacement in the detection result by constructing the deviation calculation, position correction and invariance measurement modules, and effectively improves the accuracy of the change detection result.

The invention relates to the technical field of satellite image processing, in particular to a kernel function enhanced satellite image cloud recognition and removal method, which comprises the following steps that: a satellite image and a historical image corresponding to the satellite image are acquired, the satellite image contains cloud, and the historical image does not contain cloud; carrying out matching processing on the satellite image and the historical image by adopting an image matching algorithm, and carrying out matching transformation on the historical image to obtain the historical image after matching transformation; performing color histogram analysis on the satellite image to obtain a color model, and coloring the matched and transformed historical image based on the color model to obtain a colored historical image; determining a pixel fusion kernel function, determining a pixel-level fusion weight by the pixel fusion kernel function, and performing pixel-level fusion on the satellite image and the coloring historical image by the pixel-level fusion weight to obtain a cloud-removed satellite image; according to the invention, the cloud and mist removal effect of the satellite image can be improved.

A double-linear-array image uncontrolled positioning method based on laser elevation points belongs to the field of satellite image processing, and is characterized by comprising the following steps: acquiring image connection points through double-linear-array image matching; the laser elevation point is automatically stabbed, the laser elevation point is stabbed to the double-linear-array image, and a laser connection point is obtained; the encrypted point and the exterior orientation after adjustment are obtained through adjustment of the adaptive piecewise polynomial strict model; and performing rigorous model adjustment processing based on laser elevation point assistance by using the laser connection point, correcting the elevation of the encryption point, and outputting a final exterior orientation and an encryption point file. For a strip image in which a continuous adjustment model cannot be constructed, adaptive segmentation processing can be realized, and finally adaptive rigorous model adjustment processing of the strip image is realized. And meanwhile, elevation control is realized by adopting laser elevation point assisted rigorous model adjustment, laser elevation point assisted rigorous model adjustment processing is realized, and the stripe image elevation positioning precision is improved.

The invention provides a satellite-based image processing method and device, electronic equipment and a storage medium, and relates to the technical field of satellite image processing. The satellite-based image processing method comprises the steps of determining image attributes corresponding to remote sensing image data based on acquired image acquisition target information; based on the image attributes, dividing the remote sensing image data to obtain a plurality of sub remote sensing image data, and determining data processing and transmission rules respectively corresponding to the plurality of sub remote sensing image data; target satellites corresponding to the multiple pieces of sub remote sensing image data are determined, and the target satellites are controlled to process and transmit the corresponding sub remote sensing image data based on the image processing and transmission rules. According to the invention, the applicability of satellite remote sensing image acquisition can be improved.

The invention provides an in-orbit satellite image processing method and system based on end-cloud collaboration, and relates to the technical field of satellite image processing, and the method comprises the following steps: obtaining historical remote sensing image data, carrying out the preprocessing and Patch serialization processing, and obtaining a training remote sensing image Patch sequence; constructing a remote sensing image processing model, and training the remote sensing image processing model based on the training remote sensing image Patch sequence to obtain a trained remote sensing image processing model; and acquiring real-time remote sensing image data, performing preprocessing and Patch serialization processing to obtain a real-time remote sensing image Patch sequence, and processing the real-time remote sensing image Patch sequence by using the trained remote sensing image processing model to obtain a remote sensing image processing result. Through the end-cloud collaborative architecture and discrete Token representation, the satellite image transmission bandwidth is remarkably reduced, the in-orbit processing efficiency and the real-time response capability are improved, and meanwhile, the high-fidelity reconstruction quality is ensured.

A marine waste disposal apparatus according to an embodiment of the present invention comprise a satellite image processing unit that receives satellite images to identify and locate marine waste, and generates a map of marine waste, a waste change reasoning unit for receiving at least one or more pieces of information selected from the group consisting of tide, temperature, wind direction / speed, seawater flow, and wave height information to reason changes in the marine waste map, a ship control and management units that control general ships and control waste disposal ships based on marine waste maps.

This invention relates to the field of satellite image processing technology and proposes a local orthorectification method based on video satellite imagery and range vectors. It addresses the problems of existing orthorectification processes, such as errors in direct positioning of single images, poor positioning accuracy, and feature occlusion in the generated orthorectified images due to pitch and swivel angles. This invention utilizes video satellite imagery data from multiple shooting perspectives, selecting images with small pitch and swivel angles for orthorectification. An intermediate orthorectified image is generated from a single frame of the video image, and this intermediate orthorectified image is matched with a reference base map to obtain tie points. Control point information is constructed using an auxiliary DEM, and an image-side compensation parameter model is calculated. Fourier-Melin transform matching is used during the matching process to obtain high-precision, uniformly distributed tie points. This method avoids manual editing, saving time and labor costs.

The invention discloses a multi-dimensional data co-processing ecological degradation condition identification method, which comprises the following steps of: 1, constructing a local and cloud interconnected satellite image processing system, and acquiring cloud image data by adopting a multi-dimensional metadata index mechanism; step 2, performing cloud removal processing on the obtained satellite image to obtain a satellite image with low cloud content; 3, constructing a satellite image and ecological index intercommunication processing model in a mode of fusing multiple vegetation indexes and combining satellite inversion indexes with earth surface elements; 4, constructing a land utilization and ecological element intercommunication processing model; a conversion rule from land utilization in the same period to ecological system types and ecological system succession in different periods is formulated, and attribute and spatial information processing is intelligently completed; and 5, constructing an ecological function and structural region ecological degradation identification model by using a GIS platform algorithm. The method is simple, and the working efficiency and accuracy are improved.

The invention discloses a satellite image water resource dynamic monitoring method and system based on deep learning. The water resource dynamic monitoring system comprises a satellite image processing module, a deep learning feature extraction module, a differential geometry water body boundary representation module, a curvature flow dynamic monitoring module, a multi-scale evolution prediction module, a multi-source data fusion module, a decision support module and a water body capacity estimation module. According to the system, a water body boundary is modeled into a continuous manifold by adopting a differential geometry theory, dynamic change of the boundary is represented through curvature flow, multi-scale representation is constructed by utilizing Ricci flow to carry out evolution trend prediction, the system can accurately represent water body boundary characteristics, high-precision change detection is realized, and the boundary positioning precision reaches a sub-pixel level; the method can be applied to various scenes such as drainage basin water resource assessment, urban water resource management, reservoir monitoring and disaster early warning.

The invention discloses a satellite image processing task and resource dynamic collaborative scheduling method, which comprises the following steps: inputting satellite image data, and carrying out standardization processing on the satellite image data; a three-level heterogeneous software access mechanism is adopted in the standardized flow processing process to integrate a multi-source remote sensing processing tool, and a satellite image standardized processing assembly line is constructed to achieve production process; in the production process, a multi-dimensional allocation decision matrix is adopted as a decision factor for dynamic task allocation, and the weight ratio of the multi-dimensional allocation decision matrix is adjusted through a hierarchical allocation strategy engine to realize a task dynamic allocation strategy; in the standardized flow processing process, a hyper-fusion architecture is adopted to dynamically allocate computing power resources in real time, breakpoint resume, task migration and dynamic load balancing are adopted to process satellite image data, and a task dynamic allocation strategy is fed back and adjusted. The remote sensing image production efficiency can be improved, and the timeliness and precision of remote sensing image use are guaranteed.