56 results about "Endmember" patented technology

Described herein is a process for objectively and automatically determining spectral endmembers and transforming Spectral Mixture Analysis (SMA) from a widely used research technique into a user-friendly tool that can support the needs of all types of remote sensing. The process extracts endmembers from a spectral dataset using a knowledge-based approach. The process identifies a series of starting spectra that are consistent with a scene and its environment. The process then finds endmembers iteratively, selecting each new endmember based on a combination of physically and statistically-based tests. The tests combine spectral and spatial criteria and decision trees to ensure that the resulting endmembers are physically representative of the scene.

A method is disclosed herein for compressing spectral data corresponding to an image comprising a plurality of pixels. The method includes the step of creating a set of potential endmembers. A first plurality of the potential endmembers are identified as a first set of endmembers based upon their respective correlations with a first spectral signature of a first of the plurality of pixels. The first pixel is then represented as a combination of the first set of endmembers. Processing of the image preferably continues by identifying a second plurality of the potential endmembers as a second set of endmembers based upon their respective correlations with a second spectral signature of a second of the plurality of pixels. The second pixel is then represented as a combination of the second set of endmembers.

Described herein is a process for objectively and automatically determining spectral endmembers and transforming Spectral Mixture Analysis (SMA) from a widely used research technique into a user-friendly tool that can support the needs of all types of remote sensing. The process extracts endmembers from a spectral dataset using a knowledge-based approach. The process identifies a series of starting spectra that are consistent with a scene and its environment. The process then finds endmembers iteratively, selecting each new endmember based on a combination of physically and statistically-based tests. The tests combine spectral and spatial criteria and decision trees to ensure that the resulting endmembers are physically representative of the scene.

The invention provides a method for identifying one or more materials in a scene by determining a set of spectral vectors, called endmembers, from a data set comprised of spectra from the image data, and matching the set of endmembers to predefined library materials. The image data of the scene is captured with a sensor, and comprises a plurality of spectra. The method applies an iterative mathematical criterion, termed residual minimization, to find the endmembers. The first endmember may be selected based on the largest mean square value or the largest mean magnitude value. Subsequent endmembers are determined by calculating weighting factors, such that the weighting factors are non-negative and the calculated vector differences, or residuals, generate the smallest error metric. The error metric is dependent upon the vector difference between two spectra in the image data set, and may be the mean squared vector difference between two spectra.

A method of identifying endmember spectral values from multispectral image data, where each multispectral data value is equal to a sum of mixing proportions of each endmember spectrum. The method comprises the steps of processing the data to obtain a multidimensional simplex having a number of vertices equal to the number of endmembers. The position of each vertex represents a spectrum of one of the endmembers. Processing the data is conducted by providing starting estimates of each endmember spectrum for each image data value. The mixing proportions for each data value is estimated from estimates of the spectra of all the endmembers. The spectrum of each endmember is estimated from estimates of the mixing proportions of the spectra of all the endmembers for each image data value. The estimation steps are repeated until a relative change in the regularised residual sum of squares is sufficiently small. The regularised residual sum of squares includes a term which is a measure of the size of the simplex.

A spectral unmixing scheme based on abundance sparse and endmember orthogonal constraint NMF(Non-negative Matrix Factorization) is an algorithm of a spectral image decomposition field. A classical NMFtarget function is a non-convex function, and a constraint condition needs to be added into the target function for solving the problems. The characteristics of spectral images are combined to put forward the spectral image unmixing algorithm which combines endmember orthogonality with abundance sparse constrains NMF on the basis of a linear spectral mixture model. The endmember orthogonal constraint guarantees independence among spectral endmembers, meanwhile, the abundance sparsity fully utilizes the sparsity of spectral data, two constraint conditions are introduced into the target function, a least square method is adopted to obtain the iteration rules of an endmember matrix and an abundance matrix, and a final result is obtained through the setting of an iterative termination condition. Through the experiments of simulation data and truthful data, the effectiveness of the algorithm is verified.

Provided herein are algorithms and processes to extract endmembers from hyperspectral image data in real time. A Simplex Growing Algorithm is effective to estimate a p number of endmembers to be generated, to select one or more initial endmembers as a simplex of k members and to add a k+1 endmember to the simplex that yields a maximum simplex volume until k=p, thereby extracting one or more endmembers from the data. Alternatively, N-FINDR algorithms form an initial simplex set of p endmembers obtained from the hyperspectral image data, find a maximum volume of one or more initial p endmembers therewithin, replace one or more of the p endmembers within the simplex with one or more of the found p endmembers of maximum volume, and refind a maximum volume of p endmember(s) and replace p endmember(s) until no increase in p endmember(s) volume is found.

The disclosure concerns processing of electronic images, such as hyperspectral or multispectral images. In particular, but is not limited to methods, software and computer systems for determining underlying spectra of an image of a scene. The image data comprises for each pixel location a sampled image spectrum that is a mixture of plural reflectance spectra. Processor 310 determines or accesses plural hyperplanes that each have plural linearly independent basis vectors. Each hyperplane represents an estimate of one of the plural reflectance spectra. The processor 310 then determines for each pixel location, a contribution of the plural basis vectors of each hyperplane to the image spectrum of that pixel location. The processor 310 determines or accesses plural hyperplanes and not plural endmembers directly. Hyperplanes are two-dimensional while endmembers are only one-dimensional. As a result, hyperplanes carry more information, such as the illumination spectrum and are therefore of greater use. The decomposition of the image data into hyperplanes is possible without knowing the illumination spectrum. This decomposition allows for a range of new applications such as endmember extraction and compact representation.

A method of identifying endmember spectral values from multispectral image data, where each multispectral data value is equal to a sum of mixing proportions of each endmember spectrum. The method comprises the steps of processing the data to obtain a multidimensional simplex having a number of vertices equal to the number of endmembers. The position of each vertex represents a spectrum of one of the endmembers. Processing the data is conducted by providing starting estimates of each endmember spectrum for each image data value. The mixing proportions for each data value is estimated from estimates of the spectra of all the endmembers. The spectrum of each endmember is estimated from estimates of the mixing proportions of the spectra of all the endmembers for each image data value. The estimation steps are repeated until a relative change in the regularised residual sum of squares is sufficiently small. The regularised residual sum of squares includes a term which is a measure of the size of the simplex.

Provided is a high-quality spectrum reconstruction method based on a few-wave-band high-resolution image and a low-resolution hyperspectral image. The method comprises the steps that the high-resolution image having few wave bands and the low-resolution hyperspectral image under the same scene are obtained; the low-resolution hyperspectral image is trained in a non-decomposition mode to obtain a spectrum dictionary; then, the high-resolution image having the few wave bands is subjected to sparse representation under the condition of being free of non-negative restriction, and a sparse representation coefficient is obtained; the part which cannot be represented by a sparse representation framework is estimated through spatial structure information; the dictionary, the sparse coefficient and the estimation part are utilized for reconstructing the hyperspectral image with high resolution. Accordingly, under the sparse representation framework, the non-decomposition mode is introduced to solve the spectrum dictionary, the deficiency of descriptive power of endmember decomposition to spectral characteristics of the hyperspectral image is made up, and the reconstructed spectrum precision, the effectiveness of hyperspectral image reconstruction and the spatial accuracy of reconstructing the hyperspectral image are effectively improved.

The invention relates to an automated morphological endmember extraction based hyperspectral image data unmixing method and belongs to the technical field of image data processing. According to the method, calculation is performed by utilizing a reference spectral vector to obtain an image element with highest mixing degree in a whole image, and based on this, a morphological operator is improved; an original structural element is improved with a new MEI calculation method; a structural element of an odd size is replaced with a structural element of an even size; and four candidate endmembers are selected from each structural element, so that possible information loss is effectively avoided, the accuracy of data processing is further improved, and finally the definition of the image obtained after data processing is improved; and the automated morphological endmember extraction based hyperspectral image data unmixing method is quite simple and convenient to implement.

According to one embodiment, an endmember spectrum extraction unit generates an input endmember spectrum group from a hyperspectral image. A collation unit outputs an endmember spectrum group including endmember spectra that are registered with neither a spectrum library nor an endmember spectrum database as a new endmember spectrum group. An input endmember spectrum distribution image generation unit generates an input endmember distribution image every input endmember spectrum. A new endmember spectrum distribution image selection unit selects an input endmember spectrum distribution image that is included in input endmember spectrum distribution images and that corresponds to new endmember spectrum as a new endmember distribution image. The user recognizes what kind of object corresponds to each new endmember spectrum on the basis of the new endmember spectrum distribution image, the hyperspectral image and a high resolution image, and determines and orders whether to register each new endmember spectrum with the endmember spectrum database.

Provided herein are algorithms and processes to extract endmembers from hyperspectral image data in real time. A Simplex Growing Algorithm is effective to estimate a p number of endmembers to be generated, to select one or more initial endmembers as a simplex of k members and to add a k+1 endmember to the simplex that yields a maximum simplex volume until k=p, thereby extracting one or more endmembers from the data. Alternatively, N-FINDR algorithms form an initial simplex set of p endmembers obtained from the hyperspectral image data, find a maximum volume of one or more initial p endmembers therewithin, replace one or more of the p endmembers within the simplex with one or more of the found p endmembers of maximum volume, and refind a maximum volume of p endmember(s) and replace p endmember(s) until no increase in p endmember(s) volume is found.

The invention provides a hyperspectral image emissivity spectrum extraction method based on endmember abundance inversion. First, the whole infrared hyperspectral image is decomposed into mixed pixels to obtain the pure endmembers in the image and the corresponding endmembers. The abundance value of the target, and then obtain the radiance spectrum curve of each pixel point in the image, and then perform curve integration on the radiance spectrum of the target of interest within the set temperature threshold range, and estimate the infrared hyperspectral emissivity according to the integrated area The absolute black body temperature simulated by the curve, and then the emissivity spectrum curve is obtained. When extracting the radiance curve of the target of interest, the present invention considers the influence of mixed pixels, obtains the real radiance curve of the target through end-member extraction and abundance inversion, improves the extraction accuracy of the radiance curve, and then makes the emissivity The extraction accuracy of curves has been improved.

The invention discloses a hyper-spectral image end member extraction method and a hyper-spectral image end member extraction system. Optimized variables in the problem of hyper-spectral image end member extraction correspond to nectar source locations in an artificial bee colony algorithm, and optimization of each nectar source is determined by a fitness function. Nectar collecting bees search for an optimal nectar source, and sends nectar source information to follower bees after search; the follower bees search for an optimal nectar source the second time, and use a selected optimal nectar source to update a previously obtained optimal solution; and when there is a nectar collecting bee not updating the nectar source in a preset period of time, the nectar collecting bee is transformed into an investigation bee, and the investigation bee compares the fitness value of a randomly selected nectar source with the fitting value of a nectar source used as an optimal solution and calibrates the nectar source used as an optimal solution. According to the invention, the end member extraction problem is converted into a combination optimization problem solving process based on the artificial bee colony algorithm, the advantages of the artificial bee colony algorithm are given full play, and the nectar source used as an optimal solution is calibrated by the investigation bee to reduce the risk that the optimization process is trapped into a local optimal solution.

The invention discloses a method for determining the activity of xylanase, which is characterized by comprising: A, producing a standard corresponding relation curve between an absorbance value of xylose measured by an MBTH method and concentration of the xylose; and B, using the MBTH method to detect the absorbance value which is produced after the xylanase to be tested hydrolyzes xylan and is equivalent to that of the xylose; and determining the content of reducing sugar which is produced in unit time and is equivalent to the xylose in an enzymolysis product of the xylanase to be tested according to the standard corresponding relation curve which is produced in step A between the concentration of the xylose and the measured absorbance value of the xylose to calculate the activity of thexylanase. The method uses the MBTH method to detect the quantity of terminal groups of the xylose produced after the xylanase hydrolyzes the xylan to determine the activity of the xylanase. The detectability and work concentration range of xylose determination by the method are apparently lower than those of a DNS method and a potassium ferricyanide method, so the sensitivity of the method is apparently higher than that of the DNS method and the potassium ferricyanide method.

The invention discloses a hyperspectral remote sensing image end member extraction method. Under the condition that a pure pixel hypothesis is not always realized, in the method, a linear mixing model and a particle swarm optimization algorithm are combined and a high-dimensional particle coding mode is cooperated so as to deduce a new and effective particle evolution equation. Simultaneously, an experiment proves that the method is effective for an end member extraction problem.