383 results about "Spectral curve" patented technology

Non-imaging measurement is made of misalignment of lithographic exposures by illuminating periodic features of a mark formed by two lithographic exposures with broadband light and detecting an interference pattern at different wavelengths using a specular spectroscopic scatterometer including a wavelength dispersive detector. Misalignment can be discriminated by inspection of a spectral response curve and by comparison with stored spectral response curves that may be empirical data or derived by simulation. Determination of best fit to a stored spectral curve, preferably using an optimization technique can be used to quantify the detected misalignment. Such a measurement may be made on-line or in-line in a short time while avoiding tool induced shift, contact with the mark or use of a tool requiring high vacuum.

To determine spectra, integrated multiple illuminant measurements from a non-fully illuminant populated color sensor may be converted into a fully populated spectral curve using a reference database. The reference database is partitioned into a plurality of clusters, and an appropriate centroid is determined for each cluster by, for example, vector quantization. Training samples that form the reference database may be assigned to the clusters by comparing the Euclidean distance between the centroids and the sample under consideration, and assigning each sample to the cluster having the centroid with the shortest Euclidean distance. When all training samples have been assigned, the resulting structure is stored as the reference database. When reconstructing the spectra for new measurements from the sensor, the Euclidean distances between actual color samples under measurement and each cluster centroid are measured. The spectra are then reconstructed using only the training samples from the cluster corresponding to the shortest Euclidean distance, resulting in improved speed and accuracy.

The invention relates to a cross radiation calibration method based on the optimization algorithm of a hyper-spectral sensor; the cross radiation calibration method comprises the following steps of: (1) selecting an image to be calibrated; (2) selecting a reference image; (3) cutting out a region covered jointly by the two images, and selecting a uniform ground object to be used as a cross calibration interesting region; (4) working out the entrance pupil radiant brightness of the two sensors in each waveband and a gas absorbing characteristic curve, and removing gas absorption characteristics; (5) screening out the optimal wave band and the optimal interpolation scheme; (6) carrying out the cross radiation calibration on the optimal wave band of the hyper-spectral sensor; (7) interpolating the radiant brightness of the calibrated hyper-spectral waveband, reconstructing a hyper-spectral curve, loading the gas absorption characteristics to serve as the radiant brightness of the reference sensor; and (8) realizing the cross radiation calibration on the hyper-spectral sensor by using a multispectral sensor. The cross radiation calibration method can realize the in-orbit cross radiation calibration of a space-borne imaging spectrometer and has business popularization prospect.

The invention discloses a method for automatically extracting water body pollution information. The method comprises the following steps: (1) calculating water discharging reflectivity of a water area to be detected; (2) establishing a water quality remote sensing model; and (3) performing inversion with a water body spectral curve according to the water quality remote sensing model to obtain water quality parameters. A plurality of applicable tests on the method prove that the method has high precision and basically meets practical requirements. Images of test results can inverse spatial distribution situation of water quality in an area, and the distribution characteristics of the water pollution accord with the obtained information. Transboundary pollution situation is also reflected. Large and medium-sized pollution source situations can be reflected, such as pollution source position, pollution scale and range of influence range, thus providing quite useful information for controlling and treating pollution.

The invention discloses a spectral automatic calibration device and a method of an imaging spectrometer, and the spectral automatic calibration device can automatically adjust the strength of a light source and set the starting wavelength, the end wavelength and the scanning step length, and has the self-correction function and has the function of automatically acquiring an infrared wave band spectral curve. The spectral automatic calibration device comprises a calibration light source, a grating spectrometer, a control circuit, a data acquisition electronics system and computer control and drawing software.

The invention discloses a spectral morphological characteristic-based hyperspectral water quality parameter quantitative inversion method, relating to the field of water quality remote sensing. The method comprises the steps of comparing and analyzing ground measured spectral data and hyperspectral image data, extracting a spectral curve morphological characteristic, selecting a ground measured spectral morphological characteristic significantly correlated to water quality parameters and building a ground measured spectral data-based inversion model, building a hyperspectral inversion model ofeach water quality parameter with a spectral morphological characteristic selected by the inversion model built based on the ground measured spectral as an independent variable, and applying the hyperspectral inversion model to a hyperspectral image to acquire a water quality parameter inversion result of a working area. Through adoption of the method, multivariate regression models of common chemical water quality parameters such as pH and hardness can be built, multiple types of water quality parameter information can be acquired from point to surface rapidly and accurately, and a new technological method is provided for regional water environment dynamic monitoring.

The invention relates to an information extraction method applicable to a hyperspectral image, sequentially comprising the following steps of: firstly, acquiring an imaging spectral image; secondly, resampling a ground object sample standard spectral curve; thirdly, finding an obvious wave band of the ground object sample standard spectral curve and judging the wave band; fourthly, resampling theimaging spectral image; fifthly, carrying out secondary resampling on the ground object sample standard spectral curve; sixthly, judging a type of a spectral genetic wave band; seventhly, comparing the ground object sample standard spectral curve with the type of a corresponding wave band in an image element spectral curve of the imaging spectral image, and judging whether the same ground object is represented; and eighthly, obtaining a sum of depths of all the spectral gene wave bands, wherein the larger the sum of the depths of the spectral gene wave bands is, the higher abundance of the ground object on earth surface is. By adopting the information extraction method disclosed by the invention, amount of data processing is reduced, interference of characteristic sum noise of other ground objects is reduced, processing speed and accuracy are improved, and good effect on processing of the hyperspectral image is achieved.

An aluminum nitride sintered body having an oxygen concentration of not larger than 400 ppm, a metal impurity concentration of not larger than 150 ppm, and a carbon concentration of not larger than 200 ppm, and having an average crystal grain size of 2 μm to 20 μm. The sintered body exhibits particularly excellent optical properties such as an inclination of a spectral curve in the wavelength region of 260 to 300 nm of not smaller than 1.0 (%/nm), a light transmission factor of not smaller than 86% in the wavelength region of 400 to 800 nm, and a wavelength of not longer than 400 nm when the light transmission factor reaches 60% in the spectrum.

The exploration and development of unconventional oil-gas reservoirs, especially the dense gas-oil reservoir, has become a bright spot of the oil exploration and development in recent years and a mainbattlefield of the future oil exploration and development. The wettability is one of important parameters for knowing dense oil-gas reservoir characteristics, and an operation of effectively determining the rock wettability has important significance for researching recovery percent of the oil reservoir, an oil-water ratio after water channeling, recovery ratio improvement and residual oil saturation. The invention discloses a new method for dense rock wettability determination, the disadvantages that the traditional self-absorption metering is inaccurate and nuclear magnetic resonance oil-water signals are hard to distinguish are overcome, a nuclear magnetic signal of the water is shielded by effectively utilizing manganese chloride, the self-water-absorbing oil discharge amount, water-driving oil amount, self-oil-absorbing water discharge amount and oil-driving water amount are accurately acquired by combining T2 spectral curve of the rock and weighing and weight change characteristics thereof, and the effective determination of the dense rock wettability is realized. The new method disclosed by the invention has the advantages of being small in current personal factor influence, high in data reliability, and simple and convenient.

The invention discloses an SAM weighted KEST hyperspectral anomaly detection algorithm (SKEST). The method includes the steps: firstly, deducing the SKEST algorithm; and secondly, calculating the SKEST value of each image element in a hyperspectral image by the aid of a double-rectangular window, performing threshold segmentation and detecting abnormal points. In the SKEST algorithm, based on the KEST (kernel Eigen space separation transformation) algorithm, a weight factor is introduced into each sample in a DCOR (difference correlation) matrix of a high-dimensional Eigen space detection point neighborhood by means of SAM (spectral angle mapper) measurement, and the weight factor of each sample depends on an included angle between the spectral vector of the sample and a data center of the detection window. Therefore, abnormal data in the detection window are suppressed, the contribution of main compositional data is highlighted, and the DCOR matrix can more effectively describe target and background data distribution difference. Besides, the SAM is robust to spectral energy, and by the aid of a radial basis function, the SKEST algorithm considers both spectral energy difference and spectral curve shape difference of signals, and accordingly conforms to hyperspectral data characteristics more effectively.

The invention discloses a forest-biomass remote sensing inversion method based on spectral curve characteristic differentiation. Landsat 8OLI and actually measured data serve as a data source, and a forest-biomass leaf biomass and above-ground biomass inversion model which takes Landsat 8OLI red-light band reflectivity R, near-infrared band reflectivity NIR, red-light band central wavelength CW<R> and near-infrared band central wavelength CW<NIR> as characterization parameters is constructed. The model is good in inversion effect and provides a new technical method and means for rapidly, accurately and comprehensively estimating above-ground biomass of different forest community types in large stretches of forests.

The invention discloses a color visualization method of a high spectral image. The spectral curve of each pixel in the high spectral image is extracted; the smoothed spectral curves are combined with a color matching function of a CIE1931 standard chroma system to calculate CIEXYZ tri-stimulus values, the CIEXYZ tri-stimulus values of each pixel are used to calculate the brightness, chroma and tone of a uniform color sensing space CIEL*C*h* according to white points of a display device, and the brightness coefficient, the chorma coefficient and the tone coefficient are set according to requirements for color reproduction; and the digital drive value of each pixel is calculated according to the modulated brightness, chroma and tone, the gamma coefficients of three channels of the display device as well as tri-stimulus values of primary color, and color visualization is realized. The method can be used to realize color visualization that satisfy perception characteristics of the human visual system, solves the problem that the visual effect differs due to different display devices, carries out adjustment according to different reproduction requirements, and is high in adaptability and conducive to interpretation and application of the high spectral image.

The invention discloses a method for continuously detecting physical parameters of compact rocks. The method includes the steps: selecting a rock sample, drying the rock sample, measuring the size andweight of the rock sample, taking formation water, soaking a fiber with a gap, repeatedly rolling a rock core on the fiber, uniformly soaking the outer surface of the rock core except for two end surfaces, placing the rock sample into an adjusted nuclear magnetic resonance spectrometer to obtain a T2 spectral curve of the sample, taking out the rock sample to dry the rock sample, performing saturation treatment on the rock core by the formation water, and testing a T2 spectral curve by the nuclear magnetic resonance spectrometer; taking out the rock sample, placing the taken rock sample intoa rock core clamping device, applying displacement pressure until the displacement pressure reach a suitable displacement pressure difference value, accurately recording needed parameters after a system is stabilized, continuing to measure the rock sample by a soap film flow meter, measuring a T2 spectral curve of the rock sample again, and analyzing and calculating acquired data to obtain variousof needed parameters. The method has the advantages that data are accurate and reliable, time is saved, and the method is convenient and rapid.

The invention belongs to the field of hyperspectral remote sensing application and in particular relates to a spectral matching and recognition method. The invention overcomes the defects that the conventional spectral matching and recognition method only considers the whole similarity measurement between spectral lines but neglects the local difference measurement between the spectral lines, and provides a spectral segmentation-based matching and recognition method. The method comprises the following steps of: firstly, performing transformation processing on the spectral lines by adopting the multi-scale wavelet transform taking a second-order Gaussian derived function as a wavelet function; secondly, extracting an optimized inflexion of a spectral curve by using the designed inflexion multi-scale optimizing algorithm; and finally, segmenting the spectral lines based on the extracted optimized inflexion information, and recognizing the spectral lines by adopting a segmentation matching method. The spectral matching and recognition method has the advantages that: wave bands with larger ground object spectrum difference and wave bands with smaller spectrum difference can be segmented into different segmentations through the inflexion segmentation so as to protrude the wave bands with the larger spectrum difference and enhance the effectiveness of spectral matching and recognition.

The technical scheme of the invention relates to the field of spectrum, in particular to a method for conversion of spectrum into chromaticity, which comprises the steps of: collection and digital processing of a spectral image; preprocessing of the spectral image with a computer; identification of the horizontal ordinate, vertical ordinate and spectral curve in the spectral image via the computer, and extraction of spectral data from the spectral image; display of the spectral data on the computer, and drawing of a spectral curve according to the extracted data; approximation and interpolation of the spectral data; and calculation of the values of X, Y and Z via discrete summation according to the product of the spectral intensity and the tristimulus value, and display of the chromaticity (x and y) on the computer. By adopting the method, the defects of the prior art that the spectral curve cannot be identified rapidly, the spectral data cannot be rapidly extracted and automatically converted into the chromaticity can be overcome.

The invention provides an apple interior and exterior quality fast nondestructive testing method based on the hyperspectral imaging technology. Apple spectrum information is collected through a hyperspectral imaging system, features of an apple spectrum curve are analyzed, and spectrum data is analyzed; then, single-band images are determined and subjected to secondary threshold segmentation, corresponding areas are extracted, and spectrum curves of the corresponding areas are extracted; a secondary continuous projection algorithm is disclosed according to apple exterior features like diseases and pests to extract a feature wavelength set SP1; a multi-parameter continuous projection algorithm is disclosed according to apple interior features like sweetness and hardness to extract a feature wavelength set SP2; finally, the feature wavelength set SP1 of apple interior quality and the feature wavelength set SP2 of apple exterior quality are fused, and apple interior and exterior quality fast nondestructive detection is achieved through a BP neural network. The method is easy and convenient to operate, high in detection speed and high in detection result precision, and apples are not damaged.

The invention discloses a remote-sensing calculation method of long-term variation of suspended sediments of a strong-tide high-turbidity estuary. The method comprises the following steps: step S1, receiving Landsat satellite remote-sensing data in different years and the same hydrological condition; step S2, processing the remote-sensing data; step S3, carrying out spectral measurement above spring/neap-tide water surfaces, carrying out synchronous surface layer suspended-sediment sampling, obtaining remote-sensing reflectivity, analyzing wavebands where spectral curve peak values are located, selecting waveband data which are of different sensors on Landsat and correspond to reflectivity peak values, establishing a statistical regression model between surface layer suspended-sediment concentration and remote-sensing reflectivity corresponding to sensitive wavebands, and then obtaining an optimal calculation mode of quantitative remote sensing through accuracy verification calculation, wherein wavebands corresponding to the peak values are the sediment sensitive-wavebands; and step S4, applying the optimal calculation mode, which is of quantitative remote-sensing and is obtained in the step S3, to the remote-sensing data after processing in the step S2, obtaining remote-sensing data of the suspended sediments of all regions in the different years and the same hydrological condition, and carrying out analysis of long-term variation of the suspended sediments.

The invention discloses a hyperspectral remote sensing image vector C-V model segmentation method based on wave band selection. Firstly, according to a spectral curve, wave bands high in contrast ratio between a target and the background are selected, further, according to relevant coefficients of the wave bands, the wave bands high in relevancy are removed so that a new wave band combination can be formed, and therefore according to the determined wave band assembly, a hyperspectral image vector matrix is established; on the basis, a vector C-V segmentation model based on the vector matrix is constructed, the edge guiding function based on gradient is introduced into the model, on the basis that a traditional C-V model is reserved to perform image segmentation based on area information, the capacity for capturing the target boundary in heterogeneous areas and under complex background conditions is enhanced through edge detail information of images, segmentation precision of the hyperspectral remote sensing images is improved, segmentation speed of the hyperspectral remote sensing images is increased.

The invention discloses a method for accurately measuring reflectivity of fiber Bragg grating. The method for accurately measuring reflectivity of fiber Bragg grating uses a tunable laser and the like and includes the following steps of accessing standard reflection load with known reflectivity and almost total reflection, wavelength scanning is achieved through the tunable laser and a separator, and a power meter records a spectral curve of fiber Bragg grating to be measured; compared data of the fiber Bragg grating with data of the spectral curve, and a longitudinal horizontal coordinate corresponding to a peak point is reflectivity. The wavelength scanning method is used to measure a reflection spectrum curve of fiber Bragg grating, reflectivity is further calculated, and the problem that error is large when low reflectivity fiber Bragg grating is measured by an existing method is solved.

The invention relates to an optimal distinguishing method for a red tide water body and a main algae thereof, which comprises the following steps: (1) acquisition of spectroscopic data, which comprises the processes of laboratory culture of a red tide algae, measurement of the remote sensing reflectance and the post treatment of the spectroscopic data; and (2) analysis of the spectroscopic data, i.e. Carrying out comparison of spectral absorption characteristic parameters by a spectral absorption characteristic parameter extraction method through utilizing ENVI (The Environment for Visualizing Images) software, extracting wavelengths of a reflection peak and an absorption peak by a spectral differential method and carrying out comparison analysis on the water bodies before and after the red tide main algae is inoculated. According to the invention, by the spectroscopic data of different red tide main algae, which is obtained in a laboratory and the spectroscopic data flow processing, an abnormal value is removed to obtain a remote sensing spectral curve of Karenia mikimotoi, alexandrium tamarense and AmPhidinium; and the foundation is laid for the extraction of the absorption characteristic parameters and the spectral differential method.