43 results about "Albedo" patented technology

The invention discloses a deep learning inversion method and system based on physical constraint reflectivity, and an electronic device. The method comprises the following steps: constructing a deep neural network model; hyperspectral remote sensing sample data are input into the deep neural network model, and the transmittance product combination parameter sub-model, the atmospheric intrinsic reflectivity sub-model and the atmospheric hemisphere albedo sub-model are respectively subjected to model training by utilizing the hyperspectral remote sensing sample data; carrying out model training on the physical inversion model through a physical constraint formula; and S3, acquiring hyperspectral remote sensing data of the research area, inputting the hyperspectral remote sensing data into the deep neural network model, and predicting and outputting the atmospheric roof reflectivity. According to the method, the parameter model is innovatively constructed, the transmittance product combination parameter, the atmospheric intrinsic reflectivity and the atmospheric hemisphere albedo are respectively predicted by means of the parameter model, and the output atmospheric top reflectivity is efficiently and rapidly predicted based on the constructed physical radiation transfer equation by means of the three parameters. The method has the advantages of high prediction efficiency and high precision.

The present application relates to the technical field of image processing, in particular to a remote sensing monitoring method and system for cultivated land protection, comprising the following steps: acquiring a remote sensing image sequence of a cultivated land area at different time nodes, extracting the pixel gray value of the same land block and arranging it according to time, calculating the ratio of the difference value between adjacent frames and the gray mean value in the frame, marking the position continuously exceeding the fluctuation reference ratio, and generating a pixel region set. In the present application, by adopting a sampling frequency threshold optimization strategy, combining the albedo variation rate to screen abnormal areas, reducing short-term fluctuation interference, enhancing time sequence consistency, introducing the solar incident angle and terrain slope calculation projection influence to eliminate the slope shielding area, using spatial grid marking and boundary integrity judgment to improve the coherence of cultivated land distribution information, combining time sequence analysis, sampling optimization, physical property compensation and spatial consistency correction to enhance the fine identification of cultivated land dynamic change, and improving the monitoring accuracy and data reliability.

The application discloses a kind of for satellite in-orbit real-time cloud detection fast cloud determination method, belong to optical remote sensing image processing field, the method utilizes the principle of cloud top reflection sunlight, by establishing look-up table in ground platform, the method is realized in-orbit fast cloud determination by storing look-up table information in cloud determination load computing platform.The application according to the reflectivity of cloud layer in visible light compared to ground object in remote sensing image is higher, the thicker cloud layer is, the stronger reflection ability is, the greater albedo is, by establishing the relationship between the luminance information represented by observation instrument response DN value and cloud optical thickness, the cloud optical thickness is inverted by response DN value, to judge the cloud coverage of observation target area according to cloud optical thickness, in the case where guaranteeing identification accuracy, the speed of satellite in-orbit cloud detection is effectively improved.

The invention discloses a thermal performance analysis method for a protective layer of a lunar base and a protective layer structure. The method comprises the following steps: establishing a thermal balance equation including solar radiation, lunar internal heat, non-blackbody radiation and lunar albedo radiation; performing thermal analysis based on a heat transfer theory basic theory equation and a heat balance equation to obtain a thermodynamic micro-equation of the lunar surface and the protective layer; solving the obtained thermodynamic micro-equation to obtain temperature field distribution; determining the relationship between the lunar soil protective layer thickness and temperature fluctuation based on finite element analysis; and determining the thickness of the lunar soil protective layer based on the relationship between the thickness of the lunar soil protective layer and temperature fluctuation, and constructing the protective layer based on the determined thickness of the lunar soil protective layer. By adopting the method and the protective layer, the protective capability of the protective layer structure is improved.

The invention relates to the technical field of atmospheric remote sensing monitoring, discloses a hyperspectral atmospheric methane concentration inversion method and system for a high-aerosol scene, and solves the problems of low inversion precision and poor adaptability of a hyperspectral imager in a high-aerosol heterogeneous earth surface in an existing method. The method comprises the following steps: acquiring and processing atmospheric parameters and multiband surface albedo data, and constructing a radiation transmission model to output a forward simulation vector; based on CLARS-GFIT, constructing an optical absorption coefficient lookup table of methane and interference gas containing different temperatures and pressures; correcting the forward simulation model by using GIFT3 to quantify the aerosol influence, and generating a forward simulation spectrum in combination with a spectral response function; and adjusting the state variable to converge through an iterative maximum posteriori algorithm to obtain the methane concentration. The method improves the inversion precision of a high aerosol scene, adapts to heterogeneous earth surfaces such as a coal mine area, matches AHSI characteristics, gives consideration to efficiency, and can provide support for methane point source monitoring.

The invention provides a brightness temperature simulation calculation method conforming to a radiation transfer theory. The method comprises the following steps: establishing a radiation transfer model based on a radiance Stokes vector in a (Il, Ir, U, V) T form; collecting radiance simulation parameters; if the radiation transmission model is a scalar radiation transmission model, the atmospheric thermal radiation is a vector radiation transmission model, and if the radiation transmission model is a vector radiation transmission model, the atmospheric thermal radiation is obtained by calculating atmospheric particle scattering phase function information, atmospheric single scattering albedo, surface reflectance information and surface emissivity according to radiance simulation parameters; substituting the radiance simulation parameter, the atmospheric thermal radiation, the atmospheric particle scattering phase function information, the atmospheric single scattering albedo, the surface reflectance information and the surface emissivity into a radiation transmission model for calculation to obtain a radiance simulation result; and converting the simulation result of the radiance into the brightness temperature. The invention further provides a brightness temperature analog calculation device and equipment and a storage medium. The brightness temperature simulated by the method is consistent with a theoretical value.

This invention relates to the field of solar photovoltaic power generation technology and discloses an adaptive angle adjustment method for solar cell modules, comprising the following steps: acquiring average wind speed and original pitch angle signals; performing frequency domain decoupling on the original pitch angle signals; calculating the high-frequency energy integral, low-frequency energy integral, and energy ratio parameter; calculating the actual diffuse reflectance by combining the average wind speed and the high-frequency energy integral; constructing a theoretical radiation model to obtain the optimal target tilt angle; extracting the radiation sensitivity gradient; calculating the tracking error between the actual tilt angle and the optimal target tilt angle; determining the actual dead zone by combining the energy ratio parameter and the radiation sensitivity gradient; and outputting control commands or drive commands based on the relationship between the absolute value of the error, the actual dead zone, and the high and low frequency energy integrals. This invention reduces mechanical wear by dynamically filtering out high-frequency wave disturbances through a dead zone and precisely optimizes the reflectance using high-frequency energy correction to improve power generation, achieving a balance between mechanical loss reduction and power generation benefits.

The invention relates to the technical field of freezing ring science, and provides a near-surface icing risk dynamic assessment method for a complex mountainous area, and the method comprises the steps: introducing a radiation terrain factor, an accumulated snow albedo inhibition mechanism, and air cooling correction for a rasterized weather and surface temperature input field, and constructing an energy index; integrating rainfall, snow melting, humidity, wettability and other elements to obtain a humidity index; and introducing a persistent background index of an annual / seasonal scale as a background enhancement factor, thereby integrating the energy index, the humidity index, the background enhancement factor and the icing phase state indication to obtain a corresponding icing risk score. Therefore, a systematic process framework from data preprocessing, physical mechanism diagnosis and hydrological process constraint to risk comprehensive assessment can be provided, and the risk of near-surface icing in a complex mountainous area can be assessed efficiently and accurately.

The invention discloses a remote sensing moisture index construction method combining surface temperature and short wave infrared information. The method comprises the following steps: step 1, collecting satellite remote sensing data in a research area; step 2, meteorological radiation data collection and spatial interpolation of a research area; 3, performing remote sensing inversion on the surface biophysical parameters; step 4, calculating surface net radiation and soil heat flux; step 5, constructing a remote sensing moisture index combined with the surface temperature and the short wave infrared information; and step 6, performing space-time reconstruction on the remote sensing moisture index. The remote sensing moisture index combining the surface temperature and the short-wave infrared information is constructed, the key defect that a traditional moisture index based on the surface temperature information is greatly influenced by albedo is overcome, and the supersaturation effect of the moisture index based on the short-wave infrared information in a high vegetation coverage area is optimized; satellite thermal infrared and short wave infrared observation information can be fully utilized, and large-range, data-area-free, complex and non-uniform underlying surface moisture index calculation is achieved.

The present invention presents a method for reducing heat caused by climatic influences in various environments, including liquid, solid, or gaseous surfaces. The method comprises the use of LED lighting devices capable of dynamically changing colors, as well as the application of light filtering to target surfaces to reduce albedo reflection rates. By lowering surface temperatures and reducing heat accumulation, this approach offers a versatile solution for managing the effects of heat caused by climatic influences in diverse environmental settings.

The invention provides a method and system for quantifying the influence of lake surface reflection on ozone generation, and relates to the technical field of atmospheric environment monitoring and evaluation.The method comprises the steps that measuring equipment is synchronously arranged at a lake body and a land contrast point, and reflected radiation and meteorological chemical parameters are collected; carrying out calibration, interpolation and abnormal value processing on the data; calculating the albedo of the lake surface and the additional radiation flux relative to the standard underlying surface; calculating a nitrogen dioxide photolysis frequency increment based on the additional radiation flux; a photochemical box model is used for simulating an atmospheric chemical reaction, the species concentration is updated through a time stepping method, and finally the ozone generation increment caused by lake surface reflection is obtained. A unit albedo contribution coefficient is further defined, and quantitative evaluation of the influence of lake surface reflection on ozone generation is achieved. The method overcomes the defect that in the prior art, the lake surface specular reflection enhancement effect is ignored, the contribution of lake surface reflection to ozone generation can be accurately quantified, and a scientific basis is provided for accurate prediction and prevention and treatment of ozone pollution.

The present application relates to a kind of earth and atmospheric scattering characteristic simulation measurement system, it is related to the field of measurement system, including support, radiation source simulation system and mobile platform, radiation source simulation system is erected on support, support is erected on mobile platform;Radiation source simulation system includes light source simulation system and cold background simulation system, light source simulation system provides visible light irradiation, cold background simulation system provides uniform cold background simulation, light source simulation system and cold background simulation system are integrally designed as flat array structure to have simulation uniformity and heat dissipation efficiency, the present application has the whole design of modularization, integration, mobility, realizes the quantitative simulation of earth albedo, atmospheric scattering and cold background, simultaneously, it is combined with accurate motion control and intelligent power control, constructs a set of the advantages of optical measurement background simulation system of adaptation multi-scene.

The invention discloses a geogas light scattering distribution characteristic simulation method based on spatial non-uniform parameter driving, and belongs to the technical field of scattering characteristic simulation. According to the method, firstly, the conversion relation between a global uniform longitude and latitude grid model and a detector local coordinate system is established, and accurate dispersion and coordinate unification of detection light vectors are achieved; then calculating the spatial relationship between the detection light and the earth surface, and judging a limb or non-limb detection mode; non-uniform parameters such as earth surface albedo, aerosol optical thickness and cloud optical thickness of corresponding positions are rapidly extracted based on a grid index mechanism; and finally, radiance values are calculated by adopting corresponding parameterized models according to different detection modes, and a complete radiance distribution image is synthesized, so that the authenticity and the precision of geogas light scattering distribution characteristic simulation are remarkably improved.

A method and system for creating a high-fidelity depiction of a scene including an object within the scene are provided. The method includes uniformly illuminating a target surface of the object with light to obtain reflected, backscattered illumination. The method also includes sensing via a volumetric sensor, brightness of the surface due to a diffuse component of the backscattered illumination to obtain brightness information. Backscattered illumination from the target surface is inspected to obtain geometric measurements which include sensor noise. Rotationally and positionally invariant measured surface albedo including albedo noise of the object is computed based on the brightness and the geometric measurements. A machine-learning model such as a diffusion sensor model is applied to the geometric measurements and the measured surface albedo to remove the sensor noise and the albedo noise, respectively, to obtain a prediction of actual geometry and actual albedo, respectively, of the object.

Implementations disclosed herein include a method of imaging an albedo of an astronomical object in space. The method includes arranging an imaging system in space with an orientation facing the object. The imaging system includes a lensless image sensor with the orientation that captures images of the object. The method also includes maintaining the imaging system with the orientation facing the object during an imaging data capture session, and it includes capturing near-continuous images with the imaging system during the imaging data capture session. The orientation is maintained for subsequent image data capture sessions. For some implementations, the imaging system includes a lensed image sensor, and capturing the near-continuous images includes simultaneously capturing time-correlated images of the object with the lensed image sensor and the lensless image sensor.

The utility model discloses a kind of water-leaving albedo in-situ measuring devices, including triangular support, floating assembly and measuring assembly;The floating assembly includes first float ball, second float ball and floating ring;The measuring assembly includes downlink irradiance probe, radiance probe and uplink irradiance probe;The downlink irradiance probe is fixed on the end of the intersection of the leg of triangular support, for measuring downlink irradiance E d ;Radiance probe is fixed on the end below the intersection of the leg of triangular support;Radiance probe lower end is fixed with first light shield cone;The triangular support of floating ring one end is fixed with uplink irradiance probe;The uplink irradiance probe lower end is fixed with second light shield cone;The bottom of first light shield cone and second light shield cone is immersed in water body;Radiance probe and uplink irradiance probe are located above water surface respectively, and water-leaving radiance L w And water-leaving irradiance E w It is directly measured by radiance probe and uplink irradiance probe respectively.

This invention discloses a quantitative assessment method for high-altitude geological hazard chain risks adapted to high-altitude and cold environments, relating to the field of geological hazard monitoring and prevention technology. The method includes the following steps: S100, establishing a joint irradiation and albedo observation layer, collecting the dynamic intensity of direct and reflected light, generating an exposure time-series fingerprint baseline, and determining the critical threshold of direct light intensity based on this baseline. This invention constructs an irradiation and albedo joint observation layer to extract light intensity features and generate an exposure time-series fingerprint baseline; it uses coherent phase decomposition and counterfactual playback to extract trigger point boundaries and reconstruct continuous trajectories; it predicts overexposure risk based on time-frequency scoring, and uses spectral gating and polarization rotation to regulate the exposure rhythm; and it implements inverse phase traction and phase conjugate intervention within the steady-state window to maintain boundary information and improve risk identification accuracy.

The application discloses a physical constraint reflectivity deep learning inversion method and system and electronic equipment, and the method comprises the following steps: constructing a deep neural network model; inputting hyperspectral remote sensing sample data into the deep neural network model; using the hyperspectral remote sensing sample data to train a transmittance product combination parameter submodel, an atmospheric intrinsic reflectivity submodel and an atmospheric hemisphere albedo submodel respectively; using a physical inversion model to train the model by a physical constraint formula; and S3, acquiring hyperspectral remote sensing data of a research area, inputting the data into the deep neural network model and predicting and outputting atmospheric layer top reflectivity. The application innovatively constructs a parameter model, uses the parameter model to predict transmittance product combination parameters, atmospheric intrinsic reflectivity and atmospheric hemisphere albedo respectively, realizes efficient and rapid prediction and output of atmospheric layer top reflectivity by using three parameters and based on a constructed physical radiation transmission equation, and has the advantages of high prediction efficiency, high accuracy and the like.

The invention provides a sparse remote sensing image rendering method based on seasonal specificity, which can be applied to the technical field of remote sensing image rendering. The method comprises the following steps: performing light ray sampling on a target sparse remote sensing image to obtain sampling light rays; inputting the three-dimensional coordinates of the sampling points on the sampling light, the time for generating the target sparse remote sensing image and the sun direction angle associated with the target sparse remote sensing image into a trained sparse remote sensing image rendering model for processing to obtain the volume density, the seasonal adjustment albedo, the solar visibility and the sky color; determining a volume rendering contribution weight of a sampling point on the sampling light to the target sparse remote sensing image by using the volume density; and performing shadow mask rendering and seasonal specific rendering on the target sparse remote sensing image by using the volume rendering contribution weight, the seasonal adjustment albedo, the solar visibility and the sky color to obtain a rendered target sparse remote sensing image.

The invention discloses a light-absorbing impurity aggravated accumulated snow ablation analysis method and system, and the method comprises the steps: obtaining the LAIs concentration of light-absorbing impurity aggravated accumulated snow, the LAIs concentration comprises a first LAIs concentration in which the snowfall mass in the snowfall stage is not less than the minimum mass, a second LAIs concentration in which the snowfall mass in the snowfall stage is less than the minimum mass, and a third LAIs concentration in the sublimation stage; inputting the LAIs concentration into a pre-constructed snow albedo model, and outputting the snow albedo model to obtain an albedo; and inputting the albedo into a pre-constructed air temperature index model, and outputting the air temperature index model to obtain an ablation effect of intensifying accumulated snow ablation by the light-absorbing impurities.

The application discloses a time-varying glacier degree-day factor calculation method suitable for plateau areas, and the method comprises the following steps: step 1, multi-source data collection and processing; step 2, key driving factor screening; step 3, dynamic relationship model establishment; and step 4, time-varying glacier degree-day factor calculation. The method couples albedo and solar radiation, establishes the physical connection between the degree-day factor and the net radiation, realizes the dynamic calculation of the glacier degree-day factor, significantly improves the precision of glacier ablation simulation and prediction, effectively solves the applicability problem in the complex underlying surface area, reduces the data acquisition threshold and cost of large-scale application, and provides more accurate data support for water resource management and disaster warning.

The application provides a sparse remote sensing image rendering method based on season specificity, which can be applied to the technical field of remote sensing image rendering. The method comprises the following steps: performing ray sampling on a target sparse remote sensing image to obtain a sampling ray; inputting the three-dimensional coordinates of a sampling point on the sampling ray, the time of generating the target sparse remote sensing image, and the sun direction angle associated with the target sparse remote sensing image into a trained sparse remote sensing image rendering model for processing to obtain a volume density, a seasonally adjusted albedo, a solar visibility and a sky color; determining the volume rendering contribution weight of the sampling point on the sampling ray to the target sparse remote sensing image by using the volume density; and performing shadow mask rendering and season-specific rendering on the target sparse remote sensing image by using the volume rendering contribution weight, the seasonally adjusted albedo, the solar visibility and the sky color to obtain a rendered target sparse remote sensing image.

Apparatus for obtaining fresh water by artificially generating a precipitation. The apparatus comprises at least one darkening body that forms a darkening surface which has a width and / or length of at least 3 km and an albedo of less than 0.1. The apparatus further comprises at least one base frame which is arranged on a ground and configured to support the at least one darkening body so as to keep the at least one darkening body spaced apart from the ground in order to form a space between the at least one darkening body and the ground. Still further, the apparatus comprises a precipitation collection system which is arranged at least partially within the space and configured to collect the precipitation falling on the darkening surface.

Implementations disclosed herein include a method of imaging an albedo of an astronomical object in space. The method includes arranging an imaging system in space with an orientation facing the object. The imaging system includes a lensless image sensor with the orientation that captures images of the object. The method also includes maintaining the imaging system with the orientation facing the object during an imaging data capture session, and it includes capturing near-continuous images with the imaging system during the imaging data capture session. The orientation is maintained for subsequent image data capture sessions. For some implementations, the imaging system includes a lensed image sensor, and capturing the near-continuous images includes simultaneously capturing time-correlated images of the object with the lensed image sensor and the lensless image sensor.

Implementations disclosed herein include a method of imaging an albedo of an astronomical object in space. The method includes arranging an imaging system in space with an orientation facing the object. The imaging system includes a lensless image sensor with the orientation that captures images of the object. The method also includes maintaining the imaging system with the orientation facing the object during an imaging data capture session, and it includes capturing near-continuous images with the imaging system during the imaging data capture session. The orientation is maintained for subsequent image data capture sessions. For some implementations, the imaging system includes a lensed image sensor, and capturing the near-continuous images includes simultaneously capturing time-correlated images of the object with the lensed image sensor and the lensless image sensor.

The application discloses a kind of remote sensing moisture index construction methods of joint ground surface temperature and short-wave infrared information, the method includes the following steps: step 1, research area satellite remote sensing data collection;Step 2, research area meteorological radiation data collection and spatial interpolation;Step 3, remote sensing inversion of ground surface biophysical parameter;Step 4, ground surface net radiation and soil heat flux calculation;Step 5, remote sensing moisture index construction of joint ground surface temperature and short-wave infrared information;Step 6, remote sensing moisture index space-time reconstruction.The application constructs a kind of remote sensing moisture index of joint ground surface temperature and short-wave infrared information, improves the key deficiency that traditional moisture index based on ground surface temperature information is greatly influenced by albedo, optimizes the oversaturation effect of moisture index based on short-wave infrared information in high vegetation cover area, can make full use of satellite thermal infrared and short-wave infrared observation information, realizes the moisture index calculation of large range, no data area, complex non-uniform underlying surface.

A method and system for creating a high-fidelity depiction of a scene including an object within the scene are provided. The method includes uniformly illuminating a target surface of the object with light to obtain reflected, backscattered illumination. The method also includes sensing via a volumetric sensor, brightness of the surface due to a diffuse component of the backscattered illumination to obtain brightness information. Backscattered illumination from the target surface is inspected to obtain geometric measurements which include sensor noise. Rotationally and positionally invariant measured surface albedo including albedo noise of the object is computed based on the brightness and the geometric measurements. A machine-learning model such as a diffusion sensor model is applied to the geometric measurements and the measured surface albedo to remove the sensor noise and the albedo noise, respectively, to obtain a prediction of actual geometry and actual albedo, respectively, of the object.

The invention belongs to the technical field of photovoltaic power prediction, and provides a photovoltaic power prediction method and system based on multi-source heterogeneous data, and the method comprises the steps: obtaining the multi-source heterogeneous data of photovoltaic power generation and the geometric arrangement information of a photovoltaic array; processing multi-source heterogeneous data, and constructing a multi-dimensional feature data matrix of photovoltaic power generation; processing the multi-dimensional feature data matrix based on deep learning of physical information constraint to obtain a corrected solar irradiation intensity prediction sequence; considering the dynamic earth surface albedo, and calculating a total sequence of solar irradiation intensity on the inclined plane; calculating a direct-current power output sequence of the photovoltaic module by considering a dynamic attenuation effect based on environmental stress; generating a probabilistic prediction interval based on the solar irradiation intensity prediction sequence after power prediction uncertainty quantization correction of fractional regression; and obtaining an AC power prediction result according to the DC power output sequence and the probabilistic prediction interval, and completing photovoltaic power prediction based on the multi-source heterogeneous data.

This invention discloses a model-driven deep generative network method for estimating mineral abundance in lunar hyperspectral images. It leverages the ability of an autoencoder network to learn a low-dimensional representation of the input image to obtain abundance. A recurrent generative adversarial network (GAN) implicitly learns a nonlinear mixing mechanism, and a recurrent consistency loss is introduced to achieve unsupervised unmixing using the abundance obtained from the autoencoder. Furthermore, considering the radiative transfer process, the Hapke model is simplified by converting reflectance into single-scattering albedo, establishing a correlation between the unmixing mechanism of the generative network and the physical mechanism of spectral mixing, and designing a model-driven autoencoder GAN. This invention enables nonlinear unmixing and considers the radiative transfer process, making it more suitable for mineral abundance estimation in densely mixed lunar surface scenes.

The present application relates to the polarization degree calculation method of complex space debris, and in particular to a kind of complex space debris polarization degree calculation method based on micro facet pBRDF model, solve the technical problem that the existing polarization degree calculation method is poor in authenticity and accuracy.The complex space debris polarization degree calculation method based on micro facet pBRDF model provided by the present application is aimed at the characteristics of the multi-shape and multi-material coupling of complex space debris, based on the modeling idea of the complex refractive index of different structural materials of complex space debris and the three components of specular reflection, diffuse reflection and volume scattering, and uses the albedo projection area as the reflection contribution coefficient for calculating the total light intensity of the reflected light of complex space debris, establishes the micro facet pBRDF model of different structures-different reflection contribution coefficients of complex space debris, which is closer to the multi-shape and multi-material characteristics of real space debris, so that the polarization degree of complex space debris calculated based on the micro facet pBRDF model is more accurate.