116 results about "Spectral dimension" patented technology

The invention relates to the technical field of power distribution station inspection, and discloses a power distribution station automatic inspection method based on image recognition. According to the method, multi-spectral image data of multiple areas in a power distribution station are collected in real time, and a multi-channel feature tensor of an equipment state is generated through a feature extraction network; performing feature fusion of space and frequency spectrum dimensions on the multichannel feature tensor by using a multi-scale convolutional attention network, and outputting an enhanced device feature map; inputting the data into a cascade anomaly detection module, positioning an equipment surface defect region by adopting a region segmentation algorithm, and analyzing and generating a defect evolution trend vector in combination with time sequence characteristics; on the basis of the vector, probability distribution of equipment fault risks is predicted through a space-time propagation model, and a dynamic risk field is generated; and finally, constructing a self-adaptive early warning decision tree for the dynamic risk field, and generating an inspection maintenance instruction according to risk probability threshold grading. According to the method, automation and intelligentization of power distribution station inspection are realized, and support is provided for efficient maintenance of the power distribution station.

The invention discloses an ultra-high-speed imaging device based on acousto-optic filtering modulation. The ultra-high-speed imaging device comprises a spectrum modulation system, a spectrum imaging system and a data processing system. The spectrum modulation system utilizes an acousto-optic tunable filter (AOTF) to dynamically modulate broadband continuous spectrum light, narrow-band illumination light which rapidly changes along with time is generated, and time information of a dynamic scene is effectively mapped to a spectrum dimension. And the spectral imaging system images the spectral coded dynamic scene to the hyperspectral camera to realize acquisition of spectral space-time information. The data processing system performs spectral crosstalk correction, channel separation and demosaicing reconstruction on the acquired original mosaic image, and recovers a time sequence image according to a spectrum-time mapping relation, thereby realizing single-shot superspeed imaging in a dynamic process. The device has the advantages of being simple and compact in structure, flexible in time window, high in imaging fidelity and the like, and has wide application prospects in the fields of rapid dynamics such as microfluidics, laser processing and ultrafast physics.

The invention relates to the technical field of image recognition, in particular to a red tide algae recognition method based on a hyperspectral image, and the method comprises the following steps: calculating a gradient value of each pixel in a spatial dimension and a spectral dimension according to an original hyperspectral data cube, setting a diffusion coefficient for diffusion, and obtaining a three-dimensional fidelity hyperspectral data cube. According to the method, the gradient change trends of the spatial dimension and the spectral dimension are extracted at the same time pixel by pixel, and the regional differentiation diffusion operation is matched, so that the expression ability of the alga plaque boundary and the consistency of the internal spectral characteristics are enhanced, and the fidelity of the hyperspectral data in fine-grained region identification is improved; upper convex hull calculation of a spectral reflectivity curve and construction of a continuous spectrum background baseline are introduced, and characterization of pigment absorption morphological characteristics is enhanced; and in combination with the characteristic absorption wave bands of chlorophyll and phycobiliary, the synergistic weighted response to the distribution of the algae pigment is realized, and the sensitivity and selectivity of pigment recognition are improved.

The invention provides an intelligent cashmere doping content judgment system utilizing multi-mode Raman spectrum characteristics, and relates to the technical field of intelligent spectrum analysis. The cashmere doping content intelligent judgment system utilizing the multi-mode Raman spectrum characteristics comprises a data acquisition module, a multi-wavelength laser excitation unit is used for carrying out space scanning on a sample, time sequence Raman spectrums under different polarization configurations are synchronously acquired, and the time sequence Raman spectrums are analyzed; multi-dimensional original data including space coordinates, a time sequence, spectral intensity and a polarization state is formed, and an original multi-modal data set is generated. Through multi-wavelength laser space scanning and polarization time sequence synchronous acquisition, a multi-dimensional original data set containing space coordinates, a time sequence, spectral intensity and a polarization state is constructed, the dimension limitation of a traditional spectrum is broken through, and a three-dimensional convolution kernel is adopted to perform synchronous sliding processing in space and spectrum dimensions, so that the spectrum intensity and the polarization state are obtained. And the spatial distribution rule and multi-mode spectrum correlation characteristics of the doped region are directly captured.

The invention relates to a multi-source remote sensing cooperative system based on an unmanned aerial vehicle formation and a control method, and the system can synchronously collect laser radar point cloud data, multispectral images, thermal infrared images and hyperspectral images, and breaks through the limitation of single data dimension caused by the fact that a traditional single unmanned aerial vehicle platform can only carry a single sensor. Meanwhile, the formation control module controls the heterogeneous unmanned aerial vehicle cluster to form a preset formation and perform cooperative flight to ensure spatial position cooperation of the unmanned aerial vehicles in the operation process, and the task allocation module dynamically allocates route tasks and sensor working parameters to the unmanned aerial vehicles based on target area environment information. And the data fusion module performs geometric registration, spectrum fusion and three-dimensional reconstruction processing on the multi-source remote sensing data acquired by each unmanned aerial vehicle to generate a three-dimensional spectrogram of the target area, so that accurate integration of the multi-source data in space and spectrum dimensions is realized, and the multi-source remote sensing data is acquired. Therefore, the system gives consideration to large-range area coverage capability and high monitoring precision.

The invention discloses a hyperspectral image and laser radar data fusion classification method based on an improved attention mechanism. The method comprises the following steps: obtaining hyperspectral image data and laser radar elevation data to be classified; processing the hyperspectral image data through a spectral channel attention module to extract spectral features; processing the laser radar elevation data through an elevation space attention module to extract spatial features; inputting the spectral features and the spatial features into a double-feature fusion module for coupling to generate fusion features; image classification is completed based on the fusion features; according to the method, the image features of the hyperspectral image in the spectral dimension and the elevation information of the radar image in the spatial dimension can be fully extracted, and the representation capability of the model for the spatial features and the spectral features is enhanced.

The invention discloses a computing spectrum reconstruction method based on CS-Unet and a spectral imaging system. The calculation spectrum reconstruction method comprises two parts, namely TwIST pre-reconstruction and Unet coupling reconstruction, wherein the TwIST pre-reconstruction is used for preprocessing an initial coding spectrum image to generate a pre-reconstructed spectrum image; and the Unet part couples the pre-reconstructed image and the initial coding spectral image in the spectral dimension and inputs the coupled image and the initial coding spectral image into a network for fine reconstruction, and finally a target space-spectral image is recovered. The imaging system is composed of a spectral image coding module and a CS-Unet reconstruction module, a metasurface spectral modulator applied to the spectral image coding module is composed of 36 silicon-based all-dielectric structures, and rich spectral response is achieved by adjusting the period, the size and the direction angle of three basic C4 symmetric units, namely a round unit, a cross unit and a square unit. According to the method, high-robustness and high-imaging-quality spectrum reconstruction is realized in a visible light wave band (400-700 nm), and the method has relatively high engineering applicability.

The application discloses a hyperspectral image Transformer network training and classification method, which first divides a hyperspectral image sample based on a spectral dimension to obtain a plurality of spectral subbands, wherein the hyperspectral image sample is an unlabeled training sample; each spectral subband is input into an embedding module to obtain local space-spectrum embedding features output by each embedding module, wherein the embedding module extracts space-spectrum features of the spectral subband at multiple scales; all local space-spectrum embedding features are fused according to the positions of the spectral subbands to obtain global space-spectrum embedding features; the global space-spectrum embedding features are input into a Transformer encoder, and a center region token is masked and reconstructed in a Transformer decoder to train the Transformer encoder in a self-supervised manner. Compared with the prior art, the unlabeled sample can be effectively utilized to improve the training effect of the Transformer network, and the hyperspectral image can be classified with high precision.

The invention belongs to the technical field of cleanliness detection, and particularly relates to a semiconductor adhesive tape surface cleanliness detection method and system based on Raman spectroscopy, and the method comprises the following steps: S1, obtaining a multi-spectrum image of the surface of a semiconductor adhesive tape; for each pixel point in the multi-spectrum image, calculating the weighted sum of the mahalanobis distance of the spectral dimension of the pixel point and the Gabor texture response of the spatial dimension to obtain a spatial spectrum-texture anomaly index; determining excitation points of the Raman spectrum based on the spatial spectrum-texture anomaly index, and collecting Raman spectrum data of each excitation point; s2, extracting a multi-spectrum feature vector and a Raman spectrum feature vector of the excitation point; according to the method, the full-view-field cleanliness distribution diagram is generated through interpolation based on the high-quality sparse measurement result with the confidence coefficient weight, comprehensive evaluation of the surface pollution condition of the adhesive tape is achieved, and the contradiction among detection efficiency, recognition precision and representation comprehensiveness of a traditional method is solved.

This invention relates to the field of optoelectronic detection technology and discloses an online detection system for screen color uniformity based on dynamic rotating polarization spectrum. The system includes an online transmission module for carrying and driving the illuminated screen under test to move continuously along a set direction at a set speed; and a combined optical encoding module, positioned above the screen under test, comprising a static spatial-spectral phase delay mask and a continuously rotating polarization modulator arranged sequentially along the beam propagation direction. By driving the screen to continuously translate using the online transmission module, and cooperating with the continuously rotating polarization modulator and hyperspectral acquisition module to acquire dynamic spatiotemporal aliasing data, the accuracy of the spectral dimension can be guaranteed. Furthermore, by extracting high-precision chromaticity parameter matrices and multidimensional polarization distortion feature matrices from the decoupled and reconstructed optimal polarization spectral tensor solution, the system's ability to detect complex internal quality problems can be significantly enhanced.

The invention discloses a mudflat soil salinity remote sensing inversion method, which comprises the following steps of 1, acquiring mudflat area remote sensing data and ground actually-measured soil salinity data; step 2, constructing a composite spectral index as a salt sensitive characteristic; step 3, performing multi-dimensional feature fusion on the salinity sensitive features to construct a salinity sensitive feature set, and constructing a multi-dimensional fusion feature vector data set including a spectral dimension, a spatial dimension and a statistical dimension; 4, constructing a small sample adaptive deep learning salinity inversion model with a three-layer progressive feature extraction architecture, and training the model through a multi-dimensional fusion feature vector data set; and step 5, performing inversion through the trained small sample adaptive deep learning salinity inversion model to generate a regional soil salinity spatial distribution map. According to the invention, through novel composite spectral index construction, a multi-dimensional feature fusion system, a sample equalization strategy and a small sample adaptive model, high-precision inversion of mud flat soil salinity is realized.

The application belongs to the technical field of ubiquitous computing. The application provides an end-side hyperspectral imaging and fine identification method for an open environment. The disclosure embodiment designs a spatial spectrum two-type modal encoder for extracting spatial spectrum features of RGB and NIR images and outputting a fused feature stream; a light hyperspectral imaging model is used to process the fused feature stream and reconstruct a hyperspectral image. A light decoupling loss function design is used to replace the original loss function of the light hyperspectral imaging model, and robust hyperspectral image imaging in an open environment is realized. An analysis module based on grouping attention is designed, different weights are assigned to different absorption peaks of different substances by grouping along the spectral dimension, and the huge calculation amount and calculation delay caused by full-band analysis are avoided. A mixed quantization method is used to reduce the calculation amount and storage amount of the model deployed on the end side without obvious decrease in imaging and analysis accuracy of the model.

The invention relates to the field of medical image analysis, in particular to a drug distribution and metabolism dynamic analysis system of a multispectral microscopic image, which comprises a light source module, a drug distribution module, a drug distribution module, a drug metabolism dynamic analysis module and a drug metabolism dynamic analysis module, and is characterized in that the light source module emits a multiband light source comprising broadband white light and time sequence switching narrow-band monochromatic exciting light; the data acquisition module is used for acquiring a multi-channel spectral image signal and generating a multi-spectral image containing a spatial dimension and a spectral dimension; the pharmacokinetic analysis module constructs a geometric characterization framework of drug metabolism based on a differential geometry theory, and comprises a Riemannian manifold characterization unit for mapping multispectral data to a Riemannian manifold space, a curvature tensor analysis unit for calculating manifold curvature characteristics and a parallel transmission optimization unit for evaluating drug transmission efficiency; the application analysis module carries out drug target co-localization analysis, space-time metabolic map analysis and delivery system optimization based on the analysis result, the spectrum channel limitation of traditional fluorescence imaging is broken through, and the precision and dimension of drug metabolism analysis are remarkably improved.

The invention provides a hyperspectral and multispectral image fusion method and system. The method comprises the following steps: acquiring hyperspectral and multispectral data of the same spatial region; performing up-sampling on the low-resolution hyperspectral image by adopting bilinear interpolation; one-dimensional wavelet transform is utilized to extract reflection peak and spectrum change characteristics in a spectrum dimension, two-dimensional wavelet transform is utilized to extract texture and edge information in a space dimension, and pixel-by-pixel wavelet basis adaptive selection is realized through Softmax gating; multi-granularity feature modeling is realized for the spectral branches by adopting grouped multi-scale convolution, and a self-adaptive weighting mechanism is formed for the spatial branches through global average pooling, a convolution layer and Sigmoid activation so as to enhance spatial details and compensate spatial feature loss by utilizing MSI; a cross-scale two-way attention mechanism is introduced, and long-range dependence modeling and information interaction of spectrum and spatial characteristics are achieved; and a high-resolution hyperspectral image is generated through a feature aggregation and reconstruction module.

The application relates to an abnormality detection method based on a hyperspectral image. The main body is based on a space-spectrum combined feature extraction method of filtering and low-rank decomposition to perform abnormality detection on the hyperspectral image. The specific method comprises the following steps: firstly, in the spatial dimension, a reduced dimension image is obtained through a data dimension reduction and eigenvalue weighted fusion method, and then an improved spatial filtering method is used to extract the spatial features of the image to obtain an initial spatial feature image. In the spectral dimension, a background reconstruction image of the approximate background is obtained by using a Tucker decomposition method on the original hyperspectral image, and a background dictionary of the image is obtained by using an improved k-means clustering method, then the background dictionary is input into a low-rank decomposition model to obtain a sparse matrix, and an initial spectral feature image is obtained, finally, the initial spectral feature image is fused with the spatial feature image to realize abnormality detection.

The application discloses a hyperspectral remote sensing image recognition method and device based on spectral space feature coupling, and utilizes different linear spectral filters to perform feature extraction on the whole hyperspectral remote sensing image, so that multi-class spectral features are obtained. Compared with the traditional technology, the application does not need to perform spectral dimension reduction, thereby avoiding the problem that the traditional technology weakens the subtle spectral information and high-order spectral correlation contained in the hyperspectral data. Meanwhile, the application takes the minimization of image background energy as an objective function to optimize the optimal real spectral feature vector of each linear spectral filter to the target class. Based on this, when the constructed linear spectral filter is used for feature extraction, the background energy can be minimized while the target spectral response is maintained and enhanced, so that the spectral clutter and noise in the complex environment are suppressed, and thus the detection precision of the model in the complex environment background can be improved.

This invention discloses a method for classifying defects in patternless wafers based on multi-feature joint decision-making, belonging to the field of wafer defect detection technology. The method includes the following steps: irradiating the surface of a patternless wafer with a probe beam having a preset polarization state; acquiring spatial scattered light intensity information of the scattered light from the wafer surface under different combinations of polarization states based on a probe array distributed at multiple spatial angles; extracting local optical features of the candidate defect regions and constructing a feature tensor based on these local optical features; analyzing the candidate defect regions according to the polarization degradation evolution law of the feature tensor in the spectral dimension and outputting the defect classification result. This approach avoids the low classification accuracy caused by highly similar scattering features of small defects, significantly improving the accuracy of the detection results.

This invention discloses a hyperspectral target detection method based on adversarial autoencoders and attention mechanisms, comprising: performing coarse detection on an input hyperspectral image; obtaining background samples as clean as possible as training data by adjusting the binarization threshold of the coarse detection result image; training a model based on adversarial autoencoders and attention mechanisms using the prepared training data to obtain a model that can accurately reconstruct the background of the hyperspectral image; inputting a test hyperspectral image into the network for reconstruction; calculating the spectral distance between the reconstructed result and the original hyperspectral image pixel by pixel to obtain a distance map; and applying background suppression to the distance map to obtain the final detection result image. This invention introduces a spectral attention mechanism to assign weights to the spectral dimensions, accelerating the training and convergence speed of the network; and overcomes the problem of imbalanced positive and negative samples in hyperspectral images by selecting only background samples as training data to train the image reconstruction model based on adversarial autoencoders.

The invention discloses a hyperspectral remote sensing image recognition method and device based on spectral spatial feature coupling, different linear spectral filters are utilized to perform feature extraction of the whole hyperspectral remote sensing image so as to obtain multi-category spectral features, and compared with a traditional technology, the hyperspectral remote sensing image recognition method does not need spectral dimension reduction, and is high in recognition efficiency. Therefore, the problem that the correlation between fine spectral information and high-order spectrum contained in the hyperspectral data is weakened in the traditional technology is avoided; meanwhile, minimization of image background energy is used as a target function to optimize the optimal real spectral feature vector of each linear spectral filter for the target category, so that when the constructed linear spectral filters are used for feature extraction, the background energy can be minimized while the target spectral response is maintained and enhanced, and the target spectral response is improved. Therefore, spectral clutter and noise in a complex environment are suppressed, and the detection precision of the model in a complex environment background can be improved.

The application belongs to the technical field of remote sensing data processing, and discloses a hyperspectral and laser radar data joint ground feature classification method, equipment and medium, the method comprises the following steps: acquiring hyperspectral image data and laser radar elevation data; feature extraction is carried out by adopting a three-branch parallel structure, wherein the hyperspectral image branch extracts global spectral spatial features by three-dimensional convolution and bidirectional scanning along the spectral dimension, the laser radar branch extracts elevation structure features by multi-level convolution, and the multi-modal branch extracts multi-modal fusion features by early fusion and a multi-scale feature enhancement module; a multi-head bidirectional cross attention module is used to perform deep bidirectional interaction and fusion on the features extracted by the three branches, thereby generating a final fusion feature vector; finally, the final fusion feature vector is input into a classifier to obtain a classification result. The method realizes deep interaction between modes and adaptive perception of multi-scale ground features, and significantly improves the accuracy of joint classification.

The invention discloses a liquid metal sodium leakage detection system and method based on hyperspectral imaging, and relates to the technical field of sodium-cooled leakage, the system is applied to a sodium-cooled fast reactor in a fourth-generation advanced reactor, and the system comprises an optical module, an imaging module and a data processing module; wherein the imaging module is used for guiding radiation light of a leakage area of the sodium-cooled fast reactor to the optical module; the optical module adopts an Offner optical structure and is used for converging and splitting radiation light of a leakage area of the sodium-cooled fast reactor to obtain a hyperspectral image containing spatial information and spectral dimension information of the leakage area; and the processing module is used for marking a characteristic spectral signal related to the metallic sodium in the hyperspectral image, and determining a sodium leakage position based on the marked characteristic spectral signal. By adopting the system, the leakage position of the liquid metal sodium in the sodium-cooled fast reactor can be quickly and accurately identified.

The invention relates to yaw calibration data simulation of a hyperspectral camera, and provides a simulation method for yaw calibration data of a flyback type multi-slit satellite-borne hyperspectral camera in order to solve the problems that an existing yaw calibration data simulation mode for the hyperspectral camera is relatively simple, the accuracy of obtained data is relatively low and the like. Comprising slit yaw trajectory simulation, yaw calibration data sampling and yaw data noise simulation. Wherein the slit yaw trajectory simulation comprises construction of a bottom image pixel coordinate system, construction of a camera coordinate system, conversion of a slit position and the bottom image pixel coordinate system and generation of a slit bottom image projection coordinate trajectory; the yaw calibration data sampling comprises yaw calibration data space dimension sampling and yaw calibration data spectrum dimension sampling; the yaw calibration data sampling comprises yaw calibration data space dimension sampling and yaw calibration data spectrum dimension sampling; according to the simulation method, the precision and accuracy of simulation data are effectively improved, and effective support is provided for yaw calibration algorithm research and calibration precision evaluation of the hyperspectral camera.

The invention discloses a rice grain hyperspectral image classification method based on improved feature extraction, and the method is characterized in that the method comprises the following steps: a hyperspectral imaging device is used to obtain a hyperspectral image data cube containing rice grains, the hyperspectral image data cube has a spatial dimension H * W and a spectral dimension C, each sample is endowed with a classification label of a variety category, a quality grade or a purity attribute; and performing radiation correction on the hyperspectral image data cube to eliminate response differences of an imaging system, performing bad pixel detection and restoration to reduce influence of sensor defects, and performing normalization or standardization processing to unify numerical scales of wave bands to obtain standardized hyperspectral data X.

The application belongs to the field of marine environment monitoring, and discloses a construction method, a classification method and a device of a microscopic hyperspectral image classification model, comprising the following steps: acquiring a microscopic hyperspectral image of a microalgae and microplastic mixture sample; according to an image homography transformation model, splicing the microscopic hyperspectral image in each spectral dimension to obtain a hyperspectral image corresponding to each spectral dimension; carrying out denoising processing on the splicing edges of the hyperspectral image; carrying out hyperspectral data dimension reduction processing on the hyperspectral image after denoising processing; and establishing a classification model according to the hyperspectral image after denoising processing and the hyperspectral image after dimension reduction processing, so as to identify the microscopic hyperspectral image. Based on the microscopic hyperspectral imaging technology, the application identifies microalgae and microplastics of tens of microns, and combines the microscopic image splicing technology, so that the detection limit is improved.

The invention provides a spectral imaging device and method in a fixed target scene, the imaging device comprises a wide-spectrum imaging lens which is arranged on a shell and keeps the optical axis direction unchanged, an acquisition unit is arranged in a mounting cavity, and a light-sensitive surface of a multi-spectrum image sensing assembly is in conjugate matching with a rear focal plane of the imaging lens, and a light-sensitive surface of the multi-spectrum image sensing assembly is in conjugate matching with the rear focal plane of the imaging lens; the multispectral image sensing assembly can move along the spectral dimension direction of the multispectral image sensing assembly; the precision of the displacement sensing assembly is matched with the pixel size magnitude of the multi-spectral image sensing assembly, and the displacement sensor and the multi-spectral image sensing assembly move synchronously and are used for outputting an electric signal corresponding to the physical displacement of the multi-spectral image sensing assembly in real time; and the control unit is electrically connected with the acquisition unit, and is used for triggering the multispectral image sensing assembly to acquire an image based on the electric signal, and receiving and processing image data output by the multispectral image sensing assembly so as to generate a spectral data cube of the to-be-detected target. According to the invention, target size limitation can be broken through, and high-precision and high-universality spectral imaging can be realized.

The invention discloses a spectrum optimization-based double-layer medium wide spectrum parameter simultaneous reconstruction method, relates to the technical field of spectral imaging, and aims to solve the problems that when wide optical parameter reconstruction is carried out based on a multi-layer medium model in the prior art, detection radiation optical signals are limited by a traditional contact type measurement mode, and the reconstruction precision is low. On the basis of a traditional participating medium optical parameter measurement and reconstruction method, a light field camera is introduced to more accurately detect a reflected radiation measurement signal of a medium boundary; a reconstruction optimization algorithm of searching an initial point by a steepest descent method and searching an optimal solution by a conjugate gradient method is combined, so that the efficiency of accurate reconstruction of the double-layer medium is greatly improved, the limitation of a traditional contact measurement mode is broken through, and the reconstruction precision is improved; in view of the problem that a radiation measurement signal is extremely low in sensitivity to a reduced scattering coefficient of a bottom-layer medium, prior information of a spectrum dimension is introduced, reconstruction results of an absorption coefficient and a reduced scattering coefficient of a double-layer medium, especially the bottom-layer medium, are improved, and simultaneous reconstruction of a double-layer medium absorption spectrum and a reduced scattering spectrum is realized.

The invention relates to the technical field of remote sensing image processing and hyperspectral reconstruction, and discloses a hyperspectral image reconstruction method and system based on multi-domain neural network modeling, and the method comprises the following steps: carrying out the convolution processing of an input multispectral image, improving the spectral dimension of the multispectral image to the wave band number of a target hyperspectral image, and obtaining an initial hyperspectral feature; processing the initial hyperspectral features by using a multi-scale spatial fusion module, extracting and fusing global spatial context information and local multi-scale spatial features to obtain spatial fusion features, processing the spatial fusion features by using a U-shaped spectrum enhancement modeling module, and extracting, enhancing and fusing spectral information on different scales. Through cooperative utilization of space, frequency domain and other multi-domain information, the precision and spectral fidelity of reconstructing a hyperspectral image from a multispectral image can be effectively improved. Meanwhile, a deep learning framework is adopted, good flexibility and expandability are achieved, and adjustment and optimization can be conveniently carried out according to actual application requirements.

This invention relates to the fields of aerospace remote sensing and satellite overall design technology, and particularly to a method for analyzing spectral aliasing caused by flywheel micro-vibration in hyperspectral imagers. The method involves acquiring flywheel operating parameters and satellite structural dynamics parameters, calculating the time series of line-of-sight jitter displacement caused by flywheel micro-vibration, acquiring a standard hyperspectral data cube of the target scene, performing a dynamic spectral sampling process using the line-of-sight jitter displacement time series, continuously integrating the data cube by simulating the instantaneous line-of-sight motion trajectory of the imager, and outputting a hyperspectral image affected by micro-vibration. The method also involves extracting the spectral curves of the pixels to be evaluated and the ideal spectral curve, and calculating the spectral aliasing error index. This invention quantifies the impact of micro-vibration on spectral dimensions by constructing a complete analytical chain of flywheel disturbance, structural transmission, and dynamic sampling, solving the problem of the lack of quantitative analysis of spectral aliasing in existing technologies, and significantly improving the reliability of hyperspectral remote sensing data in agricultural crop classification and growth monitoring.

The invention provides a picometer-resolution wide-spectrum calculation imaging architecture, chip and system, and the architecture comprises an obtaining module which is used for obtaining a single-frame grayscale image passing through the picometer-resolution wide-spectrum calculation imaging chip; and the calculation and reconstruction module is connected with the acquisition module and is used for dividing a preset spectral range into a plurality of continuous spectral segment sub-intervals, reconstructing hyperspectral data images of the sub-intervals based on the single-frame grayscale image, and splicing the hyperspectral data images of all the sub-intervals in the spectral dimension to obtain a full-spectral-segment hyperspectral image. The spectrum coding mask in the picometer resolution wide-spectrum calculation imaging chip is bonded on the surface of the imaging sensor, so that the module thickness is reduced, and the chip can be integrated on portable equipment; through a segmentation calculation reconstruction strategy, the reconstruction problem of the full-spectrum high-dimensional data is decomposed into parallel or serial independent solution of a plurality of continuous subintervals, the data volume of a single subinterval is reduced, and the calculation speed is improved.

The invention relates to the technical field of culture medium detection, and discloses a fruit fungus culture medium pollution identification method and system based on hyperspectral imaging, and the method comprises the steps: obtaining a data cube of a hyperspectral image of a culture medium, executing non-negative tensor decomposition, and obtaining a group of space factor graphs and corresponding spectral vectors; calculating a Bhattacharyya distance between each spectrum vector and a preset pollution strain spectrum library, and generating a pollution affinity coefficient corresponding to each space factor graph; calculating the mahalanobis distance of each pixel point in the data cube, and generating a global spectral distortion graph; initializing a pollution potential field with the same spatial size as the hyperspectral image, and carrying out iterative updating through a reaction diffusion equation until convergence; and performing threshold segmentation on the converged pollution potential field to obtain a binary mask of the pollution area. According to the method, the analysis of the spectral dimension and the structural constraint of the spatial dimension are deeply fused by constructing the reaction diffusion model, so that the early and accurate identification of the culture medium pollution is realized.