399 results about "Hyperspectral imaging" patented technology

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 the field of optical nondestructive testing, and discloses a visualization method for detecting mango quality based on hyperspectrum, which comprises the following steps of: firstly, selecting mango samples, and simulating different mango ripening stages by dividing the mango samples into a plurality of groups of samples; acquiring a high-dimensional spectral image by using a hyperspectral imaging system, and measuring the hardness, the soluble solid content and the titratable acid content; correcting and extracting the high-dimensional spectral image to obtain and optimize the average reflection spectrum of the sample, and establishing a mango maturity classification model and a quality regression prediction model; performing characteristic wavelength contribution degree analysis and screening, and optimizing a quality regression prediction model by adopting a key wave band; the method is applied to each pixel point of a high-dimensional spectral image, a two-dimensional distribution diagram is generated, and mango quality visualization is achieved. The method solves the problems that in the prior art, rapid and lossless judgment of the mango ripening stage cannot be achieved, accurate prediction and visual distribution of key internal quality indexes cannot be achieved, and intelligent grading and quality control of picked fruits are not ideal.

Aiming at the problems of low efficiency, strong subjectivity, insufficient modeling ability, complex hyperspectral data noise, weak waxiness spectrum difference and the like of a traditional rice grain waxiness discrimination method, the invention provides a hyperspectral rice grain waxiness discrimination method based on multi-branch collaborative modeling. The method comprises the following steps: S1, acquiring glutinous and non-glutinous rice grain images by using a hyperspectral imaging system to obtain spectral data; s2, preprocessing spectral data through a combined method of SG smoothing, an asymmetric weighted penalty least square method and multivariate scatter correction to reduce noise and interference; s3, constructing a multi-branch modeling architecture which comprises a CNN local feature extraction module, an SRU spectrum sequence modeling module and a HorNet global high-order modeling module, and outputting a discrimination result through a classifier after multi-branch features are subjected to fusion and pooling attention weighting; and S4, evaluating the performance. According to the method, high-precision lossless discrimination of the waxiness is realized, and a technical support is provided for germplasm screening and quality evaluation in rice breeding.

The invention discloses a coarse cereal aflatoxin detection method combining hyperspectral imaging and deep learning, and belongs to the field of agricultural product quality safety detection. The method comprises the following steps: firstly, acquiring hyperspectral imaging data of coarse cereal grains, extracting space and spectral information, forming basic data, and performing preprocessing, space-time registration and feature alignment; building a hyperspectral space-spectrum double-branch feature fusion convolutional neural network deep learning model, and importing the processed data to complete iterative training; and finally, performing same-standard data acquisition and processing on to-be-detected coarse cereals, importing the trained model, and realizing aflatoxin detection and pollution area positioning through pixel-level analysis. According to the method, deep fusion of space and spectral features is realized, the detection accuracy and generalization ability are effectively improved, the detection result is stable, reliable and traceable, and the fine detection requirement of aflatoxin in coarse cereals is met.

The invention discloses a radiation correction method and system for an airborne hyperspectral imager. The radiation correction method and system are used for improving the radiation correction precision in complex weather. The method comprises the following steps: calculating a theoretical sun position based on unmanned aerial vehicle initial coordinates, ephemeris data and atmospheric parameters; performing course and differential compensation on a solar azimuth angle by combining IMU course and wind speed and direction data, performing refraction correction on an elevation angle, and outputting a high-precision sun position parameter; hyperspectral images and irradiance data are synchronously acquired, quaternion interpolation is combined with IMU data to realize space-time alignment, and attitude disturbance is eliminated; a hemispherical space irradiance model is constructed based on alignment data, cloud layer modes (clear sky, thin cloud and thick cloud) are divided according to atmospheric optical thickness variance, and Lambert body proportion correction, a discrete longitudinal standard method or a deep convolutional spectral network are respectively adopted for radiation correction. The method remarkably improves the correction precision and generalization ability, supports large-area image splicing, and is suitable for quantitative application of geological exploration, environment monitoring and the like.

The invention discloses a field-of-view self-stabilization and self-adjustment hyperspectral camera system and a control method, and relates to the technical field of hyperspectral imaging. The system comprises a first imaging lens group, a movable slit, a two-dimensional linear driver, a slit outer layer light shield, a collimating lens group, a light splitting element, a second imaging lens group, an area array image sensor, an attitude sensor and a control acquisition circuit. Angle disturbance is obtained in real time, two-dimensional compensation displacement of the slit is calculated to drive the slit to move reversely, field-of-view collection is kept basically constant, collection is triggered according to a set rhythm, slit position and attitude data are recorded synchronously, and the pre-calibrated slit position and mapping of original coordinates and ideal coordinates are combined, so that field-of-view collection is completed. Geometric transformation and resampling dynamic correction are carried out on view field inclination aberration and spectrum bending aberration generated along with slit position changes, the environmental adaptability and reliability are improved, a hyperspectral data cube with accurate space coordinates and a stable spectrum curve is output, and the method is suitable for unmanned aerial vehicle-mounted hyperspectral remote sensing.

The invention relates to the technical field of nutritious food configuration, and discloses a system and a method for configuring personalized nutritious food, and the method for configuring personalized nutritious food comprises the following steps: collecting spectral characteristic data and chemical component data of a food material sample by adopting a hyperspectral imaging and mass spectrum combined technology, and obtaining holographic chemical fingerprint information; processing the collected spectral data by using a multi-scale feature extraction algorithm, extracting multi-level feature information of the food materials, and carrying out standardization and dimension reduction processing on the feature data; according to the method, high-precision prediction of the nutrient content of the food material is realized through combination of hyperspectral imaging and mass spectrometry technologies and a deep learning model, so that the nutrient content prediction accuracy and the configuration scheme accuracy are improved, the nutrient balance achievement rate is also improved, and meanwhile, the limitation of a static database is broken through; real-time nutritional ingredient analysis of any food material is achieved, the method can adapt to individual differences and freshness changes of the food materials, and accurate configuration based on actual food material characteristics is provided.

The invention belongs to the field of power equipment monitoring, and relates to a power equipment overheating positioning method and device based on infrared hyperspectral imaging, and the method comprises the steps: synchronously collecting infrared and hyperspectral multi-modal data, and carrying out the preprocessing of the multi-modal data; based on the preprocessed multi-modal data, complementary features are extracted from the infrared and hyperspectral data, and feature-level fusion is realized through deep learning; based on surface data inversion equipment internal heat source distribution, three-dimensional space positioning is realized; performing overheating type intelligent diagnosis, and distinguishing normal heating and fault heating of the equipment; generating a visual report and triggering graded early warning; and through online learning and model updating, the calculation efficiency and robustness are optimized. The sensitivity and the positioning precision of overheating defect detection are improved, and particularly, the recognition capability of early weak faults is enhanced; the space coordinates and intensity distribution of the internal abnormal heat source can be quantitatively calculated, visual fault position guidance can be provided, and the troubleshooting time is greatly shortened.

The invention provides a polarization hyperspectral water quality monitoring system and method, and the system comprises a tower footing polarization hyperspectral imaging module which is disposed on a building beside a to-be-detected water area, and carries out the area array staring scanning of the to-be-detected water area through a polarization hyperspectral imager, so as to obtain a water body polarization hyperspectral image signal of the to-be-detected water area; the image spectral data processing module is used for deducting dark current and spurious signals from the water body polarization hyperspectral image signals, and calculating the water body spectral reflectivity of the water area to be measured according to the water body polarization hyperspectral image signals by combining a gray value and reflectivity relation model constructed by using the hyperspectral image signals of the gray scale calibration plate; and the water quality parameter spatial distribution inversion module is used for performing inversion on the to-be-detected water quality parameters of the to-be-detected water area according to a pre-constructed water quality parameter inversion model and the water body spectral reflectivity. According to the technical scheme, the integration level is high, the automation degree is high, rapid deployment can be achieved, and all-day continuous unattended monitoring can be achieved.

The invention discloses an intelligent dynamic regulation and control method for compaction quality of a high-liquid-limit fly ash roadbed, which comprises the following steps of: establishing a mapping relation among high-spectral characteristics of high-liquid-limit fly ash, a specific surface area index, a total hydroxyl content and a water content through a first neural network model, and establishing an optimal water content prediction model through a second neural network model; the third to fifth neural network models construct the correlation of the compactness, the dielectric constant, the rigidity and the water content; two-stage water content regulation and control of a storage yard and a paving layer are realized through hyperspectral imaging of an optical fiber probe and an unmanned aerial vehicle, and pre-humidification and secondary precise regulation and control are performed in combination with an intelligent spraying system; in the compaction stage, data are collected in real time through a multi-frequency ground penetrating radar and a vibration sensor, compaction degree distribution is inverted, layered evaluation is conducted, and rolling parameters are dynamically adjusted. According to the invention, a hyperspectral technology, multi-source sensing and deep learning are fused, intelligent management of compaction quality is realized, and construction uniformity and quality stability of the fly ash roadbed are significantly improved.

The invention provides a tobacco shred wet mass online detection method based on hyperspectral imaging and time-space fusion, which comprises the following steps: acquiring a hyperspectral data cube of a tobacco shred flow, extracting a tobacco shred area and generating a mask; calculating a standardized moisture index of each pixel point in the mask according to the spectral response values of the moisture absorption wave band and the reference wave band to obtain a standardized moisture index image; based on a set index threshold value, converting the standardized moisture index image into a binary image, and performing morphological cleaning; carrying out wet group identification and marking on the cleaned binarized image to obtain an external rectangular frame of the binarized image; overlapping or adjacent external rectangular frames are combined, the combined external rectangular frames are screened based on the minimum area threshold value, the external rectangular frames which do not meet the area condition are removed, and the final tobacco shred wet mass external rectangular frames are obtained; according to the invention, misjudgment caused by shielding is significantly reduced; the high-speed and real-time online detection requirements of a cigarette production line can be completely met.

The invention relates to the technical field of agricultural intelligent equipment, in particular to intelligent monitoring and weeding equipment for gramineous weeds in a winter wheat field, and aims to solve the technical problem of high missing detection rate and false detection rate of a traditional visual identification method caused by similar forms of winter wheat and gramineous weeds in a seedling stage. The equipment comprises an autonomous mobile platform, a multi-mode sensing assembly, a data processing and decision-making unit and a targeted clearing execution mechanism. The multi-mode sensing assembly integrates a hyperspectral imaging module, a three-dimensional shape measurement module and a polarization characteristic analysis module, and pixel-level data registration is achieved in combination with the cooperative illumination system. The data processing unit performs high-confidence classification decision on the multi-source features through a hierarchical Bayesian fusion model; and the targeted clearing mechanism adopts a laser galvanometer system to carry out non-contact precise clearing on the identified weed growing points. Intelligent identification and efficient physical weeding of weeds in the winter wheat field are achieved, and the method has the advantages of being free of chemical residues and high in operation continuity.

The application relates to a hyperspectral remote sensing image quality grade evaluation method, which comprises the following steps: dividing an image into fixed-size blocks, then constructing a quality evaluation model, obtaining a feature map with spectral weight through a spatial-spectral attention module, and inputting the feature map into a frequency spectrum integration embedding module to generate an embedding feature map of local adjacent spectral bands; after linear projection, the embedding feature map is input into an encoder, and a skip connection mechanism is set to fuse the outputs of different encoders; the output of the last encoder is input into a linear layer and an activation function layer to obtain a quality evaluation result of the image; the quality evaluation model is trained, and the trained model is used for image quality grade evaluation. The application can perceive the good and bad degrees of a hyperspectral remote sensing image, and can dynamically detect and adjust the image output by an image processing system in a hyperspectral imager according to the quality grade of the image, thereby providing a more effective basis for parameter optimization of a real-time hyperspectral imager system.

The invention provides a peanut seed defect detection method and system based on hyperspectral imaging, and relates to the technical field of agricultural product quality detection.The method comprises the steps that hyperspectral images of peanut seeds are obtained, and an average spectral curve is extracted from the hyperspectral images after reflectivity correction; performing feature selection on the average spectrum curve by using an MS-SPA algorithm, and extracting a feature wavelength of the average spectrum curve; the characteristic wavelength is input into the defect detection model, the spectrum sequence conversion part is used for reshaping one-dimensional characteristic wavelength spectrum data into a sequence format adaptive to one-dimensional convolution operation, continuous correlation characteristics between wavebands are reserved, the convolutional layer extraction part adopts a multi-layer one-dimensional convolutional neural network, and the convolutional layer extraction part adopts a multi-layer one-dimensional convolutional neural network; local features and global change modes related to defects in the spectrum sequence are extracted, the defect prediction part adopts global average pooling operation and a Softmax classifier to output prediction probabilities of various defects, and a final defect detection result is obtained; according to the invention, efficient and accurate nondestructive detection of peanut seed defects can be realized.

The invention belongs to the technical field of traditional Chinese medicine tablet manufacturing, and particularly relates to an online quality prediction method for stomach invigorating and digestion promoting tablets based on multi-modal fusion. Comprising the following steps: synchronously acquiring hyperspectral imaging data and near infrared spectrum data of a stomach invigorating and digestion promoting tablet sample; processing the near infrared spectrum data through a one-dimensional convolutional neural network to extract a first spectrum feature; parallel extraction of spatial features and spectral features is carried out on the hyperspectral imaging data through a double-branch feature extraction network; performing intra-modal fusion on the extracted spatial features and the second spectral features through a cross attention mechanism to obtain hyperspectral fusion features, and performing cross-modal fusion on the hyperspectral fusion features and the first spectrum through cross-modal multi-head attention to generate fusion features; and outputting a quality index prediction value of the stomach invigorating and digestion promoting tablets through a regression prediction module based on the fusion features.

The application discloses a hyperspectral imaging chip, device and system based on an adjustable reconfigurable metasurface, and the chip comprises a metasurface unit array, a metasurface reconfiguration module, a metasurface adjustment module, a CMOS image sensor and a spectrum reconstruction and identification module, the metasurface unit array comprises a plurality of metasurface units arranged on a silicon layer; the metasurface reconfiguration module is used for adjusting the number of metasurface units included in the reconfigurable metasurface unit array; the metasurface adjustment module is used for connecting the silicon layer of the metasurface unit array through wires, and is used for changing the spectral response of the metasurface unit by adjusting the voltage of the wires; the CMOS image sensor is used for detecting light signals from the reconfigurable metasurface unit array after modulation; and the spectrum reconstruction and identification module is used for performing hyperspectral reconstruction and imaging according to the light signals detected by the CMOS image sensor. The application has adjustable spectral response, ultrahigh spectral resolution, real-time dynamic monitoring capability and self-adaptive imaging capability.

The invention relates to the field of fruit quality nondestructive testing, in particular to a winter peach cold storage shelf life classification and quality nondestructive testing method based on spectrum-image feature fusion and stacked model fusion, and the method is used for accurately classifying the winter peach cold storage shelf life and predicting the key quality of the winter peach cold storage shelf life based on hyperspectral imaging and deep learning technologies. The method comprises the following steps: acquiring a hyperspectral image in a wave band range of 400-1000nm, and extracting a region of interest through mask processing; phenotype data of the samples are measured and classified, and abnormal samples are removed; through spectrum preprocessing and characteristic wavelength screening, effective pixel size and area parameters are extracted, and a multi-task integration model is constructed. The model not only realizes 100% accurate classification of the shelf life of the winter peaches, but also predicts the sugar degree (R2 = 0.8451) and the hardness (R2 = 0.8798), and supports a pseudo-color visual distribution diagram and fruit diameter and weight prediction. The method is high in precision, good in stability and high in automation degree, and effective technical support is provided for quality management and market circulation after agricultural products are harvested.

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

The invention discloses a portable automatic focusing hyperspectral imaging system and method, and the system comprises a spectral data collection module, a spectral analysis module, a displacement platform, an inertia measurement module, and a control module. Wherein the spectral data acquisition module comprises a focusable lens, an optical splitter, a first collimating lens group and an image sensor. And the spectral analysis module is provided with an electronic shutter, a slit, a second collimating lens group, a grating, a third collimating lens group and an imaging element along the light path direction. And the displacement platform is used for driving the spectral analysis module to perform internal swing-sweeping motion. The control module executes an automatic focusing algorithm, automatic exposure control and multi-source data fusion and preprocessing. According to the invention, focusing precision and spectral data space registration precision in a complex field depth scene can be improved, exposure parameters are intelligently and adaptively adjusted, geometric distortion is compensated, unattended continuous operation is supported, invalid operation cost is reduced, and reliability and availability of hyperspectral data are improved.

The invention discloses an esophageal anomaly detection method and system based on an endoscope image, and relates to the technical field of medical image processing, and the method comprises the steps: extracting a brightness component from an enhanced image, generating a smooth layer and a detail layer through weighted least square filtering, calculating a spectrum guide weight based on a dimension-reduced hyperspectral image, and carrying out the detection of the esophageal anomaly. Fusing the detail layer and the smooth layer to generate a de-noised brightness map, generating a de-noised image through color conversion, calculating spectral intensity characteristics based on the dimension-reduced hyperspectral image, generating an initial foreground mask, and generating an optimized mask through morphological closed operation optimization; through hyperspectral imaging, multi-modal feature fusion and local gamma value enhancement, the spectral feature capture capability of the lesion and the visualization effect of the lesion area are improved, and the accuracy and robustness of lesion segmentation and classification are significantly improved.

The invention relates to the technical field of adulteration detection methods, in particular to a fritillaria powder layered adulteration detection method based on hyperspectral imaging, which specifically comprises the following steps: step 1, sample preparation and hyperspectral data acquisition, specifically comprising sample preparation, hyperspectral image acquisition, black and white plate correction and spectrum extraction; step 2, spectral data preprocessing, specifically including noise suppression processing and baseline correction and feature enhancement; according to the method, sample preparation fits a real market adulterated scene, hyperspectral acquisition is combined with black and white plate correction and spectrum extraction, high-quality and interference-free original data is obtained, and pretreatment steps of noise suppression, baseline correction and feature enhancement are matched, so that interference such as equipment noise and light scattering is eliminated, and the sample essential spectrum difference is highlighted; and the problem of identification difficulty caused by poor data quality in traditional detection is solved.

The invention discloses a flower grading method, system and device based on hyperspectral imaging and logistics data, and relates to the technical field of flower grading. Comprising the following steps: constructing a first reference model for reflecting a relationship between colors, forms or combinations of specific areas on various fresh flowers and freshness, and a second reference model for reflecting a relationship between moisture content and distribution positions in various fresh flowers and freshness, the third reference model is used for reflecting the relationship among the freshness of various fresh flowers, the logistics duration and the logistics environment parameters; the method comprises the following steps: acquiring and identifying the color and form of to-be-graded flowers, the moisture content and distribution position in the flowers, and logistics environment parameters and logistics duration during flower logistics, generating a first reference value, a second reference value and a third reference value, configuring reference weights, generating a comprehensive judgment value after weighted summation, and performing classification on the comprehensive judgment value. According to the scheme, the grading accuracy is ensured while the grading speed of the fresh flowers is remarkably increased.

The invention relates to the technical field of artificial intelligence, and discloses an AI-based water quality parameter hyperspectral data inversion method and system. Comprising the following steps: acquiring a hyperspectral image through a hyperspectral imager, acquiring water temperature and illumination intensity auxiliary parameters by combining a GNSS positioning module and an environment sensor, and constructing a multi-dimensional data set of space-time alignment; inputting the multi-dimensional data set into a denoising model based on a GAN (Generative Adversarial Network), distinguishing a real spectrum and noise through a discriminator, and reconstructing a pure spectrum through a generator to suppress atmospheric scattering and instrument noise; constructing a spectrum-space joint feature vector; calculating the contribution degree of each wave band to a target parameter by utilizing a wave band attention mechanism and a spectrum-space joint feature vector, and weighting to generate an enhanced spectrum feature; modeling the superpixel spatial adjacency relation through a graph neural network GNN to capture spatial correlation, and generating spatial graph features; and inputting the enhanced spectral features and the spatial diagram features into a cross-modal interaction layer, and finally outputting predicted values of the water quality parameters.

This invention discloses a design method for a plane-multiplying relay structure of a wide-spectrum, wide-swath hyperspectral imaging telescope, relating to the field of optical design technology. The method includes establishing a model for realizing a controllable aberration-multiplying relay structure. This model consists of four closely related parts: field segmentation, plane multiplication, telecentricity adjustment, and field curvature / distortion adjustment. Based on this model, the interrelationships between the number of field segmentations, plane multiplication rate, image-side telecentricity adjustment, field curvature / distortion adjustment, and relay structure parameters of the hyperspectral imaging system are clarified. Based on these parameter relationships, a design method for the initial relay structure is further proposed. Using the plane-multiplying relay structure of this invention, wide-spectrum, wide-swath hyperspectral imaging is achieved, breaking through the technical bottleneck of traditional telescopes' difficulty in connecting multiple large-field-of-view spectrometers and large-scale detectors.

The invention provides a hyperspectral imaging system. The hyperspectral imaging system comprises a telescope group, a collimator objective, a prism, a volume holographic diffraction grating, an imaging objective and an imaging sensor which are arranged in sequence, the telescope group is composed of a primary reflector, a secondary reflector, a third reflector, a plane reflector and an entrance slit; the primary reflector, the secondary reflector and the third reflector form an off-axis three-reflector structure; the plane reflector is used for folding a light path; the collimating objective lens, the prism, the volume holographic diffraction grating, the imaging objective lens and the imaging sensor form an imaging spectrometer, and an entrance slit is used as object space input; the formed off-axis three-mirror and folded light path and an imaging spectrometer are in coaxial butt joint by taking an entrance slit as a node, and are used for joint correction and splicing to form a hyperspectral imaging system. According to the optical path architecture, optimal matching of the unobstructed total reflection front end and the transmission type rear end is realized, and the detection sensitivity of crop weak spectrum signals is greatly improved while a better modulation transfer function of a full view field is ensured.

This invention discloses a method for preparing and intelligently identifying microscopic stealth codes by integrating artificial intelligence and hyperspectral imaging, belonging to the field of anti-counterfeiting technology. The method comprises two parts: preparation and identification. During preparation, photochromic materials are doped into a substrate to form microscopic code points. A hyperspectral feature library is established, and a digital twin initial model containing partial differential equations is constructed, followed by pre-training of a physical information neural network. During identification, measured hyperspectral data of the code points to be tested are collected. Combined with environmental parameters and historical data, the expected state is predicted using the trained physical information neural network. The authenticity is determined by comparing the measured data with the expected state. This invention embeds physical laws into the identification process through a digital twin model and a physical information neural network, achieving proactive prediction of the evolution of the microscopic stealth code state, significantly improving the accuracy, robustness, and anti-counterfeiting capability of anti-counterfeiting identification.

The invention provides a micro-nano plastic detection method and system, and relates to the technical field of micro-nano plastic detection.The method comprises the steps that a to-be-detected sample is obtained, and the positions of all suspicious micro-nano plastics in the sample are determined based on hyperspectral imaging; eliminating a biological matrix on the surface of the suspicious micro-nano plastic by utilizing laser with stepped power based on a photo-thermal ablation pretreatment technology; the magnitude of the stepped power is determined according to a biological matrix; and collecting the Raman spectrum of the suspicious micro-nano plastic after photo-thermal ablation pretreatment, and outputting a detection result according to the Raman spectrum. The positions of all suspicious micro-nano plastics in a sample are determined based on hyperspectral imaging, and the sample is detected by using Raman spectrum, so that the efficient positioning capability of nanoscale hyperspectral imaging and the accurate identification capability of laser confocal Raman spectrum are organically combined, and a photo-thermal ablation pretreatment technology is introduced, so that the biological matrix interference is effectively overcome, and the detection accuracy is improved. The high-flux, high-resolution and high-accuracy detection on the micro-nano plastic is realized.

The invention discloses an on-chip spectrum sensing structure, a pixel unit and a preparation method and application thereof. The on-chip spectrum sensing structure comprises a metal layer, a dielectric layer and a detector, nano-pore channels are formed in the metal layer and the dielectric layer, and the nano-pore channels extend into the dielectric layer from the surface of the metal layer; near-infrared light forms surface plasmon resonance at the interface of the metal layer and the dielectric layer in the nanopore channel, so that part of near-infrared light in a selected narrow wavelength range is transmitted to the detector, and a corresponding electric signal is formed. According to the on-chip spectrum sensing structure provided by the invention, near-infrared broadband calculation spectrum sensing with high spectral fidelity can be realized, radiation and ohmic loss are effectively inhibited by a nonlinear parameterized design framework, and high transmission efficiency is ensured; the multi-channel super-pixel system structure generated by the method realizes simultaneous point spectrum and snapshot hyperspectral imaging, and shows the potential of the compact platform for field deployment and chip spectrum sensing application.

This invention discloses a coaxial telecentric polarization hyperspectral imaging device and a biological tissue detection system. The device includes: a light source emitting unpolarized light; a circular polarization generator converting the unpolarized light emitted by the light source into circularly polarized light; the circularly polarized light being coupled into a coaxial telecentric lens via a coaxial optical interface; the coaxial telecentric lens reflecting the circularly polarized light, which then illuminates the target object plane; the signal reflected from the target object re-enters the coaxial telecentric lens and is transmitted to a polarization analyzer module via a beam splitter. The polarization analyzer module includes a first waveplate and a first linear polarizer. The first waveplate receives the signal transmitted from the beam splitter and transmits the signal to the first linear polarizer. The first linear polarizer sends the output light to a hyperspectral camera, and the hyperspectral camera sends the output image to a computer for image analysis.

This invention proposes a rapid verification method for the imaging performance of an aperture-coded infrared hyperspectral imaging system. This method uses an N×M filter array as the indoor imaging target. This array includes a central infrared window and multiple filters with different center wavelengths surrounding the infrared window, containing rich spectral feature information and geometric and spatial positional information of the filters. Utilizing a large-area blackbody and a collimator with a large focal plane, the system can capture all the necessary information from the filter array. After image reconstruction, using the data from the central window as a reference, the spectral transmittance curves of each filter can be calculated quickly and accurately, thereby verifying the system's ability to acquire spectral information. The system's ability to acquire geometric information can also be verified by observing the geometry and relative positions of the filters in each spectral channel. This invention provides an efficient, reliable, and rapid verification method for aperture-coded infrared hyperspectral imaging systems.