1010 results about "Remote sensing satellite" patented technology

The invention discloses a geographic information big data-oriented multi-source data processing system, and relates to the technical field of data acquisition and sensors, and the system comprises a data acquisition module which accesses a remote sensing satellite, an unmanned aerial vehicle, an Internet of Things sensor and social media in real time through a multi-source heterogeneous interface, and carries out the adaptive analysis of a data format and metadata marking; the distributed storage module is used for performing partition storage on the geographic information data based on a space-time database and an object storage architecture, and establishing dynamic space-time index and version control; the data fusion module is used for realizing coordinate system conversion, time sequence calibration and semantic knowledge graph matching based on a multi-source data alignment method of dynamic weight distribution; and an intelligent analysis module and a security control module. According to the method, core pain points such as data splitting, low storage efficiency, extensive analysis and compliance risks in the geographic information field are systematically solved, and a full-stack type technical base is provided for scenes such as smart cities, emergency disaster relief and environment monitoring.

Disclosed is an intelligent monitoring method for soil moisture status based on remote sensing technology, including the following steps: dividing a monitoring region into a plurality of sub-regions by a processing end, acquiring image data of various sub-regions on the basis of remote sensing satellites, acquiring multi-source data related to soil moisture status of the monitoring region on the basis of sensor devices, and pre-processing the image data and the multi-source data; comprehensively analyzing the image data and the multi-source data, and assessing soil moisture status of the various sub-regions; and comprehensively analyzing overall soil moisture status of the monitoring region according to the soil moisture status of the various sub-regions. The soil moisture status of the monitoring region is assessed after the multi-source data is comprehensively analyzed by the monitoring method, bringing about a more comprehensive and accurate analysis.

The invention provides a flood monitoring and early warning method and system based on multi-source remote sensing satellite data fusion. The method comprises the following steps: collecting various types of remote sensing satellite data in a flood area; preprocessing the collected remote sensing satellite data; integrating different scale features of the preprocessed remote sensing satellite data to obtain feature fusion; popularizing feature fusion based on a revisit channel attention module from a same-layer scene to a cross-layer scene; flood recognition is carried out by using the fused remote sensing image, and the boundary and range of a flood area are extracted; according to the identified flood area and the historical flood data, flood early warning is carried out in combination with meteorological and hydrological information, and an early warning result is obtained; and comprehensively analyzing the flood cause, the flood process and the flood influence of the flood event. By integrating multi-source satellite data, the invention aims to improve the accuracy of flood large-range detection and strengthen the prediction capability of flood trend, thereby ensuring the accuracy and reliability of flood space-time early warning.

The invention relates to the technical field of natural resource investigation, in particular to a GIS-based natural resource investigation system and method, which comprises data acquisition, data preprocessing, spatial data integration, dynamic monitoring and analysis, resource utilization evaluation and decision support. Firstly, environment data, human activity data and resource distribution data are collected in real time by arranging multiple kinds of sensors, remote sensing satellites, unmanned aerial vehicles and other devices, and cleaning and denoising processing is carried out. And then, spatial integration is performed on data from different sources by utilizing a GIS platform, and a geographic information data set is formed by combining regional map information. Through a dynamic monitoring system, in combination with spatial-temporal change analysis, resource change and fluctuation of environmental factors are monitored in real time, and resource consumption and environmental influence trends are predicted. Based on the analysis results, the system generates a resource optimization utilization scheme and environmental protection measures, scientific decision support is provided for managers, and sustainable utilization of resources and protection of the ecological environment are achieved.

The invention discloses an on-satellite full-autonomous task planning system for an agile remote sensing satellite. Comprising an imaging target database module, a high-precision orbit prediction and orbit calculation module, a target visible arc section calculation module, an imaging task preprocessing module, a subsystem constraint model module, an imaging task planning module, a data transmission task planning module, a solid memory autonomous erasing module, an instruction data block generation module and a satellite state real-time monitoring module. By carrying out constraint modeling on subsystems or single machines such as load, data transmission, control and the like, an on-satellite full-autonomous operation process from receiving of subsystem or single machine instruction blocks from user requirements is established, satellite control is effectively simplified, and remote control uploading efficiency and on-satellite autonomous operation capacity are improved. And through a satellite state real-time monitoring module, the execution condition of tasks on the satellite, satellite state changes and the like are monitored in real time, task execution stopping, whole satellite sun-facing or re-planning are further triggered, task requirements and environment changes are quickly responded, task plans are formulated and adjusted in real time, and the task execution efficiency is improved.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

A remote sensing sub-pixel runoff inversion method based on an improved vegetation index method includes: extracting an image of a target water from remote sensing satellite data; calculating a width of the target water using remote sensing data, inversing a width of the target water using remote sensing subpixels, and using one of the widths of the target water with a higher accuracy as a target width; remote sensing and estimating a flow velocity and a depth of the target water, and calculating a runoff of a small river. The improved vegetation index method can decompose pixels to extract a river width with a higher accuracy, which can meet accuracy requirements of some small rivers in mountainous areas, and then effectively applied in flow inversion of the small rivers with high accuracy requirements to improve runoff inversion effect, which has good economy, practicability, flexibility, and a certain physical mechanism.

The invention provides a wetland vegetation intelligent monitoring method and system based on multi-dimensional data fusion, and the method comprises the following steps: collecting a water area unmanned plane image of a wetland water area through a monitoring unmanned plane, and obtaining a water area remote sensing image of the wetland water area through a remote sensing satellite; the water area unmanned aerial vehicle image and the water area remote sensing image are fused into a water area fusion image, the submerged vegetation distribution condition is recognized, and a submerged vegetation monitoring route is planned; the underwater monitoring robot is controlled to continuously detect a wetland water area, and a submerged vegetation standard image and a submerged vegetation multispectral image are collected at the same time; performing image enhancement on the submerged vegetation standard image through a submerged vegetation identification model, and identifying and marking target submerged vegetation from the submerged vegetation enhanced image; selecting a target multispectral region containing target submerged vegetation from the submerged vegetation multispectral image; and vegetation feature data of the target submerged vegetation are calculated based on the target multispectral region. According to the invention, related data of submerged vegetation can be continuously and accurately monitored.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention discloses a meta reinforcement learning scheduling method for earth observation satellite task planning, and the method comprises the steps: carrying out the prediction of a target visible window for all visible time windows of a task target, carrying out the preprocessing of a satellite visible window, designing a dynamic reward mechanism, building a self-adaptive dynamic reward task Markov decision environment, and carrying out the prediction of the target visible window. Building a reinforcement learning algorithm based on meta learning, and finally loading the algorithm to input test data and output an imaging task planning strategy. A task dynamic reward mechanism is introduced, task generality is captured by utilizing a meta-learning layer, and through multiple rounds of training, an intelligent agent can quickly adapt to and efficiently make a decision in a variable environment, so that a satellite task planning result is obtained. The method can be effectively suitable for remote sensing satellite task planning with a dynamic task scene.

The present invention relates to the technical field of soil monitoring, and disclosed is an intelligent soil moisture content monitoring method based on remote sensing technology. The method comprises: a processing terminal dividing a monitoring area into multiple sub-areas, wherein the division of the sub-areas may be by means of equal parts by area, acquiring image data of each sub-area on the basis of a remote sensing satellite, acquiring, on the basis of a sensor device, multi-source data related to the monitoring area and soil moisture content, and performing pre-processing on the image data and multi-source data; comprehensively analyzing the image data and the multi-source data, and evaluating the soil moisture content conditions of each sub-area; and, by means of the soil moisture content conditions of each sub-area, comprehensively analyzing the overall soil moisture content conditions of the monitoring area. The present monitoring method comprehensively analyzes the multi-source data to then evaluate the soil moisture of the monitoring area, such that analysis is more comprehensive, and accuracy is higher.

The invention discloses a dynamic scheduling method and system for a satellite-ground cooperative computing task, and the method comprises the following steps: constructing a satellite-ground cooperative computing system comprising a remote sensing satellite cluster, a satellite server node, a ground server / data center and the like, and enabling the satellite cluster to generate an in-orbit task and to describe dependence through DAG; available computing power, task queues and other resource states of a satellite and a ground server are collected in real time, and global observation is formed; on the basis of an MADRL framework, each node is regarded as an independent agent, a task allocation action is generated through a strategy network, and a collaborative decision is modeled through POMDP; optimizing the task execution sequence of the same node by using an EFT algorithm; issuing a task and monitoring execution; task completion time and energy consumption are collected, and a network optimization strategy is updated through a reward function and experience playback; the scheduling strategy is adjusted through loop iteration, satellite offline and link fluctuation are adapted, and the resource utilization rate and the task processing efficiency are improved.

The invention provides a multi-parameter GNSS-R soil humidity inversion method based on a random forest model. The multi-parameter GNSS-R soil humidity inversion method comprises the following steps: satellite-borne GNSS-R data quality control; satellite-borne GNSS-R data observation quantity extraction and surface reflectance calculation are carried out; eliminating satellite-borne GNSS-R observation data influenced by the open water area; carrying out surface reflectance gridding; preprocessing the SMAP data; constructing an inversion model; and carrying out spaceborne GNSS-R soil humidity inversion and precision evaluation. The method has the beneficial effects that by utilizing the performance advantages of processing a non-linear relationship, good generalization performance, strong robustness and the like of the random forest, after observation data influenced by a water body is accurately eliminated, an inversion model is constructed by combining multi-parameter auxiliary data of a soil humidity active and passive remote sensing satellite; the space-time coverage, the inversion precision and the reliability of soil humidity data are remarkably improved, and powerful data support is provided for the fields of climate forecast, agricultural production, hydrological modeling and the like.

The invention discloses a priori image constraint-based self-supervised remote sensing image space-spectrum fusion method and device, and relates to the technical field of remote sensing satellites. The method comprises the following steps: acquiring a panchromatic image and a multispectral image; a prior image is generated based on the spatial information of the panchromatic image and the spectral information of the multispectral image, and the prior image is used for constraining the fusion process of the panchromatic image and the multispectral image by a remote sensing image spatial-spectral fusion network; inputting the panchromatic image, the multispectral image and the prior image into the remote sensing image spatial-spectral fusion network, and obtaining a target fusion image output by the remote sensing image spatial-spectral fusion network; by implementing the technical scheme provided by the invention, the generalization ability of the model in processing different sensor data is enhanced, and a more stable fusion effect is realized.

The invention discloses a surveying and mapping method for a complex terrain in a surveying and mapping process, which relates to the technical field of surveying and mapping, and comprises the following steps of: constructing a convolutional neural network model, carrying out sub-region division on a whole region, and taking terrain complexity data, terrain complexity level data and a preset surveying and mapping method as a training set for training; according to the method, the trained convolutional neural network model is utilized, only remote sensing satellite image data of a to-be-mapped area needs to be acquired, sub-area division can be automatically carried out on the area, the terrain complexity level and the corresponding mapping method of each sub-area are output at the same time, and the mapping efficiency of the whole area is improved while the mapping precision of the complex terrain is guaranteed. Meanwhile, surveying and mapping data of each sub-region are obtained in real time and transmitted back to a ground preset platform; and the ground preset platform processes all the surveying and mapping data, so that the processing efficiency of the surveying and mapping data is improved.

The invention provides a lightweight visible light ship target detection method based on edge feature guidance, and relates to the technical field of ship detection image data processing, and the method comprises the steps: collecting remote sensing satellite images, and carrying out the random distribution of the images after screening and marking, and obtaining a training set and a verification set; the backbone network module comprises a plurality of Conv modules and C3k2 modules which are mutually stacked; the neck module comprises a detail-enhanced convolution module and a hierarchical pyramid module based on dynamic feature aggregation; in the head module, after the features of all detection layers are subjected to independent convolution processing, feature transformation is carried out through a multi-branch detail enhancement convolution module; performing data enhancement on the training set; and obtaining a trained ship target detection model through a back propagation algorithm and a gradient descent optimization method. According to the invention, the lightweight and precision improvement of the detection head are realized, the robustness of the model to the illumination change is enhanced, and the global semantic information and the local detail features are fused to balance the detection of the small target and the large target.

The invention relates to the technical field of water and soil monitoring, in particular to a water and soil loss intelligent monitoring system based on the Internet of Things, which comprises an earth surface remote sensing image data acquisition module for acquiring remote sensing image data covering a monitoring area through a remote sensing satellite; the perturbation perception compensation module is used for repairing data by adopting a compensation mechanism aiming at abnormal fluctuation of the remote sensing image caused by landslide geological disturbance, and specifically comprises a disturbance detection unit and a disturbance area compensation unit; and the disaster tracing module is used for reconstructing a loss path based on the remote sensing image data restored by the perturbation perception compensation module, simulating a diffusion path of water and soil loss and tracing the loss based on the diffusion path. According to the method, water and soil loss prevention and control guidance of the key area is provided, the accuracy and efficiency of water and soil loss tracing are improved, and powerful technical support is provided for source identification and prevention and control decision of water and soil loss.

The invention discloses a distributed remote sensing method and device based on a satellite internet and a storage medium, and the method comprises the steps: receiving a remote sensing image obtaining request corresponding to a target region and sent by a user terminal through the satellite internet, and uploading a remote sensing image obtaining task to a first remote sensing satellite through the satellite internet and an inter-satellite link; remote sensing images corresponding to the target areas are collected respectively; based on the remote sensing image, generating a space confidence map corresponding to the first feature map, and determining a target feature image block in the first feature map; receiving target feature image blocks sent by the second remote sensing satellites, and splicing and fusing the plurality of target feature image blocks corresponding to the second remote sensing satellites and the first feature map to generate a second feature map; and transmitting the second feature map to the user terminal in real time through the satellite internet and the inter-satellite link. The technical effect of improving the distribution capability of the satellite for the remote sensing information and the distributed computing capability of the remote sensing task is achieved.

The invention discloses an agile satellite attitude fusion method and system considering multi-source reference difference, and belongs to the field of remote sensing satellite ground data processing, and the method comprises the steps: calculating a thermal environment factor based on satellite measurement data and satellite sensor installation parameters; extracting a space-time low-frequency error based on observation data of the star camera and the star sensor; performing normalization processing on the thermal environment factor, and constructing a relation model of the space-time low-frequency error and the thermal environment factor by adopting a multi-layer perceptron network to obtain compensated attitude data of the star sensor and the star camera; establishing a multi-source absolute attitude sensor data fusion model based on the compensated attitude data of the star sensor and the star camera; and carrying out absolute and relative attitude data fusion filtering processing on the processed gyroscope data and the fusion result of the star sensor and the star camera by adopting a bidirectional dynamic unscented Kalman filter to obtain a high-precision attitude data result. According to the method, the attitude determination precision of the agile satellite is improved, and the requirement of a high-precision remote sensing task is met.

The invention discloses a large-area crop rapid mapping method integrating multi-source remote sensing data, and the method comprises the steps: obtaining a multi-source remote sensing satellite image, carrying out the preprocessing, amplifying a sample based on the spectrum similarity of a field collection sample point and an amplification sample point, and screening the amplification sample point based on a dynamic time warping distance; drawing a crop distribution reference map of the research subarea in cooperation with multi-source data; and based on the crop distribution reference diagram of the research sub-region, constructing a research region sub-pixel drawing sample set and predicting a sub-pixel distribution diagram of each crop type. According to the method, existing field collection sample points are fully utilized, the drawing potential of multi-source remote sensing data and different classifiers is explored, and crop sub-pixel rapid drawing of a large-scale research area is achieved.

The invention belongs to the field of crop growth monitoring, and particularly relates to a crop irrigation decision-making method and system based on crop growth period water demand characteristics, and the method comprises the steps: firstly obtaining a remote sensing satellite image of a to-be-decided crop soil region, and the segmented growth period and transition time period attributes of each crop; secondly, monitoring the soil water content of a crop soil area to be decided by using a temperature vegetation drought index according to a remote sensing satellite image; and finally, combining the soil water content with the related attributes of the crop growth period, and generating a variable irrigation prescription map by means of a preset variable precise irrigation decision model, so as to realize the irrigation decision of the crop soil area to be decided. By comprehensively utilizing the remote sensing data and the crop growth period information, the irrigation scheme can be reasonably planned according to the water demand characteristics of different crops in different growth stages, the water resource utilization efficiency is effectively improved, and the crop growth is guaranteed.

The invention relates to the technical field of remote sensing spectrums, in particular to an urban black and odorous water body recognition method and system based on remote sensing spectrums. The method comprises the following steps: acquiring infrared spectrum and visible spectrum data based on a remote sensing satellite or an unmanned aerial vehicle, processing the data, eliminating noise and abnormal values in the data, and extracting spectral features to obtain a spectral feature data set; according to the invention, through infrared and visible spectrum data collected by a remote sensing satellite or an unmanned aerial vehicle and through combination with urban water body data of a geographic information system, the dimension and quality of the data are improved, so that monitoring of the urban black and odorous water body is not limited to surface coverage, and sensitivity of data analysis can be accurately regulated and controlled by dynamically adjusting a spectrum band weight; the method has the advantages that the method adapts to variable environmental conditions, recognition precision of various polluted water bodies is improved, continuity and accuracy of long-term monitoring are guaranteed by continuously updating and optimizing the processing flow, and urban water quality management and decision-making processes are effectively supported.

The invention provides a river flow estimation method for deducing a river channel open area by using a height measurement satellite, which comprises the following steps of: extracting and preprocessing an optical image river width, preprocessing a radar image and extracting the river width; extracting a height measurement satellite preprocessing water level; inverting the river topography by the elevation boundary; inverting a riverway terrain by a remote sensing image; constructing a riverway terrain; and carrying out river flow inversion. According to the river flow estimation method for deducing the river channel open area by using the height measurement satellite, river flow prediction is realized by extracting river width and water level inversion river channel topography based on multi-source remote sensing satellite data and establishing a cross section-flow empirical formula.

The invention discloses a remote sensing satellite autonomous task planning method and system, and the method comprises the steps: constructing an orbit prediction model, an attitude maneuver model, a load model and other models, and calling the models in a planning process for task sorting, constraint checking and meta-task generation; only point / line / surface related information, load related parameters, task imaging intervals and other necessary task demand parameters need to be injected on the ground, path planning, task decomposition, task sorting, constraint check, autonomous instruction generation and other operations are carried out through an on-satellite autonomous task planning system, and an instruction sequence related to task execution is generated and distributed to a related subsystem. The load subsystem, the data transmission subsystem, the control subsystem, the satellite service subsystem and the like cooperate to complete tasks. After the autonomous task planning system receives task information injected on the ground, satellite pre-planning information can be fed back to the ground in time, and the satellite pre-planning information can be used for ground synchronous task situations.

The invention discloses an atmosphere and sea color parameter joint inversion method under sparse remote sensing satellite data. The method comprises the following steps: firstly, acquiring various satellite remote sensing and sea color data in a unified format; secondly, using the obtained satellite remote sensing and sea color data to construct a sparse atmosphere sea color data set through pixel mask and combining observation geometric information carried by the satellite remote sensing data; then, based on remote sensing waveband data processing, a sparse remote sensing depth inversion network comprising a sparse processing network, an external depth estimation network and a scale fusion network is constructed; and finally, training the sparse remote sensing depth inversion network through the sparse atmospheric sea color data set, outputting an inversion result image, and performing model evaluation. According to the method, joint estimation of atmosphere and sea color multiple parameters can be realized, the inversion precision in a complex ocean and atmosphere interaction scene is improved, and reliable technical support is provided for high-precision atmosphere correction and ocean carbon flux estimation.

The invention discloses a landslide identification and early warning system and method based on remote sensing satellite images, and belongs to the technical field of geological monitoring. The problem that the accuracy and reliability of data are easy to interfere due to the fact that the precision and stability of part of monitoring equipment need to be improved in the prior art is solved, the satellite image of the target mountain area is obtained through the remote sensing satellite, landslide feature extraction is conducted on the satellite image through the synthetic aperture radar interferometry technology, and the landslide feature extraction accuracy is improved. Identifying a potential landslide area in the target mountain area; a landslide risk assessment model is constructed in combination, the landslide risk assessment model is utilized to predict real-time mountain data, the degree of landslide risks existing in a potential landslide area is obtained, and a risk assessment report is generated and fed back to the man-machine interaction unit to give an early warning; through fusion of satellite remote sensing images and ground monitoring data, real-time monitoring and early warning of landslide are realized, accuracy and reliability of identification and early warning are improved, and loss caused by geological disasters is reduced.

The invention provides a multi-satellite parallel data transmission resource scheduling method based on a genetic algorithm, and belongs to the technical field of ground task management and control of spaceflight earth observation satellites. The method comprises the steps that historical data transmission resource use rules of all satellites are mined, and data transmission resource use rule models of all the satellites are established; in a data transmission resource scheduling process, each satellite generates a corresponding resource demand set according to a data transmission timeliness requirement, a priority degree and a data transmission resource use rule model of an important task, conflict resolution is carried out on a conflicting resource use demand set by utilizing a genetic algorithm, and a data transmission resource scheduling result is obtained through a multi-round parallel resource matching and conflict resolution process. The multi-satellite data transmission resource planning scheme is obtained, large-scale satellite data transmission resource scheduling is achieved, the large-scale remote sensing satellite group resource use efficiency is effectively improved, and the implementation rate of reconnaissance tasks is improved. According to the method, the data transmission resource distribution income among multiple satellites can be comprehensively planned, and the intelligent scheduling capability level of large-scale remote sensing satellite group resources can be effectively improved.

The invention discloses a vertical rail scanning remote sensing satellite natural multi-coverage image resolution improving method and system, and relates to the field of remote sensing image processing. The method solves the problems that due to the fact that the distance between an existing vertical rail scanning imaging sensor and a ground target is increased, image pixels cover a wider ground range, the actually-measured spatial resolution is reduced, and fine detection and recognition of ships, aircrafts and the ground target cannot be supported. SIFT feature point extraction is carried out, and feature points extracted from different images are matched by using a Euclidean nearest distance matching strategy; taking one registered image as a reference, and constructing an initial high-resolution image through up-sampling; establishing an imaging degradation model from a high-resolution image to a low-resolution observation image; calculating a residual image of the simulation image and the real observation image; and back-projecting the residual error back to the high-resolution image space, and updating high-resolution image estimation.

The invention provides a low-orbit satellite internet heterogeneous federated learning method based on dynamic aggregation, and relates to the technical field of federated learning, and the method comprises the following steps: S1, a central server distributes a local training task for each low-orbit remote sensing satellite through a data set sharing method; s2, the satellite participating in local training receives the shared data and the parameters of the current global model, and uploads the updated local model parameters to a central server; s3, the central server calculates a weighted value according to the frequency at which each satellite participates in aggregation and the time at which the model participates in aggregation last time; judging whether convergence occurs based on the weighted value; s4, outputting a current global model; and S5, replacing the initial global model in the step S1 with the current global model, executing the steps S1 to S3 again until convergence, and outputting the current global model. According to the method, the efficiency, precision and stability of model training can be improved, and the generalization ability of a global model is enhanced, so that cooperative work between different satellites and efficient convergence of the model are ensured.

The invention discloses a remote sensing image hue information keeping method based on a diffusion super-division model, and the method comprises the steps: constructing a data set, carrying out the image preprocessing of an original high-resolution image, obtaining a feature image after preprocessing, and dividing the feature image into a test set and a verification set; constructing a diffusion super-division model; designing a residual function of the diffusion super-division model; performing cross reference on the training set and the verification set, combining a residual function, and optimizing model parameters by continuously minimizing the residual function to train a diffusion super-division model to obtain a trained diffusion super-division model; deploying the trained diffusion super-division model on a remote sensing satellite to form a hue information keeping module based on the diffusion super-division model; a remote sensing satellite generates an original image needing to be processed, the original image is input into a hue information keeping module based on a diffusion super-division model, model reasoning is conducted through the trained diffusion super-division model, a high-resolution image is obtained from a low-resolution image in a super-division mode, and image details are restored on the basis of keeping hue information.