435 results about "Remote sensing satellite" patented technology

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 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 discloses an optical remote sensing satellite high and low frequency relative radiation error correction method and system, and the method comprises the steps: S1, carrying out the systematic high frequency radiation error calibration of a remote sensing image, so as to eliminate stripe noise; s2, performing frequency domain decomposition on the image after high-frequency correction, and separating high and low frequency components; s3, calculating a global statistical moment of the low-frequency component as a correction reference; s4, calculating a local statistical moment of the low-frequency component column by column; s5, calculating an independent correction coefficient for each column according to the global and local statistical moments; s6, correcting the low-frequency component by using the correction coefficient; and S7, recombining the corrected low-frequency component with the original high-frequency component to generate a final image. The method is suitable for improving the quality of the remote sensing image with multiple composite radiation errors, high-frequency stripes and low-frequency brightness gradient can be effectively eliminated, original texture details of ground features are reserved to the maximum extent in the correction process, and the radiation fidelity is guaranteed.

The invention discloses a remote sensing image low-frequency noise correction method and system, relates to the field of remote sensing image processing, realizes accurate removal of stripe noise under the condition of reducing satellite image detail loss, and relieves the problems of unstable correction effect, easiness in losing image details, difficulty in accurately depicting prior characteristics and the like of the existing remote sensing image low-frequency noise correction technology. The remote sensing image low-frequency noise correction process takes a column mean value or a row mean value as a basic processing unit; fine noise intensity calibration is carried out; low-frequency noise of the image is corrected in a mean value compensation mode, and detail loss of the corrected image is avoided. The method is suitable for multispectral and panchromatic remote sensing images of a remote sensing satellite, and is not influenced by ground feature types.

The invention discloses a remote sensing satellite network security situation awareness method and system with dual prevention mechanisms, and the method comprises the steps: obtaining multi-source heterogeneous data from a remote sensing satellite ground system network, and generating a structured data set through cleaning, denoising and standardization processing; and according to the structured data set, recording network assets by adopting an asset identification algorithm, and generating a risk thermodynamic diagram of the network assets through a Bayesian network model and an entropy weight method. And according to the risk thermodynamic diagram and the structured data set, adopting a deep learning model to detect abnormal traffic and behaviors, and generating a hidden danger list marked with priorities through a general vulnerability scoring standard and a threat intelligence library. And according to the risk thermodynamic diagram and the hidden danger list, constructing a dynamic security situation map by adopting a graph database and a community discovery algorithm. Therefore, the real-time fusion capability of multi-source heterogeneous data is improved, a dynamic attack chain reasoning mechanism is enhanced, and collaborative decision-making of risk early warning and hidden danger treatment is realized.

The invention discloses a typhoon intensity prediction method and device fused with a space-time deep network, and a medium, and the method comprises the following steps: 1), collecting typhoon historical observation data, and obtaining typhoon data; 2) data preprocessing; 3) extracting data features; 4) feature fusion; 5) establishing a typhoon intensity prediction model; 6) model training; and 7) performing prediction by using the trained model, outputting a final prediction value, and restoring a prediction result into an actual typhoon intensity value through reverse normalization processing. The influence of the remote sensing satellite image and the characteristics of the tropical cyclone on the tropical cyclone strength estimation accuracy is considered, the typhoon strength prediction integrated with the spatio-temporal information is carried out, the spatio-temporal representation of the typhoon focus area is enhanced through the integration of the spatial characteristics and the time sequence characteristics of the typhoon, and the accuracy of the strength prediction result is improved.

The invention provides a remote sensing image classification method based on a spatial spectrum attention network, and relates to the technical field of remote sensing image processing, and the method comprises the steps: obtaining a hyperspectral remote sensing satellite image, carrying out the data preprocessing, and constructing a hyperspectral remote sensing image classification data set; constructing a classification model DSAFNet based on a double-branch spectrum-space attention fusion network, and taking the classification model DSAFNet as an initial hyperspectral classification model; training the initial hyperspectral classification model through the hyperspectral remote sensing image classification data set to obtain a trained classification network model; obtaining a to-be-classified hyperspectral remote sensing image; and inputting a to-be-classified hyperspectral remote sensing image into the trained classification network model to obtain an image classification result. According to the technical scheme, generation of the hyperspectral remote sensing image classification model with high recognition accuracy and efficiency is realized.

The invention relates to the technical field of offshore wind plant area marine environment monitoring, and discloses an offshore wind plant wide-area environment panoramic sensing method and system, and the method comprises the steps: S1, laying seabed bottom type comprehensive observation equipment and low-altitude meteorological observation equipment in a target area of an offshore wind plant; the seabed bottom type comprehensive observation equipment comprises an acoustic Doppler flow velocity profiler, a tide gauge and a temperature-salinity-depth instrument; the low-altitude meteorological observation equipment comprises a meteorological station and a wind measuring radar; s2, collecting field data through the seabed bottom type comprehensive observation equipment and the low-altitude meteorological observation equipment, collecting remote sensing data through the meteorological remote sensing satellite, performing inversion on the remote sensing data to obtain remote sensing inversion data, and transmitting the field data and the remote sensing inversion data to a software processing platform and the like; according to the method and the system, collaborative acquisition, precise processing and fusion prediction of air-sky-sea multi-dimensional data can be realized, and the comprehensiveness and the accuracy of offshore wind plant environment perception are improved.

The invention provides a satellite service data analysis method and system based on a dynamic protocol, belongs to the technical field of satellite real-time data processing, and is used for analyzing satellite data downloaded by a data transmission link so as to complete closed-loop verification on a satellite data transmission core data flow path. The method depends on the support capability of an on-satellite data transmission system for a multi-channel downloading mode, and the independent, lossless and time sequence fidelity binary disk falling capability of ground detection equipment for each channel data. The method comprises the following steps: acquiring original downlink data highly consistent with an on-orbit state; secondly, adopting a scheduler model to carry out structured segmentation and affiliation allocation on the AOS frame after the original data stream is restored; and finally, dynamically loading a corresponding protocol description file for each data unit, and performing field-by-field decoding according to a mapping rule of binary data and readable engineering quantity to generate structured table data. The method can be widely applied to whole-satellite integration test scenes of various remote sensing satellites such as optics, SAR and infrared.

The invention belongs to the technical field of satellite remote sensing application, and discloses an active and passive satellite remote sensing fused ocean three-dimensional chlorophyll field reconstruction method and system. According to the method, satellite-borne laser radar data is subjected to correction, signal processing and biological optical model inversion, and a chlorophyll a concentration vertical section along an orbit is obtained; forming a training set by the passive satellite observation optical variable and the marine environment variable of which the profile is matched with the space-time, so as to train a long short-term memory (LSTM) neural network model; and utilizing the trained model to reconstruct a three-dimensional chlorophyll a concentration field of a target area according to passive observation and environment variables of the target area. By fusing the advantages of a satellite-borne laser radar ICESat-2 satellite and a passive optical remote sensing satellite, three-dimensional chlorophyll a concentration field detection based on active and passive fusion remote sensing is developed, the structure and function of a marine ecosystem can be deeply known, and three-dimensional dynamic observation of the ocean is realized.

The invention discloses a neural network-based remote sensing image farmland plot extraction method, system and product, and the method comprises the steps: firstly obtaining a high-resolution remote sensing satellite image, and carrying out the data preprocessing of the image; inputting the preprocessed image into a high-resolution remote sensing image farmland plot extraction neural network SED-GCNet to obtain a semantic and edge prediction image of the farmland; the SED-GCNet uses a semantic branch encoder, an edge branch encoder and a graph convolution fusion encoder to form a three-encoder, and a semantic branch decoder and an edge branch decoder form a double-decoder; and finally, fusing the semantics of the farmland and the edge prediction image through post-processing to obtain a final farmland plot extraction result. According to the method, the farmland plot extraction precision is improved on the plot scale and the pixel scale.

The invention discloses a mining area surface environment monitoring method, aiming at the current situation of surface ecological environment damage and prevention of a plain mining area, and researching a mining area dynamic change monitoring benchmark; for surface movement and deformation caused by mining area disturbance, a D-InSAR technology is applied to perform surface subsidence monitoring on a deformation block. According to the invention, technical means such as InSAR, GPS, remote sensing satellite images and the like are comprehensively utilized to obtain multi-source data of the mining area surface environment, key technologies of integration and application of the multi-source monitoring data are researched, a mining area surface environment multi-source monitoring technical system is established, and on the basis of multi-source high-resolution remote sensing data, technical means such as image processing, mode recognition and the like are adopted, so that the mining area surface environment multi-source monitoring system is established. Land utilization changes and ecological influence conditions are studied, InSAR and GNSS technologies are applied to perform high-precision monitoring analysis research, the overall monitoring capability, reliability and precision are improved, the technical defects of single technologies for mining area geological environment monitoring are overcome, and the mining area geological environment monitoring system has wide popularization and application prospects.

The invention relates to an agile satellite visibility analysis method based on an along-longitude push-broom mode, relates to the technical field of satellite task planning and visibility analysis, and solves the technical problem that an existing target visibility analysis method is difficult to accurately reflect a real visibility state in a satellite continuous imaging process. The method comprises the following steps: acquiring an imaging center point coordinate, an imaging duration, satellite imaging parameter information, satellite orbit information and a target shooting period; the visibility of the imaging center point in the shooting period is calculated, division is carried out according to the number of orbital circles, and a visible information set of the center point is obtained. The agile satellite visibility analysis method based on the along-longitude push-broom mode is suitable for efficient visibility calculation of an agile optical remote sensing satellite and oriented to an along-longitude imaging scene, the visibility state of a target in the imaging process can be accurately evaluated on the basis of considering satellite attitude maneuver characteristics and orbit constraints, and the accuracy of the visibility analysis is improved. Therefore, accurate support is provided for task arrangement and resource scheduling.

The invention discloses a GIS-based arid and semi-arid region farmland irrigation priority dynamic evaluation and zoning method, system, device and medium, and belongs to the technical field of farmland irrigation priority evaluation and zoning, and the method comprises the steps: collecting various types of remote sensing satellite data of a to-be-irrigated region; performing pretreatment; identifying the boundary and range of the to-be-irrigated area, and extracting a remote sensing image of the to-be-irrigated area; based on the remote sensing image of the to-be-irrigated area and the basic geographic information, parameter calculation is carried out, and normalization processing is carried out; performing superposition calculation on each normalized parameter according to a set weight to obtain an irrigation priority index; partitioning the to-be-irrigated area based on the irrigation priority index to generate an irrigation priority map; and judging whether agricultural irrigation management requirements are met or not according to preset conditions. According to the method, temperature vegetation drought indexes, normalized moisture indexes, water resource accessibility and crop economic value parameters are coupled; the irrigation priority index weight can be automatically adjusted according to a season strategy.

The invention discloses a hot melt slump area identification method and system, and relates to the technical field of hot melt slump prediction, and the method comprises the steps: obtaining a remote sensing satellite image of a target area, and carrying out the preprocessing of the remote sensing satellite image, and obtaining a preprocessed remote sensing satellite image; inputting the preprocessed remote sensing satellite image into a hot melt slump area identification model to obtain a hot melt slump area identification result; the identification result of the hot melt slump area is a remote sensing satellite image of the target area marked with the hot melt slump area; the hot melt slump area identification model is obtained by training a U-Net neural network model by adopting a training set, and the training set comprises a plurality of sample remote sensing satellite images marked with a hot melt slump area; the hot melt slump area in the sample remote sensing satellite image marked with the hot melt slump area is obtained by extracting deformation features, gradient features, wave spectrum features and vegetation features from the sample remote sensing satellite image based on constraint conditions. According to the invention, the identification accuracy of the hot melt slump area is improved.

The invention provides a carbon satellite application cloud resource dynamic allocation method based on data volume prediction, and relates to the crossing field of cloud computing and remote sensing satellites. A fine-grained process monitoring module and an application feature labeling module are used for respectively acquiring a plurality of process performance data and a plurality of task feature tags of a plurality of data processing tasks in a carbon satellite application system; and performing cloud resource dynamic optimization and outputting an elastic cloud resource optimization strategy to dynamically adjust the resource ratio of the basic computing power resource pool to a plurality of data processing tasks for pre-allocation. The technical problems that the GPU / CPU utilization rate is in an extremely unbalanced state and a high-priority task is interrupted due to the fact that supply and demand of computing power resources are unbalanced in a carbon satellite service system in an existing resource allocation method in a cloud environment are solved. The technical effect of zero interruption of a high-priority carbon emission data analysis task is guaranteed while accurate matching of resource supply and demand is realized and the resource utilization rate is improved.

The invention discloses a road weak information extraction method and system based on ASU-Net, and relates to the technical field of image feature recognition, and the method comprises the steps: employing an encoder-decoder structure to construct a road extraction network model ASU-Net, employing ResNet-50 as an encoder, introducing a cavity convolution module into the encoder, and introducing a strip convolution module into the decoder; inputting a remote sensing satellite road image into an ASU-Net model, performing down-sampling operation on the image by an encoder, and expanding a receptive field through parallel cascade branches with preset voidage in a void convolution module; up-sampling is carried out on features learned by the encoder through transposition convolution, and meanwhile, road linear feature extraction is carried out by adopting banded convolution in four different directions through a banded convolution module. Through the technical scheme of the invention, the performance of road extraction in a complex environment is improved, better balance between the accuracy rate and the recall rate is realized, and the accuracy of road extraction is improved.

The invention relates to a remote sensing data processing method and device, electronic equipment and a computer program product, and belongs to the technical field of satellite communication. The method comprises the following steps: a remote sensing satellite sends a session establishment request to a ground network, wherein the session establishment request carries a user identifier and a joint task identifier of the remote sensing satellite; the ground network establishes a session, and judges whether the remote sensing satellites belonging to the same joint task have established or have established sessions according to the joint task identifier; if yes, the ground network returns a session establishment response and a data processing instruction of the joint task to the satellite network; and the satellite network performs cooperative processing on the target service data of the remote sensing satellites belonging to the same joint task based on the data processing information in the data processing instruction, and sends a service data packet of the joint task to a ground network through a session of any remote sensing satellite in the joint task. The processing efficiency of the multi-satellite cooperative task can be improved, and the data transmission amount between the satellites and the ground can be reduced.

The invention discloses a vehicle-mounted hyperspectral comprehensive detection system and method, and the system comprises an unmanned plane system, an airborne spectrometer system, a handheld spectrum system, a satellite data transmission receiving and transmitting system, a central control system, a power system, and optional roof equipment which are integrated on an engineering vehicle. The detection method comprises the steps that firstly, the remote sensing satellite detects a target area in a large range, detection data are transmitted to the vehicle-mounted system in real time, the vehicle-mounted system analyzes the satellite detection data and marks a suspected target position, and an unmanned aerial vehicle detailed investigation task is generated; the unmanned aerial vehicle returns detection data of a target area in real time, the vehicle-mounted system analyzes the returned data of the unmanned aerial vehicle, and according to an analysis result, the other unmanned aerial vehicle carries a handheld device to carry out synchronous flight matching and fine checking. Compared with the prior art, the method is characterized in that: a star sky ground link is quickly established; establishing satellite-ground reference rapid transmission; wide-range real-time high-quantification application of satellite-ground data is realized; the invention relates to star-sky ground-communication remote-guiding large-range coordinated monitoring.

The embodiment of the invention discloses a surface temperature inversion method and device based on remote sensing satellite data, and a medium, and relates to the technical field of remote sensing, and the method comprises the steps: receiving a heat island effect analysis demand triggered by a user, obtaining satellite remote sensing data of a target region and a corresponding metadata file based on the heat island effect analysis demand, and storing the satellite remote sensing data in the target region; processing the satellite remote sensing data to determine earth surface radiation parameters; the atmospheric features and the earth surface features of the target area are evaluated through the metadata file and a pre-obtained normalized vegetation index, a dynamic adjustment factor corresponding to the target area is determined, a preset single-window algorithm model is optimized through the dynamic adjustment factor, and an optimized single-window algorithm model is determined; and inputting the surface emissivity, the atmospheric transmittance, the brightness temperature value and a pre-acquired atmospheric average action temperature into an optimized single window algorithm model for calculation, and determining a surface temperature distribution diagram of the target area for heat island effect analysis.

The invention relates to a three-dimensional map surveying and mapping method, device and equipment based on an unmanned aerial vehicle and a medium. The method comprises the following steps: acquiring a global remote sensing image of a to-be-mapped area, and performing scene semantic analysis on the global remote sensing image to obtain scene semantic information; the global remote sensing image is collected by a remote sensing satellite; generating an initial flight path according to the scene semantic information, and obtaining three-dimensional surveying and mapping data collected by the unmanned aerial vehicle along the initial flight path; the three-dimensional surveying and mapping data comprises multi-view image data and corresponding positioning and attitude determination data; generating an initial point cloud of the to-be-mapped area based on the three-dimensional mapping data; carrying out ground feature category division on the initial point cloud to generate a hierarchical semantic point cloud; and generating a three-dimensional map model corresponding to the to-be-mapped area based on the hierarchical semantic point cloud. By adopting the method, the layered three-dimensional map with surface feature semantic information can be generated, and dynamic optimization can be realized.

A method of sun-synchronous orbit design for remote sensing satellite based on revisit characteristics is proposed. The relationship between revisit and ground synchronization is established by Q value. The mathematical model of Q is established by node revisit sequence in a certain interval. Then the coverage rate is obtained by the mixed node access sequence and coverage area of orbit altitude and observation angle. Finally, the coverage rate of remote sensing satellite in sun-synchronous orbit is demonstrated from three aspects of time-varying coverage rate, minimum coverage time and minimum observation angle, which provides a reference for the design of sun-synchronous orbit of remote sensing satellite.

The invention discloses a remote sensing satellite area coverage method based on greedy search, and belongs to the technical field of remote sensing satellite earth observation. The method comprises the following steps: firstly, gridding an area target to be observed according to the imaging breadth of a remote sensing satellite; secondly, calculating a grid set of potential coverage of the satellite based on the breadth and the side-sway capability of the satellite within the top-crossing access time of the satellite to the regional target; and then local greedy search and multi-round iteration are carried out to determine a final imaging satellite set and an imaging sequence, and an optimized multi-remote sensing satellite collaborative observation region target scheme is obtained. The method has the advantages of being simple in processing logic, high in flexibility, high in resource utilization rate, high in optimization efficiency and the like, and is suitable for the problem that multiple remote sensing satellites cooperatively cover area targets.

The present application relates to the field of unmanned aerial vehicle offshore rescue, in particular to a kind of rainbow unmanned aerial vehicle multi-source remote sensing all-weather ocean search and rescue system and method, for solving the problem of existing unmanned aerial vehicle offshore search and rescue efficiency bottom, difficult to bear high-intensity search and rescue task, the method comprises the following steps: determining the original search and rescue area of rescue target;Select remote sensing satellite, and obtain the weather condition and geographic information of the original search and rescue area and surrounding sea area using the remote sensing satellite;According to the weather condition and the geographic information, console formulates the search and rescue task of rainbow unmanned aerial vehicle;According to the search and rescue task, the rainbow unmanned aerial vehicle carries search and rescue equipment and searches and rescues the rescue target;Using the rainbow unmanned aerial vehicle identifies the rescue target and drops rescue materials, and provides guidance for rescue personnel to implement rescue on the rescue target.

The invention provides an Antarctic sea ice snow melting characteristic monitoring method and system based on wind cloud satellite active and passive microwave collaboration, and relates to the technical field of remote sensing satellites, and the method comprises the steps: constructing a characteristic parameter data set containing brightness temperature daily poorer and / or backscattering daily poorer; aiming at each space pixel, based on the time sequence data of the brightness temperature daily worse and / or the backscattering daily worse of the space pixel, automatically determining a freeze-thaw state discrimination threshold value of the pixel through an iterative optimization algorithm taking inter-class difference maximization as a target; for each pixel, comparing the day-by-day characteristic parameter value of the pixel with an adaptive threshold value, judging the freeze-thaw state of the pixel every day according to a preset continuity condition, and generating a time-space continuous freeze-thaw state sequence; and generating a snow melting monitoring result on the surface of the Antarctic sea ice based on the freeze-thaw state sequence. According to the invention, the accuracy of annual freeze-thaw state identification and spring and summer typical thawing initial day detection is improved.

The invention provides a sea reclamation change remote sensing image target classification method and system, and the method comprises the steps: obtaining a remote sensing satellite image of a monitored sea reclamation region, carrying out the geometric correction, radiation correction and tide level normalization correction of the remote sensing satellite image, and carrying out the fusion of the corrected remote sensing satellite images; a fused remote sensing satellite image is cut into training slices for training, an MCFCN model is optimized, test slices are predicted, and CRF correction and morphological corrosion post-processing and splicing are carried out to form a final result. Through the method and the corresponding system, the segmentation efficiency and the detection accuracy of the sea reclamation change remote sensing image can be improved.

The invention discloses a communication method for a remote sensing satellite and a high-orbit communication satellite, and relates to the technical field of aerospace, and the specific implementation scheme is as follows: obtaining at least one communication time period of the remote sensing satellite and the high-orbit communication satellite; determining at least one antenna azimuth angle and pitch angle of the remote sensing satellite pointing to the high-orbit communication satellite based on the at least one communication time period; generating antenna wave control codes at different moments based on the at least one antenna azimuth angle and pitch angle; and based on the antenna wave control code, generating antenna beams pointing to the high-orbit communication satellite at different moments, and sending a satellite communication signal to the high-orbit communication satellite through the antenna beams, so that the high-orbit communication satellite receives the satellite communication signal after searching and locking the antenna beams, thereby solving the problem of cross-orbit layer data transmission of aerospace information network development, and improving the data transmission efficiency. And meanwhile, the real-time response capability of the remote sensing satellite is improved.

The invention discloses a multi-temporal remote sensing crop extraction method in combination with MoCo self-supervised learning. Relates to the technical field of agricultural resource monitoring, in particular to a multi-temporal remote sensing crop extraction method combined with MoCo self-supervised learning. According to the method, label-free satellite images are utilized, MoCo self-supervised learning is adopted to carry out label-free pre-training on a space encoder, so that a crop classification model can still learn spatio-temporal characteristic representation with discriminative power under limited label data, and the classification precision problem caused by insufficient labeled samples is effectively relieved. The method comprises the following steps: acquiring a multi-temporal crop label-free remote sensing satellite image data set and a label data set; constructing a time-phase crop classification model: pre-training a spatial feature encoder by MoCo self-supervised learning; performing supervised learning on the crop classification model by adopting the labeled data set to obtain a final time phase crop classification model; and classifying the multi-temporal crops through the final temporal crop classification model.

The invention discloses a giant remote sensing satellite group task planning method based on atomic task decomposition, and relates to the technical field of task planning of remote sensing satellites, and the method comprises the steps: obtaining an observation task submitted by a user, and determining an observation mode of the observation task; performing atomic task decomposition to obtain a corresponding subtask set; determining a constraint condition and a target function in combination with the subtask set; minimizing the objective function to obtain an optimal task observation scheme; and allocating each sub-task to the corresponding remote sensing satellite based on the optimal task observation scheme so as to execute the observation task. According to the method, a complex multi-satellite collaborative observation task is refined into an operable atomic task, so that the complexity of the problem can be reduced, the allocation process of satellite resources is optimized, efficient integration and execution of a dynamic user target and an autonomous observation target are realized, the adaptability and response speed of a remote sensing satellite when facing a complex observation demand are improved, and the remote sensing system is suitable for being popularized and applied. Therefore, the rapid change of user requirements can be adapted.

The invention relates to a disaster monitoring task-oriented remote sensing satellite system application performance evaluation method. The method comprises the following steps of S1, constructing an evaluation index system of a remote sensing satellite system application task layer, a capability demand layer and a system index layer; s2, in combination with the constructed evaluation index system, establishing an index measurement model for different index characteristics, and carrying out standardization processing on an index result; and S3, obtaining all index measurement results of the remote sensing satellite system, determining an index weight based on an analytic hierarchy process, and performing comprehensive effectiveness evaluation of the system. According to the method, the reasonable and feasible method for performing multi-dimensional comprehensive evaluation on the disaster monitoring task execution application efficiency of the remote sensing satellite system is realized, and a guiding effect is provided for design and evaluation work of the remote sensing satellite system.