High spectrum mineral map plotting method

A hyperspectral and mineral technology, applied in the application field of hyperspectral remote sensing geology, can solve the problems of uncertain spectral features, insufficient prior knowledge, and difficult acquisition, and achieve the effect of improving the accuracy of mineral mapping

Inactive Publication Date: 2008-01-23
BEIHANG UNIV
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Problems solved by technology

[0005] Aiming at the problems of uncertain spectral characteristics, insufficient prior knowledge and difficulty in obtaining hyperspectral data due to spectral mixing and differences in geological conditions in geoscience applications, a mineral identification method based on independent component analysis and mixed modulation matched filtering is proposed. method

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Embodiment Construction

[0026] As shown in Figure 1, the specific implementation method of the present invention is as follows:

[0027] 1. Establishment of ICA model based on kurtosis

[0028] ICA is a multivariate data analysis method that produces statistically independent components. When using it to classify ground objects, each category is expressed as an independent component, so the separability of the categories is maximized. Suppose the observed signal X=[x 1 , x 2 ,...,x m ] T is an m-dimensional random vector, the source signal S=[s 1 ,s 2 ,...,s n ] T is an n-dimensional independent random vector, and the mixing matrix A is an m×n-dimensional non-singular matrix, then their linear combination can be described as:

[0029] X = AS (1)

[0030] (1) is called the ICA model. The essence of ICA is that when the source signal s and the mixing matrix A are unknown, according to the known observation signal X and the statistical characteristics of the source signal S, determine the sepa...

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Abstract

The invention relates to a method for mapping mineral by taking use of the statistics characteristics of data without prior information for a high spectrum data, which comprises the following procedures: reading in the high spectrum data, centralizing the data, sphericalizing the data, setting up an analysis model of independent components, rating the independent components, extracting end members of mineral, min. noise separating and transforming, mixing, modulating, matching and filtering, mapping mineral. The method allows to extract end members of eroded mineral based on the analysis model of independent component by taking use the hi-order statistics feature of data themselves without unknowing any prior information, and to map eroded mineral by a way based on mixing, modulating, matching and filtering. The method avoids in the mineral identification the influence of uncertainness of spectrum feature of mineral caused by spectrum mixing, illumination, and mineralizing condition, combines the restricting condition of end-member content to be positive and 1 in the linear mixing decomposition theory, has effectively improves the detection limit for mineral and mapping mineral accuracy.

Description

technical field [0001] The invention relates to the application field of hyperspectral remote sensing geosciences, in particular to a method for realizing hyperspectral mineral mapping under the condition of unknown prior information. Background technique [0002] The hyperspectral imager is a new type of remote sensing payload. Its spectrum is compact and continuous, and it can simultaneously record the spectral and spatial information characteristics of the same ground object. It can be detected in spectral remote sensing. At present, the spectral recognition models of surface objects developed at home and abroad can be divided into the following three models in essence: (1) pattern recognition models based on spectral absorption band parameters; (2) based on reconstructed spectral Spectral matching model with reference spectral similarity measurement; (3) Intelligent recognition model based on mineralogy, spectroscopy, optics and other knowledge. Due to the errors in the...

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Application Information

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IPC IPC(8): G01J3/28G01N21/31
Inventor 赵慧洁李娜贾国瑞
Owner BEIHANG UNIV
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