44 results about "Cross Calibration" patented technology

The invention relates to the technical field of optical detection and calibration, in particular to an optical detection and calibration method based on a laser cross calibration method, which comprises the following steps of: 1, suppressing dynamic ambient light and adjusting laser parameters; step 2, extracting anti-distortion feature points; 3, multi-view error collaborative optimization: inputting the corrected coordinates output in the step 2 and mechanical motion parameters of a rotating shaft encoder into a splicing optimization model, constructing a coordinate transformation matrix with curvature constraint, and obtaining a three-dimensional calibration point cloud under a global coordinate system through iterative optimization; and 4, dynamic closed-loop compensation: dynamically correcting the laser power compensation coefficient in the step 1 and the distortion correction parameter in the step 2. Through close cooperation of multiple steps, high-precision, stable and self-adaptive laser calibration is realized, and reliable optical detection and calibration results can be provided in various environments and working conditions.

The invention relates to the technical field of computer vision and remote sensing image processing, in particular to a super-resolution remote sensing image target detection method based on multi-modal fusion, which is realized on the basis of a target detection network obtained by improving a YOLOv5 network. The improvement comprises the following steps: introducing a dynamic cross calibration module and a lightweight super-resolution auxiliary branch behind an input layer; replacing part of common convolution in the backbone network with space-to-depth convolution; the method comprises the following steps: inputting a visible light image and an infrared image into a dynamic cross calibration module, carrying out feature extraction and fusion on the visible light image and the infrared image through the dynamic cross calibration module, and outputting fusion features; inputting the visible light image and the infrared image into a lightweight super-resolution auxiliary branch, and performing super-resolution reconstruction through the branch to generate a high-resolution feature; the problems that an existing remote sensing image target detection method is low in accuracy and prone to loss of detail features when facing small targets, low resolution and complex backgrounds are solved.

The invention provides a wide-area power waveform synchronous monitoring system and a synchronous monitoring method, in the synchronous monitoring system, firstly, a terminal layer node adopts a multi-channel synchronous sampling technology in combination with cross calibration, phase error detection and correction filtering, so that data of different signal sources can be ensured to be collected at the same time; and delay caused by hardware difference and different transmission paths is eliminated, so that the synchronization precision and accuracy of data uploaded to the edge layer node are relatively high, then the edge layer node can maintain the time synchronization precision at a relatively high level by executing a cross-hierarchy clock synchronization mechanism, and finally, synchronous analysis is performed through the master station layer node, so that the synchronization precision of the data uploaded to the edge layer node is improved. And the reliability of a monitoring result is ensured. According to the invention, the terminal layer node, the edge layer node and the master station layer node execute corresponding synchronous monitoring tasks at different levels, the synchronization precision can be improved, and the synchronization error can be reduced, so that the strict requirement of high-precision time synchronization is met, and the reliability of a synchronous monitoring result is improved.

The invention discloses a calibration board and wind tunnel feature point-based binocular system wind tunnel coordinate system calibration method, and belongs to the field of aerodynamic tests. Comprising the following steps: reconstructing surface mark points of a calibration plate by photogrammetry outside a hole and establishing a local coordinate system to obtain coordinates under the local coordinate system; a binocular system is arranged in the hole, and binocular internal and external parameters are calibrated through a special cross calibration ruler; arranging a plurality of circular feature mark points on the surface of the hole wall with overlapped visual fields of the binocular system; the calibration plate is correctly placed according to the geometrical relationship, so that each vector of the local coordinate system is parallel or perpendicular to an incoming vector, and the binocular system reconstructs global points of the calibration plate and registers the global points to achieve alignment of a measurement coordinate system of the binocular system and a wind tunnel coordinate system; reconstructing a three-dimensional coordinate of the fixed point in a wind tunnel coordinate system through a binocular system; and in a subsequent test, adopting resection to realize the recalibration of the wind tunnel coordinate system. According to the invention, high-precision and high-efficiency alignment requirements of a measurement coordinate system and a wind tunnel coordinate system of a binocular system can be met.

The invention relates to the technical field of metabolite monitoring, and discloses a continuous multi-metabolite monitoring method based on multi-modal cross calibration. The method comprises the following steps: acquiring a multi-source metabolite signal sequence containing an electrochemical signal, an optical signal and a thermodynamic signal through an implantable biosensor array; performing time sequence segmentation on the multi-source metabolite signal sequence to generate metabolite concentration time sequence fragments; and synchronously inputting the metabolite concentration time sequence fragment into a metabolite dynamic response model and a metabolite interaction characteristic model, and respectively generating a first metabolite concentration track and a second metabolite concentration track. According to the method, by means of multi-modal signal fusion and cross calibration, in combination with time sequence segmentation and multi-model analysis, dynamic changes and interaction of metabolites can be comprehensively captured, the integrity and accuracy of metabolite monitoring are improved, and the method is suitable for continuous multi-metabolite monitoring scenes.

The invention belongs to the technical field of detection and measurement, and discloses a gas inspection robot multi-source harmful gas monitoring method based on image recognition, and the method comprises the steps: obtaining an inspection area image through a visible light camera and an infrared camera, extracting features, and generating a leakage probability distribution diagram with a coordinate index; combining the probability graph with laser ranging data to form a target area set; self-adaptive sampling tracks of three modes of grid, spiral and jumping are generated in a grading manner according to the confidence degree; synchronously starting infrared, electrochemical and photoacoustic spectrum sensors along the track to collect concentration data, and carrying out zero cross calibration between adjacent sampling points; combining the calibrated multi-source data with environmental parameters, dynamically updating the weight through Kalman filtering, and outputting a fusion concentration result; on the basis, a Gaussian plume diffusion model is adopted to reconstruct a concentration field, the leakage level is judged, sound-light alarm is triggered when the leakage level exceeds the limit, and meanwhile the concentration gradient serves as feedback to correct the path of the next stage.

The invention discloses an SAR radiation cross calibration method based on an urban wide strong scattering reference point target, and the method comprises the steps: analyzing a meta-file of to-be-calibrated SAR data, extracting corresponding system parameter information and imaging region information, selecting multi-scene reference SAR data from calibrated historical SAR data, and extracting an absolute calibration constant; cutting and registering an area which coincides with the imaging area of the reference SAR data and the to-be-calibrated SAR data and is located in an urban area to make an SSDH data set; carrying out one-round screening and two-round screening on the SSDH data set based on a standard strong scattering point model, and extracting a radiation-stable high-value reference point target appearing in a full time domain; rCS data of the reference point target in the to-be-calibrated SAR data and RCS data of the reference point target in the reference SAR data are calculated; and comparing the RCS data of the same reference point target in the to-be-calibrated SAR data with the RCS data of the calibrated reference SAR data, and performing cross calibration to obtain an absolute radiometric calibration constant of the to-be-calibrated SAR data. According to the invention, low-cost and normalized SAR radiometric calibration processing is realized.

The invention relates to the technical field of mass spectrometer radio frequency power supply control, and discloses a mass spectrum radio frequency voltage loop stable control circuit comprising a power supply module used for supplying power to an operational amplifier through a + 12V power supply pin and used for supplying power and establishing a potential reference; the radio frequency power supply acquisition module is used for inputting a signal to an operational amplifier of the hardware PID control module through a resistance potential-divider network and is used for acquiring and monitoring a voltage signal; according to the radio frequency voltage loop stability control circuit for the mass spectrum, through a cross calibration mechanism in which software control and a hardware PID controller are combined and output voltages of software and hardware circuits are mutual references and mutual constraints, the problem of voltage drift instability caused by long-time work of a mass spectrometer radio frequency power supply is avoided. Meanwhile, hardware PID ensures rapid dynamic response to suppress real-time voltage fluctuation, software control provides long-term stability to overcome the influence of element aging and temperature drift, and the synergistic effect of the hardware PID and the software control realizes voltage stability superior to that of a pure hardware or pure software scheme.

The invention aims to provide a full-spectrum on-orbit cross radiation calibration method for a micro-nano hyperspectral satellite sensor. The method comprises the following steps of: a, screening a to-be-calibrated satellite-reference satellite image data pair meeting a cross calibration basic condition; b, Gaussian analysis and parameter extraction of the satellite sensor SRF; c, wave band matching and parameter calculation of the sensor SRF; d, matching reference image simulation of a to-be-calibrated satellite sensor spectrum; e, site BRDF simulation and image BRDF correction are carried out; and f, executing cross calibration of the target sensor. The method is applied to the technical field of remote sensing data processing methods and applications.

The invention discloses a multi-source data fusion processing method for dynamic optimization and adjustment of a public traffic network, and the method comprises the steps: obtaining multi-source heterogeneous original data, executing the standardization processing, and generating a unified time reference and a multi-source standardized flow; the credibility of the data source is estimated online through a reinforcement learning strategy, and event signals after credibility weighting are output; the event signals after credibility weighting are combined with the multi-source standardized flow, and line segment level advanced demand distribution is predicted and generated; aiming at the multi-source standardized flow and the multi-source heterogeneous original data, applying a time-varying nonlinear cross calibration model to obtain real-time congestion degree and short-time station dwell time length prediction; and fusing the line segment-level advanced demand distribution, the real-time congestion degree and the short-time stop duration prediction to generate a fine tuning scheme. According to the invention, the accuracy of demand prediction and stop prediction is improved.

The invention discloses a satellite height measurement data optimal interpolation fusion method based on cross calibration and spectrum analysis, and the method comprises the steps: carrying out the sea surface height anomaly calculation and power spectrum analysis, and calculating the sea surface height anomaly of a satellite altimeter according to the precise orbit determination coordinates of a satellite and the distance measurement value of the altimeter; the related scale of adjacent observation points of the to-be-fused satellite altimeter is obtained through the power spectrum analysis of the sea surface height anomaly of the to-be-fused satellite altimeter; cross calibration: performing cross calibration on sea surface height anomaly of the fusion satellite altimeters by using the sea surface height anomaly of the reference satellite altimeter to obtain system deviation in the to-be-fused satellite altimeters; performing self-crossing calibration by using sea surface height anomaly of the satellite altimeter to be fused to obtain a diagonal element of an observation error covariance matrix; constructing a covariance matrix, wherein a non-diagonal element of the observation error covariance matrix is a product of a standard deviation at the two measurement points and a correlation function of the two measurement points; and fusing the fused satellite altimeter and the background field data by using an optimal interpolation method.

The invention discloses a method for calculating an inter-satellite cross calibration coefficient according to particle phase space density correlation characteristics. The method comprises the following steps: obtaining particle flux sequences of a standard satellite A and a to-be-calibrated satellite B; respectively calculating a corrected third adiabatic invariant and a corrected second adiabatic invariant of the satellite A and the satellite B under a T89c external magnetic field model, and calculating the local magnetic field intensity; performing uniform grid division on the time and the third adiabatic invariant, and performing logarithmic uniform grid division on the first adiabatic invariant and the second adiabatic invariant; solving a local throwing angle and energy in the grid; points with data in the same grid are screened out, and the particle phase space density of the A star and the particle phase space density of the B star are obtained respectively; calculating a particle phase space density mean value in the grid; respectively establishing particle phase space density matrixes of the satellite A and the satellite B; and screening out points with data in the same grid of the particle phase space density matrixes of the two satellites, and calculating to obtain a cross calibration coefficient of the satellite B.

The present invention discloses a method for on-site rapid calibration of a large field-of-view camera applicable to complex working conditions, including: on-site measurement scene arrangement, camera placement and adjustment, left camera acquisition of cross calibration target images, decoding of cross calibration target images, orientation of cross calibration target images, optimization of camera internal parameters, calibration of right camera internal parameters, synchronous acquisition of a frame of measurement scene images by the left and right cameras, feature point detection, feature point matching, solution of initial external parameter values, total station measurement of point distances, correction and optimization of camera external parameters, and export of calibration data. The present invention combines close-range photogrammetry technology, binocular stereo vision technology and total station ranging technology, uses a small-sized cross calibration target and the distance between a pair of feature points that are far apart in the measurement scene obtained by the total station as a flexible calibration target, realizes the calibration of a large field-of-view camera under complex working conditions, and can complete the on-site rapid calibration of the internal and external parameters of a large field-of-view camera with a field of view of dozens of meters to hundreds of meters within a few minutes.

An analog-to-digital converter (ADC) includes: a time-domain ADC core; and a calibration circuit. The time-domain ADC core includes: a first delay-to-digital stage having a terminal; a second delay-to-digital stage having a terminal; a third delay-to-digital stage having a terminal. The calibration circuitry is coupled to the terminal of the first delay-to-digital stage, the terminal of the second delay-to-digital stage, and the terminal of the third delay-to-digital stage of stages. The calibration circuitry is configured to calibrate the first delay-to-digital stage, the second delay-to-digital stage, and the third delay-to-digital stage based on a zero-crossing calibration and an over-range calibration. The over-range calibration sets a maximum threshold and a minimum threshold for the time-domain ADC relative to a reference voltage.

The application discloses a flight confidence calculation method and system based on multidimensional sensing data, and the method comprises the following steps: data acquisition, multidimensional feature extraction, establishment of safety value mapping, multidimensional confidence fusion calculation and decision feedback. The application belongs to the technical field of data processing, and specifically refers to a flight confidence calculation method and system based on multidimensional sensing data. According to the scheme, dynamic resource allocation is adopted to guarantee the quality of core data acquisition, dynamic Kalman filtering is adopted to reduce noise, high-value historical data is screened, and the double mechanisms of multi-sensor cross calibration and Bayesian inference verification are adopted to output an optimal spatial parameter estimation set and a corresponding confidence interval, so that the accuracy of confidence evaluation is improved. The safety value target is defined, the cause-and-effect chain is constructed, the cause-and-effect contribution degree of features to the safety value is quantified, the direct correlation between the features and the safety value is established, the multidimensional confidence is dynamically fused to generate the final flight confidence, and the problem that the confidence is high but the decision is unsafe is avoided.

This application discloses an adaptive oscillation method, system, and storage medium for blood products. The method includes: S1: acquiring and reconstructing multidimensional physical features of the blood bag surface to obtain three-dimensional morphological data and heat distribution data of the blood bag surface; S2: based on the three-dimensional morphological data and heat distribution data, using a fluid dynamics proxy model to inversely deduce the fluid shear stress and comprehensive physical suspension feature vector of the blood inside the blood bag, and performing cross-calibration on both, continuously outputting a mixed completion confidence score; S3: adjusting the motion parameters of the oscillation tray and clamping mechanism in real time according to the mixed completion confidence score, and stopping oscillation when the mixed completion confidence score continuously exceeds a preset threshold. This application achieves real-time, non-invasive sensing of the blood state inside a sealed blood bag, ensuring uniform suspension of red blood cells while avoiding mechanical hemolysis, significantly improving the safety, adaptability, and processing efficiency of blood product oscillation processing.

The application discloses a kind of large-scale constellation-oriented autonomous calibration task cooperation planning method and device.The method includes S1, obtains calibration resource information, satellite information and constellation resource information;The calibration resource information includes star calibration field, cold air calibration field, moon reference source, ground comprehensive field, ground uniform field and cross calibration field;The satellite information includes satellite state information and satellite task type information;The satellite state information includes the on-orbit state information of N satellites, N is positive integer;The on-orbit state information includes fast evaluation phase information, on-orbit test phase information or operation guarantee phase information;S2, based on the calibration resource information, the satellite information and the constellation resource information, determine task allocation information;S3, based on the task allocation information, determine the execution task sequence.It can be seen that the present application is beneficial to improve the on-orbit calibration efficiency under the current cluster giant optical constellation networking condition.

A method of automatically calibrating a toxic gas detector 100 wherein the same calibration gas is used to calibrate one or more gas sensors 110, 112, 114 which detect the presence of a different toxi

To cross-calibrate two cameras (4, 5) of a vehicle (1) mounted on the vehicle (1) with different poses and overlapping fields of view (6, 7), first image features are generated by applying a first branch (13a) of an encoder module (13) of a trained MLM (12) to first input data dependent on a first camera image (10) from the first camera (4) of the two cameras (4, 5). Second image features are generated by applying a second branch (13b) of the encoder module (13) to second input data dependent on a second camera image (11) from the second camera (5) of the two cameras (4, 5). A relative pose (8) between the first camera (4) and the second camera (5) is calculated by applying a pose decoder module (17) of the MLM (12) to the first and second image features.

This invention relates to the field of metabolite monitoring technology and discloses a multimodal cross-calibrated method for continuous multi-metabolite monitoring. The method acquires multi-source metabolite signal sequences containing electrochemical, optical, and thermodynamic signals through an implanted biosensor array; it performs time-series segmentation of the multi-source metabolite signal sequences to generate metabolite concentration time-series segments; and it synchronously inputs these metabolite concentration time-series segments into a metabolite dynamic response model and a metabolite interaction feature model to generate a first metabolite concentration trajectory and a second metabolite concentration trajectory, respectively. This method, by leveraging multimodal signal fusion and cross-calibration, combined with time-series segmentation and multi-model analysis, can comprehensively capture the dynamic changes and interactions of metabolites, improving the completeness and accuracy of metabolite monitoring, and is suitable for continuous multi-metabolite monitoring scenarios.

The present application relates to the technical field of optical detection calibration, and more particularly to an optical detection calibration method based on laser cross calibration, comprising: step 1: dynamic ambient light suppression and laser parameter adjustment; step 2: anti-distortion feature point extraction; step 3: multi-view error collaborative optimization: input the corrected coordinates output by step 2 and the mechanical motion parameters of the rotary shaft encoder into a splicing optimization model, construct a coordinate transformation matrix with curvature constraint, and obtain a three-dimensional calibration point cloud in a global coordinate system through iterative optimization; step 4: dynamic closed-loop compensation: dynamically correct the laser power compensation coefficient of step 1 and the distortion correction parameter of step 2. Through the close cooperation of multiple steps, high-precision, stable and adaptive laser calibration is achieved, which can provide reliable optical detection and calibration results in various environments and working conditions.

The present application relates to a kind of quantitative methods of spectral imager, specifically, a kind of high spectral remote sensor cross calibration method based on spectral matching factor, existing high spectral remote sensor only relies on single spectral channel in cross calibration process, leading to the technical problem of poor cross calibration precision.This method includes the following steps:1】Obtain spectral channel characteristics;2】Calculate simulated spectral radiance;3】Calculate spectral matching factor;4】Obtain cross calibration test area;5】Calculate the real spectral radiance of each spectral channel of reference remote sensor B;6】Obtain weight coefficient;7】Obtain final spectral matching factor;8】Calculate the real spectral radiance of each spectral channel of to-be-calibrated remote sensor A;9】Calculate the radiation calibration coefficient of each spectral channel of to-be-calibrated remote sensor A, complete the cross calibration of high spectral remote sensor of spectral matching factor.This method reduces error, improves data accuracy and reliability.

The invention discloses a flight confidence calculation method and system based on multi-dimensional sensing data. The method comprises the steps of data collection, multi-dimensional feature extraction, safety value mapping establishment, multi-dimensional confidence fusion calculation and decision feedback. The invention belongs to the technical field of data processing, and particularly relates to a flight confidence coefficient calculation method and system based on multi-dimensional sensing data. According to the scheme, the core data collection quality is guaranteed through dynamic resource allocation, noise is reduced through dynamic Kalman filtering, and high-value historical data is screened; an optimal spatial parameter estimation set and a corresponding confidence interval are output through a dual mechanism of multi-sensor cross calibration and Bayesian reasoning verification, so that the accuracy of confidence evaluation is improved; a final flight confidence coefficient is generated by defining a safety value target, constructing a causal chain and quantifying the causal contribution degree of features to the safety value, establishing direct association between the features and the safety value and dynamically fusing multi-dimensional confidence coefficients, and the problem that the confidence coefficient is high but the decision is not safe is avoided.

The invention discloses an SAR radiation cross calibration method, device and equipment based on building area feature weight distribution and a storage medium, and relates to the technical field of remote sensing, and the method comprises the steps: carrying out the mask extraction of a target reference SAR image and a target to-be-calibrated SAR image based on a land coverage type product, and obtaining a target to-be-calibrated SAR image; obtaining a target reference building area pixel and a target to-be-calibrated building area pixel; performing data clustering on the target reference building area pixel and the target to-be-calibrated building area pixel through a target clustering algorithm to obtain a reference area set and a to-be-calibrated area set; performing feature calculation based on weight distribution on the reference region set and the to-be-calibrated region set to obtain reference stable features and to-be-calibrated stable features; and performing radiation correction on the target to-be-calibrated SAR image based on the reference stability feature and the to-be-calibrated stability feature to obtain a target calibrated SAR image. Radiation correction is carried out on the SAR image through a target clustering algorithm and weighted feature calculation, and the accuracy of radiation correction is improved.

The invention provides a remote sensing giant satellite group absolute radiation reference unification method, equipment and a storage medium, which are used for establishing a ground absolute radiation reference and comprises the following steps: acquiring the surface reflectance and atmospheric parameters of a ground radiation calibration field through automatic observation equipment; constructing an on-satellite absolute radiation reference, namely selecting a plurality of satellites from a satellite group as calibration satellites based on satellite radiation stability, orbit distribution and redundant backup requirements, and carrying on-satellite calibration devices by the calibration satellites to obtain a traceable on-satellite reference; satellite-ground reference unification: respectively carrying out site calibration and on-satellite calibration on a calibration satellite, and carrying out weighted fusion on two calibration results based on calibration uncertainty to obtain an optimal absolute radiation coefficient; and inter-satellite reference transmission: transmitting the optimal absolute radiation coefficient to other satellites of the satellite group through one-to-many mode cross calibration, thereby realizing radiation reference unification of the whole satellite group. According to the invention, high-precision, high-stability and automatic absolute radiometric calibration of the remote sensing giant satellite group is realized.

The present invention discloses a synthetic aperture radar radiation cross-calibration method based on weighted least squares, including: obtaining a calibrated SAR image and a SAR image to be calibrated, extracting the backscattering coefficient of the calibrated SAR in a uniform area, and the amplitude value of the SAR to be calibrated in a uniform area, as a cross-calibration data pair; obtaining the historical cross-calibration data pair of the calibrated SAR and the SAR to be calibrated; calculating the overall uncertainty of the historical cross-calibration data pair according to the calibration error influencing factors; calculating the weight coefficient of the cross-calibration data pair according to the overall uncertainty; constructing a calibration coefficient model based on a weighted least squares model, and substituting the cross-calibration data pair and the weight coefficient into the calibration coefficient model to calculate and obtain the calibration coefficient. The present invention adopts an uncertainty analysis method to calculate the corresponding uncertainty of the data pair and assign a weight coefficient, and then obtains the calibration coefficient of the satellite to be calibrated by a weighted least squares fitting method, which can further improve the accuracy of cross-calibration.

The application discloses a kind of multi-source data fusion processing methods for public transport network dynamic optimization adjustment, comprising: obtaining multi-source heterogeneous original data, performing standardization processing, generating uniform time reference and multi-source standardized flow;Through reinforcement learning strategy, the data source reliability is estimated on-line, and the event signal after reliability weighting is output;The event signal after reliability weighting is combined with multi-source standardized flow, and line segment level advance demand distribution is predicted and generated;For multi-source standardized flow and multi-source heterogeneous original data, time-varying nonlinear cross calibration model is applied, and real-time congestion degree and short-time stop duration prediction are obtained;Fusion line segment level advance demand distribution, real-time congestion degree and short-time stop duration prediction, generate fine-tuning scheme.The application improves the accuracy of demand prediction and stop prediction.

The invention provides a cross calibration method for the effective wave height of a wave buoy, and relates to the technical field of marine environment monitoring. According to the cross calibration method for the significant wave height of the wave buoy, a multi-source reference data system of a shore-based radar, satellite remote sensing and a networking buoy is constructed, and standardized preprocessing is firstly carried out on different-source data; then dynamically distributing each reference data weight based on the sea condition complexity to avoid calibration deviation caused by a fixed weight; then, a hierarchical strategy of coarse calibration and fine calibration is adopted, and a calibration result is optimized in combination with an adaptive least square algorithm; and finally verifying the calibration effectiveness through a root-mean-square error and a correlation coefficient, and realizing 24-hour dynamic updating. According to the invention, the problems of low calibration precision and limited application scene under complex sea conditions in the prior art are solved, the effective wave height calibration error can be controlled within 0.15 m, the method is suitable for wave buoy calibration of different sea areas such as coastal and open sea, and the reliability of ocean wave monitoring data is improved.

The invention relates to a spectral imager quantification method, in particular to a hyperspectral imager visible near-infrared full-band cross calibration method. The technical problems that in an existing hyperspectral imager cross calibration method, a multi-spectral imager is adopted as a reference sensor, a spectral channel is limited, and the calibration precision is low due to spectral response differences are solved. The method comprises the following steps: S1, determining the number of channels for performing cross calibration on a hyperspectral imager A by a multispectral imager B; s2, obtaining a cross calibration coefficient of the hyperspectral imager A corresponding to a waveband range in which the spectral response overlapping ratio beta is greater than or equal to 90%; s3, obtaining a cross calibration coefficient of the hyperspectral imager A corresponding to a wave band range of which the spectral response overlapping ratio beta is less than 90%; and S4, all cross calibration coefficients of the hyperspectral imager A are obtained, and visible near-infrared full-band cross calibration of the hyperspectral imager A is completed.

The application relates to the technical field of computer vision and remote sensing image processing, in particular to a super-resolution remote sensing image target detection method based on multi-modal fusion, which is realized based on a target detection network improved from a YOLOv5 network, and the improvement comprises the following steps: introducing a dynamic cross-calibration module and a light-weight super-resolution auxiliary branch after an input layer; replacing part of ordinary convolution in a backbone network with space-to-depth convolution; the method comprises the following steps: inputting a visible light image and an infrared image into the dynamic cross-calibration module, performing feature extraction and fusion on the visible light image and the infrared image through the module, and outputting fused features; inputting the visible light image and the infrared image into the light-weight super-resolution auxiliary branch, performing super-resolution reconstruction through the branch, and generating high-resolution features; the method solves the problems that an existing remote sensing image target detection method has low accuracy and is prone to losing detailed features when facing small targets, low resolution and complex backgrounds.