9 results about "Spectral response function" patented technology

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.

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 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 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.

This invention discloses an on-orbit method and system for determining the spectral response function of a hyperspectral remote sensor, based on the solar reflection band. The method includes: step S1, acquiring multiple sets of input light sources to obtain a system of multiple linear regression equations; and step S2, calculating the system of multiple linear regression equations using the least squares method to obtain the pixel-by-pixel spectral response function of the remote sensor. This on-orbit method for determining the spectral response function of a hyperspectral remote sensor solves the problem of whether or not on-orbit observation of the spectral response function of a hyperspectral remote sensor is possible. It also shortens the observation cycle from months or even years to minutes, thereby improving the accuracy of satellite-based monitoring of global greenhouse gases, environmental pollutants, aerosols, and other atmospheric components, contributing to the high-quality development of national undertakings such as climate research, environmental protection, and low-carbon emission reduction.

The application discloses a kind of spectral knowledge embedding and physical constraint remote sensing image generation method, belong to remote sensing image generation field.This method designs double random mask condition diffusion training strategy, simulates spectral response function and randomly discards effective band, and dynamically constructs incomplete condition input;Design physical guiding sampling mechanism, input preliminary reconstruction image into differentiable physical forward model, calculate the difference loss with preset physical target, and move the sampling trajectory to the solution space that satisfies remote sensing physical constraint in reverse direction;Build multiscale physical consistency joint loss function, pixel level forces each band spectral correlation to comply with priori, regional level requires that the generated image and the real image ground object index distribution remain consistent, and image level is constrained by simplified differentiable radiative transfer model The overall radiation consistency.This application solves the technical problems of spectral distortion and insufficient physical credibility of existing generation model, and the generation result meets the accuracy requirements of quantitative remote sensing analysis and ground object classification.