81 results about "Spectral response function" patented technology

The invention relates to the field of photovoltaic power prediction, in particular to a photovoltaic power prediction method, and the method comprises the steps: obtaining the spectral response function data of different photovoltaic modules in a photovoltaic power station, and measuring the spectral irradiance distribution of the surface of each photovoltaic module in real time; according to the spectral response function data and the spectral irradiance distribution, calculating the effective spectral irradiance of each component type in a grouping manner according to the component types; counting the temperature coefficient of the batch to which each photovoltaic module belongs according to historical data, and establishing a module-level temperature-power correction factor; according to the string topological relation, string-level temperature correction power is obtained, and the string-level temperature correction power, the effective spectral irradiance and the global irradiance data of the numerical weather forecast are output into a corrected power station-level photovoltaic power predicted value; the problem of low photovoltaic power prediction precision caused by inaccurate modeling due to parameter difference and temperature influence of photovoltaic modules in photovoltaic power station power prediction is solved.

The invention discloses a hyperspectral image super-resolution reconstruction method based on multi-scale cavity convolution guidance, and the method comprises the steps: carrying out the space downsampling of HrHSI to generate LrHSI, and carrying out the spectrum degradation to generate first LrMSI; after the HrHSI generates HrMSI through a spectral response function, performing space degradation to generate second LrMSI; constructing an initialized image generation module by adopting a double-branch network comprising multi-stage cascaded CCEs (Control Channel Element), and generating high-resolution initial estimation; a dual-path cavity cooperative enhancement module DDCA is constructed, a main path reinforces local details, an auxiliary path compresses global information, and feature calibration and residual fusion are realized through a gating mechanism; wherein the multi-scale cavity fusion module integrates a channel and a space attention mechanism so as to enhance context and detail information extraction capability; a depth image generation network of a double U-Net architecture is constructed based on DDCA, and reconstruction is realized through joint optimization. According to the invention, the feature distinguishing capability and the detail recovery capability are enhanced.

The invention discloses a visible light wave band spectral response function inversion method which comprises the following steps: acquiring a satellite on-orbit spectral response function and atmospheric top hyperspectral reflectivity simulation data through data simulation, and calculating satellite on-orbit reflection data through a forward model; determining a target function of the inversion model according to the satellite in-orbit reflection data; determining constraint conditions of the inversion model; training the inversion model through a linear programming solver to obtain a trained inversion model; and inputting the atmospheric top hyperspectral reflectance data and the satellite reflectance value into the trained inversion model to obtain a corresponding spectral response function. According to the satellite visible light wave band and the satellite characteristics, an inversion algorithm based on linear programming is used, and sufficient prior information of a target satellite is fully utilized to formulate reasonable constraints; and a visible light wave band spectral response function inversion method with universality and robustness is obtained.

The invention relates to a modeling method for multi-scale fruit environment characteristic parameterization classification quantitative detection, which comprises the following steps: S10, obtaining illumination and temperature space distribution characteristics through high-precision satellite remote sensing image data, carrying out gridding processing on environment characteristics of a to-be-detected area, and obtaining a data matrix according to a solar radiation model and a surface temperature inversion model; classifying and partitioning the temperature and illumination features of the gridded orchard area through ground experiment control points; s20, establishing a multi-angle profiling calibration field through the temperature and illumination characteristics of the gridding orchard area; s30, accurate angle calibration is carried out, fruit reflectivity characteristic angles are selected, and a multi-angle polarization reflectivity database is established; and S40, acquiring a reflectivity change rule of fruits in the same region at different angles and polarization directions by using an unmanned aerial vehicle multispectral camera, extracting key parameters with obvious environment temperature and illumination characteristics, and fitting a region scale spectral reflectivity curve model and a typical environment multi-angle polarization spectral response function. The fruit partition quantitative detection precision is improved.

The invention provides a phyllostachys pubescens forest phyllostachys pubescens moth hazard remote sensing detection scale parameter optimization method, which comprises the following steps: (1) resampling hyperspectral data to a multi-source satellite sensor spectral scale through a Gaussian spectral response function, and analyzing and quantifying the response stability of spectral indexes to insect pests under different spectral scales by adopting a variable coefficient, screening spectral indexes with sensitive response and high stability; (2) based on the remote sensing images with different spatial resolutions, extracting moso bamboo forest and insect pest features by utilizing a machine learning algorithm and combining a recursive feature elimination technology, and determining an optimal spatial resolution through precision evaluation; (3) constructing an insect pest time sequence set, analyzing an insect pest grade conversion path through dynamic attitude analysis and a hazard transfer matrix, and determining a suitable monitoring period; and (4) synthesizing the spectral scale, the spatial resolution and the time period parameters output in the steps (1)-(3) to generate an optimized scale combination for remote sensing detection of the harm of the phyllostachys nigra.

The invention discloses an early-stage enteromorpha biomass monitoring method which comprises the following steps: collecting an early-stage enteromorpha frond sample, and obtaining actually measured reflectivity hyperspectral data under different biomass levels through a water tank control experiment; in combination with a spectral response function of a high-resolution satellite sensor, acquiring the equivalent reflectivity of enteromorpha at different biomass levels; calculating a plurality of spectral indexes based on the equivalent reflectivity; establishing an empirical inversion model between various spectral indexes and actually measured biomass, and screening out an optimal inversion model through precision comparison; applying the optimal model to a high-resolution satellite image to generate an early enteromorpha biomass remote sensing monitoring product; according to the invention, refined and quantitative monitoring of early enteromorpha is realized, and effective technical support is provided for scientific early warning and prevention and control of green tide disasters.

The invention provides a spectral reconstruction method and system based on an optimized digital camera spectral response function. The method comprises the following steps: measuring the spectral reflectivity of a training sample; shooting to obtain original format response values of the test sample in different imaging scenes, and recording camera imaging parameters; synchronously measuring light source spectral power distribution of the imaging scene; then, on the basis of a digital camera full-parameter imaging model, the raw response value of a training sample set under the same imaging condition is predicted, maximum and minimum normalization is carried out on the original format response value of a test sample and the predicted training sample set response value in RGB channels, spectral characterization modeling is carried out on the camera, and the spectral characteristic of the camera is obtained; and finally, performing multispectral reconstruction on the test sample under the same imaging condition to obtain reconstructed spectral data of the test sample.

The invention provides a satellite-borne hyperspectral load spectrum calibration method independent of ground synchronous observation, which is applied to the technical field of satellite remote sensing and hyperspectral, and comprises the following steps: constructing an actual apparent reflectance matrix of a calibration site referring to multispectral load observation based on various standard mode apparent reflectance matrixes; determining a weight coefficient matrix of spectrum reconstruction based on the spectrum matching factor and the actual apparent reflectivity matrix; constructing hyper-spectral apparent reflectivity of the calibration site; performing convolution operation based on the spectral response function of the spaceborne hyperspectral load and the hyperspectral apparent reflectivity to obtain channel radiance at the entrance pupil of the spaceborne hyperspectral load; performing spectrum normalization on the radiance of the channel at the entrance pupil of the spaceborne hyperspectral load and the actual observation value at the entrance pupil of the spaceborne hyperspectral load, and establishing a spaceborne hyperspectral load on-orbit spectrum calibration model; according to the invention, the defect that a satellite-borne hyperspectral load on-orbit spectrum calibration method excessively depends on ground synchronous observation data can be solved.

The invention discloses a remote sensing data-based cultivated land resource dynamic monitoring and checking method and system, and the method comprises the steps: introducing atmospheric scattering parameter estimation in an atmospheric correction process, carrying out the refined estimation of the atmospheric path radiation through combining with the water vapor content and aerosol concentration, and optimizing the atmospheric correction parameters, thereby achieving the dynamic monitoring and checking of cultivated land resources. And the interference of the atmospheric effect on the surface reflectance is effectively weakened. Spectral response function matching and spectral feature standardization processing are implemented, and a spectral difference correction model is constructed, so that the spectral features of the multi-temporal images tend to be consistent. The spectral deviation caused by atmospheric condition fluctuation is remarkably reduced, and the comparability of spectral response between images is improved, so that the accuracy and the stability of cultivated land change detection are improved. And finally, change feature extraction and region identification are performed on the basis of spectrum consistency enhancement, so that the actual change condition of cultivated land resources can be reflected more truly, misjudgment caused by atmospheric noise is reduced, and the reliability of a dynamic monitoring result is enhanced.

The application discloses a FY satellite spectral imager atmospheric correction method and device based on 6S radiation transmission mode, medium and product, and relates to the field of atmospheric correction. The method comprises the following steps: determining the threshold range of a first parameter according to MERSI observation data and determining the threshold range of a second parameter according to atmospheric reanalysis data; generating a target coefficient by applying a 6S radiation transmission mode and a spectral response function of MERSI; constructing a lookup table; obtaining matching data values of the second parameter corresponding to each pixel in target MERSI observation data by applying an interpolation algorithm; determining the target coefficient of each pixel in the target MERSI observation data from the lookup table; obtaining the corrected reflectivity of each pixel by applying the 6S radiation transmission mode; and correcting the true color quick view of the visible light channel according to the corrected reflectivity of each pixel to obtain a corrected true color composite image. The application can perform atmospheric correction on MERSI wide-width global images.

The invention discloses a spectral imaging calculation system based on spectral response enhancement of a color detector, and belongs to the technical field of spectral imaging, the spectral imaging calculation system comprises a birefringence light splitting module and a color detector, the birefringence light splitting module comprises a polarizer, a birefringence crystal and a polarization analyzer, the polarizer, the birefringent crystal, the polarization analyzer and the color detector are sequentially and coaxially arranged along an optical axis; the color detector is also electrically connected with a signal processing module; incident light is converted into linearly polarized light through the polarizer, the linearly polarized light is decomposed into o light and e light through the birefringent crystal, and the o light and the e light are combined into emergent light through the polarization analyzer. The emergent light passes through a color detector to obtain a measurement signal; and the signal processing module receives the measurement signal and reconstructs a spectral image in combination with the spectral response matrix of the birefringence light splitting module and the spectral response function of the color detector. The spectral response characteristics of multiple color channels of the color image sensor are combined with the birefringence color development polarization principle, the rank of a spectral response matrix is expanded, and the spectral resolution and the imaging precision are improved, so that the spectral information of a substance is more accurately acquired and analyzed.

The invention provides a single-pixel calculation hyperspectral imaging system and method, and the technical scheme of the invention comprises a spatial light modulator which is used for carrying out the spatial coding of a light beam from an imaging scene; and the reconfigurable spectrum detector is used for detecting the light beams after space coding. The reconfigurable spectrum detector is provided with a group of spectral response functions which are tunable along with external bias voltage, broadband and correlative, and the reconfigurable spectrum detector forms a core sensing unit of the micro-computing spectrometer. A barrel signal sequence is obtained by synchronously collecting light intensity integral values under different space coding patterns and different bias voltages. And finally, in a data processing module, a spectral data cube of the target scene is jointly solved from the bucket signal sequence according to the spatial coding matrix and the spectral response function set by utilizing a calculation reconstruction network. According to the invention, two computational imaging technologies are creatively fused, the structure is very compact, and real-time and wide-spectrum spectral imaging can be realized at a low sampling rate.

The invention discloses a color image sensor based on a two-dimensional semiconductor heterostructure and application of the color image sensor in image convolution processing. The device comprises a bipolar two-dimensional semiconductor and an N-type two-dimensional semiconductor which are deposited on a substrate, and the bipolar two-dimensional semiconductor and the N-type two-dimensional semiconductor form a horizontal heterojunction structure. By adjusting the voltage of the back gate, the energy band arrangement of the heterojunction is controlled, a spectral response function capable of continuously changing is realized, and image information under multiple wavebands can be obtained without a color filter. Meanwhile, the photocurrent response of heterojunctions is regulated and controlled through source and drain voltages, then different weights are given to output signals, a plurality of heterojunctions with the same size as a convolution kernel are connected in parallel, and the sum of read currents is mapped into a gray value, so that a convoluted image under the wave band can be obtained. And image convolution processing can be completed by respectively applying convolution operation under different wavebands. The device has excellent performance in the aspects of integrated image acquisition and convolution calculation, effectively reduces the cost of calculation resources, and remarkably improves the image processing efficiency.

The invention relates to a water quality remote sensing inversion method based on an interpretable hybrid physical guidance neural network (HPGNN) model. The method comprises the following steps: collecting geotagged in-situ water quality parameter concentration with global representativeness and 1nm hyperspectral remote sensing reflectivity subjected to quality assurance and radiation correction from global lakes; converting the reflectance into equivalent reflectance of a sensor wave band by utilizing a spectral response function of a remote sensor; extracting space-time matched water surface remote sensing reflectivity by utilizing GEE; defining a mixed self-defined loss function fusing data driving, regularization and physical loss; and aiming at each water quality index, developing and training an interpretable high-precision spectrogram neural network model HPGNN using the converted reflectivity and the mixed self-defined loss function. Based on global representative data, the accurate water quality evaluation method is developed by mixing the self-defined loss function, short-term and long-term space-time dynamic analysis is achieved, an effective technical means is provided for water pollution prevention and control, and the method has wide application and popularization value.

The invention provides an absolute radiometric calibration method for target remote sensing equipment and related equipment, and the method comprises the steps: carrying out the regularization processing of a linear flight imaging image based on a target ground object, and obtaining a first regularized image and an actual DN value of the regularized target ground object; performing radiation transfer calculation according to the TOA environment parameters of the target ground object and the equipment parameters of the target remote sensing equipment which are synchronously acquired in the straight flight imaging period to obtain the TOA radiance of the reference detector, and performing absolute radiometric calibration according to the DN value of the target ground object to obtain the absolute radiometric calibration coefficient of the reference detector. And finally, carrying out absolute radiometric calibration coefficient transmission based on the absolute radiometric calibration coefficient of the reference detector and the function relationship between the spectral response functions of other detectors and the reference detector to obtain the absolute radiometric calibration coefficients of all detectors of the target remote sensing equipment. Therefore, the accuracy of absolute radiometric calibration of multiple detectors can be effectively improved.

The invention discloses a moisture prediction method and system based on airborne multispectral sensitive wave band combination, and relates to the technical field of agricultural remote sensing and image processing, and the method comprises the steps: on the basis of the response characteristics of soil and plant canopy reflection spectrums to moisture, combining with the physical mechanism of O-H bond vibration in water molecules, and predicting the moisture content of the soil and plant canopy reflection spectrums; determining the bandwidths of all central wavelengths by considering the influence of atmospheric water vapor on the near infrared spectrum; generating reflection spectrums of the soil and the vegetation canopies, generating a spectral response function by using the relationship between the central wavelength and the bandwidth, converting the simulated spectrums into a moisture sensitive wave band, and calculating the reflectivity and the spectral index; and collecting reflection spectrum images of the soil and the vegetation canopies at the selected positions, and inputting the reflection spectrum images into the moisture prediction model to predict moisture. By optimizing wave band selection and bandwidth allocation, the problems that when an existing airborne multispectral sensor executes a moisture monitoring task, the pertinence of wave band selection is insufficient, and the monitoring precision is not high can be effectively solved.

The present application relates to the field of photovoltaic power prediction, and particularly relates to a photovoltaic power prediction method, which obtains spectral response function data of different photovoltaic components in a photovoltaic power station, and measures spectral irradiance distribution of surfaces of each photovoltaic component in real time; according to the spectral response function data and the spectral irradiance distribution, effective spectral irradiance of each component type is calculated according to component type grouping; temperature coefficients of batches to which each photovoltaic component belongs are counted according to historical data, and a component-level temperature-power correction factor is established; string-level temperature correction power is obtained according to string topology relationship; the string-level temperature correction power, effective spectral irradiance and global irradiance data of numerical weather prediction are used to output a corrected power station-level photovoltaic power prediction value; the problem of low photovoltaic power prediction accuracy caused by inaccurate modeling due to photovoltaic component parameter difference and temperature influence in photovoltaic power station power prediction is solved.

The invention relates to the technical field of water quality parameter remote sensing inversion, and discloses a simple method suitable for high-turbidity estuary particulate organic carbon concentration remote sensing inversion, which comprises the following steps: collecting and preprocessing suspended particulate matter (SPM) concentration, particulate organic carbon (POC) concentration data and corresponding remote sensing reflectivity data of a water body; according to a wave spectrum response function of each wave band of a common optical satellite sensor, the equivalent remote sensing reflectivity Rrs (lambda i) of each wave band is obtained through simulation, and by comparing the correlation between the remote sensing reflectivity Rrs (lambda i) of the visible light-near infrared wave band and the actually measured suspended particulate matter (SPM) concentration, the POC inversion model constructed by the method adopts a subsection modeling strategy, so that the concentration of the SPM can be calculated. The concentration of the suspended particulate matters with strong correlation is preferably selected as a correlation parameter, so that a relatively large error generated by an existing multi-parameter model is effectively avoided, and the precision of the inversion of the concentration of the particulate organic carbon in the high-turbidity estuary region is remarkably improved.

The invention discloses a water quality inversion method based on multi-strategy improved sparrow search optimization machine learning. The method comprises the following steps: firstly, collecting the reflectivity of a water sample by using a spectrograph, and converting the collected reflectivity into an equivalent albedo of a corresponding wave band of a Sentinel-2 satellite through a spectral response function; collecting a water sample on site, and measuring the TSM concentration and Chla concentration; the obtained equivalent albedo is constructed into combined features in a single-band and multi-band mode, and the first four combined features of the correlation are selected as feature input; inputting the selected combined features into a plurality of machine learning models for contrastive analysis to invert the performance of TSM and Chla concentration, and determining an optimal machine learning model; then constructing a multi-strategy optimized sparrow search algorithm; and performing water quality inversion on the Sentinel-2 image by using the optimal machine learning model obtained by optimization of the obtained multi-strategy optimized sparrow search algorithm. According to the invention, the model inversion precision is improved.

The application discloses an infrared image adaptive atmospheric correction method and device based on a radiation transmission model, and belongs to the technical field of remote sensing image radiation correction. The method first extracts the spatial range, imaging time and observation geometry parameters of an image scene, constructs a scene-based atmospheric profile grid by distance inverse ratio weighted interpolation based on a reanalysis atmospheric profile, and combines a digital elevation model and a view zenith angle to correct and apply terrain and observation geometry constraints to generate control node input parameters. Then, the atmospheric radiation transmission model is called in batches to obtain the hyperspectral results of each control node, and the transmittance and path radiance parameters of the infrared target band are extracted by spectral response function convolution. Finally, the control node parameters are reprojected to the original image coordinates, the four-neighbor-domain bilinear interpolation and nine-neighbor-domain weighted interpolation results are adaptively fused for each pixel, the full-resolution atmospheric parameter field is recovered, and the observed radiance at the ground is inversed.

The invention discloses a RadCalNet data application range expansion method oriented to radiation calibration of a satellite-borne large-view-field sensor. The method comprises the following steps: 1) constructing a RadCalNet site earth surface BRDF model; (2) RadCalNet data missing at the transit moment of the satellite-borne large-field-of-view sensor are simulated; 3) simulating MODIS five-waveband equivalent surface reflectance under observation geometry and vertical angle of a sensor to be calibrated, and calculating a BRDF correction factor; 4) weakening the relative spectral response function difference of the sensor; 5) calculating a full-spectrum global correction factor to obtain the equivalent surface reflectance of the to-be-calibrated sensor; and 6) calculating the radiometric calibration coefficient of the to-be-calibrated sensor after obtaining the equivalent apparent reflectance of the to-be-calibrated sensor by using a 6S radiation transfer equation. The method disclosed by the invention well solves the problem that the high-frequency radiometric calibration of the satellite-borne large-view-field sensor cannot be realized due to angle limitation and data missing of RadCalNet data.

The present invention relates to a full Stokes vector measurement calibration method for a polarization camera with an integrated compensator, belonging to the field of polarization light measurement technology. The method comprises the following steps: S1, constructing a normalized equivalent comprehensive spectral response function of a polarization imaging system; S2, constructing an intensity vector expression received by a DoF polarization sensor based on the normalized equivalent comprehensive spectral response function; simplifying the intensity vector expression received by the DoF polarization sensor based on a depolarization factor of a micropolarizer array; S3, correcting the simplified intensity vector expression received by the DoF polarization sensor based on the azimuth angle and phase delay of the compensator; rewriting the corrected intensity vector expression received by the DoF polarization sensor based on the depolarization factor of the compensator to obtain a final intensity vector expression received by the DoF polarization sensor; and S4, solving the azimuth angle, phase delay, and depolarization factor of the compensator. The method is highly efficient, accurate, and easy to generalize.

The embodiment of the invention discloses an infrared band multi-mode calibration method and device. The method comprises the following steps: calculating a point spread function (PSF) of an original infrared image, and generating a spatial response matching filter function (SRMF) according to the PSF; carrying out two-dimensional convolution calculation on the SRMF to obtain an infrared image after effective space calibration; generating a possible spectral response function of ice pollution, and evaluating an optimal ice pollution spectral response through a cross calibration method; according to the optimal ice pollution spectral response, a spectral response function after possible spectral offset is generated, the optimal possible spectral offset is evaluated through a cross calibration method, and a spectral response function after spectral calibration is obtained through optimal sampling; calculating a linear coefficient, a nonlinear coefficient and a constant coefficient of radiometric calibration; and according to each coefficient of radiometric calibration, calculating the radiation quantity of the infrared image after multi-modal calibration. The method can overcome the existing problems and promote the quantification and multi-source fusion application of the satellite infrared data.

The application discloses a spectral imaging method based on an RGB camera and a broadband filter coding. According to a camera spectral response function, an optimized parameter of a broadband filter and a spectral reconstruction network construction design stage, an end-to-end optimization framework is constructed. Then, the end-to-end optimization framework is trained according to a hyperspectral dataset to obtain a trained end-to-end optimization framework of the design stage and obtain a theoretical design parameter of the broadband filter. Then, an actual spectral transmittance function of the combined broadband filter and a final spectral reconstruction network are obtained. Finally, an RGB imaging system is built and a scene image is obtained, which is input into the final spectral reconstruction network to obtain a hyperspectral image. The application is based on the wavelength coding spectral imaging principle, a specially designed broadband filter is added in the imaging light path, and the input image of the reconstruction algorithm is increased from 3 channels to multiple channels through multiple imaging, so that the spectral accuracy of the reconstruction result can be effectively improved.

The present invention provides a spectral reconstruction method and system based on optimizing the spectral response function of a digital camera, comprising: measuring the spectral reflectance of a training sample; photographing and obtaining the original format response values of the test sample under different imaging scenes and recording the camera imaging parameters; synchronously measuring the spectral power distribution of the light source of the imaging scene; then, based on a full-parameter imaging model of the digital camera, predicting the raw response values of a training sample set under the same imaging conditions, performing maximum and minimum normalization on the original format response values of the test sample and the predicted response values of the training sample set in three RGB channels, performing spectral characteristic modeling on the camera, and finally performing multi-spectral reconstruction on the test sample under the same imaging conditions to obtain reconstructed spectral data of the test sample.

The application discloses an unsupervised hyperspectral image super-resolution method based on a matrix decomposition network and belongs to the technical field of hyperspectral image processing. The application is used for processing a hyperspectral image, generating a simulated low spatial resolution hyperspectral image Y and a high spatial resolution multispectral image Z; first, inputting the generated data pair (Y, Z) into a designed auto-encoder network, training iteration to obtain a point spread function and a spectral response function; for a target high spatial resolution hyperspectral image X, the target high spatial resolution hyperspectral image X can be assumed to be a linear combination of a terminal member matrix A and a corresponding abundance matrix S, that is, X = AS, a spectral and spatial degradation model is combined to model, a deep CP decomposition module is designed to calculate A, A and S are iteratively solved, and finally a fusion result is obtained. The application can obtain more rich spectral and spatial features, obtain a better fusion result, and has good performance in practice.

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.

Example embodiments relate to a computer-implemented method for determining water content of a moist sample of porous material from a spectral reflectance spectrum of the moist sample. The computer-implemented method comprising: obtaining (201) a hyperspectral measurement of the moist sample comprising the spectral reflectance spectrum (212), and a spectral response function (211) associated with the hyperspectral measurement; determining (202) a water-layer thickness (215) within the moist sample based on the spectral response function, the spectral reflectance spectrum of the moist sample, a spectral reflectance spectrum of an air-dried sample of the porous material, and an absorption spectrum of water; and determining (204) the water content of the moist sample (221) according to a first predetermined relationship (220) or a second predetermined relationship (230) based on (203) the water-layer thickness within the moist sample.

The present invention provides a method for constructing and looking up a Rayleigh scattering lookup table for the hyperspectral imager of the Environmental Disaster Reduction Satellite No. 2A / B to solve the problem that the existing Rayleigh scattering lookup table for water color satellite remote sensors is not applicable to the hyperspectral imager of the HJ-2A / B satellite and its accuracy is not high. According to the spectral response function of the hyperspectral imager of the HJ-2A / B satellite, this method calculates the solar irradiance at the top of the atmosphere, the Rayleigh scattering optical thickness of atmospheric molecules under standard atmospheric pressure, and the specific absorption coefficient of ozone per unit volume concentration, revises the anisotropic band difference of atmospheric molecules and the atmospheric pressure of the ocean-atmosphere coupled vector radiative transfer model, and calculates the Rayleigh scattering radiation vector under the equivalent Rayleigh scattering optical thickness of each band under different wind speeds and solar elevation angles, so as to generate a high-precision Rayleigh scattering lookup table for the hyperspectral imager of the HJ-2A / B satellite.

The embodiments of the present disclosure disclose an infrared waveband multi-modal calibration method and device. The method comprises: calculating a point spread function (PSF) of an original infrared image, and generating a spatial response matching filter function (SRMF) according to the PSF; performing two-dimensional convolution calculation on the SRMF to obtain an infrared image after effective spatial calibration; generating a possible spectral response function affected by ice pollution, and evaluating an optimal ice pollution spectral response by a cross-calibration method; generating a possible spectral response function after possible spectral shift according to the optimal ice pollution spectral response, and evaluating an optimal possible spectral shift amount by the cross-calibration method, and obtaining a spectral response function after spectral calibration by optimal sampling; calculating a linear coefficient, a nonlinear coefficient and a constant coefficient of radiation calibration; and calculating an infrared image radiation after multi-modal calibration according to the coefficients of radiation calibration and. The method can overcome existing problems and promote quantitative satellite infrared data and multi-source fusion application.