96 results about "Hyperspectral reflectance" patented technology

The invention discloses an optical performance detection system and method for a camouflage material, and relates to the technical field of camouflage material detection, and the system comprises a structure identification and illumination control module which is used for collecting a surface image of a camouflage material sample, identifying the surface structure of the sample, starting a full-spectrum light source, and simulating step-by-step illumination from weak light to strong light; carrying out RGB imaging based on the standard illumination; and the region division module is used for applying a superpixel segmentation algorithm to the image, automatically dividing sub-regions, performing hyperspectral reflectivity scanning on each region, synchronously acquiring a light intensity image in a polarization direction, calculating a Stokes vector and constructing a polarization enhanced image. According to the method, frequency domain quantitative analysis of microcosmic interference fringes and accurate mask extraction of polarization abnormal areas can be realized, optical exposure risks of camouflage materials under complex illumination and visual angle conditions are effectively quantified through multi-angle reflection sampling and scoring function modeling, and the detection precision, the area pertinence and the automation level are remarkably improved.

The invention relates to a heavy metal content quantitative inversion method and system combining a GF-5 remote sensing image and a ground soil hyperspectrum, and the method comprises the steps: obtaining a bare soil image based on a normalized vegetation index value of the GF-5 remote sensing image, and carrying out the DS correction and first-order derivative spectrum transformation of the bare soil image; obtaining ground hyperspectral reflectivity data, soil heavy metal content and remote sensing image hyperspectral reflectivity data of the sampling points; dS correction and first-order derivative spectrum transformation are carried out on the hyperspectral reflectivity data of the remote sensing image; screening remote sensing image hyperspectral reflectivity data after first-order derivative spectral transformation through a Boruta algorithm, and constructing a heavy metal inversion model in combination with the soil heavy metal content; and predicting the bare soil image after the first-order derivative spectral transformation based on a heavy metal inversion model to obtain a heavy metal content pollution spatial distribution diagram. The soil heavy metal pollution condition in a large range can be rapidly and accurately monitored, and a basis is provided for ecological restoration.

The invention discloses a strawberry internal quality and maturity nondestructive testing method based on an MPCD-Mama model. The strawberry internal quality and maturity nondestructive testing method comprises the following steps: acquiring a strawberry sample, acquiring a hyperspectral image by using a hyperspectral reflection imaging system, measuring SSC, PH and hardness, and marking maturity; correcting the hyperspectral image, performing spectrum extraction to form a data set, and dividing the data set into a training set, a test set and a verification set; the method comprises the following steps: constructing an MPCD-Mama model, training the MPCD-Mama model by using a training set, testing the performance of the MPCD-Mama model by using a verification set on the basis of a cross validation method, predicting related indexes on the basis of the pre-trained MPCD-Mama model, generating heat maps of corresponding components at different positions of strawberries, and displaying the distribution conditions of the different components. According to the method, nondestructive testing of multiple quality parameters of strawberries is achieved, compared with a traditional method, the prediction precision is remarkably improved, and technical support is provided for nondestructive testing of the quality of the strawberries.

The invention discloses a vegetation coverage and management measure remote sensing inversion method combining multi-angle remote sensing data and layered vertical vegetation coverage, and the method comprises the following steps: (1), obtaining a multi-angle remote sensing data set, and carrying out the preprocessing of the multi-angle remote sensing data set; (2) selecting an optimal wave band through Borson correlation analysis and XGBoost feature screening; (3) estimating a C factor and a structured vegetation index S-Cs; (4) simulation of leaf reflectivity and transmissivity is carried out, and then vegetation canopy reflectivity is simulated; (5) converting the high spectral reflectivity of the vegetation canopy into equivalent remote sensing reflectivity of a satellite wave band, calculating vegetation indexes, and selecting the vegetation index with the highest correlation to participate in inversion and modeling of a leaf area index LAI; (6) determining an optimal angle combination and evaluating an inversion result of LAI; (7) establishing a novel quantitative coupling relation model between the multi-angle LAI and the SCs and between the SCs and the C factor, and then inverting the C factor; the invention provides a technical means with high practicability for monitoring the dynamic change and expansion process of soil erosion.

The invention relates to the technical field of shoe body detection, in particular to a shoe body quality detection method based on image visual analysis, which comprises the following steps: synchronously acquiring three-dimensional contour and surface spectral information of a to-be-detected shoe body through three-dimensional scanning and spectral imaging equipment to obtain original point cloud spectral data; performing registration and fusion processing on the original point cloud spectral data to construct a registered hyperspectral three-dimensional grid model containing space coordinates and hyperspectral reflectivity values; and calculating a normal vector field and a curvature distribution diagram of the surface based on the hyperspectral three-dimensional grid model so as to quantify geometric features of the surface of the shoe body. According to the invention, through registration and fusion processing, surface spectral information collected by a spectral imaging device is reversely projected and interpolated to each space coordinate vertex of an initial three-dimensional point cloud constructed by a three-dimensional scanning device, so that a registered hyperspectral three-dimensional grid model is constructed, and a surface chemical characteristic spectrum is endowed with three-dimensional space geometric coordinates. And the detection capability is improved.

The invention relates to the technical field of salicornia europaea spectral feature extraction, and discloses a salicornia europaea lithium stress time sequence spectral feature database construction method which comprises the following steps: culturing salicornia europaea in a greenhouse by utilizing water culture vessels containing different lithium concentrations to obtain a plurality of salicornia europaea water culture vessels containing different lithium concentrations; measuring hyperspectral images of the salicornia europaea in the sampling intervals to obtain hyperspectral images of the salicornia europaea and the reference white board in each sampling interval; extracting a hyperspectral reflectivity image and hyperspectral data of the salicornia europaea in each sampling interval so as to obtain absorption characteristic parameters and red edge positions of each continuum removal spectrum under different set wavelengths, and calculating the hyperspectral reflectivity of the salicornia europaea according to the hyperspectral reflectivity image of the salicornia europaea in each sampling interval. Respectively constructing a time sequence spectrum library and a standard spectrum library of salicornia europaea at different sampling intervals under the stress of different lithium concentrations; according to the method, the spectral feature precision of salicornia europaea is improved, and meanwhile, the spectral database of salicornia europaea is enriched.

The embodiment of the invention provides a hyperspectral inversion method, system and equipment for the iron content of soil and a medium, and belongs to the field of soil analysis. The method comprises the following steps: selecting a plurality of measuring points in a to-be-measured soil sample area to collect surface soil samples; performing spectral measurement on the surface soil sample to obtain spectral reflectivity, and performing data transformation on the spectral reflectivity; constructing a correlation curve of the spectral reflectance at different wavelength intervals after data conversion and the iron content of the soil, selecting the wavelength with the highest correlation coefficient as an initial inversion index, and constructing a hyperspectral inversion model of the iron content of the soil; and inverting the iron content of the to-be-detected soil sample area based on the soil iron content hyperspectral inversion model. The soil iron content hyperspectral inversion model suitable for different space-time ranges is established by performing simulation transformation on the soil hyperspectral reflectivity and analyzing a wave band sensitive to the soil iron content, so that space and time barriers of soil spectral data in soil iron content research are broken, and high data sharing is realized.

The invention discloses a visible light wave band spectral response function inversion method which comprises the following steps: acquiring a satellite on-orbit spectral response function and atmospheric top hyperspectral reflectivity simulation data through data simulation, and calculating satellite on-orbit reflection data through a forward model; determining a target function of the inversion model according to the satellite in-orbit reflection data; determining constraint conditions of the inversion model; training the inversion model through a linear programming solver to obtain a trained inversion model; and inputting the atmospheric top hyperspectral reflectance data and the satellite reflectance value into the trained inversion model to obtain a corresponding spectral response function. According to the satellite visible light wave band and the satellite characteristics, an inversion algorithm based on linear programming is used, and sufficient prior information of a target satellite is fully utilized to formulate reasonable constraints; and a visible light wave band spectral response function inversion method with universality and robustness is obtained.

The invention belongs to the technical field of image processing, and particularly relates to an intelligent agricultural product pesticide residue detection method and system based on a spectrum technology, and the method comprises the steps: obtaining the hyperspectral reflectivity according to the original hyperspectrum, dark current and whiteboard image data; calculating an illumination confidence factor by using the average spectral intensity; performing linear scaling on the obtained standard biological matrix background spectrum according to the illumination confidence factor to obtain a local background estimated value and obtain a net residual spectrum; generating a pesticide residue distribution thermodynamic diagram by combining the cosine similarity of the net residual spectrum and the target pesticide standard fingerprint spectrum and the noise suppression weight based on the illumination confidence factor; and pesticide residue detection is carried out according to the statistical characteristics of the connected region of the suspected defect mask. Through adaptive background deduction and noise suppression, the problems of uneven curved surface illumination and strong background interference are solved, and the detection accuracy is improved.

The invention discloses a visible light hyperspectral measurement method based on multispectral sensing, and belongs to the technical field of spectrum detection. According to the method, visible light eight-channel data is collected through a multispectral sensor, after optical crosstalk correction and filtering preprocessing, key principal components are reserved through PCA dimension reduction, a multispectral and hyperspectral mapping model is constructed through an SVR algorithm, and finally the hyperspectral reflectivity is reconstructed through PCA inverse transformation. The problems that traditional hyperspectral equipment is high in cost and poor in portability are solved, low-cost and high-precision visible light hyperspectral measurement is achieved, and the device and the method can be widely applied to food freshness detection, agricultural product quality evaluation and other scenes.

The invention discloses a short-term prediction and early-warning method and system for water blooms of water quality of near-sensitive lakes and reservoirs based on hyperspectrum, and belongs to the field of water quality prediction and early-warning technical methods. Combining the preprocessed water body proximity hyperspectral reflectivity with the time-space attribute information to generate a water body proximity hyperspectral reflectivity-time sequence data set; constructing a preset water quality inversion model set according to a space-time matching principle in combination with synchronously measured water quality parameters; generating a multi-water quality parameter-time sequence data set by using a preset water quality inversion model set; and building a prediction and early warning model based on the multiple water quality parameter-time sequence data set, and outputting a water quality and algal bloom prediction and early warning result. Measurement data are transmitted to a cloud server in real time, hyperspectral reflectivity data are converted into water quality parameters easy to understand by a user by constructing a water quality inversion model set, and the data are integrated on a software platform based on multi-parameter water quality; and current water quality information, historical long-term change and future short-term algal bloom prediction and early warning information can be checked anytime and anywhere.

The invention provides a Mars mineral abundance inversion method and system. The Mars mineral abundance inversion method comprises the steps of firstly obtaining target Mars hyperspectral reflectivity data; and then generating simulated Mars hyperspectral image data based on the target Mars hyperspectral reflectivity data. Secondly, training the constructed mineral abundance inversion deep learning model based on the simulated Mars hyperspectral image data, and determining the trained mineral abundance inversion deep learning model; thirdly, acquiring to-be-processed Mars hyperspectral image data, and performing preprocessing; and finally, performing mineral abundance inversion on the preprocessed to-be-processed Mars hyperspectral image data through the trained mineral abundance inversion deep learning model to obtain a mineral abundance inversion result. Therefore, the weight distribution of each wave band of the hyperspectral image can be enhanced through the mineral abundance inversion deep learning model, and the non-linear modeling capability and the wave band characteristic response capability of abundance inversion are improved, so that the accuracy and the efficiency of Mars mineral abundance inversion can be effectively improved.

The invention provides a method for calibrating hyperspectral reflectivity data of field tea ridges, which comprises the following steps of: acquiring outdoor hyperspectral data and indoor hyperspectral data of the field tea ridges, and performing cubic B spline fitting on the indoor hyperspectral data; according to the wave band position and number of the outdoor hyperspectral data, the reflectivity of the corresponding wave band position is obtained from the indoor spectral reflectivity curve, and an indoor standard reflectivity curve is obtained. Searching the modulation coefficient of the k-th iteration under the condition that the constraint function obtains the minimum value; calculating a correction factor function according to the modulation coefficient of the kth iteration; substituting the training set and the correction factor function into a calibration formula, and substituting the verification set, the kth environmental interference error and the correction factor function into the calibration formula; and if the kth residual matrix meets the preset condition, iteration is stopped. According to the method, the optimal modulation coefficient can be searched, so that the outdoor spectral reflectivity can be calibrated adaptively, and the outdoor hyperspectral data of the field tea ridge can be measured more accurately.

The invention relates to the technical field of structural safety detection of steel structural members, in particular to a steel structural damage identification and repair method, system and device and a medium, and the method comprises the steps: obtaining a multi-source image data set and a standard corrosion test block database of a steel structural member through an unmanned aerial vehicle, the multi-source image data set comprises a polarized visible light image, a long-wave infrared temperature matrix and hyperspectral reflectivity data, and the standard corrosion test block database comprises spectral feature vectors of different corrosion grades and corresponding physical parameters; and carrying out sub-pixel level registration and feature extraction on the multi-source image data set. According to the method, the unmanned aerial vehicle synchronously carries the three cameras to carry out real-time multi-dimensional data acquisition, the problem of data splitting in traditional single-mode detection is solved, the multi-spectral data acquisition efficiency and the sub-millimeter level hidden damage identification precision are remarkably improved, and dynamic environment interference is inhibited in real time through an intelligent parameter inversion mechanism.

The invention discloses an automobile wire harness detection system based on artificial intelligence and machine vision, and relates to the technical field of intelligent detection, and the system comprises a feature extraction module which carries out the noise reduction and feature enhancement of hyperspectral reflection data through a low-rank sparse decomposition method, extracts the principal component features and local sparse features of an automobile wire harness sheath, and carries out the recognition of the local sparse features of the automobile wire harness sheath; spectral features are formed; the material identification module is used for identifying the category of the automobile wire harness sheath material according to the similarity distribution of the spectral characteristics in the multi-dimensional spectral space; the state analysis module is used for combining the spectral characteristics with the types of the automobile wire harness sheath materials, identifying the surface state of the automobile wire harness sheath and carrying out quantitative analysis to obtain an aging grade evaluation result; and the quality evaluation module is used for carrying out comprehensive quality evaluation on the automobile wire harness sheath according to the automobile wire harness sheath material category and the aging grade evaluation result, and generating a detection report. According to the invention, the capability of distinguishing aging modes of different materials is remarkably improved.

The invention discloses a fuzzy theory fused tongue diagnosis characteristic quantitative analysis method and system, and relates to the technical field of traditional Chinese medicine tongue diagnos.The method comprises the steps that a micro piezoelectric sensor array and a hyperspectral imaging module are integrated, tongue surface Young modulus distribution data and hyperspectral reflectivity data are synchronously collected, and a multi-mode tongue picture fusion data set is generated; further performing crack region segmentation and super-resolution reconstruction through a neural network model, outputting a crack network diagram and a tongue papilla form entropy, generating topological fingerprints in combination with continuous homology analysis, identifying a mechanical gradient mutation region and extracting a metabolic index, constructing a mechanical-metabolic associated feature set, and obtaining a mechanical-metabolic associated feature set; topological fingerprints and mechanical-metabolic characteristics are fused in a three-dimensional space-time coordinate system, a dynamic intuitionistic fuzzy decision field is constructed, finally, tongue picture pathology evolution risk indexes and a visual early warning report are output through a pathology prediction model, and the system provides quantifiable decision support for'prevention before illness' in traditional Chinese medicine.

The invention provides a pegmatite type lithium ore prospecting method based on aviation hyperspectral remote sensing. The method comprises the following steps: step 1, inputting hyperspectral reflectivity data; step 2, calculating continuum removal data of a characteristic spectrum section; step 3, calculating a characteristic absorption wavelength position DL of the characteristic spectrum section; step 4, calculating the matching degree of the characteristic spectrum sections; 5, recognizing mineral end members; 6, inputting pegmatite vein distribution information; 7, delineating an abnormal pegmatite vein; and 8, delineating the lithium-containing pegmatite vein. The invention provides a muscovite and spodumene end member identification and lithium-containing pegmatite vein identification method, the fine identification of seven subclasses of muscovite can be realized, the identification of the subclasses in the existing aviation hyperspectral data is the most and finest, and meanwhile, the muscovite and spodumene information can be finely distinguished. Based on spodumene and muscovite information, the lithium-containing pegmatite vein can be rapidly screened from a large number of pegmatite veins, and therefore the prospecting efficiency of pegmatite type lithium ore is improved.

The present invention discloses a method for improving the prediction accuracy of soil iron oxide based on an amplified hyperspectral dataset. The method comprises the following steps: amplifying the hyperspectral dataset and performing multiple fractional-order differential transformations on the original hyperspectral spectrum using a fractional-order differential algorithm; then combining the fractional-order differential spectra with the original spectrum to form different amplified hyperspectral databases to amplify the amount of original spectral data; establishing a 1D-CNN neural network model for each of the different amplified hyperspectral databases, where the independent variable is the hyperspectral reflectance value and the dependent variable is the soil iron oxide content; and selecting the optimal 1D-CNN model based on the models established for the different amplified databases. The present invention effectively solves the problem of low accuracy of deep learning models under small sample conditions, improves the applicability and effectiveness of deep learning models to a certain extent, effectively improves the existing prediction accuracy of soil iron oxide based on hyperspectral data, and provides new methods and ideas for hyperspectral prediction research of other soil properties.

The invention relates to the technical field of paper detection, in particular to a paper finished product detection system based on a framework paper production process. By extracting defect information, modeling based on a graph structure and carrying out graph convolution operation to smooth an image, a defect area is accurately identified. Large-scale trend and small-scale local anomaly are separated through a wavelet decomposition technology, errors caused by environmental factors are eliminated through a local adaptive regression method, and the accuracy of thickness deviation is ensured. The density uniformity is calculated through ultrasonic feature extraction, the hyperspectral reflectivity is corrected in combination with a physical compensation method of chemical residues, and the chemical residue index is accurately obtained. The comprehensive quality index of the paper is obtained through comprehensive index calculation, so that whether the finished paper is qualified or not is judged, and a basis is provided for paper production adjustment of a production line.

The invention relates to a tea tree chlorophyll content inversion method based on hyperspectral remote sensing. The method comprises the following steps: S1, acquiring remote sensing reflectivity data of tea tree leaves in a hyperspectral band; s2, performing spectral derivative processing on the hyperspectral reflectivity data obtained in the S1, and extracting enhanced spectral feature information; s3, selecting at least one characteristic wave band or wave band combination related to the chlorophyll content of the tea tree from the spectral derivative data; s4, establishing an inversion model between the chlorophyll content and the characteristic wave band reflectivity value; and S5, calculating the chlorophyll content of the target tea tree based on the inversion model and the hyperspectral remote sensing data. According to the method, rapid, lossless and high-precision monitoring of the chlorophyll content of the tea tree is realized, and the chlorophyll inversion precision and reliability are improved. The method has the advantages of high monitoring precision, abundant information amount, high model adaptability and the like, and can be widely applied to the fields of tea garden growth monitoring, nutrient diagnosis, precise fertilization, tea tree physiological research and the like.

The invention belongs to the technical field of nondestructive testing of plant diseases, and discloses a nondestructive testing method and system for Candidatus Liberobacter asiaticum based on hyperspectral reflection and transmission fusion, and the method comprises the following steps: respectively obtaining hyperspectral reflection spectrum data and hyperspectral transmission spectrum data of to-be-detected citrus leaves, single reflection characteristic wave band data and single transmission characteristic wave band data are obtained after preprocessing and wave band selection; based on the single reflection characteristic wave band data, the single transmission characteristic wave band data and the spectrum data after the characteristic level fusion of the single reflection characteristic wave band data and the single transmission characteristic wave band data, obtaining a first prediction calculation result, a second prediction calculation result and a third prediction calculation result through respective optimal classification models; and determining a corresponding weight according to the classification accuracy of each optimal classification model, and performing decision-level fusion calculation on the first, second and third prediction calculation results to obtain the citrus Huanglongbing grade. According to the method, feature level and decision level fusion is introduced, so that the method has the capability of distinguishing the surface physical form and the internal structure components of the sample, and can be used for rapidly and nondestructively detecting the citrus huanglongbing.

The invention provides a wheat leaf area index whole growth period estimation method based on canopy pure hyperspectral reflectivity, which comprises the following steps of: firstly, extracting wheat canopy spike, leaf and soil pure pixels based on an RGB (Red, Green and Blue) image combined with a deep learning algorithm; secondly, separating the canopy pure leaf hyperspectral reflectivity by using the extracted pure pixels and canopy hyperspectral reflectivity data in combination with a non-negative matrix factorization algorithm; and finally, calculating a vegetation index based on the canopy pure leaf hyperspectral reflectivity, and establishing a whole-growth-period LAI estimation model in combination with a simple linear regression algorithm. According to the method, the problem that the LAI inversion precision is reduced after heading is solved by combining near-end remote sensing and a machine learning algorithm, and the wheat LAI estimation method based on the canopy pure hyperspectral reflectivity has the potential of accurately predicting the LAI of the wheat in the whole growth period and can be expanded to accurate management of field crops and breeding of high-yield wheat varieties.

The invention discloses a crop photosynthetic parameter prediction method and device based on deep learning, a medium and a product, and relates to the field of photosynthetic parameter prediction, and the method comprises the steps: obtaining a hyperspectral data cube of a to-be-predicted crop; according to the hyperspectral data cube, determining the hyperspectral reflectivity of the to-be-predicted crop; according to the hyperspectral reflectivity, utilizing a photosynthetic parameter prediction model to determine photosynthetic parameters of the to-be-predicted crop; the photosynthetic parameters comprise a photosynthetic rate, a chlorophyll relative content, chlorophyll a, chlorophyll b and a leaf nitrogen content under specific light intensity; the photosynthetic parameter prediction model is obtained by training a deep learning model by using a training data set. According to the method, the photosynthetic parameter prediction model is determined by using the true values of the photosynthetic parameters and the corresponding hyperspectral reflectivity, so that the plurality of photosynthetic parameters of the crops can be determined quickly, losslessly and accurately.

The invention discloses a coastal salt marsh wetland non-photosynthetic vegetation coverage inversion method, which comprises the following steps: S1, obtaining hyperspectral reflection curves of soil, non-photosynthetic vegetation and photosynthetic vegetation of the coastal salt marsh wetland of the Yellow River Delta, simulating and measuring spectral characteristics of samples under different moisture contents (dry, low humidity, medium humidity and saturation) in a laboratory, and calculating the coverage of the non-photosynthetic vegetation in the coastal salt marsh wetland; key wave bands of non-photosynthetic vegetation and other ground features are screened and distinguished; s2, constructing a moisture-insensitive hyperspectral NPV index (MINI), and determining an optimal wave band combination through wave band traversal; and S3, constructing mixed data of soil, non-photosynthetic vegetation and photosynthetic vegetation under different moisture conditions in a laboratory, and evaluating inversion precision and stability of MINI under different moisture conditions. S4, acquiring a coastal salt marsh wetland hyperspectral image, and calculating an NDVI value and an MINI index value based on the processed image; and S5, performing spectral unmixing on the mixed pixels based on a triangular space method, and inverting the non-photosynthetic vegetation, photosynthetic vegetation and soil coverage of the research area. The method provided by the invention solves the technical problems of low precision and poor stability of the traditional NPV index inversion coverage under the condition that the coastal salt marsh wetland is influenced by tides and the moisture change is obvious.

The invention discloses a copper alloy surface wiredrawing quality detection method and system, and the method comprises the steps: carrying out the multi-source defect feature collaborative analysis of hyperspectral reflectivity data, linear array image data and surface three-dimensional point cloud data, and obtaining an oxidation defect marking graph, a scratch distribution graph and a texture anomaly graph; inputting the oxidation defect mark graph, the scratch distribution graph and the texture anomaly graph into a pre-trained convolutional neural network, wherein the convolutional neural network outputs and obtains defect space coordinates; and calculating the unit area defect density, the maximum defect area and the deepest scratch depth according to the defect space coordinates, and determining the surface wire drawing quality of the copper alloy according to the unit area defect density, the maximum defect area and the deepest scratch depth. The industrial problems of multi-modal data splitting, missed judgment of tiny defects and misalignment of grading standards can be well solved.

A method for performing point-to-point whiteboard parameter ratio correction on a hyperspectral image includes: capturing hyperspectral data of a standard reference whiteboard in advance as white(x,y,w), and storing the records, then captures hyperspectral data of a sample as sample(x,y,w); then selecting an unobstructed and unshaded whiteboard area within a certain range of the hyperspectral data sample(x,y,w) of the test sample, and labeling the area as Area A; calculating a spectral average of the ROI on the sample image as SA(w); calculating a spectral average of whiteboard data in the same position as the ROI as WA(w); dividing the two spectral averages, and obtaining a correction coefficient alpha(w)=WA(w). / SA(w); multiplying the whiteboard parameter ratio correction coefficient alpha(w) by a sample reflectance image matrix after whiteboard parameter ratio correction to obtain a final hyperspectral reflectance image matrix REFL(x,y,w)=alpha(w).*sample(x,y,w). / white(x,y,w).

The invention provides a hyperspectral data-based automatic extraction method and a hyperspectral data-based automatic extraction system for tetracalamus mongolicus, and the method comprises the steps: building a data collection platform, collecting the ground canopy hyperspectral reflectivity of each species and the hyperspectral image data of an unmanned aerial vehicle in a target region, and synchronously collecting the geographic coordinates of each shrub; carrying out dimension reduction processing on the ground canopy high-spectral reflectivity data and constructing classification characteristic variables of the tetragos, wherein the classification characteristic variables comprise a vegetation index variable, a continuum removal parameter variable and a high-spectral characteristic parameter variable; screening key feature variables of the classification feature variables of the tetrazygote; a vegetation index threshold value classification method and a classification method based on machine learning are adopted to construct classification models to extract the tetragos in the target area, and an optimal classification model is selected; and based on the optimal classification model, combining the hyperspectral image data of the unmanned aerial vehicle to carry out identification and classification on the quadrangle of the target area, and obtaining space distribution data of the quadrangle. By means of the method, rapid, accurate and automatic extraction of the quadrangle tree can be achieved.

The invention belongs to the field of functional ceramic materials, and relates to a boron nitride fiber-based ceramic material as well as a preparation method and application thereof. The material is prepared by calcining BN powder at 700-900 DEG C for 12-48 hours, and the content of B2O3 is 1t; the BN powder is of a fibrous structure, the radial width of the BN powder is 1-5 microns, and the length-diameter ratio of the BN powder is gt; 10: 1. The preparation method comprises the following steps: dissolving a B source and an N source in a low-hydroxyl-density cosolvent-water mixed solvent, evaporating the solvent or cooling to obtain a precursor product, calcining the product in NH3-N2 to obtain fibrous BN powder, and calcining the molded powder to obtain the BN fiber-based ceramic material. The material has good high-temperature oxidation resistance, thermal shock resistance, heat conduction anisotropy and ultraviolet-visible-near infrared band hyperspectral reflectivity, and can be applied to a thermal protection layer of a spacecraft, a thermal protection layer of a high-temperature pipeline and a reactor or a high-energy laser protection layer.

This invention discloses a method for inverting equivalent water thickness in plant leaves across species. This method selects equivalent water thickness with more explicit physical meaning as a representative indicator of plant leaf water content. Based on traditional vegetation indices, it combines continuous removal of preprocessing parameters to construct absorption feature parameters, thereby reducing noise in hyperspectral data while deeply mining its inherent spectral characteristics. It innovatively introduces one-heat coding technology to explicitly process species information and employs a Bayesian optimized machine learning model, effectively improving the accuracy, stability, and computational efficiency of cross-species water inversion. This invention can accurately invert the water status of crops across species, providing a new method for rapidly acquiring crop water status based on hyperspectral reflectance characteristics. It can be widely applied in intercropping and relay cropping patterns and large-scale survey and analysis scenarios, greatly improving the accuracy and speed of water stress diagnosis.

A non-invasive ophthalmic light-based detection device for detecting pathologies associated with Alzheimer's disease (AD) in the retina of an eye. The device uses two imaging modalities, one of which directs a specific area to be interrogated by the second imaging modality. A hyperspectral reflectance imaging unit detects light reflected and / or backscattered from the retina from a broadband light source, which is used to determine one or more regions of interest. A Raman spectroscopy unit detects light from a laser that is re-emitted by the retina through the phenomenon of Raman scattering, which is targeted to the region of interest determined based on the hyperspectral reflectance information. The detection information from the hyperspectral reflectance imaging unit and the Raman spectroscopy unit is used to determine the presence of the one or more pathologies associated with AD. The detection device allows identification of high-risk groups, diagnosis and tracking of patient response to treatment.