WO2016000088A1 - Procédé d'extraction de gamme d'ondes hyperspectrales selon un procédé de coefficient de corrélation de facteur d'indice optimal - Google Patents

Procédé d'extraction de gamme d'ondes hyperspectrales selon un procédé de coefficient de corrélation de facteur d'indice optimal Download PDF

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WO2016000088A1
WO2016000088A1 PCT/CN2014/000676 CN2014000676W WO2016000088A1 WO 2016000088 A1 WO2016000088 A1 WO 2016000088A1 CN 2014000676 W CN2014000676 W CN 2014000676W WO 2016000088 A1 WO2016000088 A1 WO 2016000088A1
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index
correlation coefficient
data
band
bands
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PCT/CN2014/000676
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Chinese (zh)
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程志庆
张劲松
郑宁
王鹤松
李春友
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中国林业科学研究院林业研究所
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands

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  • the invention relates to the field of spectral data processing, in particular to a method for extracting a hyperspectral remote sensing band.
  • Hyperspectral technology is the use of light from objects Spectral characteristics, the spectral information of the object is obtained by a high-resolution spectroscopic instrument, and the characteristic band of the object is analyzed by means of analysis Extract and distinguish to obtain useful spectral information. Due to the high resolution of the hyperspectral, the amount of data obtained is large and More redundant information, therefore, the ability to extract useful spectral information from hyperspectral data for hyperspectral techniques has Great significance.
  • the main analytical tool is to reduce the spectral information and extract the useful band.
  • the correlation coefficient analysis method is more commonly used and applied to remote sensing image processing.
  • the Optimum Index Factor can obtain the band combination with the most information. It has the advantages of rich information and small redundancy of band information, which can provide important reference for hyperspectral data processing. Comprehensive In addition, if the above two methods can be combined, it will be beneficial to improve the detection and simulation capabilities of hyperspectral data.
  • the object of the present invention is to overcome the conventional hyperspectral data band selection method and use a single band to meet the target requirements. Inversion is very susceptible to interference from other factors, and the use of multiple bands lacks the relationship between each band and the target. A method for selecting a band of hyperspectral remote sensing data is provided.
  • the invention comprises the following steps: a method for extracting hyperspectral bands based on an optimal exponential-correlation coefficient method, including the following step:
  • Step A Sort and sort the original hyperspectral data. The specific steps are as follows:
  • the unwanted information in the single raw hyperspectral data obtained is removed, and then the reflectivity of all the individual data is The data is integrated into the same file as the basic database for the following processing;
  • Step B Perform the optimal index calculation process after classifying and sorting the original hyperspectral data.
  • the specific method is as follows:
  • the optimal combination band needs to select three relevant bands for calculation at the same time, and the best index OIF is used as the evaluation index of the optimized combination.
  • the calculation formula is: Where: S i is the standard deviation of any i-band of the three bands, R ij is the correlation coefficient of any i and j bands of the three bands, and r is the combination number of any i and j bands;
  • Step C The 3-band correlation coefficient is simultaneously selected to calculate the maximum value, and the calculation method is as follows:
  • R std Rr std , where: R std is the correlation coefficient evaluation index of the 3 bands. The larger the R stf is, the larger the correlation coefficient values of the three bands are at the same time; R is the sum of the three bands and the target data, and r std is the standard deviation of the correlation coefficient between each of the three bands and the target data;
  • Step D Using the calculation method of the commercial power index to establish a comprehensive index evaluation system for the best index and correlation coefficient of the data, And on this basis, the selection of the hyperspectral band is as follows:
  • the calculation method of the commercial power index is to use the best index calculation result and the correlation coefficient calculation result as the two input index values.
  • Negative indicator data optimization processing formula is where: r ij ′ is the optimization index evaluation value of the i-th item under the j-th index, and max(r ij ) and min(r ij ) are the maximum and minimum values of the optimization index evaluation of all i items in the j-th index;
  • L 1 is the selection index of the hyperspectral band extraction method based on the optimal exponential-correlation coefficient method.
  • this experiment selects 5 nm interval hyperspectral data for processing and analysis, with 108 Spectral samples are modeled.
  • the measured data of chlorophyll data is directly observed by instruments, and the observation instrument is Konica's SPAD. Type chlorophyll meter.
  • Spectral data is processed by Matlab R2012b programming, and the extracted band is used to minimize the second Multiplication is used for regression analysis, and the accuracy ratio is compared with the regression model obtained by using the best index method or the correlation coefficient method alone. Compared.
  • the OIF value of the three-band combination of Figure 1, 2 is obtained by calculating the optimal index (OIF) of the pre-processed spectral data, OIF
  • the maximum value is mainly distributed in three band intervals: the first band is located at 740nm-1115nm, and the second band is located at 1850nm-1860nm, the third band is located at 1930nm-2010nm.
  • the three-band combination of the maximum values obtained by the OIF algorithm is: 745 nm, 1860 nm, 1950 nm and 750 nm, 1860 nm, 1950 nm.
  • the numbers are -0.679 and -0.692, respectively, and the bands with the largest correlation coefficients in the two groups are: 696 nm (correlation coefficient -0.728) And 1890nm (correlation coefficient -0.775); chlorophyll content and its original spectral reflectance at the 740-1140nm band Significant positive correlation, but the correlation coefficient is small, where the position correlation coefficient is the largest at 770 nm (correlation coefficient is 0.46). Studies have shown that the main absorption peak of chlorophyll is blue and red light, and in the green light area is absorption low. Therefore in the selection of the relationship When modeling the number of chlorophyll, the band with the largest correlation coefficient between 350 nm and 800 nm is used at 696 nm.
  • the model of the various methods obtained in Table 1 and the decision coefficients of the model show that the decision method of the selected three methods is The number has reached extremely significant, and the coefficient of determination from large to small is OIFC>OIF>MCC, and the 3-band pass obtained by the OIFC method.
  • the coefficient of determination for PLS modeling reached 0.739, which was 0.027 and 0.1383 higher than OIF and MCC, respectively.
  • the verification data randomly selected 22 sets of measured data in different growth periods of wheat to predict the predicted values of the above five groups of models. Line verification ( Figure 6).
  • the predicted values of the three sets of models have a significant linear correlation with the measured values.
  • the model prediction effect established by the extraction band of the invention has high precision.

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  • Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
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  • Investigating Or Analysing Materials By Optical Means (AREA)
  • Complex Calculations (AREA)

Abstract

La présente invention concerne un procédé d'extraction de gamme d'ondes hyperspectrales selon un procédé de coefficient de corrélation de facteur d'indice, comprenant les étapes suivantes : étape A : effectuer un traitement tel que le tri et l'organisation de données originales hyperspectrales ; étape B : effectuer un calcul de facteur d'indice optimal après le tri et l'organisation des données originales hyperspectrales ; étape C : calculer les 3 coefficients de corrélation de gamme d'ondes maximum ; étape D : sélectionner une gamme d'ondes hyperspectrales en fonction du calcul de facteur d'indice optimum, du calcul de corrélation et du calcul d'indice quotient-poids des données. Le procédé résout les défauts d'un procédé classique de sélection de gamme d'ondes de données hyperspectrales sensible à une interférence provenant d'autres facteurs en cas d'inversion d'une exigence cible à l'aide d'une seule gamme d'ondes et d'absence d'une relation entre chaque gamme d'ondes et une cible lors de l'utilisation d'une multibande.
PCT/CN2014/000676 2014-07-02 2014-07-16 Procédé d'extraction de gamme d'ondes hyperspectrales selon un procédé de coefficient de corrélation de facteur d'indice optimal WO2016000088A1 (fr)

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