32 results about "Spectral distance" patented technology

A method of characterizing a sample from spectrometric data using calculation of spectral distance values is disclosed, for use in the field of mass spectrometry. Molecular formula assignment of peaks in mass spectral data is difficult and time-consuming, and the invention provides a computer implemented method of finding a most likely elemental composition of a measured spectral peak of interest. The method analyzes isotopic peaks in a portion of the spectrum, using both their mass positions and intensities, to determine a spectral distance between those peaks and isotopic peaks of a candidate composition, finding peaks that match (140). A pattern spectral distance is determined (150) to provide a measure of the correspondence between a set of those peaks in the measured spectrum and peaks of each of a number of candidate compositions. The spectral fit is used to determine a most likely candidate composition.

A coding efficiency of coding spectral coefficients of a spectrum of an audio signal is increased by en / decoding a currently to be en / decoded spectral coefficient by entropy en / decoding and, in doing so, performing the entropy en / decoding depending, in a context-adaptive manner, on a previously en / decoded spectral coefficient, while adjusting a relative spectral distance between the previously en / decoded spectral coefficient and the currently en / decoded spectral coefficient depending on an information concerning a shape of the spectrum. The information concerning the shape of the spectrum may have a measure of a pitch or periodicity of the audio signal, a measure of an inter-harmonic distance of the audio signal's spectrum and / or relative locations of formants and / or valleys of a spectral envelope of the spectrum, and on the basis of this knowledge, the spectral neighborhood which is exploited in order to form the context of the currently to be en / decoded spectral coefficients may be adapted to the thus determined shape of the spectrum, thereby enhancing the entropy coding efficiency.

A system is configured so that signals passed through an all-pass filter using warp parameter λ are whitening-filtered, and so that the frequency axis is restored by an all-pass filter using warp parameter λ. This optimizes the whitening filter by determining the optimum λ. First, the AR order p is automatically estimated using λ=0. A spectral distance dλ is computed using a discrete Fourier transform spectrum value passed through an all-pass filter using warp parameter λ and a discrete AR spectrum value passed through an all-pass filter using warp parameter λ. The λ which minimizes the spectral distance dλ is set as the warp parameter, and a whitening filter is configured in which the warp parameter has been optimized.

The invention discloses a KNN-based near-infrared abnormal spectrum identification method, aiming at the problem that the existence of abnormal spectral data seriously affects the accuracy and reliability of the spectral analysis model in the near-infrared spectral analysis. The steps of the method include: selecting a similarity measure, selecting a hyperparameter k, calculating a distance measure between spectra, finding k-shortest distance samples, calculating a sample abnormal measure, sorting samples according to the abnormal measure, identifying and removing samples with high abnormal measure. The invention is mainly used for identifying and eliminating the abnormal spectrum in the construction of the near-infrared spectrum analysis model.

The invention belongs to the technical field of remote sensing data processing, and particularly discloses a mixed pixel decomposition method based on geometric spatial spectral structure information, so as to solve the problems that classifications of hyper-spectral image mixed pixel point surface features are not distinct and distribution is not accurate. The method comprises steps: 1) hyper-spectral data are inputted, and the data are arranged in a matrix after pretreatment; 2) a VD method is used for estimating the number of pure end members; 3) an edge contour of the image is extracted; 4) a formula for computing a spatial distance is brought forward according to the edge and the position; 5) a formula for computing a spectral distance is brought forward according to spectral statistic information; 6) a geometric spatial spectral binding term is built according to the spatial distance and the spectral distance, and the binding term is added to an NMF model; and 7) an output end member matrix and an abundance matrix are unmixed in new NMF algorithm, and the scene surface feature classifications and the distribution ratio are judged. The method is well applicable to different hyper-spectral data, and compared with the prior method, the precision of mixed pixel decomposition is improved, and great value is provided for target detection and recognition.

The invention discloses a hyperspectral remote sensing image segmentation method based on spectral curve spectral distance. Firstly, a neighborhood spectral curve spectral distance model of a target pixel is constructed; Hilbert transform is used to obtain parity filtering results through convolution operations, and Use the square root of the square sum of the parity filtering results as local energy to construct an edge feature enhancement model; combine the two to obtain an edge feature enhancement model based on neighborhood spectral features, which is used to obtain edge feature enhancement results; use the edge feature enhancement results as gradient data Input the watershed segmentation algorithm and optimize the watershed segmentation algorithm to achieve high-precision segmentation of remote sensing images, which can effectively suppress the over-segmentation phenomenon of hyperspectral remote sensing images, and solve the problem of weak and false edge features in hyperspectral remote sensing images affecting remote sensing image segmentation Resulting technical issues.