Hyperspectral image classification method based on wavelet packet transformation and grey prediction model

A grey prediction model and wavelet packet transform technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of poor classification accuracy of traditional algorithms, complex mechanism and action process, and high computational complexity of algorithms, and achieve suitable The effect of wide range, high classification accuracy and small space complexity

Inactive Publication Date: 2012-09-12
BEIHANG UNIV
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AI Technical Summary

Problems solved by technology

[0006] 3) Additive noise
[0008] 5) The mechanism and action process from ground object to image spectral response are very complicated
[0013] In the hyperspectral data processing system, due to the requirement of computational complexity, the less calculation of the classification algorithm, the better, but the classifica

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  • Hyperspectral image classification method based on wavelet packet transformation and grey prediction model
  • Hyperspectral image classification method based on wavelet packet transformation and grey prediction model
  • Hyperspectral image classification method based on wavelet packet transformation and grey prediction model

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Embodiment Construction

[0030] Further illustrate the application method of the present technology invention with example below.

[0031] 1) Obtain the hyperspectral data to be processed:

[0032] This example uses Washington D.C.Mall hyperspectral data, with a size of 1280×307 pixels and a wavelength range of 0.4 to 2.4 μm. After removing the water vapor absorption band and the low signal-to-noise ratio band, 191 bands are retained, and one of the sizes is 562× A 307-pixel submap, which contains 7 types of ground objects, namely: roofs, grass, trees, paths, streets, water, and shadows.

[0033] 2) Apply wavelet packet transform to decompose the hyperspectral response curve of each pixel:

[0034] Set the wavelet mother function ψ as Haar wavelet, and the maximum decomposition depth j=3. Then X=(x 1 ,...,x i ,...,x K ) after 3 layers of wavelet packet decomposition, 8 components can be obtained, including 1 approximate component and 7 detail components. Remember that their corresponding energy ...

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Abstract

A novel hyperspectral image classification method based on wavelet packet transformation and a grey prediction model belongs to the hyperspectral image processing field. The method comprises the following steps: firstly, acquiring hyperspectral data to be processed; secondly, using the wavelet packet transformation to decompose a hyperspectral response curve of each pixel; thirdly, using the grey prediction model to process a decomposition result; fourthly, using a characteristic construction result to supervise and classify hyperspectral data; fifthly, outputting a hyperspectral image ground object classification result. The method is an automatic hyperspectral image classification method. By using the method, wave band correlation can be effectively removed; data redundancy can be reduced; a negative effect of a dimension disaster on classification precision can be avoided; an application range is wide.

Description

technical field [0001] The invention relates to a novel hyperspectral image classification method based on wavelet packet transform and gray prediction model, which belongs to the field of hyperspectral image processing. Background technique [0002] Hyperspectral Remote Sensing (Hyperspectral Remote Sensing) technology is a ground remote sensing technology that has developed rapidly in the past three decades. Whether in commercial, military or civilian fields, it has important theoretical value and broad application prospects. Hyperspectral remote sensing technology uses imaging spectrometers to obtain spectral responses with narrow intervals from the target to be measured, which can capture features that are difficult to find with conventional remote sensing technology, thus laying a solid physical foundation for quantitative analysis of material components. my country is one of the few countries in the world that independently developed a complete set of hyperspectral rem...

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Application Information

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IPC IPC(8): G06K9/62
Inventor 尹继豪徐胤高超顾则通孙建颖李辉
Owner BEIHANG UNIV
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