17 results about "Satellite image processing" patented technology

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 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 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 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.

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.

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.

The application discloses a kind of based on diffusion model remote sensing image plot extraction method and device, belong to deep learning, remote sensing satellite, image processing and other technical fields, the method can accurately capture the boundary information of field plot by the powerful feature learning and generation ability of diffusion model, effectively avoid the adhesion between adjacent field plots, realize the independent, accurate extraction of each field plot, Even in the similar or complex farmland scene of field plot feature, it can also guarantee the clarity and accuracy of field plot boundary, meet the demand of large-scale, fine farmland resource management;The method introduces cross attention mechanism and other technologies, directly fuses original image information in the denoising process, so that the generated field plot boundary is more pure and fine, without relying on complex post-processing operation to obtain high-quality extraction result, thereby greatly simplifying the entire field plot extraction process;The method has stronger robustness to different light conditions, seasonal changes and image noise.

This invention discloses a feature hierarchy optimization method for small target detection based on remote sensing, belonging to the field of satellite image processing. The method includes: extracting multiple basic feature layers at different scales from a remote sensing image; obtaining the target's attitude parameters from the deepest basic feature layer and encoding them as attitude embedding vectors; generating a corresponding fusion weight set for each basic feature layer based on the attitude embedding vectors; weighting and summing the upsampling results of the current basic feature layer, adjacent upper-layer features, and adjacent lower-layer features according to the fusion weight set to reconstruct an output feature layer; and detecting the category and location of small targets based on the output feature layer. It also includes a ground-on-orbit continuous learning mechanism, which records closed-loop training samples in the on-orbit system, performs on-orbit adaptive updates to the model, and transmits the samples or model parameters back to the ground. After retraining on the ground, the updated model is pushed back to the on-orbit system to continuously improve the mission success rate.

The application discloses an InSAR regional network adjustment method based on physical driving and data learning, belongs to the field of satellite image processing, and effectively extracts common nodes of multi-strip DEM data based on an optimal registration method of minimum height difference, so that the robustness of the common node constraint condition is avoided to be reduced due to the position deviation of the DEM; in combination with the constraint conditions of the common nodes of the multi-strip DEM data and height control points, a spatial error optimization model based on a polynomial is established to effectively fit the error joint distribution of the multi-strip DEM data; the common nodes of the multi-strip DEM data and the height control points are used as constraint conditions of adjustment equations to perform InSAR regional network adjustment, which is beneficial to unifying the system difference between the DEM data obtained from different strips and ensuring the consistency of the error distribution of the DEM product; and the common nodes of the multi-strip DEM data are combined to perform regional network adjustment, which is beneficial to reducing the requirement for the density of the ground control points and improving the numerical stability of the adjustment equations.

The application discloses a kind of optical remote sensing data registration methods of micro-nano satellite, storage medium and electronic equipment, it is related to satellite image processing technical field;Same-named point deviation data of registration band data and datum band data is acquired;The distribution of same-named point deviation data is counted, and representative data of same-named point deviation data is acquired;According to representative data, the difference parameter of registration band data relative to datum band data is determined;According to difference parameter, registration band data is registered to datum band data.This application is by the deviation data between band statistics, with the distribution characteristics of data to determine representative data as the representation of all same-named point deviation data, can quickly determine an effective difference parameter to adapt to the adjustment of all same-named point deviation, avoid point-by-point detailed analysis deviation, reduce processing period, can efficiently deal with the processing of a large amount of data, improve the quality of satellite shooting image same-named point registration.