38 results about "Spectral response function" patented technology

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 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 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 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 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 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 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 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.

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.

The invention relates to the technical field of absolute response calculation of in-orbit optical remote sensing satellites, in particular to an in-orbit satellite response calculation method based on a point light source, which comprises the following steps of: calculating entrance pupil radiance and a response value of a remote sensing camera; calculating a spectral response function and a spectral response bandwidth of the remote sensing camera; equivalent entrance pupil radiance is calculated through wavelength infinitesimal elements; performing least square fitting calculation through the equivalent entrance pupil radiation brightness and the response value to obtain an absolute radiation calibration coefficient; calculating the absolute radiation brightness under the equivalent extended light source; the atmospheric upper bound absolute radiation brightness is calculated through the atmospheric transmittance and the absolute radiation brightness under the equivalent extended light source, and the equivalent radiation brightness is calculated through the spectral response bandwidth; and the output response value of the remote sensing camera is calculated according to the equivalent spectral radiation brightness. According to the method, on-orbit satellite response calculation based on the point light source is realized by utilizing a point source target radiation transmission principle and combining a basic principle of optical remote sensing camera radiation calibration.

This invention belongs to the field of environmental monitoring technology and discloses a remote sensing inversion method for dissolved inorganic nitrogen and silicate in estuaries based on salinity synergy. The method includes the following steps: S1. During a field survey in the land-sea interaction zone of the estuary, remote sensing reflectance, salinity, and nutrient data are collected simultaneously. The measured hyperspectral reflectance is simulated as the equivalent reflectance of the satellite band using the spectral response function of the target satellite, which is used to construct the training and validation datasets for the model. S2. A nonlinear inversion model of remote sensing reflectance and salinity is established, and a nutrient mixture model of salinity and nutrients is constructed. The training dataset is used for model training. S3. The satellite remote sensing reflectance data of the estuarine area to be predicted is input into the trained nonlinear inversion model and combined with the nutrient mixture model to generate the spatial distribution of DIN concentration and DSi concentration in the estuarine area. This method achieves high-precision remote sensing inversion prediction of DIN concentration and dissolved silicate DSi concentration in the estuarine area.

The invention provides a hyperspectral image fusion method and device for image adaptive registration, and relates to the technical field of image processing, and the method comprises the steps: taking a first hyperspectral image as a reference, carrying out the feature point extraction and matching based on the first hyperspectral image and a first multispectral image, and obtaining a projection matrix; performing projection transformation processing on the first multispectral image based on the projection matrix to obtain a second multispectral image; determining a spectral response function based on the second multispectral image and the first hyperspectral image; estimating a spectrum dictionary in the first hyperspectral image based on a VCA algorithm; estimating a sparse coefficient matrix in the first multispectral image based on the spectral response function and the spectral dictionary; constructing a second hyperspectral image based on the spectral dictionary and the sparse coefficient matrix; wherein the spatial resolution of the first multispectral image is higher than that of the first hyperspectral image. Therefore, the spatial resolution of the fused image is remarkably improved while the spectral information integrity of the hyperspectral image is kept.

The invention discloses a satellite inversion method for the canopy chlorophyll content based on a row-sowing rice geometrical optics-radiation transfer composite model, and the method comprises the steps: 1, simulating the spectral reflectance of rice canopies at an interval of 1 nm in different planting scenes based on agricultural priori knowledge through the row-sowing rice geometrical optics-radiation transfer composite model; 2, mapping the simulated canopy spectral reflectivity data set into a satellite image broadband canopy spectral reflectivity data set by using a spectral response function; step 3, constructing red edge chlorophyll index characteristics based on the simulated broadband reflection spectrum data set; and 4, establishing a hybrid inversion model between the red edge chlorophyll index and the paired canopy chlorophyll content CCC, and carrying out canopy chlorophyll content satellite inversion by using the hybrid inversion model to realize large-range rice chlorophyll content space-time dynamic monitoring. According to the method, the precision of the rice canopy chlorophyll content hybrid inversion model is remarkably improved in the early stage of rice growth.

The application provides a hyperspectral super-resolution reconstruction method and system fusing phase priori and degradation perception, the method comprising: acquiring an observed image and a spectral response function matrix, and constructing an optimization objective function comprising a data fidelity term and a space-spectrum joint priori term; using a semi-quadratic splitting algorithm to decouple the objective function into three constraint terms of physical fidelity, spectral low rank and depth priori; using a Fourier phase-amplitude mixed processing model, a truncated singular value decomposition model and a double-branch proximal mapping network model to optimize and solve the three constraint terms respectively, to obtain and update a first auxiliary variable, a second auxiliary variable and a target hyperspectral image; through cyclic iteration until the iteration number reaches a number threshold, the target hyperspectral image output in the last iteration cycle is taken as a high-resolution hyperspectral reconstruction image. The application effectively improves the spatial texture recovery capability and spectral fidelity of the image.

The present application is to solve the technical problem that most of the existing total suspended particulate matter remote sensing inversion algorithm is constructed on the basis of 550 nm remote sensing reflectance, and the remote sensing reflectance appears double peak characteristics with the increase of total suspended particulate matter concentration, which makes the expression of total suspended particulate matter concentration change of high turbidity water body gradually ineffective, and provides a turbid water total suspended particulate matter inversion method suitable for stationary orbit satellite GOCI. The inversion method takes the in-situ observed remote sensing reflectance and the equivalent remote sensing reflectance calculated by the satellite spectral response function as the input, determines the absorption peak and the two shoulder band positions of the non-absorption baseline of the spectral absorption characteristic curve through the correlation coefficient between the equivalent remote sensing reflectance, the first derivative, the second derivative and the total suspended particulate matter concentration, fits the linear relationship between the spectral absorption index and the total suspended particulate matter based on the spectral absorption index, and finally establishes the remote sensing inversion algorithm of the total suspended particulate matter suitable for the stationary orbit water color satellite of the high turbidity water body.

The application discloses a microwave fast radiative transfer calculation method and equipment based on a CDF / EOF atmospheric profile library, and the method comprises the following steps: constructing a CDF / EOF standardized atmospheric profile library; constructing a microwave line-by-line transmittance database, and obtaining channel equivalent transmittance by combining an instrument spectral response function; constructing a prediction factor set based on atmospheric profile parameters in the CDF / EOF standardized atmospheric profile library, and obtaining microwave fast transmittance coefficients through regression fitting; performing microwave radiative transfer calculation based on profile matching results of the CDF / EOF standardized atmospheric profile library and the microwave fast transmittance coefficients, and correcting and verifying the calculation results. The application aims to construct a CDF / EOF standardized atmospheric profile library, fit high-universality fast transmittance coefficients, realize high-precision and fast calculation of microwave radiative transfer, and thus solve the technical problems that existing profile libraries are single in coverage, poor in coefficient universality, and difficult to balance calculation precision and speed.

The application discloses a kind of color image sensor based on two-dimensional semiconductor heterostructure and its application in image convolution processing.The device contains deposited on substrate bipolar two-dimensional semiconductor and N-type two-dimensional semiconductor, which form horizontal heterojunction structure.By adjusting back gate voltage, the energy band arrangement of heterojunction is controlled, and a continuously variable spectral response function is realized, so that multi-band image information can be obtained without color filter.Meanwhile, by source-drain voltage, the photocurrent response of heterojunction is controlled, and then the output signal is given different weights.Several heterojunctions with the same size as the convolution kernel are connected in parallel, and the sum of the currents read is mapped to the gray value, and the convolved image under this waveband can be obtained.By applying convolution operation under different wavebands respectively, image convolution processing can be completed.The device performs well in integrated image acquisition and convolution calculation, effectively reduces the computing resource overhead, and significantly improves the image processing efficiency.

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 application discloses a spectrometer based on light waveguide leakage mode light splitting and a spectrum reconstruction method thereof. The spectrometer comprises an image sensor and at least one light waveguide, the surface of the image sensor is coated with a substrate layer, the first end of the light waveguide is connected with a light source as an entrance, and the second end of the light waveguide is adsorbed on the surface of the substrate layer after being heated and drawn. The spectrum reconstruction method comprises the following steps: calibration: the photocurrent value of the sensor unit at each wavelength is saved as a spectral response function; collecting photocurrent: collecting the photocurrent value of the to-be-detected light in any sensor unit; constructing an equation set and solving: obtaining k actual spectral functions of the to-be-detected light, solving the corresponding photocurrent equation and composing a linear equation set, and solving to obtain the reconstructed actual spectral function of the to-be-detected light. The application has the advantages of small physical volume, multi-channel detection and accurate detection result.

This invention belongs to the field of environmental monitoring technology and discloses a remote sensing inversion method for dissolved inorganic nitrogen and silicate in estuaries based on salinity synergy. The method includes the following steps: S1. During a field survey in the land-sea interaction zone of the estuary, remote sensing reflectance, salinity, and nutrient data are collected simultaneously. The measured hyperspectral reflectance is simulated as the equivalent reflectance of the satellite band using the spectral response function of the target satellite, which is used to construct the training and validation datasets for the model. S2. A nonlinear inversion model of remote sensing reflectance and salinity is established, and a nutrient mixture model of salinity and nutrients is constructed. The training dataset is used for model training. S3. The satellite remote sensing reflectance data of the estuarine area to be predicted is input into the trained nonlinear inversion model and combined with the nutrient mixture model to generate the spatial distribution of DIN concentration and DSi concentration in the estuarine area. This method achieves high-precision remote sensing inversion prediction of DIN concentration and dissolved silicate DSi concentration in the estuarine area.

The invention provides an RGB image spectral super-resolution reconstruction method and system of a fusion point hyperspectrum. The method comprises the following steps: determining an acquisition position of point hyperspectral data from an RGB image to be reconstructed; acquiring point hyperspectral data based on the data acquisition position; determining a spectral response function according to the point hyperspectral data and the RGB image, and calculating a sparse coefficient matrix for reconstruction based on the spectral response function; and carrying out fusion processing on the sparse coefficient matrix and the point hyperspectral data to obtain a high-resolution hyperspectral image. The method is used for solving the problems that in the prior art, the high-resolution hyperspectral image obtaining cost is high, and the map fusion algorithm is high in difficulty.

The invention relates to a GA-BPNN-based spectral calibration method for a prism dispersion type spectral imager, which is mainly used for solving the problems that in spectral calibration, Gaussian fitting is carried out by using a least square method, so that the requirement on data distribution is high, and a local optimal solution is easily caused; and the technical problems of Runge phenomenon, sensitivity to data noise, local inflexibility, boundary error accumulation and the like when polynomial fitting is used for nonlinear interpolation are solved. According to the GA-BPNN-based spectral calibration method for the prism dispersion type spectral imager, Gaussian fitting is carried out through a genetic algorithm GA to obtain a spectral response function of a selected channel, and nonlinear interpolation is carried out through construction of a BP neural network to determine a spectral response function of a whole channel. The method can adaptively find a global optimal solution of Gaussian fitting and nonlinear interpolation, has strong nonlinear mapping capability and fault-tolerant capability, effectively solves the problem of dispersion nonlinearity of the prism, and improves the quantitative measurement capability of the prism dispersion type spectral imager.

The invention provides a spectral radiometer calibration method, system and device based on multiple light sources, and belongs to the technical field of optical measurement. The method comprises the following steps: acquiring spectral response functions of two light sources by using a target spectral radiometer; fusing the spectral response functions of the two light sources based on the coincident wavebands of the spectral response functions of the two light sources to obtain a full spectral response function; performing absolute calibration by using a third standard light source to obtain a third spectral response function; and based on the third spectral response function and the full spectral response function, obtaining a final calibrated full spectral response function. According to the method, relative calibration is completed by using the combination of two light sources for the broadband spectrum radiometer, and the calibrated wave band is successfully broadened. On the basis, the high-stability standard light source is used for absolute calibration of the radiation quantity, the calibration process of the wide-spectrum light source is simplified, and the reliability of the calibration result is guaranteed.