CN102663420A - Hyperspectral image classification method based on wavelet packet transformation and grey prediction model - Google Patents

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

Hyperspectral image classification method based on wavelet package transforms and grey forecasting model

Technical field

The present invention relates to a kind of novel hyperspectral image classification method, belong to the high spectrum image process field based on wavelet package transforms and grey forecasting model.

Background technology

High-spectrum remote-sensing (Hyperspectral Remote Sensing) technology is fast-developing in the last thirty years remote sensing of the earth technology, no matter is at commerce, military affairs or civil area, and it all has important theory and is worth and wide application prospect.High spectrum resolution remote sensing technique utilizes imaging spectrometer from target to be measured, to obtain the spectral response with narrow interval, can capture the indiscoverable characteristic of conventional remote sensing technology, thereby establish solid physical basis for quantitative test material composition.China is that a few stand-alone development goes out one of country of complete high spectrum resolution remote sensing technique in the world; China's researcher is carried out high spectrum resolution remote sensing technique applied research comprehensive, multi-level, wide-range at categories such as mineral prospecting, medical diagnosis, reconnaissance behind enemy lines, battlefield monitoring, vegetation measurement, city plannings in recent years, all reaps rich fruits.

Compare with conventional remote sensing image processing, the principal feature of high-spectrum remote sensing has the following aspects:

1) data volume is big.Immediately observe the numeric field data amount be exponential growth to same ground, this efficient to Processing Algorithm has higher requirements.

2) correlativity is strong.Exist stronger correlativity between the high spectrum image adjacent band; And the correlativity between this wave band is more much better than than spatial coherence, topographic shadowing that this relevant generation reason comprises is relevant between the spectrum of the nature that is produced by the substance spectra reflecting attribute, produced by terrain slope and the spectrum sensitivity between the sensor adjacent band overlapping.

3) additive noise.The noise that the radiation characteristic of spectrometer record has superposeed and produced by atmosphere, sensor apparatus, quantification treatment and data transmission etc., it can regard this classical problem of signal noise silencing as, can the basis signal treatment theory solve.

4) mixed pixel point.Because resolution is limited; What the spectrum that single pixel place obtains reflected not necessarily is a kind of characteristic of material; And possibly be that ground observes territory (Ground Instantaneous Field of View) locate the mixing of several kinds of different material spectrum immediately, the complexity of mix depends on concrete ground characteristics.

5) mechanism of response and mechanism are very complicated from the ground object target to the image spectrum.Even commaterial, its spectrum performance also has very big difference usually, the phenomenon that promptly has the different spectrum of so-called jljl and compose foreign matter together.

In sum; The superiority of high-spectrum remote sensing is to be cost with its bigger data volume and higher data dimension; Therefore conventional Remote Sensing Image Processing Technology will be difficult to be applicable to the high-spectrum remote sensing process field, and some image processing method and technology to traditional remotely-sensed data face the challenge.

Wavelet package transforms and grey forecasting model are a kind of classical ways of Dynamic Data Processing.

Wavelet package transforms is one of instrument of being used widely in current application mathematics and the engineering discipline.Compare with Fourier transform, wavelet transformation is the partial transformation of space and frequency, thereby can effectively information extraction from signal.The fundamental purpose of signal analysis is to seek a kind of simple and effective signal transformation method, and the important information that signal is comprised can display.Say from physical significance; Wavelet package transforms can carry out multiple dimensioned refinement analysis to function or signal through calculation functions such as flexible and translations; Inherit and developed the thought of short time discrete Fourier transform localization; Simultaneously overcome window size again, a time-frequency window with frequency shift is provided, solved the indeterminable many difficult problems of Fourier transform not with shortcomings such as change of frequency.

If system has randomness and achievement data incomplete or uncertain of ambiguity, the dynamic change of structural relation, claim that then this system has grey property, the system with grey property is called gray system.Gray system is both to have contained Given information, contains unknown message or the non-system that knows information again.In gray system theory, utilize original data sequence less or inapt expression gray system behavioural characteristic to do to set up after the generating transformation, in order to describe the model of the continuous change procedure of the inner things of gray system, be called gray model.One of important content of research gray system is abstract and set up a model how unclear from one, that Global Information the is not enough system; The factor that this model can make gray system to clear and definite, develops into the more research basis that provides of knowledge by indeterminate by knowing little about it.Gray system theory is the product that cybernatic viewpoint and method extend to society, economic field, also is to control the result that science combines with mathematics of operations research method automatically.

In the high-spectral data disposal system, from the computation complexity requirement, the calculated amount of sorting algorithm is few more good more, and it is poor to reach the traditional algorithm nicety of grading of this requirement.On the other hand, from the requirement of nicety of grading, sorting algorithm will have the good robustness of difference classification scene, and reaches the algorithm computation complexity height of this requirement.Therefore, need to seek a kind of algorithm of taking all factors into consideration computation complexity and this two aspects balance of nicety of grading, make that its time complexity is low, robustness good.

Summary of the invention

To the problems referred to above; The object of the present invention is to provide a kind of novel hyperspectral image classification method, handle the method that high spectral response curve carries out latent structure and then classifies according to the characteristic completion of structure different pixels in the high spectrum image thereby propose a kind of wavelet package transforms and grey forecasting model of using based on wavelet package transforms and grey forecasting model.Present technique invention calculated amount is little, has kept the accuracy of classification simultaneously again, is applied to have good robustness in the high-spectral data disposal system.

Method flow involved in the present invention may further comprise the steps: (1) obtains pending high-spectral data; (2) use the high spectral response curve that wavelet package transforms decomposes each pixel; (3) use grey forecasting model and handle decomposition result; (4) use characteristic structure result is to the high-spectral data classification that exercises supervision; (5) output high spectrum image terrain classification result.Be elaborated in the face of each step of this method flow down.

(1) obtains pending high-spectral data,, be designated as X=(x arbitrary high spectrum pixel X ₁..., x _i..., x _K), wherein K is the wave band sum of high-spectral data, x _i, i=1 ..., K represents the spectral response numerical value of i wave band.

(2) use the high spectral response curve that wavelet package transforms decomposes each pixel: given wavelet mother function ψ and the maximum degree of depth j that decomposes, use ψ to X=(x ₁..., x _i..., x _K) carry out j layer wavelet package transforms, obtain 2 altogether ^jIndividual component is comprising 1 approximate component A and 2 ^j-1 details component

Their corresponding energy coefficients be respectively a with

And satisfy relational expression (1):

$d_1+...+d_{2^j-1}=1-a---\left(1\right)$

Wherein, j is the maximum degree of depth of decomposing.

(3) Application of gray prediction model processing decomposition results: details of the calculation of the energy component of the coefficient sequence

is an order accumulation and sequence

${\stackrel{\‾}{d}}_k=\underset{i=1}{\overset{k}{\mathrm{\Σ}}}{d}_i,k=1,...,2^j-1---\left(2\right)$

Wherein, j is the maximum degree of depth of decomposing.

Order $Y={(d_2,...,d_{2^j-1})}^T,$ $B=\left(\begin{array}{cc}-({\stackrel{\‾}{d}}_1+{\stackrel{\‾}{d}}_2)/2& 1\\ -({\stackrel{\‾}{d}}_2+{\stackrel{\‾}{d}}_3)/2& 1\\ .& .\\ .& .\\ .& .\\ -({\stackrel{\‾}{d}}_{2^j-2}+{\stackrel{\‾}{d}}_{2^j-1})/2& 1\end{array}\right),$ Then:

$\left(\begin{array}{c}b\\ \hat{b}\end{array}\right){\left(B^{T}B\right)}^{-1}{B}^{T}Y---\left(3\right)$

Wherein, B is called the development coefficient, and

is grey action.

Keep the energy coefficient a and development coefficient b of approximate component, then the latent structure result of arbitrary pixel is made up of energy coefficient a and development coefficient b.

(4) use characteristic structure result is to the high-spectral data classification that exercises supervision.

(5) output high spectrum image terrain classification result.

The present invention has the following advantages: be used for the high-spectral data disposal system, the latent structure result does not receive the interference of other pixel, strong robustness, and space complexity is little, and time complexity and the sexual intercourse of sample points retention wire, nicety of grading is high, and is applied widely.

Embodiment

Further specify the application process of present technique invention below with instance.

1) obtain pending high-spectral data:

This instance adopts Washington D.C.Mall high-spectral data; Size is 1280 * 307 pixels, and wavelength coverage is 0.4～2.4 μ m, remove water vapor absorption wave band and low signal-to-noise ratio wave band after; Keep 191 wave bands; And intercepting wherein a size be the subgraph of 562 * 307 pixels, subgraph comprises 7 types of atural objects altogether, is respectively: roof, meadow, trees, path, street, water, shade.

2) use wavelet package transforms and decompose the high spectral response curve of each pixel:

Setting wavelet mother function ψ is the Haar small echo, the maximum degree of depth j=3 that decomposes.Then with X=(x ₁..., x _i..., x _K) carry out 3 layers of WAVELET PACKET DECOMPOSITION after, can access 8 components, comprising 1 approximate component and 7 details components.Remember that their corresponding energy coefficients are respectively a and d ₁..., d ₇

3) use grey forecasting model and handle decomposition result:

The energy coefficient sequence d of computational details component ₁..., d ₇1 rank accumulations and sequence ${\stackrel{\‾}{d}}_7=d_1+d_2+...+d_7.$ Make Y=(d ₂..., d ₇) ^T, $B=\left(\begin{array}{cc}-({\stackrel{\‾}{d}}_1+{\stackrel{\‾}{d}}_2)/2& 1\\ -({\stackrel{\‾}{d}}_2+{\stackrel{\‾}{d}}_3)/2& 1\\ .& .\\ .& .\\ .& .\\ -({\stackrel{\‾}{d}}_6+{\stackrel{\‾}{d}}_{7^{}})/2& 1\end{array}\right),$ Then $\left(\begin{array}{c}b\\ \hat{b}\end{array}\right){\left(B^{T}B\right)}^{-1}{B}^{T}Y.$ B is called the development coefficient, It is grey action.

Keep the energy coefficient a and development coefficient b of approximate component, the latent structure result of then arbitrary high spectrum pixel is made up of energy coefficient a and development coefficient b.

4) use characteristic structure result is to the instance classification that exercises supervision.

5) output instance classification results.

The present invention can effectively avoid the interference of dimension disaster and Hao Si phenomenon through the practical implementation of analogue system, under the condition that keeps target atural object principal character, accomplishes the supervised classification of high spectrum image.The present invention is used for the high-spectral data disposal system, and the latent structure result does not receive the interference of other pixel, strong robustness, and space complexity is little, and time complexity and the sexual intercourse of sample points retention wire, nicety of grading is high, and is applied widely.

Claims (1)

1. based on the hyperspectral image classification method of wavelet package transforms and grey forecasting model, it is characterized in that this method comprises the steps:

(1) obtains pending high-spectral data,, be designated as X=(x arbitrary high spectrum pixel X ₁..., x _i..., x _K), wherein K is the wave band sum of high-spectral data, x _i, i=1 ..., K represents the spectral response numerical value of i wave band;

(2) use the high spectral response curve that wavelet package transforms decomposes each pixel: given wavelet mother function ψ and the maximum degree of depth j that decomposes, use ψ to X=(x ₁..., x _i..., x _K) carry out j layer wavelet package transforms, obtain 2 altogether ^jIndividual component is comprising 1 approximate component A and 2 ^j-1 details component Their corresponding energy coefficients be respectively a with

And satisfy relational expression (1):

$d_1+...+d_{2^j-1}=1-a---\left(1\right)$

Wherein, j is the maximum degree of depth of decomposing;

(3) Application of gray prediction model processing decomposition results: details of the calculation of the energy component of the coefficient sequence is an order accumulation and sequence

${\stackrel{\‾}{d}}_k=\underset{i=1}{\overset{k}{\mathrm{\Σ}}}{d}_i,k=1,...,2^j-1---\left(2\right)$

Wherein, j is the maximum degree of depth of decomposing;

Order $Y={(d_2,...,d_{2^j-1})}^T,$ $B=\left(\begin{array}{cc}-({\stackrel{\‾}{d}}_1+{\stackrel{\‾}{d}}_2)/2& 1\\ -({\stackrel{\‾}{d}}_2+{\stackrel{\‾}{d}}_3)/2& 1\\ .& .\\ .& .\\ .& .\\ -({\stackrel{\‾}{d}}_{2^j-2}+{\stackrel{\‾}{d}}_{2^j-1})/2& 1\end{array}\right),$ Then:

$\left(\begin{array}{c}b\\ \hat{b}\end{array}\right){\left(B^{T}B\right)}^{-1}{B}^{T}Y---\left(3\right)$

Wherein, B is called the development coefficient, and

is grey action;

Keep the energy coefficient a and development coefficient b of approximate component, then the latent structure result of arbitrary pixel is made up of energy coefficient a and development coefficient b;

(4) use characteristic structure result is to the high-spectral data classification that exercises supervision;

(5) output high spectrum image terrain classification result.