79 results about "Earth observation" patented technology

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention discloses an airborne laser radar sounding data processing method and system, and relates to the technical field of marine surveying and mapping and earth observation, and the method comprises the following steps: collecting original full-waveform data of an airborne laser radar sounding system, the original full-waveform data comprising infrared laser channel data and blue-green laser multichannel data; joint denoising processing is carried out on the original full-waveform data to obtain denoised waveform data, and the joint denoising processing comprises background noise modeling and removing based on machine learning and random noise filtering based on frequency domain low-pass filtering; a collaborative joint denoising strategy is formed by fusing background noise modeling based on machine learning and frequency domain low-pass filtering based on signal-to-noise ratio optimization. According to the method, complex background noise can be accurately estimated and removed, random noise can be adaptively filtered out, weak underwater echo signals are reserved to the maximum extent, and the problem of water depth information loss caused by excessive smoothness is effectively avoided.

This disclosure relates generally to system and method for estimating crop water requirement using multi-sensor data fusion. Increasing global population is imparting pressure on both agriculture for food demand and limited freshwater resources for consumption. Estimating crop water requirement reduces water demand for crop production. The method divides soil and crop into multiple vertical and horizontal profiles to estimate water balance thereby reducing the errors in estimation of crop water requirement. Additionally, the method has capability to interlink the multiple data sets such as satellite based earth observations, weather observations from IoT sensors, Weather forecasts from global circulation models, and crop knowledge base for crop water requirement estimation. The method is based on spatio-temporal modeling for multi-layer crop and soil water balance and helps to generate the additional insights on crop water requirement like moisture at different levels in soil profile, crop canopy growth at different locations.

The invention discloses a large-scale complex correlation earth observation task planning method based on reinforcement learning, and relates to the technical field of task resource planning. The method comprises the following steps: acquiring historical data of earth observation task planning, and constructing a sample data set; constructing a graph neural network and a reinforcement learning network; using the sample data set to train the graph neural network and the reinforcement learning network at the same time; acquiring a demand information list, a satellite information list, a data transmission task information list and an observation information list, and processing the information lists into demand feature vectors, satellite feature vectors, data transmission task feature vectors and observation feature vectors; and inputting the four feature vectors into a trained graph neural network to obtain an environment feature vector, inputting the environment feature vector and the demand feature vector into a trained reinforcement learning network, and outputting an earth observation task planning result. The method can solve the scheduling optimization problem under the condition of multiple tasks and limited resources, improves the task planning efficiency, and can be widely applied to the fields of satellite task scheduling, intelligent manufacturing, automatic driving and the like.

An aspect of the disclosure provides An earth observation apparatus to be carried by a moving aerial platform or satellite for obtaining images of the surface of the earth, the apparatus comprising: an optical train having an optical field of view for imaging a region of the surface of the earth and being configured to form an image of the region at an image plane; an image sensor disposed at the image plane providing an imaging field of view; a view adjuster configured to control the optical train to: provide forward motion compensation for a stare time; and to displace the image, relative to the imaging field of view, in an across-track direction in a sequence of discrete displacement steps during each stare time; wherein the image sensor comprises a plurality of active areas, each comprising an area array detector and the active areas being spaced apart by inactive areas at the image plane wherein each active area captures a frame of image data for each discrete displacement step thereby to capture a plurality of frames for each discrete displacement step and the plurality of frames captured for each discrete displacement step are displaced in the across-track direction, relative to the imaging field of view, from the plurality of frames captured for the next discrete step.

The invention discloses a differential measurement-based satellite-borne synthetic aperture microwave radiometer imaging correction method, which comprises the following steps of: respectively selecting a reference area and a correction area in a global range, and respectively intercepting scientific data and auxiliary data in a specific time period in the reference area and the correction area based on load normal earth observation data; averaging the visibility functions intercepted in the reference area to obtain a reference visibility function; generating a reference brightness temperature and a correction brightness temperature through a simulation model by using the auxiliary data intercepted in the reference area and the correction area respectively; performing difference and inversion processing on the normal earth observation visibility function to be subjected to imaging processing by using the reference visibility function to obtain a difference inversion brightness temperature; calculating a correction coefficient by using the reference visibility function, the reference brightness temperature, the visibility function intercepted in the correction area and the correction brightness temperature; and correcting and compensating the differential inversion brightness temperature by using the correction coefficient and the reference brightness temperature to obtain the final inversion brightness temperature.

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 invention relates to the technical field of satellite observation task scheduling, in particular to a low-orbit satellite large-scale earth observation task self-adaptive scheduling method and system. The method comprises the following steps: generating a task allocation result according to a task candidate resource mapping table, namely generating an initial allocation scheme by using a greedy strategy of double factors of time and load, and establishing an allocation frequency memory matrix; constructing an inverse correlation selection probability model by utilizing the distribution frequency memory matrix, and outputting a task distribution result considering global revenue and load balance; task scheduling optimization is carried out based on a task allocation result, wherein conflict resolution fine adjustment is carried out by utilizing a time elastic adjustment mechanism of conflict perception; inter-satellite link collaborative rescheduling based on task characteristics; and global optimization and real-time response capability are effectively considered. According to the method, a complex joint scheduling problem is decoupled into two sub-problems with clear logic and low coupling degree, and the calculation complexity of single scheduling is greatly reduced.

This application provides a method, device, and computer-readable storage medium for evaluating the on-orbit calibration error of a microwave radiometer calibration system used in geostationary orbit. The application evaluates the on-orbit calibration error of the microwave radiometer calibration system by determining the errors of the calibration cold source, calibration heat source, Earth observation radiation brightness temperature, and Earth target brightness temperature. By separately evaluating the errors of the calibration cold source, calibration heat source, Earth observation radiation brightness temperature, and Earth target brightness temperature in the microwave radiometer calibration system used in geostationary orbit, each error is quantified, thus quantifying the on-orbit calibration error of the microwave radiometer calibration system. Furthermore, the different radiation transmission paths are considered during the evaluation of each error, making the evaluation of the sources of on-orbit calibration error of the microwave radiometer calibration system used in geostationary orbit not only effective and accurate but also highly implementable.

This invention discloses a method for collaborative Earth observation and data transmission scheduling across heterogeneous space platforms. The method employs integer encoding to represent the execution sequence of observation tasks from multiple space platforms, proposes a three-stage decoding strategy to repair infeasible chromosomes, and designs feasible observation time window selection strategies, as well as observation task insertion strategies based on time-based benefits, task urgency, and attitude transition time, to construct an initial chromosome population. Crossover operations are performed on the current population, constructing three neighborhood search operators: inter-platform exchange, intra-platform exchange, and insertion based on relaxation time, to perform local searches on the chromosomes within the current population. The algorithm terminates if the maximum number of iterations or the maximum computation time limit is reached, generating a scheme for collaborative Earth observation and data transmission scheduling across heterogeneous space platforms, providing technical support for space-based Earth observation.

The application discloses a kind of multi-star on-orbit cooperation observation task planning method and system, by designing heuristic initial solution generation strategy, roulette selection algorithm based on Boltzmann selection probability, adaptive crossover operator and mutation operator based on population dispersion degree, stop condition based on population convergence coefficient, from initial solution, selection operator, crossover operator, mutation operator, stop condition etc. Various angles improve the genetic algorithm for multi-star cooperative observation task planning, compared with the genetic algorithm based on circle next cross, variation, significantly improve the execution benefit of observation task and the observation number of target point, construct the scheduling system of constellation cloud computing system, reach the purpose of on-orbit cooperation multi-star efficient execution observation task.

The invention discloses an in-orbit calibration method for correcting self-radiation of a heat mirror of a microwave imager based on reference radiation. The method comprises the following steps: calculating an included angle between the heat mirror of the microwave imager and the sun; calculating the earth observation brightness temperature of the microwave imager; calculating the observed brightness temperature of the reference microwave radiometer; simulation brightness temperature of the reference microwave radiometer and the microwave imager is obtained based on radiation transmission model simulation; calculating an observation brightness temperature deviation and a simulation brightness temperature deviation, and calculating a system error between the reference microwave radiometer and the microwave imager; based on the system error, the included angle between the microwave imager heat mirror and the sun is adopted for function relation fitting, and the heat mirror self-radiation brightness temperature of the reference microwave radiometer is calculated; the included angle between the microwave imager heat mirror and the sun is calculated on orbit in real time, and the earth observation brightness temperature of the microwave imager is corrected based on the self-radiation brightness temperature of the heat mirror. The self-radiation of the heat mirror is solved in real time by taking double deviations of observation brightness temperature and simulation brightness temperature as constraints, and the problem that the temperature of the heat mirror of the microwave imager cannot be actually measured in orbit is solved.

The present application belongs to the technical field of space-to-earth observation (satellite remote sensing) and watershed hydrology, geographical environment disciplines, and discloses a watershed scale high spatio-temporal resolution river network remote sensing information extraction method and system. After cloud and cloud shadow processing of optical remote sensing images, a simple non-iterative clustering superpixel segmentation algorithm is used to segment homogeneous landscape objects, and a hierarchical decision tree is constructed using a water body index and a vegetation index. In the area where the optical remote sensing image is seriously covered by clouds or the image is missing, the SAR image is filtered, the dual polarization water body index is calculated using the VV and VH polarization images, the maximum inter-class variance method is executed, and the image is binarized. After obtaining the water body result extracted from the remote sensing image, the river cut-off is repaired in combination with the water system diagram generated by the DEM model. The present application can automatically extract accurate, continuous and high spatio-temporal resolution river network remote sensing information, and can be expanded to global scale river network feature extraction.

We provide the system. [Solution] As a means of data collection, it has the function of receiving remote sensing data from Earth observation instruments, A function to acquire weather data from weather information providers, It has a function to collect soil data from soil condition detectors installed on the ground, A method for integrating collected data and performing data cleaning, A means of analyzing data using a generative model to generate optimized agricultural management guidelines based on crop growth and environmental changes, A system that includes means for notifying users of the above guidelines.

This invention discloses a method and apparatus for extracting three-dimensional deformation data of mining subsidence in desert areas, relating to the field of Earth observation technology. The invention calculates the angle between the normal vectors of each point in the point cloud data and performs tangent plane projection analysis to form multiple pairs of point cloud contour sets. The overall approach is based on the local geometric characteristics of the point cloud, accurately extracting feature points reflecting the shape of the point cloud contours. Then, using these multiple pairs of point cloud contour sets as registration objects, the K-4PCS and VGICP registration algorithms are employed to register the three phases of point cloud data, ensuring precise alignment and obtaining the registered point cloud and transformation matrix. The three-dimensional deformation of the land surface is extracted from the transformation parameters in the transformation matrix. This process eliminates the need for subsidence extraction based on point DEMs and planar displacement extraction based on rigid body matching, enabling accurate extraction of planar displacement in desert areas and precise acquisition of three-dimensional deformation data of the land surface caused by mining in desert regions.

This invention proposes a satellite-ground collaborative mission management method for Earth observation space cloud services, belonging to the field of satellite application and management technology. This method first identifies three main roles in satellite-ground collaborative mission management; secondly, it selects a management mode based on the urgency and complexity of the needs; then, it generates mission plans and instructions for the remote sensing satellite constellation and telemetry and control station network, and executes the missions; finally, after mission execution, data is received through the space edge cloud service center and the ground station network, thereby achieving management under a cloud architecture. This method overcomes the shortcomings of existing methods, such as difficulty in coordinating satellite-ground missions, difficulty in coordinating multiple types of users, and slow data transmission speeds. It improves the collaborative management performance of remote sensing satellite missions under a satellite-ground integrated system and can meet the application requirements of rapid, accurate, and comprehensive remote sensing satellite mission planning and management.

Synthetic aperture radar (SAR) is a type of imaging technology that can be used for a wide range of applications, including Earth observation, mapping, object tracking, change detection (e.g., Arctic ice), natural disaster monitoring, and many other uses. A high-altitude unmanned aerial vehicle for performing synthetic aperture radar (SAR) imaging comprises a fuselage, wings attached to the fuselage to provide lift to the aircraft, one or more electric propellers to provide thrust to the aircraft, a SAR payload for SAR imaging the Earth's surface as the aircraft flies, solar cells to generate power to drive one or more electric propellers and operate the SAR payload, and one or more batteries to store the electrical energy generated by the solar cells.

This invention discloses a method and system for identifying planted and natural forests based on multi-source remote sensing and spatiotemporal joint optimization, belonging to the fields of remote sensing image processing, earth observation, and dynamic monitoring of forest ecology. Addressing the problems of high label noise and disordered temporal logic in existing technologies, this invention obtains high-purity training samples through multi-stage iterative purification by fusing CCDC mutation history and deep semantic features; obtains pixel-level confidence through independent initial classification by geographic partitioning; constructs an HMM model integrating ecological prior constraints and dynamic adaptive emission probabilities for Viterbi decoding; and performs constrained spatial restoration with cross-dimensional temporal security checks. This invention effectively suppresses temporal pseudo-variables and salt-and-pepper noise, achieving high-resolution thematic mapping year by year with no temporal logical contradictions and regular spatial patches, suitable for fine-grained forest classification and compliance monitoring of zero-deforestation laws in tropical and subtropical regions.

The invention discloses a remote sensing image multi-granularity collaborative recognition method and system based on satellite edge calculation, and the method comprises the steps: obtaining a high-resolution remote sensing satellite image, carrying out the in-orbit coarse granularity screening through maximizing an efficiency score, and obtaining an interested target region; based on a distributed task unloading method, an interested target area is sent to an optimal intelligent satellite through a remote sensing satellite; and the optimal intelligent satellite performs fine-grained target recognition processing on the interested target area to obtain a ground target recognition result. According to the invention, decision making can be carried out under the condition of meeting performance and time delay constraints, and the precision of a ground target identification result is improved. The remote sensing image multi-granularity collaborative recognition method and system based on satellite edge calculation can be widely applied to the technical field of satellite earth observation.

The invention provides a rotating arc ultra-light compact space remote sensing concave mirror device aiming at the pain points that an earth observation space telescope is difficult to deploy due to a large aperture, high in cost and inconvenient to transport, an integrated double-fan-shaped paraboloid primary mirror is taken as a core, and two fan-shaped sub-mirrors are symmetrically divided based on a complete paraboloid mirror of 10m or above; a stable-shape, high-temperature-resistant and low-temperature-resistant high-performance ceramic material including ultrahigh-modulus silicon carbide is adopted, water-based colloidal forming is used as a core process, and a'thickness gradient + honeycomb 'lightweight design is matched, so that the weight is reduced by more than 40%, and the surface precision reaches lambda / 50; a mainstream rocket fairing is adapted through vertical storage, a mirror body does not need a folding mechanism, a view field is complemented by means of coaxial rotation, fan-shaped scanning equivalent view field distortion generated by rotation of a double-fan-shaped primary mirror is accurately corrected through a ground AI image restoration system, 0.1 m-level and higher ground resolution and wide-view-field collaborative observation is achieved, and the system is simple in structure and convenient to use. The device has the advantages of being sufficient in rigidity, high in reliability and adaptive to the space environment, and meets the requirements of accurate measurement and wide coverage of earth observation.

The invention discloses a hyperspectral image compression network and compression method based on multi-scale spectrum and spatial feature enhancement, and mainly solves the problems of insufficient hyperspectral image spectrum modeling, insufficient spatial feature extraction and high network complexity in the prior art. The network comprises a main encoder, a main decoder, a super-prior encoder, a super-prior decoder and an entropy model. The main encoder comprises a spectral attention gating data unit, a convolution unit and a multi-scale spatial adaptive feature attention enhancement unit, and is used for converting an input image into potential representation and removing spatial and spectral redundancy; the main decoder and the main encoder are symmetrical in structure; the super-prior encoder comprises a convolution layer and an activation layer and is used for extracting auxiliary information from the output of the main encoder; the super-prior decoder and the super-prior encoder are symmetrical in structure; the entropy model is Gaussian distribution based on output parameters of a super-prior decoder. After the network is trained, lossy compression of a hyperspectral image can be realized. The method reduces the network complexity and spectral distortion, improves the reconstruction quality of complex ground feature details, and is suitable for earth observation, meteorological monitoring and the like.

This invention discloses a space-ground coordinated radiometric characteristic calibration method for spaceborne active microwave sensors in the field of radar Earth observation technology. It constructs a space-ground coordinated calibration system consisting of a radiation reference source satellite and a ground calibration processing station. The method includes: determining the orbital parameters of the radiation reference source satellite; the satellite transmitting signals to the ground station in Earth observation mode, using a known RCS target on the ground to calibrate its own antenna gain and link gain; receiving signals from the satellite to be calibrated in Earth observation mode, accurately calculating and transmitting echo signals based on the calibrated parameters; and the satellite to be calibrated calibrating its own transmit / receive link gain based on the received echo signals. This invention can be widely applied to the on-orbit radiometric calibration of active microwave sensors such as synthetic aperture radar, altimeters, and microwave scatterometers, significantly improving calibration frequency and accuracy, supporting multi-band, high-stability quantitative remote sensing tasks, and providing reliable technical support for precise observation in fields such as ocean monitoring, weather forecasting, and topographic mapping.

The application provides a long-distance imaging camera internal and external parameter separation calibration method based on a precision three-axis turntable. In the calibration process, two groups of light point sub-pixel coordinates are automatically collected through the precision three-axis turntable, and the corresponding relationship of the homonymic points under the pure rotation motion is constructed, so that the initial estimated value of the main internal and external parameters is linearly solved, and finally, all the camera internal and external parameters are iteratively optimized with the minimum homonymic point corresponding error as the target. In the solving process, the calibration point vector is eliminated without solving. The application is mainly applied to the high-precision calibration of long-distance and large-view-field imaging cameras in the aviation and aerospace fields before leaving the factory, and has important significance for realizing the high-precision visual measurement of the aviation and aerospace earth observation and celestial body sensing.

The invention discloses a sea surface reflectivity remote sensing missing interpolation method based on mask self-encoding and a related device, and belongs to the technical field of satellite remote sensing earth observation. According to the method, a space-time cube structure is constructed, and a system extracts multi-dimensional space-time context features; a cooperative mechanism of spatial position coding and time sequence coding is adopted, so that the feature characterization capability is remarkably enhanced; based on a residual connection architecture, a Transform encoder and a decoder, accurate reconstruction of missing pixel information is realized. The method breaks through the limitation of a traditional method in the aspect of complex space-time mode modeling, and has the technical advantage of remarkably improving the interpolation precision and space-time consistency of the marine reflectivity remote sensing data.

This invention relates to the field of aerospace Earth observation and remote sensing imaging technology, and discloses a target-driven dual-camera heterogeneous payload dynamic cooperative aiming method and device. The device includes a base, a wide-area search unit, a high-precision staring unit, a field-of-view deflector, and a cooperative controller. The wide-area search unit and the high-precision staring unit are distributed forward and backward along the flight direction. The cooperative controller identifies potential targets and calculates their geographical locations. It calculates the delay time based on the physical distance and real-time velocity. The cooperative controller calculates the track-crossing swing angle based on the target's position and attitude and sends commands. The field-of-view deflector drives the high-precision staring unit to deflect to the track-crossing swing angle within the delay time. The cooperative controller triggers imaging at the end of the delay time. This invention achieves pointing adjustment and exposure synchronization through heterogeneous payload spatiotemporal coordination and a delay time window, realizing wide-area detection and high-resolution fine imaging of potential targets during a single flight.

The invention relates to the technical field of earth observation and climate change analysis, in particular to a global temperature change comprehensive evaluation method and system based on multi-dimensional time sequence analysis and mode fusion, equipment and a medium. Target indexes are obtained based on the surface temperature data, the target indexes comprise the average temperature, the highest temperature, the lowest temperature and the temperature difference, and multi-dimensional feature extraction is carried out on the target indexes; three key dimensions of directivity and rate (linear analysis), dominance (trend signal-to-noise ratio analysis) and dynamic characteristics (nonlinear mutation analysis) of the trend are creatively integrated. And all classification standards are based on data-driven statistics instead of subjective thresholds, so that the objectivity and repeatability of classification are ensured.

There is provided a sinkage prediction device including: a variation data acquisition unit 10 analyzing time-series observation data measured by using an earth observation technology and acquiring horizontal direction variation data which is data indicating a horizontal direction variation on a ground surface; and a sinkage prediction unit 20 determining, based on the acquired horizontal direction variation data, whether at least one of the variation in the time axis and the variation in the space axis on the ground surface is in a predetermined state and predicting a location on the ground surface indicating the variation determined to be in the predetermined state as a potential sinkage location where there is a possibility of occurrence of sinkage, wherein it is possible to predict the location where the sinkage may be likely to occur by determining, based on the horizontal direction variation data obtained by analyzing the observation data measured by using the earth observation technology, whether the variation in the time axis or the variation in the space axis on the ground surface is in the predetermined state, by using the predetermined state related to the variation in the horizontal direction viewed on the ground surface where there is a sign that the sinkage is likely to occur as a determination criterion.

The invention relates to a multi-satellite cooperative computing and routing joint optimization method for a low-orbit satellite network, and belongs to the technical field of satellite communication. The method comprises the following steps: constructing a low earth orbit satellite network topology model; the satellite updates the satellite resource state representation through the time memory map attention network; establishing a multi-satellite cooperative computing and routing joint optimization model of the low-orbit satellite network, specifically, comprehensively considering storage resources, transmission queue states, computing queue states and task characteristics of satellites, and establishing an optimization model with the goal of minimizing the total time delay of earth observation tasks; and solving the optimization problem by adopting a deep reinforcement learning method, specifically, realizing off-line training of a deep reinforcement learning model based on satellite network data provided by a terrestrial digital twin system until algorithm convergence, and periodically sending a strategy obtained by training to an on-orbit satellite. According to the invention, support can be provided for timely and reliable transmission and processing of massive earth observation tasks.

Systems and methods are described for receiving, by processing circuitry, a plurality of maps of the geographic area, wherein each map of the plurality of maps is generated based on a respective plurality of overhead images captured during a respective portion of a time period, and each overhead image of the respective pluralities of overhead images comprises a respective plurality of pixels, and each pixel is designated as being of a particular mapping category of a plurality of mapping categories. The systems and methods may be configured to train, using the processing circuitry and the plurality of maps, the machine learning model to identify the expected distribution for the mapping categories of the geographic area at the given time.