CN111488822A - Tree species information identification method based on full spectrum segment correlation analysis algorithm - Google Patents
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Abstract
The invention discloses a tree species information identification method based on a full spectrum segment correlation analysis algorithm, which comprises the following steps: collecting a plurality of leaf samples of different tree species; measuring the spectral data of the tree species leaf sample by using an ASD spectrometer; screening partial tree species leaf samples as training samples, and taking the rest tree species leaf samples as verification samples; taking the mean value of leaf spectrum data of all training samples of each tree as a standard spectrum for tree species identification; processing the spectrum data of the verification sample by using a spectrum transformation method, disordering the sequence of the spectrum data of the verification sample, then, carrying out correlation analysis on the spectrum data of the verification sample and the standard spectrum identified by the tree species, and taking the tree species corresponding to the standard spectrum with the highest correlation as the tree species type of the verification sample. The tree species information identification method based on the full spectrum segment correlation analysis algorithm is simple in method, convenient to operate and high in tree species identification precision, and is beneficial to improvement of forestry information supervision level.
Description
Technical Field
The invention relates to the technical field of tree species information identification, in particular to a tree species information identification method based on a full spectrum segment correlation analysis algorithm.
Background
The forest covers almost one third of the land surface, and plays a great role in the aspects of water and soil conservation, climate regulation and the like as the most important land ecosystem; the trees are used as the main body of the forest, and the mastering of the types and the distribution conditions of the trees has great significance for forest ecological system protection, forest continuous operation and the like. Therefore, how to quickly and accurately acquire the distribution information of the forest tree species in a large range is very important.
In recent years, with the rapid development of remote sensing technology, the remote sensing technology is widely applied to tree species classification research; the hyperspectral data is always a hot spot in tree species classification research due to the advantages of abundant spectral characteristic information, easiness in highlighting fine information and the like. Trees are influenced by the environment (hydrology, climate, soil and the like) and show periodic natural phenomena, including germination, leaf expansion, leaf discoloration, leaf falling and the like, namely, the phenological phenomena, and the spectrum of leaves of canopy of the same kind of trees is also different due to the restriction of illumination, geometric structure of canopy, height and the like; therefore, the difficulty of tree species classification based on a few spectral characteristic bands is high, ideal precision is difficult to obtain, and the classification precision gradually decreases with the increase of the number of tree species, so that exploring a new tree species information identification method is extremely important for tree species classification, and how to reasonably utilize limited spectral information becomes an urgent problem to be solved.
The essence of the spectral variation is the change of target physicochemical characteristics, and the source of the spectral difference among the tree species is that different tree species have difference in the aspect of physicochemical characteristics; considering the environmental influence, the leaves of the same tree species have difference, but the physical and chemical properties of the leaves have higher similarity, and the spectrum of the same tree species has higher correlation, so the invention provides the tree species information identification based on the full spectrum segment correlation analysis algorithm, and provides necessary technical support for improving the identification precision of the tree species.
Disclosure of Invention
The invention aims to provide a tree species information identification method based on a full spectrum segment correlation analysis algorithm, which is simple, convenient to operate, high in tree species identification precision and beneficial to improvement of forestry information supervision level.
In order to achieve the purpose, the invention provides the following scheme:
a tree species information identification method based on a full spectrum segment correlation analysis algorithm comprises the following steps:
s1, collecting a plurality of leaf samples of different tree species;
s2, measuring the spectrum data of the tree seed leaf sample by using an ASD spectrometer;
s3, screening partial tree leaf samples from the tree leaf samples as training samples, and using the rest tree leaf samples as verification samples;
s4, taking the mean value of the leaf spectrum data of all training samples of each tree as the standard spectrum of the tree species identification;
s5, processing the spectrum data of the verification sample by using a spectrum transformation method, disordering the sequence of the spectrum data of the verification sample, then, carrying out correlation analysis on the spectrum data of the verification sample and the standard spectrum identified by the tree species, and taking the tree species corresponding to the standard spectrum with the highest correlation as the tree species type of the verification sample.
Optionally, in step S1, the measuring the spectrum data of the tree seed leaf sample by using the ASD spectrometer specifically includes: 18 spectral data were collected for each tree.
Optionally, in step S3, screening a part of the tree species leaf samples as training samples and the remaining tree species leaf samples as verification samples from the tree species leaf samples, specifically including: 2/3 tree leaf samples are screened as training samples, and 1/3 tree leaf samples are screened as verification samples.
Optionally, the tree species information identification method further includes: in step S6, an accuracy check is performed using the known information based on the analysis result in step S5.
Optionally, the ASD spectrometer employs a field portable surface feature spectrometer.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the tree species information identification method based on the full spectrum segment correlation analysis algorithm fully utilizes the difference of different tree species in the aspects of physical and chemical characteristics of leaves as an entry point, utilizes the full band as analysis data, analyzes from the key point of correlation, and develops the research of the tree species identification technology; the invention is provided on the basis of deeply analyzing the inherent correlation of the physicochemical characteristics and the spectrum of the tree species, has the advantages of high precision, simplicity, convenience, easy operation and the like, and can provide basic technical support for the identification of the tree species; the tree species information identification is basic data for developing forest information detection of biomass, ecology and the like, and the improvement of the tree species information identification precision is beneficial to improving the monitoring precision of forest information; the prior art is relatively deficient in tree species information identification, the method can effectively make up for the defects in the field, and the method is higher in detection precision and better in robustness and universality.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flowchart of a tree species information identification method based on a full spectrum segment correlation analysis algorithm according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a geophysical spectrometer according to an embodiment of the present invention;
FIG. 3 is a graph of spectra of leaves of 15 trees in an example of the present invention;
FIG. 4 is a diagram illustrating tree classification accuracy based on the full-band correlation analysis method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a tree species information identification method based on a full spectrum segment correlation analysis algorithm, which is simple, convenient to operate, high in tree species identification precision and beneficial to improvement of forestry information supervision level.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in FIG. 1, the tree species information identification method based on the full spectrum segment correlation analysis algorithm provided by the invention comprises the following steps:
s1, collecting a plurality of leaf samples of different tree species;
s2, measuring the spectrum data of the tree seed leaf sample by using an ASD spectrometer;
s3, screening partial tree leaf samples from the tree leaf samples as training samples, and using the rest tree leaf samples as verification samples;
s4, taking the mean value of the leaf spectrum data of all training samples of each tree as the standard spectrum of the tree species identification;
s5, processing the spectrum data of the verification sample by using a spectrum transformation method, disordering the sequence of the spectrum data of the verification sample, then, carrying out correlation analysis on the spectrum data of the verification sample and the standard spectrum identified by the tree species, and taking the tree species corresponding to the standard spectrum with the highest correlation as the tree species type of the verification sample.
In step S1, the measuring the spectrum data of the tree seed leaf sample by using the ASD spectrometer specifically includes: 18 spectral data were collected for each tree.
In step S3, screening a part of the tree leaf samples as training samples and the remaining tree leaf samples as verification samples from the tree leaf samples, specifically including: 2/3 tree leaf samples are screened as training samples, and 1/3 tree leaf samples are screened as verification samples.
The tree species information identification method further comprises the following steps: and step S6, based on the analysis result of step S5, using the spectrum data and the corresponding tree species to perform accuracy test.
Wherein, the ASD spectrometer adopts a field portable surface feature spectrometer. The surface feature spectrum is the comprehensive embodiment of the surface feature, the source of the change of a certain surface feature spectrum is the change of the physical and chemical properties of a substance, and therefore, the spectrum is the external embodiment of the physical and chemical properties (constituent components, proportion and the like) of a certain surface feature; therefore, the leaf spectra of the same tree species have similar physicochemical properties, so that the same leaf species have higher correlation; the leaves of different tree species have relatively large difference in physical and chemical properties, so that the heterogeneous leaves have low correlation; considering that different tree species have certain similarity and cannot be distinguished with high precision by using one or more characteristic wave bands, the invention develops the research of the identification technology of the tree species by fully utilizing the difference of the different tree species in the aspect of physical and chemical characteristics of leaves as an entry point, utilizing the full wave band as analysis data and analyzing from the key point of correlation.
In step S2, the tree leaf spectrum measurement method is performed indoors, and the feature spectrometer is measured by using a field portable feature spectrometer manufactured by ASD corporation of usa, and the wavelength coverage of the device is 350-2500 nm, the output spectrum resolution is 1nm, and the specific schematic diagram is shown in fig. 2. The measurement method can effectively avoid the interference of external uncontrollable factors and improve the quality of spectral data. 10 spectra were measured for each sample and averaged as the final spectrum. Because the indoor environment is stable, the quality of the acquired spectral data is high, and smooth denoising is not needed.
In step S5, the spectral transformation method is to enhance the spectral feature and enhance the classification accuracy or inversion accuracy by processing the analyzed spectral data using the spectral transformation method, the present invention processes the spectral data using 11-fold methods such as first-order differentiation (R '), second-order differentiation (R "), low-pass filtering (L ow _ filter), normalization (N), logarithm (L og (R)), logarithm first-order differentiation ((L og (R)) '), logarithm normalization (N (L og (R))), logarithm normalization minus (N (L og (R)) -R), logarithm minus reciprocal (1/L og R)), reciprocal (1/R), reciprocal first-order differentiation ((1/R) ') and the like.
Wherein, the correlation analysis comprises the following steps: the correlation analysis and the regression analysis have close relation in practical application. In regression analysis, however, the functional form of the dependence of one random variable Y on another (or a group of) random variables X is heavily studied. While in correlation analysis the variables in question are in the same place, the analysis focuses on a myriad of correlation features between random variables. The correlation coefficient is calculated according to a product difference method, and the correlation degree between the two variables is reflected by multiplying the two dispersion differences on the basis of the dispersion difference between the two variables and the respective average value; the linear single correlation coefficient is heavily studied. The correlation coefficient, or linear correlation coefficient, generally denoted by the letter R, is used to measure the linear relationship between two variables: the calculation method is as follows:
wherein Cov (X, Y) is the covariance of X and Y, Var [ X ] is the variance of X, and Var [ Y ] is the variance of Y. The study focuses on the analysis of linear correlation by calculating the correlation coefficient R between variables to analyze the correlation between the two.
In this embodiment, 15 species of buxus sinica, clove, holly, paper mulberry, kumquat, forsythia, camellia, pomegranate, persimmon, obelia, camptotheca, apricot, poplar, elm, crape myrtle and the like are selected as classification objects, samples of different tree leaves are collected, spectral data of the leaves of each tree is measured by an ASD spectrometer, and as shown in fig. 3, standard spectra of different tree species are prepared; and (3) disordering the verification spectrum data, performing correlation analysis on each spectrum and the tree standard spectrum, taking the tree with the standard spectrum with the highest correlation as the class of the spectrum, adopting the third step, and performing precision test by using known information according to the result, wherein the third step is shown in the table 1 and the figure 4.
In addition, different tree species have greater similarity in physical and chemical composition and proportion, which results in higher similarity of spectral characteristic curves; but the proportions of the constituent components of different tree species are different, the spectral response of the difference has a specific range, and therefore the tree species classification provides possibility; the same tree species has more similar components and proportions, the higher the correlation among the spectra, and the larger the difference between the proportions of the components of different tree species, the lower the correlation among the spectra.
TABLE 1 Tree species Classification accuracy evaluation Table
The tree species information identification method based on the full spectrum segment correlation analysis algorithm fully utilizes the difference of different tree species in the aspects of physical and chemical characteristics of leaves as an entry point, utilizes the full band as analysis data, analyzes from the key point of correlation, and develops the research of the tree species identification technology; the invention is provided on the basis of deeply analyzing the inherent correlation of the physicochemical characteristics and the spectrum of the tree species, has the advantages of high precision, simplicity, convenience, easy operation and the like, and can provide basic technical support for the identification of the tree species; the tree species information identification is basic data for developing forest information detection of biomass, ecology and the like, and the improvement of the tree species information identification precision is beneficial to improving the monitoring precision of forest information; the prior art is relatively deficient in tree species information identification, the method can effectively make up for the defects in the field, and the method is higher in detection precision and better in robustness and universality.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (5)
1. A tree species information identification method based on a full spectrum segment correlation analysis algorithm is characterized by comprising the following steps:
s1, collecting a plurality of leaf samples of different tree species;
s2, measuring the spectrum data of the tree seed leaf sample by using an ASD spectrometer;
s3, screening partial tree leaf samples from the tree leaf samples as training samples, and using the rest tree leaf samples as verification samples;
s4, taking the mean value of the leaf spectrum data of all training samples of each tree as the standard spectrum of the tree species identification;
s5, processing the spectrum data of the verification sample by using a spectrum transformation method, disordering the sequence of the spectrum data of the verification sample, then, carrying out correlation analysis on the spectrum data of the verification sample and the standard spectrum identified by the tree species, and taking the tree species corresponding to the standard spectrum with the highest correlation as the tree species type of the verification sample.
2. The method for identifying tree species information based on full spectral band correlation analysis algorithm as claimed in claim 1, wherein in step S1, the measuring the spectral data of the tree species leaf sample by using the ASD spectrometer specifically comprises: 18 spectral data were collected for each tree.
3. The method for identifying tree species information based on the full spectrum segment correlation analysis algorithm as claimed in claim 1, wherein in the step S3, the step of screening partial tree species leaf samples as training samples and the step of screening the remaining tree species leaf samples as verification samples comprises: 2/3 tree leaf samples are screened as training samples, and 1/3 tree leaf samples are screened as verification samples.
4. The tree species information identification method based on the full spectrum segment correlation analysis algorithm according to claim 1, further comprising: in step S6, an accuracy check is performed using the known information based on the analysis result in step S5.
5. The tree species information identification method based on the full spectrum segment correlation analysis algorithm as claimed in claim 1, wherein the ASD spectrometer adopts a field portable surface feature spectrometer.
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| CN108304791A (en) * | 2018-01-23 | 2018-07-20 | 山东农业大学 | Seeds multispectral remote sensing recognition methods is easily obscured in a kind of mountain area based on cloud model |
| CN109870421A (en) * | 2019-03-27 | 2019-06-11 | 东北林业大学 | It is a kind of based on visible light/near-infrared spectrum analysis incrementally timber varieties of trees classifying identification method |
| CN110390028A (en) * | 2019-04-16 | 2019-10-29 | 杭州电子科技大学 | A kind of method for building up in plant spectral library |
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- 2020-04-09 CN CN202010271493.8A patent/CN111488822A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN1831515A (en) * | 2006-04-03 | 2006-09-13 | 浙江大学 | Method for nondistructive discriminating crop seed variety using visible light and near-infrared spectrum technology |
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| CN108280473A (en) * | 2018-01-19 | 2018-07-13 | 山东农业大学 | A kind of main deciduous species remote sensing recognition method in Mount Taishan based on sensitivity spectrum index and SVM |
| CN108304791A (en) * | 2018-01-23 | 2018-07-20 | 山东农业大学 | Seeds multispectral remote sensing recognition methods is easily obscured in a kind of mountain area based on cloud model |
| CN109870421A (en) * | 2019-03-27 | 2019-06-11 | 东北林业大学 | It is a kind of based on visible light/near-infrared spectrum analysis incrementally timber varieties of trees classifying identification method |
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