1090 results about "Hyperspectral imaging" patented technology

The invention provides a semiconductor chip defect automatic detection method and system based on hyperspectral imaging, and relates to the technical field of chip detection, and the method comprises the steps: collecting a chip hyperspectral image, carrying out the image enhancement and illumination non-uniformity correction through an improved neuromorphic adaptive processing network and a dual-path diffusion model, and carrying out the detection of the hyperspectral image. Extracting multi-level features by using a dynamic mixed feature enhancement network based on neural architecture search, fusing the features through a cross-layer attention mechanism, generating an initial defect detection result by using a multi-branch expert decision-making module and an uncertainty dynamic integration strategy, constructing a space-time knowledge inference graph, and determining a defect detection result; and performing reliability evaluation and optimization on the initial defect detection result in combination with the form similarity, the spatial position relationship and the process parameter influence model to obtain a final defect detection result.

Disclosed in the present invention is a method for whole-time space-air-ground integrated hyperspectral stereoscopic remote sensing, tracing and prediction of greenhouse / pollution gas, comprising: performing remote sensing of multi-source heterogeneous data: utilizing a hyperspectral stereoscopic remote sensing device, a hyperspectral imaging device, a hyperspectral unmanned aerial vehicle remote sensing device, a hyperspectral greenhouse gas remote sensing device based on grating light splitting and a night hyperspectral stereoscopic remote sensing device to perform the remote sensing of the multi-source heterogeneous greenhouse / pollution gas data; performing tracing and early warning of greenhouse / pollution gas components: progressively realizing tracing of greenhouse / pollution gas components at different locations based on the multi-source heterogeneous greenhouse / pollution gas data, and performing emission early warning according to a traced result; and performing stereo fusion and prediction of the multi-source heterogeneous data: performing data feature fusion based on the multi-source heterogeneous greenhouse / pollution gas data, and performing prediction of the greenhouse / pollution gas components at a future moment based on a fusion result. The system can complement the deficiencies of existing greenhouse / pollution gas monitoring and tracing technologies.

The invention discloses an automatic fine ore sorting method and system based on XRT (X ray tomography) and hyperspectrum, which are used for realizing comprehensive fusion of ore surface and internal information by combining X-ray tomography and hyperspectral imaging technologies so as to improve the recognition precision and the sorting efficiency. XRT is used for extracting the internal structure and density characteristics of the ore, hyperspectral imaging is used for identifying the mineral composition characteristics of the ore surface, and information complementation is achieved. The system adopts a multi-modal convolutional neural network to perform independent feature extraction on XRT and hyperspectral data, and the extracted features are fused through a deep residual network. And the fused features are processed by a cross-modal attention mechanism to ensure that key features of different modals are fully integrated and optimized, and finally high-precision ore classification and sorting are realized. According to the method, the limitation of a single data source on feature extraction is overcome, the ore recognition and sorting precision is greatly improved, and the refined and intelligent development of ore sorting is promoted.

The invention discloses a method and a system for rapidly screening peanut endophytic mildew, particularly relates to the technical field of nondestructive testing of agricultural products, and is used for solving the problems of false detection and missing detection caused by low contrast and gradient boundary in an initial mildew stage and an internal diffusion sample in an existing detection method. Based on a hyperspectral imaging technology, a filtering kernel size is dynamically adjusted through conjoint analysis of frequency domain energy distribution and spatial local variance, and detail features of a gradient boundary in an image are enhanced; verifying and screening high-confidence candidate regions by adopting spectrum and spatial feature consistency, analyzing and extracting principal component features representing a mildew diffusion trend in combination with a texture direction, and dynamically splicing the principal component features with a spectrum gradient direction to construct a fusion feature vector; quantizing boundary confidence through a Bayesian probability classification model, combining with a neighborhood similarity constraint region growing algorithm to generate a mildewed mark graph with continuous space and consistent features, and introducing a dynamic threshold correction mechanism to adaptively optimize a segmentation standard; and the detection precision and the anti-interference capability of the fuzzy boundary are improved.

The invention discloses a wheat single grain appearance anomaly detection method based on a deep learning model and hyperspectral imaging. The method comprises the following steps: step 1, collecting different types of wheat grain samples; step 2, acquiring hyperspectral image data of the wheat grains by using a visible light-near infrared and short wave near infrared hyperspectral imaging system, and extracting spectral information and image information; step 3, preprocessing the spectral data, and verifying a preprocessing effect; 4, screening spectral characteristic wave bands, extracting texture characteristics and morphological characteristics in combination with a gray-level co-occurrence matrix, and constructing a middle-level data fusion model; step 5, constructing an atlas feature fusion deep learning model to realize high-level fusion of spectrum and image features; and step 6, pixel-level classification is carried out on the hyperspectral image, and a spatial distribution visualization result of the appearance abnormity of the wheat grains is generated. The method has high precision, nondestructive testing and strong generalization ability, and is suitable for wheat quality grading, processing and sorting and storage safety management.

The invention relates to the field of landfill methane emission remote sensing monitoring, and discloses a landfill methane emission flux measuring and calculating method and device and a medium, and the method comprises the following steps: based on regional satellite TROPOMI observation data, carrying out qualitative analysis on annual average methane emission signals of a landfill, and determining a target landfill according to a qualitative analysis result; based on data of a satellite-borne hyperspectral imager, the methane emission flux of the target landfill is quantitatively measured and calculated, and the position of a methane emission hot spot area in the target landfill is determined; and positioning the position of an emission source in the target landfill through an active methane laser detection lens carried by an unmanned aerial vehicle platform, and independently calculating the methane emission flux of the target landfill by adopting an upper and lower section method. Based on the method, the annual average methane emission condition of the landfill can be rapidly and accurately evaluated, the methane emission flux of the whole landfill is obtained, and the internal emission hot spot area of the landfill is positioned.

The invention relates to the technical field of ecological environment monitoring, and particularly discloses a system and a method for monitoring distribution of plant communities along an expressway. An unmanned aerial vehicle carries a hyperspectral imager and a multi-parameter sensor to synchronously obtain vegetation spectral data and environmental parameters of soil salinity, water content, pH value, temperature and humidity; constructing a multi-dimensional data set with space-time matching; an improved end member extraction algorithm is combined with a soil salinity threshold triggering mechanism to realize self-adaptive updating of a dynamic end member library; an environmental constraint adversarial generative network is introduced, virtual end members conforming to ecological characteristics are generated and verified, and the coverage capability of an end member library is improved; performing physiological and ecological dual verification on the end members based on the knowledge graph to form an optimized end member set; and an environmental adaptability weight is introduced in the unmixing process, confidence evaluation and fuzzy logic analysis are combined, a graded credibility vegetation map is output, and accurate identification and spatial positioning of invasive plants and local vegetation are realized.

The invention relates to the technical field of bridge monitoring, in particular to a bridge crack real-time monitoring method based on edge calculation, which comprises the following steps of: acquiring bridge crack data by using a hyperspectral imaging camera, a thermal imaging device and a laser radar, adaptively adjusting edge calculation nodes by using an edge learning technology, and calculating the edge calculation nodes according to the edge learning technology. A deep learning model is used for crack analysis, an anomaly monitoring model is established, an augmented reality technology is introduced to assist field investigation, and through comprehensive application of technologies such as multi-source data acquisition, edge calculation, deep learning and augmented reality, the problems that a traditional bridge crack monitoring method is low in efficiency, low in precision, incapable of real-time monitoring and the like are solved. The system has the advantages of comprehensive data acquisition, real-time and efficient processing, strong adaptive ability, comprehensive abnormity monitoring, high system reliability and visual and efficient field investigation.

The invention discloses a pork quality grade lossless evaluation method based on hyperspectral imaging, and relates to the technical field of pork detection.The pork quality grade lossless evaluation method comprises the following steps that pork samples of different qualities are prepared, and standardization processing is conducted on the pork samples; performing chromatic aberration analysis on the pork samples with different qualities by using a colorimeter, and performing quality grade division on the pork samples with different qualities based on the analysis results of the muscle color, the fat color and the gloss of the pork. According to the pork quality grade lossless evaluation method based on hyperspectral imaging, reflection or transmission information of a pork sample in a plurality of continuous spectral wavebands is captured by using a hyperspectral imaging technology, accurate evaluation of the pork quality grade is realized by analyzing spectral data, higher-dimension data is provided, and the evaluation accuracy of the pork quality grade is improved. Meanwhile, rapid scanning can be achieved, the detection time is greatly shortened, the overall detection efficiency is improved, and pork quality monitoring is more efficient and timely.

The invention discloses a seed quality evaluation system and device based on multi-modal data fusion and a control method, and relates to the technical field of agricultural engineering. The system comprises a data acquisition module, a data processing module, a quality evaluation module and a control module. The data acquisition module acquires multi-modal data; the data processing module performs preprocessing, feature extraction and fusion on the multi-modal data to generate a comprehensive feature vector; the quality evaluation module performs seed quality evaluation based on the comprehensive feature vector by using a machine learning model; the control module controls all the modules to operate and outputs results. The device comprises a closed box body, a temperature and humidity control unit, an electronic scale, a hyperspectral imaging unit, a visible light imaging unit, a mechanical arm and a main control unit. The method realizes rapid, accurate, lossless and automatic evaluation of seed quality, overcomes the defects of low efficiency, strong subjectivity, easy damage of seeds and the like in a traditional method, and improves the accuracy and efficiency of seed quality evaluation.

The invention relates to a multi-modal data-based printed cloth surface defect intelligent identification method, which comprises the following steps of: S1, acquiring a global image of a printed cloth surface by using a high-resolution RGB (Red, Green and Blue) camera, and preprocessing; s2, identifying potential defect candidate areas based on an image identification model, and reducing overlapped defect candidate areas in combination with non-maximum suppression; s3, obtaining an image of the defect candidate area through near-infrared camera equipment, and obtaining a hyperspectral image through hyperspectral imaging; s4, extracting features from the near-infrared image and the hyperspectral image of the defect candidate area, and performing feature fusion; s5, according to the fused features, based on a multi-modal detection model, performing fine-grained defect identification and positioning on the defect candidate area; and S6, through a post-processing step, false positive is removed, adjacent areas are combined, the defect detection precision is improved, and finally a detection report is generated. According to the method, the defects of the printed cloth surface can be effectively identified and evaluated, and high-precision and high-efficiency detection is realized.

The invention discloses a textile fabric polyester fiber detection method based on spectral imaging and curve fitting, and relates to the technical field of hyperspectral imaging analysis, and the method comprises the steps: carrying out the characteristic decomposition and principal component analysis of a reflectivity function after baseline correction, and obtaining a characteristic curve; extracting feature information of the hyperspectral image, and fusing the feature information of the hyperspectral image with the feature curve to generate a multi-dimensional feature vector; and constructing a hyperspectral fiber content detection model, inputting the multi-dimensional feature vector and the feature parameter vector into the hyperspectral fiber content detection model, and generating the polyester fiber content. Not only are the problems of background noise and drift interference solved, but also the multi-dimensional characteristics of the hyperspectral data are fully mined. The content of the polyester fibers in the waste textile fabrics can be efficiently detected on line. Finally, according to the method, the detection precision and reliability are remarkably improved, and an efficient, environment-friendly and accurate technical means is provided for resource utilization of the waste textile fabrics.

The invention relates to the technical field of intelligent agricultural equipment, in particular to a tomato disease diagnosis method based on multi-modal data analysis, which comprises the following steps of: 1, synchronously acquiring and preprocessing multi-modal data, synchronously triggering a hyperspectral imaging device and a microscopic camera, respectively acquiring a plant canopy hyperspectral image and a stem microscopic image, and acquiring a plant canopy hyperspectral image and a stem microscopic image; meanwhile, temperature, conductivity and dissolved oxygen environment parameters are continuously collected in the root zone; 2, self-adaptive feature extraction and fusion in the growth stage are carried out, reflectivity correction and leaf segmentation are carried out on the hyperspectral image, and leaf surface spectrum curve features are extracted; step 3, hybrid model construction and space-time analysis: constructing a hybrid model comprising spectrum, microscopy and environment analysis networks, and dynamically adjusting each network weight through a gating network; and 4, generating a disease decision. The method can realize accurate, efficient and real-time tomato disease diagnosis, has high practical value, and can effectively improve the disease prevention and control capability in agricultural production.

The invention relates to the technical field of fruit detection, in particular to a blueberry acidity detection method based on hyperspectral imaging, which comprises the following steps: randomly selecting blueberry samples to be detected, and constructing a reference sample database; acquiring full-band hyperspectral image data of the to-be-detected blueberry sample by using hyperspectral imaging equipment; performing spectrum preprocessing on the hyperspectral image data; eliminating redundant information of redundant wave bands by combining a continuous projection algorithm to obtain a final characteristic wave band set, and extracting a spectral intensity matrix corresponding to the final characteristic wave band set from the hyperspectral image data based on the final characteristic wave band set; constructing a blueberry acidity detection model; and outputting the acidity predicted value of the blueberries. According to the method, the data dimension is greatly reduced, the sensitivity and modeling efficiency of the characteristic wave band are improved, and compared with a traditional destructive detection method, the method has the advantages of being high in speed, free of damage and suitable for large-scale sample detection.

The embodiment of the invention discloses a novel fish acute septicemia detection system based on hyperspectral imaging. The system comprises a data acquisition module, a model detection module and a user interaction module, wherein the data acquisition module acquires hyperspectral image data on the surface of a target fish body; the model detection module carries out diagnosis by taking the hyperspectral image data as input based on a trained deep learning diagnosis model, and the deep learning diagnosis model obtains a pixel space structure and a pixel spectrum sequence of the fish body surface based on the hyperspectral image data; identifying spatial features representing related clinical symptoms based on a pixel spatial structure, and identifying spectral features representing an inositol metabolism condition based on a pixel spectral sequence; and the user interaction module outputs a diagnosis result of the target fish body based on a result output by the model. The characteristic that the aeromonas hydrophila utilizes inositol as a unique carbon source and the map change of typical infection symptoms on the surface of a fish body are combined, and accurate and rapid detection is achieved through hyperspectral imaging and a deep learning algorithm.

The invention relates to the technical field of intelligent forestry management, and particularly discloses a forest farm intelligent comprehensive management and control system, which comprises a space-air-ground collaborative sensing module, a fire danger dynamic modeling module and an intelligent decision execution module. The method comprises the following steps: acquiring temperature field data through multispectral satellite remote sensing, constructing a high-precision terrain model through a laser radar, extracting vegetation features through hyperspectral imaging, extracting a thermal anomaly structure by adopting topological continuous coherence analysis and a Morse-Smal complex method, and establishing an adaptive fire danger model in combination with a quantum annealing optimizer. The system can generate a fire danger thermodynamic diagram in real time and divide risk levels, and drives the unmanned aerial vehicle to perform priority patrol and obstacle avoidance path planning. The problems of difficulty in pseudo hot area identification, low fire danger prediction precision, non-intelligent path planning and the like in a complex environment are solved, and the intelligent level of forest farm fire prevention and control is improved.

The present invention relates to a solution for assessing alterations in biological tissue in a non-invasive manner based on computer-implemented analysis of hyper and / or multispectral images of the biological tissue of interest. Alterations can be related to stresses, aging, pests, diseases, changes related to concentration of an active ingredient, and / or resistances against active ingredient, or the like as well as combination thereof. In a particular embodiment such alterations can be assigned to an alteration type and / or the degree of alteration can be quantified over time.

The invention discloses a carbon fiber composite material surface modification spraying system and a control method thereof. The system comprises a multi-axis robot, a plasma spray gun, a contact angle measuring probe, a 3D line laser scanner, a hyperspectral imager, an environment sensor and a controller. According to the method, the plasma power and the robot speed are adjusted in real time through contact angle measurement and plasma treatment feedback control, so that the CFRP surface can accurately reach a target value, and the coating adhesive force is improved; 3D line laser scanning and hyperspectral imaging are combined, and the posture, the distance, the wet film thickness and the component uniformity of the spray gun are monitored in real time; based on a prediction model fusing a physical model and a neural network, a self-adaptive fuzzy PID control algorithm is adopted, and the coating flow and the track posture of a spray gun are accurately regulated and controlled in real time. The problems of weak coating binding force, uneven thickness, orange peel, sagging and serious coating waste in traditional spraying are effectively solved, and self-adaptive, high-quality and green spraying of workpieces with complex curved surfaces is achieved.

The invention relates to an indoor lighting rate simulation system for indoor design. The system comprises a dynamic meteorological simulation module which generates a dynamically changing sky model based on real-time meteorological data; the building attribute analysis module analyzes building optical attribute parameters by using the building information model data and the hyperspectral imaging data; the light ray tracing processing module performs light ray tracing based on the dynamic sky model and the building optical attribute parameters to obtain indoor glare distribution data; a reverse parameter optimization module generates window form parameters and sunshade device layout parameters according to the target illumination interval and the indoor glare distribution data; and the real-time visual verification module carries out visual rendering on the window form parameters and the sunshade device layout parameters to obtain an updated lighting rate simulation result for verifying the lighting effect of the design scheme. According to the system, through dynamic meteorological fusion, non-uniform material modeling and real-time optimization feedback, the precision and adaptability of indoor lighting simulation can be improved.

The invention discloses a snapshot type hyperspectral imaging industrial online sorting system which comprises a support assembly, an optical imaging assembly and a motion control assembly. The bracket assembly consists of a bottom plate, and an adjustable bracket and a fixed bracket which are fixed on the bottom plate; the optical imaging assembly comprises a lighting unit, an imaging objective lens and a snapshot type hyperspectral camera, and can synchronously obtain space image information of a measured object and spectral information of a continuous wave band in a single exposure process; the motion control assembly realizes automatic transmission of the measured object through the transmission platform; the data processing module is integrated with an image preprocessing unit, a spectral analysis unit and a spectral database, and is used for end-to-end closed-loop control from original hyperspectral data generation to sorting decision making; the system adopts a metasurface light splitting structure to realize real-time decoding, performs material or category identification by using a pre-trained classification model, and finally automatically generates a sorting decision signal according to an identification result to drive a motion control assembly to complete sorting operation.

The invention discloses a marine plankton hyperspectral imaging detection system, relates to the technical field of spectrum detection, and is used for solving the problem of poor spectrum classification and recognition in a water body environment. According to the method, the hyperspectral image and the environmental disturbance parameters are synchronously acquired, and the disturbance mapping sequence is constructed, so that accurate alignment between the image and disturbance is realized. A disturbance contribution weight matrix is constructed based on a dominant wave band, pixel-level spectrum stripping is carried out, a plankton purification spectrum is extracted, the spectrum purity and the recognition accuracy are improved, and a spectrum abnormal drift region is precisely recognized in combination with derivative spectrum change and image texture features; a coupling relation between the drift region and background disturbance is further established, a dynamic interference factor map is generated, and identification model parameters are dynamically adjusted, so that the identification stability and classification accuracy of the system in a complex interference environment are improved, and the method is suitable for multi-scene marine ecological monitoring.

The invention provides a method for preparing a single-material VMPE recoverable hose through dynamic temperature control multi-layer co-extrusion. The method comprises the steps that infrared temperature measurement modules are installed on all sections of an extruder respectively to construct a dynamic temperature control system; feeding the pretreated raw materials into a corresponding extruder, and adjusting a heating device and a cooling device of a charging barrel of the extruder in real time according to a preset extrusion temperature allowable range through a dynamic temperature control system to obtain a plasticized melt; carrying out bonding treatment on the plasticized melt by utilizing an ultrasonic vibration composite technology to obtain a multi-layer reinforced melt; detecting the thickness of the pipe wall of the multi-layer reinforced melt through a hyperspectral imager and judging whether the thickness of the pipe wall is qualified or not; if not, pipe wall thickness deviation data are fed back to a dynamic temperature control system and an extruder control system to conduct linkage adjustment on extrusion parameters; and if the multi-layer reinforced melt is qualified, cooling shaping and fixed-length cutting treatment are conducted on the multi-layer reinforced melt, the finished recoverable hose is obtained, and the hose forming precision and the recovery performance are effectively improved.

Hyperspectral imaging apparatus and methods in which flat optic metasurfaces encode wavelengths of light using their spatial geometry to produce a desired transmission response. The metasurfaces are inversely designed using machine learning techniques to retain in their transmission response information that is sufficient to facilitate substantial reconstruction of the original hyperspectral scene for given applications.

The invention discloses a fruit and vegetable sugar degree detection model construction and sugar degree detection method based on hyperspectral imaging. The method comprises the following steps: acquiring a correction sample set of target fruits and vegetables; acquiring near infrared spectrum data and first sugar degree actual measurement data of each correction sample in the correction sample set; according to the near infrared spectrum data and the first sugar degree actual measurement data of all the correction samples, modeling by adopting a chemometrics algorithm to obtain a sugar degree detection model; constructing a first relation curve between the cross validation root-mean-square error of the sugar degree detection model and the number of characteristic wavelengths and a second relation curve between the cross validation root-mean-square error and the number of latent variables by adopting a cross validation method; respectively determining the number of target characteristic wavelengths and the number of target latent variables according to the first relation curve and the second relation curve; and obtaining a fruit and vegetable sugar degree detection model according to the number of the target characteristic wavelengths and the number of the target latent variables. And the sugar degree can be accurately and nondestructively detected.

Hyperspectral imaging methods, devices and systems are described that improve the hyperspectral accuracy, and reduce manufacturing costs, by using two images that enable the recovery of hyperspectral images with high fidelity. One hyperspectral imaging device includes one or more imaging lenses, one or more sensors positioned to receive light associated with an object from the one or more imaging lenses, and a spectral filter. The hyperspectral imaging device is configured to capture a first image produced without using the spectral filer and to capture a second image produced with the spectral filter. The first image and the second image have different spectral contents, and the first and the second images are processed using a trained neural network for producing hyperspectral imaging data associated with the first and second images.

The invention relates to the technical field of automatic sorting of agricultural products, in particular to an egg quality intelligent sorting system and method based on multi-modal sensing. The system comprises a multi-mode sensing module, a data processing module and a sorting execution module. The multi-mode sensing module integrates a hyperspectral imaging unit, a voiceprint vibration detection unit and a weight sensing unit, and comprehensively collects egg surface images, vibration frequency and weight data; the data processing module analyzes the image data through deep learning and a data fusion algorithm to detect cracks and blood spots, and fuses multi-modal data to generate features to adapt to characteristic changes of different batches of eggs; the sorting execution module determines the sorting priority according to the fusion features and executes accurate sorting; the system utilizes the synergistic effect of the multi-mode sensing technology to comprehensively evaluate the internal and external quality of eggs, the detection precision is remarkably improved, efficient and intelligent egg sorting is realized, and the automatic sorting technology level of agricultural products is improved.

The invention relates to the field of farmland ecological protection, in particular to a salinization farmland protection forest configuration optimization system and method, and the system comprises a salinization monitoring module, an edge calculation module, a database, a three-dimensional model module, a model optimization module and a configuration scheme optimization module. The salinization monitoring module acquires data through an unmanned aerial vehicle and a hyperspectral imaging spectrometer and determines a soil salinity distribution state, the edge calculation module receives the data and carries out protection forest configuration model calculation, the database stores a calculation model and soil salinity and vegetation growth state data, and the three-dimensional model module generates a three-dimensional spatial-temporal model of forest growth. The model optimization module utilizes deep reinforcement learning to optimize a soil salinity and underground water level simulation model, the protection forest configuration effect is improved, the configuration scheme optimization module generates an optimal protection forest configuration scheme, a complex interaction process is accurately represented, and an efficient and accurate technical means is provided for ecological protection of salinized farmland.

The invention provides a method for carrying out corn quality seed selection by using a hyperspectral imaging technology, and belongs to the technical field of corn quality seed selection. Corn kernels are subjected to spectrum scanning in a wavelength range of 400nm to 2500nm, spectrum data are preprocessed through a continuous wavelet transform algorithm, baseline drift and noise interference are removed, and a high-quality seed selection result is obtained. And accurately extracting spectral characteristic peaks of protein, starch, grease and moisture. And establishing a characteristic peak distribution matrix to record peak intensity distribution, and calculating a characteristic peak intensity weight coefficient and a position offset. And constructing a characteristic peak quality incidence matrix, establishing a numerical mapping relationship between the spectral characteristics and the quality parameters, and obtaining a peak width parameter and a spectral noise level. The spectral quality fusion function is adopted to process the multi-dimensional characteristic parameters, and the comprehensive quality evaluation index and the single quality evaluation index are calculated, so that the technical problem that the multi-dimensional quality characteristics of the corn kernels cannot be accurately identified and accurately graded is solved.

The invention relates to the technical field of color measurement, in particular to a color measurement method based on hyperspectral imaging, which comprises the following steps: judging whether the chromatic aberration of a to-be-measured sample needs to be calculated or not, and if the chromatic aberration of the to-be-measured sample needs to be calculated, presetting a standard color value, and acquiring dark background hyperspectral data of the to-be-measured sample based on a hyperspectral imaging device, otherwise, directly acquiring the dark background hyperspectral data of the to-be-measured sample based on the hyperspectral imaging device. According to the method, the dark background hyperspectral data, the reflectivity calibration plate data and the real-time hyperspectral data of the sample are collected and corrected, so that the color measurement precision can be remarkably improved, the interference of an ambient light source and equipment noise is eliminated, the finally obtained standard real-time hyperspectral data is ensured to reflect the real spectral characteristics of the sample to be measured, and the accuracy of color measurement is improved. And the corrected standard real-time hyperspectral data can more accurately reflect the reflectivity spectrum of the sample to be detected, so that the calculation of the color value is more accurate.

The invention relates to the technical field of detection and identification, in particular to a semen cassiae authenticity identification method based on hyperspectral imaging, which comprises the following steps: S1, acquiring a hyperspectral image of a semen cassiae sample by using hyperspectral imaging equipment; s2, correcting the spectral drift of the hyperspectral image by using a spectral baseline correction method; s3, segmenting the corrected spectral image, distinguishing a target region and a background region of the semen cassiae sample, and extracting an effective spectral region of the semen cassiae sample in combination with a segmentation result; s4, separating the key spectral features related to the chemical components of the semen cassiae from the redundant features influenced by interference; s5, training an authenticity identification model based on an LGBM algorithm; and S6, inputting the key spectral features of the cassia seed sample to be detected into the authenticity identification model, and outputting an authenticity identification result. According to the invention, interference factors such as ambient light change, equipment deviation, surface impurities and the like are effectively eliminated, so that high-quality input data are provided for true and false identification.