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High-spectrum image classification method based on linear prediction cepstrum coefficient

A hyperspectral image and cepstral coefficient technology, applied in the field of spectral data classification, can solve the problems of difficult wide application, poor real-time performance, and high algorithm complexity, and achieve high real-time performance, good classification effect, and low algorithm complexity.

Inactive Publication Date: 2013-01-16
XIDIAN UNIV
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Problems solved by technology

However, since this method requires two matrix transformations, it has the disadvantages of high algorithm complexity and poor real-time performance.
[0007] The LDA method is a transformation method based on prior information. Because its transformation results depend on the rationality of sample selection, it is difficult to be widely used.

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  • High-spectrum image classification method based on linear prediction cepstrum coefficient

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[0023] Refer to attached figure 1 , the detailed implementation steps of the hyperspectral image spectral classification method based on the linear predictive cepstral coefficient of the present invention are as follows:

[0024] Step 1, read the reference hyperspectral image of the known surface morphology category.

[0025] The reference hyperspectral image adopts the standard spectral library or the hyperspectral image of known ground object types. Such as figure 2 The image shown is the reference hyperspectral image of the known surface morphology category, which comes from the Indina pink image in the airborne infrared imaging spectrometer AVRIS, with a size of 145×145 and a total of 220 bands.

[0026] Step 2, extract the linear predictive cepstral coefficient h of the benchmark hyperspectral data s .

[0027] (2.1) Yes figure 2 The reference hyperspectral data shown is average filtered, that is, spectral noise is filtered to reduce the interference of spectral no...

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Abstract

The invention discloses a high-spectrum image classification method based on linear prediction cepstrum coefficient, and solves the shortages in the prior that the complexity is high, real-time capability is bad, Huges phenomenon exists, prior information of a sample is needed, and wide application is difficult to realize. The method provided by the invention applies the linear prediction cepstrum coefficient in voice signal identification in spectrum data of a spectrum image and comprises the following steps: firstly, performing spectrum noise filtering on the high-spectrum data; secondly, performing pre-emphasis on the spectrum data subjected to the noise filtering for enhancing characteristics of the spectrum data; after that, utilizing Levinson-Durbin algorithm for solving a linear prediction coefficient and converting the linear prediction coefficient to the linear prediction cepstrum coefficient; and finally, matching the linear prediction cepstrum coefficient, and performing description with vector quantity included angles, wherein the smaller the included angles, the higher the similarity between classification results and standard surface configurations is. The method provided by the invention has the advantages of low complexity, high real-time capability, good classification effect and no-prior-information for the sample, and can be applied in aspects such as surface features classification and mineral identification of the high-spectrum image, etc.

Description

technical field [0001] The invention belongs to the technical field of remote sensing data processing or pattern recognition, and in particular relates to spectral data classification of hyperspectral images, which can be used for remote sensing object type identification, mineral composition identification and the like. Background technique [0002] With the advancement of remote sensing technology, hyperspectral remote sensing that can simultaneously record ground spatial distribution information and spectral information has emerged. The emergence of hyperspectral remote sensing also puts forward new requirements for hyperspectral image processing. The salient feature of hyperspectral images is that there are many imaging bands, narrow band ranges, and the bands are connected to each other, which provides a physical basis for the fine identification of ground object types. Therefore, the study of spectral classification technology suitable for hyperspectral image character...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46
Inventor 刘德连宋碧霄韩亮宗靖国何国经黄曦张建奇
Owner XIDIAN UNIV
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