71 results about "Hyperspectral reflectance" patented technology

A device to provide hyperspectral reflection spectrum, hyperspectral depolarization, and hyperspectral fluorescence spectrum data in a portable, remote sensing instrument. The device can provide a large range of remotely-sensed optical property data, presently only obtainable in laboratories, in a low-cost field instrument. Among its many uses, the present invention can be used by farmers as a tool for determining the nitrogen content of crops to optimize fertilizer laydown.

This invention relates to a non-destructive precise determination method for biophysical parameters of cotton. To the feature and growth characteristic of cotton, selecting SAVI2 and RVI (precision more than 80%) with high precision for LAI and CCD measurement on middle and final period; finding out a optimal wave band combination for NDVI as 705nm (wave width 20nm) and 780nm (wave width 40nm); and presents an empirical regression equation as Y=0.5287*R780 -0.202*R705+2.4575 for LAI measurement by high-spectrum reflection rate. Compared with prior art, this invention has precision more than 81.8% with simple process and no damage.

The invention relates to the field of detection of heavy metal pollution in soil , in particular to an unmanned-aerial-vehicle hyperspectral inversion based monitoring method of heavy metal pollution in soil. The method is characterized by including: field sampling; sample pretreatment; using an x-ray fluorescence analyzer to collect content of main research elements of heavy metal pollution sources of samples; using a field spectrometer to collect laboratory hyperspectral reflectance of the samples; performing data processing on original spectral reflectance data; performing correlation analysis on measured content of the main research elements respectively with laboratory hyperspectral original reflectance data, reciprocal, logarithms, first derivative data and second derivative data with a partial least squares regression algorithm and verification and modification on models to acquire an optimal transformation method, using an unmanned aerial vehicle equipped with a hyperspectral imaging spectrometer to collect research-area hyperspectral reflectance data as to-be-measured data, and performing extensive inversion of the heavy metal content. The unmanned-aerial-vehicle hyperspectral inversion based monitoring method has the advantages of large range detachable, non-invasive and non-contact quick sample detection and the like.

The invention relates to a chlorophyll content measurement method and an apparatus thereof. The method comprises the following steps: acquiring the chlorophyll content of a preset sample, and calculating a corresponding spectral vegetation index according to the hyperspectral reflectivity data of the preset sample; constructing a model of a predication relationship between the chlorophyll content of a vegetation to be measured and the spectral vegetation index according to the chlorophyll content of the preset sample and the corresponding spectral vegetation index; obtaining the hyperspectral reflectivity data of the leaf and canopy of the vegetation to be measured; calculating the spectral vegetation index of the vegetation to be measured by using a spectral index algorithm according to the hyperspectral reflectivity data; and inversing by adopting the pre-constructed predication relationship model according to the spectral vegetation index of the vegetation to be measured in order to obtain the chlorophyll content of the vegetation to be measured. An optical remote sensing technology is used in the chlorophyll content measurement field, so accurate, simple, fast and timely measurement of the chlorophyll content is realized, and the method and the apparatus are of great significance to monitor the growth vigor of crops and estimate the output.

A construction and merging classification method for high spectrum data multi-characteristic space comprises the following steps: (1) obtaining high spectrum reflection rate data and constructing high spectrum initial characteristic space; (2) utilizing a real-dimension analysis method to determine the category number of objects to be classified; (3) utilizing a semi-automatic image unit trainingsample selection method to obtain training samples to be classified; (4) determining a single category separability measurement criterion according to a largest separability principle; (5) utilizing an optimizing algorithm to obtain weight optimizing characteristic space according to the separability measurement criterion determined in the step (4); (6) performing single category optimizing linear transformation on the weight optimizing characteristic space to obtain linear transformation characteristic space specific to single category optimization; (7) performing classification on the linear transformation characteristic space to respectively obtain a classification result specific to the single classification optimization; and (8) designing merging rules, merging the classification result of the single category optimization and obtaining an accurate merging classification result.

The invention relates to the field of remote sensing monitoring of water quality, in particular to a multi-model collaborative inversion method used for water quality parameters and based on deterministic set modeling. According to the method, sampling points are distributed in a research area, and water quality parameter concentration and corresponding hyperspectral remote sensing reflectivity of the sampling points are acquired; actually measured hyperspectral remote sensing reflectivity of a water body is preprocessed, and multiple water quality parameter inversion models are constructed by selecting preprocessed hyperspectral remote sensing reflectivity with better correlation with the water quality parameter concentration or band combination; weight of each single model is determined with an entropy weight method and a set pair principle, weighted summation is performed on inversion results of the single models, and multi-model collaborative inversion of the water quality parameters is realized. With adoption of the method, multi-model collaborative inversion of the water quality parameters can be realized with the two deterministic set modeling methods including the entropy weight method and the set pair principle by integrating characteristics of different water quality parameter inversion models, and the stability of the water quality parameter inversion results is improved.

The invention discloses a band ratio method based maize embryo segmentation method in a high-spectral reflection image. The high-spectral image of a maize seed is collected with the embryo surface of the seed upwards; multiple pixels and spectrums thereof are extracted from the endosperm and embryo and normalized; the spectral ratio of two random wave bands is calculated and serves as the band ratio, and the optimal band ratio is selected; a band subset is selected, variance of all pixels in the band subset is calculated, a seed binary image is obtained via threshold segmentation, and a background removed high-spectral image of the maize seed is obtained via mask operation; and a gray scale image of the background removed high-spectral image of the maize seed under the optimal band ratio is calculated via the high-spectral image, the gray scale image is segmented in a region growing method to obtain an embryo binary image, the embryo binary image is processed to obtain a seed embryo binary image, and the high-spectral image of the maize seed embryo is obtained via mask operation. The method can be used to segment the maize seed embryo in the visible near-infrared high-spectral image effectively, the segmentation effect is good, and the practicality is higher.

The present invention is directed to methods and apparatus for pest management using remote sensing technology. One aspect of the present invention relates to a method for detecting plant-parasitic nematodes using hyperspectral reflectance data. Another aspect of the present invention relates to a device for determining the population of reniform nematode in a target. The further aspect of the present invention relates to a method for managing nematode population with variable rate applications of nematicides.

The invention provides a multi-model cooperative water quality parameter concentration inversion method and device, the method is as follows: constructing multiple candidate remote sensing inversion models by use of measured concentration and hyperspectral reflectance data of water quality parameters at a sampling point; calculating the comprehensive error of the candidate remote sensing inversion models; according to the comprehensive error, screening the candidate remote sensing inversion models to obtain multiple remote sensing inversion models; using bayesian model average method for weighted summation of the multiple remote sensing inversion models to obtain a water quality remote sensing collection inversion model; establishing an optimization model for calculation of weight and variance; solving the optimization model to obtain weight and variance of each remote sensing inversion model in the water quality remote sensing collection inversion model; using the water quality remote sensing collection inversion model with substituted weight for water quality parameter inversion to obtain water quality parameter concentration inversion value; and according to Monte Carlo method and the weight and the variance of each remote sensing inversion model, calculating the concentration inversion interval of the water quality parameters in the water quality remote sensing collection inversion model.

The invention provides a method for structuring a wheat blumeriagraminis speer early detection model by extracting sensitive parameters on the basis of subwindow permutation analysis (SPA). The method comprises the following steps of 1) acquiring the hyperspectral reflectance of an infected wheat; 2) extracting a sensitive wave band from the original wave band of the hyperspectral reflectance through the SPA algorithm; 3) selecting spectral indexes possibly related to diseases in existing research, and extracting sensitive spectral indexes from the spectral indexes through the SPA algorithm; 4) through partial least square-linear discriminant analysis, taking the sensitive wave band or the sensitive spectral indexes as component input variables to structure the wheat blumeriagraminis speer early detection model; 5) testing the wheat blumeriagraminis speer early detection model through the dichotomy algorithm, evaluating model performance through leave-one-out cross validation on the basis of independent susceptible varieties. Therefore, a wheat blumeriagraminis speer sensitive spectrum extracted on the basis of SPA is accurate in characteristic and few in wave bands, and the structured wheat blumeriagraminis speer detection model is simple, high in accuracy and good in stability.

The invention relates to a hyperspectral reflectance data spectrum characteristics extracting method based on global sensitivity analysis. The method comprises the following steps of (1) sensitivity analysis: calculating sensitivity of the hyperspectral reflectance data on each waveband position by utilizing a global sensitivity analysis method; (2) characteristic waveband selection: selecting a characteristic waveband according to the sensitivity analysis result; (3) canonical transformation: calculating a characteristic vector by utilizing a canonical transformation method when the divisibility of the class is maximal; (4) spectrum characteristic establishment: establishing a first canonical axis to be used as a spectrum characteristic by utilizing the linear combination of the characteristic waveband reflectance value and the elemental value corresponding to the characteristic vector. Compared with the traditional hyperspectral reflectance data spectrum characteristics extracting method, the spectrum characteristic established by adopting the method enables the divisibility of different classes to be maximized while the data dimension is reduced, and the method is particularly applicable to the remote sensing application problem such as crop stress detection, target recognition, ground feature classification and the like, and has wide prospect in the technical field of the hyperspectral reflectance data processing and application.

The invention discloses a remote-sensing estimation method for chlorophyll of apple leaves. The remote-sensing estimation method comprises synchronously acquiring hyperspectral reflectivity and corresponding chlorophyll relative content of the apple leaves by virtue of an SVC HR-1024i type hyper-spectrometer and an SPAD-502 chlorophyll meter, carrying out relevant analysis on original spectral reflectivity and a first-order derivative derivative spectrum so as to extract spectrographic red edge parameters of the apple leaves, and optimizing an artificial neural network by virtue of a traditional univariate regression algorithm, a BP neural network and a radial basis function network, and establishing a chlorophyll content inversion model. Compared with a traditional univariate model, the regression precision of an artificial network model is obviously increased, and the radial basis function network is high in study speed and precision and has a relatively reliable fitting result; and the artificial network model is the chlorophyll content inversion model which is worthy of being popularized.

The embodiment of the invention discloses a remote sensing estimation method and remote sensing estimation model building method of an SPAD value of a whole-growth-period cotton canopy. The cotton planted in the Hanyuan area of the Weihe river is taken as a test material; the SPAD value of the whole-growth-period cotton canopy and a canopy reflectance spectrum are measured; correlation analysis is carried out on original hyperspectral reflectance, first-order differential spectral reflectance and remote sensing spectrum parameters combined by different wavebands and the SPAD value separately; an SPAD prediction model of five important spectrum parameters is built; meanwhile, a full-spectrum SPAD estimation model is built by adopting PLSR and check is carried out; and the model with the highest precision is finally screened out. A full-spectrum PLSR model built on the basis of important spectral variables monitors the SPAD value of the cotton by using a hyperspectral technology; the prediction effect is significant; a basis can be provided for remote sensing monitoring of whole-growth-period cotton growth; and a positive guiding role in guidance of cotton cultivation and production is put into play.

The invention discloses an alteration extraction method for imaging high-spectral boring rock core data, and belongs to the field of high-spectral remote sensing application. The method comprises that a reference whiteboard and a rock core sample to be scanned are placed; the whiteboard and a rock core are scanned via a high spectrum, and original imaging high-spectral data is obtained; the original imaging high-spectral data is preprocessed to obtain imaging high-spectral reflectivity data; the format of the imaging high-spectral reflectivity data is converted to obtain data of a BIP format; the format of the BIP data is further converted to obtain ENVI Spectral Library data; spectral analysis is carried out on the ENVI Spectral Library data via software The Spectral Geologist, and an alteration analysis result is obtained; and format conversion is carried out on the alteration analysis result to obtain an alteration extracted image. The method can be used to extract imaging high-spectral rock core scanning data automatically.

The invention provides a method for extracting a combined kernel minimum noise fraction characteristic of a high spectral image, wherein the method belongs to the technical field of high spectral image data processing and application. The method comprises the following steps of 1), acquiring high spectral reflectivity data; 2), estimating image noise; 3) constructing a least noise separation transform model; 4) constructing a dual mode least noise separation transform model; 4), constructing a combined kernel function through using advantages of high learning capability of a Gaussian kernel function and high generalization capability of a polynomial kernel function; 5), constructing a combined kernel minimum noise fraction model; and 6), utilizing combined kernel minimum noise fraction forextracting the characteristic of the high spectral image. According to the method of the invention, through the combined kernel function, the original non-separatable high spectral data are mapped toa kernel characteristic space so that the high spectral data are separatable, thereby obtaining a high spectral image characteristic extraction effect which is better than kernel minimum noise separation transform and traditional least noise separation transform.

The invention discloses a chlorophyll content detection method insensitive to a leaf surface structure, and relates to the technical field of methods for measuring the chlorophyll content through an optical means. The method comprises the steps that hyperspectral reflectance of the plant leaf surface is obtained; the chlorophyll content is measured; a regression equation is established for nine types of vegetation indexes and the chlorophyll content, and a correlation coefficient and a root mean square error are calculated; a regression equation is established for 36 types of published vegetation indexes and the chlorophyll content, and a correlation coefficient and a root mean square error are calculated; the wave bands which are most sensitive and least sensitive to a change of the chlorophyll content are determined; a vegetation index type which is least sensitive to the leaf surface structure in the nine types of the vegetation indexes is determined; new vegetation indexes estimating the chlorophyll content are developed based on spectral information of the front and back of leaves, and the new vegetation indexes which are not sensitive to the leaf surface structure are used for detecting the chlorophyll content of the plant leaves. By means of the chlorophyll content detection method insensitive to the leaf surface structure, the content of chlorophyll in the plant leaves can be detected rapidly, accurately and nondestructively.

A high-spectrum image classification method based on nonlinear time series analysis is to analyze and process high-spectrum reflectance curves through nonlinear time series analysis so as to perform feature construction for different pixels in high-spectrum images and then finish classification according to constructed features. The method includes: 1, obtaining high-spectrum data to be processed through a man-machine interaction interface, 2, obtaining a feature combination used for ground object classification through a high-spectrum feature construction module, 3, performing ground object classification for cases through a high-spectrum ground object classification module by aid of feature construction results, and 4, outputting classification results through a classification result output module. The high-spectrum image classification method based on nonlinear time series analysis has the advantages of being strong in robustness, small in space complexity, high in classification accuracy and wide in application range, and time complexity and the number of sample points keep linear relation.

The invention relates to a first lens structure for a Tokamak type magnetically-confined nuclear fusion device and a realization method thereof. Particularly, the first lens is a reflector that has anti-high-energy plasma and neutron and anti-high-energy ray irradiation and has a high spectral reflectivity and high stability. The first lens is provided with a protective layer, a functional thin-film layer, a welding brazing filter material transition layer, and a micro channel cooler. The functional thin-film layer is plated on the protective layer by using a vacuum film-plating process; and the functional thin-film layer and the micro channel cooler are processed by using a vacuum brazing process and are welded together by the welding brazing filter material transition layer to form an integrated multi-layer composite structure. The protective layer is resistant to the high-energy plasma, the high-energy neutron, and the high-energy ray irradiation. The functional thin-film layer has the feature spectrum reflection function; and the micro channel cooler dissipates the heat deposited by the protective layer. The first lens can be applied to the high-energy plasma, high-energy neutron, and high-energy ray irradiation conditions and can be applied to a nuclear fusion reaction device.

The invention discloses a canopy hyper-spectral triticum aestivum leaf dry weight monitoring method based on continuous wavelet analysis. The method comprises the following steps: selecting sampling plots, acquiring the triticum aestivum canopy hyper-spectral reflectance, and measuring the triticum aestivum leaf dry weight, wherein the sampling plots are selected from different test points, are of different varieties, different nitrogen levels and different planting density, and are from different years; carrying out continuous wavelet transform on the acquired triticum aestivum canopy hyper-spectral reflectance data to get a wavelet coefficient under a specific wavelength and a specific scale; using the wavelet coefficient to analyze the quantitative relation between triticum aestivum leaf dry weight and wavelet coefficient, screening out an optimal wavelet function sensitive to triticum aestivum leaf dry weight and a characteristic value corresponding to the optimal wavelet function, and building a triticum aestivum leaf dry weight quantitative model based on continuous wavelet analysis; and using independent triticum aestivum test data to assess the reliability and applicability of the quantitative model, and using a determination coefficient R<2> and relative root mean square error RRMSE between predicted and observed values to evaluate the quantitative model.

The invention relates to a hyperspectral and multispectral remote sensing information fusion method and system. The method comprises the following steps: performing hyperspectral atmospheric correction according to pre-acquired hyperspectral remote sensing image data; carrying out multispectral atmospheric correction according to multispectral remote sensing image data acquired in advance; establishing a wave band mapping model based on a hyperspectral reflectivity image result and a multispectral reflectivity image result of the earth surface generated after hyperspectral atmospheric correction and multispectral atmospheric correction; carrying out weighted calculation of the spectral reflectivity value on the virtual hyperspectral reflectivity image result by taking the original hyperspectral reflectivity image value as a reference, and generating a hyperspectral reflectivity fusion result; based on the hyperspectral reflectivity fusion result, adopting an atmospheric radiation transmission model for simulation, achieving atmospheric radiation transmission conversion of a reflection fusion image, and generating a fusion image result of a hyperspectral remote sensing image and a multispectral remote sensing image.

The invention provides a method for measuring the anthocyanin content of purple corn blades, and a system thereof. The method comprises the following steps: acquiring the first hyperspectral reflectivity of the purple corn blades in samples at different wavelengths, and acquiring the second hyperspectral reflectivity of first generation purple corn blades in the samples at different wavelengths; determining the anthocyanin content of the first generation purple corn blades; carrying out addition operation on the first hyperspectral reflectivity and the second hyperspectral reflectivity; determining the wavelength corresponding to the strongest correlation in a correlation curve corresponding to the above operation result as a sensitive wavelength; and establishing an anthocyanin content test model according to the anthocyanin content, the sensitive wavelength, the addition hyperspectral reflectivity corresponding to the sensitive wavelength and the anthocyanin content of the first generation purple corn blades. Data adopted for modeling comprises the sensitive wavelength, the addition hyperspectral reflectivity corresponding to the sensitive wavelength and the anthocyanin content of the first generation purple corn blades, so the anthocyanin content obtained through modeling in the above mode is accurate.