Method and device for selecting hyperspectral image band based on key band extraction

A hyperspectral image and band selection technology, which is applied in the field of image processing, can solve the problem of low detection accuracy of hyperspectral image anomalies, achieve the effect of reducing the amount of calculation, achieving good results, and improving the efficiency of band selection

Active Publication Date: 2017-05-31
HANGZHOU DIANZI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a hyperspectral image band selection method and device based on key band extraction, improve the extraction effect of hyperspectral image key ba

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  • Method and device for selecting hyperspectral image band based on key band extraction
  • Method and device for selecting hyperspectral image band based on key band extraction
  • Method and device for selecting hyperspectral image band based on key band extraction

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Example Embodiment

[0059] Example one

[0060] Step 1: Use the HFC (Harsanyi-Farrand-Chang) method to determine the number of endmembers.

[0061] (1) Calculate the covariance matrix K of the image data L×L And autocorrelation matrix R L×L .

[0062] (2) Calculate the eigenvalue sets of the covariance matrix and the autocorrelation matrix respectively, denoted as {λ 1 ≥λ 2 ≥…λ L }with Where L is the number of spectral bands.

[0063] (3) Calculate the approximate noise variance value of the lth band of the spectral image Among them, M×N represents the number of elements in the image.

[0064] (4) Calculate the probability density function

[0065] (5) Given false alarm probability P F ,according to with Find τ l value

[0066] (6) Satisfaction The number of eigenvalues ​​is the number of bands sought.

[0067] Step 2: Use FNSGA (Fast New Simplex Growing Algorithm) monomorph growth algorithm to achieve endmember extraction and obtain endmember spectrum curve.

[0068] (1) For each pixel r in the image...

Example Embodiment

[0104] Example two

[0105] Steps 1 to 4 of the second embodiment are exactly the same as those of the first embodiment.

[0106] Step 5: Use the band selection method based on the maximum amount of information for the data corresponding to the key band subset to perform band selection.

[0107] Assuming that there are k bands in the key band set, the data of k bands are denoted as Φ 2d ={B 1 ,B 2 ,...,B k }∈R MN×k , Where MN=M×N. The number of bands required for band selection is set to num(num

[0108] For the C matrix obtained in step 4

[0109] (1) Calculate maxC=max(C), define the set

[0110] (2) Calculate the minimum value of each row element of the C matrix and record it as minR i , (I=1,2,3,...,k);

[0111] (3) Calculation And adjust SET=SET∪g;

[0112] (4) Modify the elements of the C matrix so that C(g,i)=C(g,i)=maxC;

[0113] (5) If the number of elements in SET is less than k-num, skip to step (3), otherwise skip to step (6);

[0114] (6) Remove the elements in SET from...

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Abstract

The invention discloses a method and device for selecting a hyperspectral image band based on key band extraction. The method is specifically implemented by: determining the number of end members of a hyperspectral image, and extracting end member spectrums; extracting a key point subset for each end member spectrum by using a method based on three-point vector included angle and variation amplitude analysis, and combining all the key point subsets to construct a candidate band subset; according to the characteristic that inter-band similarities have clustered distribution, constructing a visibility graph of a local information divergence matrix, and determining a range of an optimum band number; and finally, selecting the best one of an information quantity measuring method and an optimum subset selection criteria method, thereby determining the optimum band subset. Key bands of the end member spectrums provided in the method disclosed by the invention are most distinguishing feature bands among different ground objects, an optimum band is selected from the key band subset, and the time of subsequent band selection can be shortened, so that the band selection method disclosed by the invention can improve the efficiency of band selection.

Description

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Claims

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Application Information

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Owner HANGZHOU DIANZI UNIV
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