CN110188735A - A kind of instruction plant recognition methods based on EO-1 hyperion - Google Patents
A kind of instruction plant recognition methods based on EO-1 hyperion Download PDFInfo
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Abstract
The instruction plant recognition methods based on EO-1 hyperion that the invention discloses a kind of, the recognition methods include the following steps: to acquire the high spectrum image of the instruction plant under laboratory environment and under field environment respectively, are denoted as source sample；Rejecting abnormalities sample process is carried out to the instruction plant sample obtained under two kinds of environment, is denoted as and does not make pretreatment sample；The instruction plant data of two kinds of data sources are pre-processed respectively, corresponding instruction plant feature set is obtained, is denoted as primary features collection；Dimensionality reduction is carried out to the primary features collection of the instruction plant under laboratory environment, obtained new feature collection is denoted as standard feature Ji Ku；Dimension-reduction treatment is carried out with all band and local wave band to the primary features collection of instruction plant under field environment；Vector machine modeling is supported to all band of instruction plant mid-level features collection in the field environment and local wave band respectively, optimal preprocess method is obtained by comparison；Primary features collection is optimized.Recognition methods in the present invention is high-efficient.
Description
Technical field
The present invention relates to the Eigenvalue Extraction of a kind of pair of Alien Invasive Plants, specially a kind of entering based on EO-1 hyperion
Plants identification method is invaded, the characteristics extraction applied technical field of Alien Invasive Plants is belonged to.
Background technique
Instruction plant is low with visual recognition degree under attached main plant visible light, and irregular distribution, field environment is complicated and changeable, mesh
Preceding artificial on-site survey is one of the method the most universal to instruction plant detection, and artificial on-site survey is the subjective judgement by people,
Instruction plant can not be detected and make accurate judgement, and artificial on-site survey mode low efficiency, time-consuming, accuracy rate is low etc.；But
The present invention is high with spectral resolution using high spectrum image monitoring technology, wave band is more, nicety of grading is high, accurate positioning, information
The features such as amount is abundant, has great advantage to the detection of instruction plant, small, cost which overcome the coverage areas manually trampleed on
The disadvantages of time is more, to improve to instruction plant detection efficiency.
The Image Acquisition of EO-1 hyperion is mainly satellite remote sensing and unmanned plane, for satellite remote sensing technology, is suitable for advising greatly
The high spectrum image of mould acquires, but for the often ignored of middle and small scale.The present invention using unmanned rack carry EO-1 hyperion at
As instrument obtains instruction plant high spectrum image, compared with satellite remote sensing, the features such as flexibility is high, high resolution, for invasion
The detection of plant is advantageously.
Past detects instruction plant using satellite remote sensing, but due to the small-scale instruction plant of satellite remote sensing centering
Ignore, miss the early prevention stage of instruction plant, existing instruction plant is largely invaded, and the production for arriving crops is seriously endangered
Amount.Therefore it is very important for the detection of instruction plant.
It to sum up, is at present by artificial and carry out using satellite remote sensing technology, but manually to the detection of instruction plant
All there is relatively large detection error with satellite remote sensing technology detection；And the present invention is planted using instruction plant as object in conjunction with invasion
The characteristics of object, and unmanned air vehicle technique is used, thus the method for identifying instruction plant feature；The present invention is by a variety of common method knots
It closes, forms the method more effectively new to instruction plant detection.Therefore, a kind of entering based on EO-1 hyperion is proposed regarding to the issue above
Invade plants identification method.
Summary of the invention
The object of the invention is that providing a kind of instruction plant identification based on EO-1 hyperion to solve the above-mentioned problems
Method.
The present invention is achieved through the following technical solutions above-mentioned purpose, a kind of instruction plant identification side based on EO-1 hyperion
Method, the recognition methods include the following steps:
Step 1. acquires the high spectrum image of the instruction plant under laboratory environment and under field environment respectively, is denoted as source sample
This；
Step 2. carries out rejecting abnormalities sample process to the instruction plant sample obtained under two kinds of environment, is denoted as and does not do pre- place
Manage sample；
Step 3. respectively pre-processes the instruction plant data of two kinds of data sources, obtains corresponding instruction plant feature
Collection, is denoted as primary features collection；Wherein the instruction plant preprocess method under laboratory environment includes putting down smoothly and after first differential
Sliding, the instruction plant volume preprocess method under field environment includes not pre-processing, and 9 gliding smoothings, S-G is smooth, and single order is micro-
Divide, second-order differential, polynary scatter correction, standard normal variable transformation, after first differential smooth (first differential+S-G is smooth), first marks
Quasi- normal variate transformation goes trend to handle nine kinds of preprocess methods again；
Step 4. carries out dimensionality reduction to the primary features collection of the instruction plant under laboratory environment, and obtained new feature collection is denoted as
Standard feature Ji Ku；
Step 5. carries out dimension-reduction treatment to the primary features collection of instruction plant under field environment with all band and local wave band,
Wherein dimensionality reduction is carried out using primary features collection of the principal component analysis to all band instruction plant；And to the instruction plant of local wave band
Primary features collection dimensionality reduction be pass through instruction plant bloom Spectral characteristics analysis and combine statistical analysis technique realize；It is obtained after dimensionality reduction
Data be denoted as stage data collection；
Step 6. respectively props up all band of instruction plant mid-level features collection in the field environment and local wave band
Vector machine modeling is held, optimal preprocess method is obtained by comparison；
Step 7. optimizes primary features collection.
Preferably, the step 2 is to the instruction plant sample mahalanobis distance rejecting abnormalities sample obtained under two kinds of environment
Processing, to improve the differentiation quality of established model.
Preferably, the processing of primary features collection is carried out based on pretreated data in the step 4, and its dimensionality reduction
Processing is obtained by bloom Spectral characteristics analysis and statistical analysis technique.
Preferably, the standard feature collection that dimensionality reduction obtains under laboratory environment in the step 4 is in order to research field ring
Feature set is extracted under border and does basis, and the data under laboratory environment are only involved in the analysis of local wave band under field environment, number
According to as standard set.
Preferably, during carrying out dimensionality reduction for the mid-level features collection of local wave band instruction plant in the step 5, ginseng
According to the standard feature Ji Ku in step 4, corresponding mid-level features collection is obtained.
Preferably, the step 5,6 are to be based respectively on the primary features collection that nine kinds of preprocess methods obtain to be handled.
Preferably, instruction plant feature set is optimized in the step 7, i.e., the feature in instruction plant feature set carries out single
Feature ordering optimization.
Preferably, it is excellent to be that the primary features collection converted for the quasi- normal variate of optimal pretreatment carries out for the step 7
Change.
The beneficial effects of the present invention are: instruction plant data of the present invention pass through laboratory non-imaged bloom spectra system and field
High spectrum image system acquisition, the mainly identification to field instruction plant.Instruction plant identification in field is based on full wave
Instruction plant feature extraction and instruction plant feature extraction two schemes based on local wave band.The method of the present invention is planted to invasion
Object feature set is simplified comprising many algorithms, that is, preprocess method, Method of Data with Adding Windows, data classification and optimization method lead to
The method of the present invention is crossed, identification instruction plant can be quickly obtained, and discrimination is high.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow chart of data processing figure of local wave band；
Fig. 2 be the present invention is based under full wave flow chart of data processing to the collecting flowchart of instruction plant high-spectral data
Figure.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Embodiment:
A kind of instruction plant recognition methods based on EO-1 hyperion, the recognition methods include the following steps:
Step 1. acquires the high spectrum image of the instruction plant under laboratory environment and under field environment respectively, is denoted as source sample
This；
Step 2. carries out rejecting abnormalities sample process to the instruction plant sample obtained under two kinds of environment, is denoted as and does not do pre- place
Manage sample；
Step 3. respectively pre-processes the instruction plant data of two kinds of data sources, obtains corresponding instruction plant feature
Collection, is denoted as primary features collection；Wherein the instruction plant preprocess method under laboratory environment includes putting down smoothly and after first differential
Sliding, the instruction plant volume preprocess method under field environment includes not pre-processing, and 9 gliding smoothings, S-G is smooth, and single order is micro-
Divide, second-order differential, polynary scatter correction, standard normal variable transformation, after first differential smooth (first differential+S-G is smooth), first marks
Quasi- normal variate transformation goes trend to handle nine kinds of preprocess methods again；
Step 4. carries out dimensionality reduction to the primary features collection of the instruction plant under laboratory environment, and obtained new feature collection is denoted as
Standard feature Ji Ku；
Step 5. carries out dimension-reduction treatment to the primary features collection of instruction plant under field environment with all band and local wave band,
Wherein dimensionality reduction is carried out using primary features collection of the principal component analysis to all band instruction plant；And to the instruction plant of local wave band
Primary features collection dimensionality reduction be pass through instruction plant bloom Spectral characteristics analysis and combine statistical analysis technique realize；It is obtained after dimensionality reduction
Data be denoted as stage data collection；
Step 6. respectively props up all band of instruction plant mid-level features collection in the field environment and local wave band
Vector machine modeling is held, optimal preprocess method is obtained by comparison；
Step 7. optimizes primary features collection.
Further, the step 2 is to the instruction plant sample mahalanobis distance rejecting abnormalities sample obtained under two kinds of environment
Present treatment, to improve the differentiation quality of established model.
Further, the processing of primary features collection is to be carried out based on pretreated data, and it is dropped in the step 4
Dimension processing is obtained by bloom Spectral characteristics analysis and statistical analysis technique.
Further, the standard feature collection that dimensionality reduction obtains under laboratory environment in the step 4 is in order to research field
Feature set is extracted under environment and does basis, and the data under laboratory environment are only involved in the analysis of local wave band under field environment,
Data are as standard set.
Further, during carrying out dimensionality reduction for the mid-level features collection of local wave band instruction plant in the step 5,
Referring to the standard feature Ji Ku in step 4, corresponding mid-level features collection is obtained.
Further, the step 5,6 are to be based respectively on the primary features collection that nine kinds of preprocess methods obtain to be handled
's.
Further, instruction plant feature set is optimized in the step 7, i.e., the feature in instruction plant feature set carries out
Single feature ordering optimization.
Further, the step 7 is that the primary features collection converted for the optimal quasi- normal variate of pretreatment carries out
Optimization.
Acquisition by two approach (under laboratory environment and under field environment) to instruction plant high-spectral data, and it is right
Data source is used based on the feature extraction of all band instruction plant and based on the instruction plant feature two of local wave band under field environment
The characteristics extraction of kind mode, describes in terms of two individually below, such as Figure of description 1-2:
1. acquiring the instruction plant high-spectral data sample of two kinds of data sources.
2. pair two kinds of data source samples carry out rejecting abnormalities sample (be denoted as and do not make pretreatment sample)
Achieve the purpose that rejecting abnormalities sample using mahalanobis distance (MD), the specific method is as follows:
Wherein, k=1 ... ..., n are sample number, XkFor the spectral value of each sample, μ is that the spectrum of sample set is average
Value, ∑-1For covariance matrix.Calculation formula are as follows:
Wherein, k=1 ... ..., n are sample number, are every XkThe spectral value of a sample, μ are that the spectrum of sample set is average
Value, ∑-1For covariance matrix.
Threshold value is arranged to mahalanobis distance, is exceptional sample beyond threshold value, and rejected.
3. two kinds of data source samples are pre-processed
Data sample under 3.1 laboratory environments is pre-processed
Using smooth two kinds and processing method after first differential processing and first differential.
High spectrum image sample preprocessing under 3.2 field environments
3.2.1 being based on the feature extraction of all band instruction plant, carry out as follows:
3.2.1.1 smooth (two kinds of preprocess methods)
Achieved the purpose that by two kinds of algorithms it is smooth, it is specific as follows:
Rolling average exponential smoothing: averaging to single sample data gliding smoothing window, to carry out noise reduction to data.
Savitzky-Golay convolution exponential smoothing (S-G is smooth) is that one kind is based on local multinomial least square in time domain
The filtering method of method fitting.Its operational formula are as follows:
Wherein, yiFor the value filtered out in point i；yi+jFor the value not filtered out in point i；J specifies smoothed data on the left of the point
Points；M is to specify data points smooth on the right side of the point；cjFor deconvolution parameter.
3.2.1.2 differential (two kinds of pretreatments)
The differential method mainly has first differential (FDR) and second-order differential (SDR), and calculation formula respectively indicates are as follows:
3.2.1.3 multiplicative scatter correction (MSC)
The summation of all spectroscopic datas is averaged again first, using counted average spectral data as " ideal spectrum ", is calculated
Formula are as follows:
Then the intercept constant and regression coefficient of every spectroscopic data and average spectral data are calculated:
Finally calculate the relative tilt of the amendment curve of spectrum:
3.2.1.4 standard normal variable converts
By original spectral data standard normal calculation formula such as formula:
3.2.1.5 after first differential smooth (first differential+S-G is smooth)
3.2.1.6 first standard normal variable transformation goes trend to handle again
4. dimensionality reduction
4.1 dimensionality reduction based on all band instruction plant feature --- principal component analysis
By original higher-dimension variable data (x1, x2..., xp) it is converted into new generalized variable data (y according to the following formula1,
y2..., ym) i.e. principal component, it is as follows to solve formula:
Wherein, it is necessary to meet following condition for solution formula solution procedure:
A) principal component (y1, y2..., ym) between it is irrelevant each other, with achieve the effect that reduce data redundancy.
B) according to the descending sequence of the variance yields of principal component, i.e. Var (y1) > Var (y2) > ... > Var (ym), it will
After main feature information aggregation to fraction principal component analysis in original high dimensional data in data.
C) coefficient in formula need to meet condition bm1+bm2+...+bmp=1.
Based on there are m n-dimensional vector x of certain correlationiExtract the PCA algorithm concrete implementation of its main feature information
Process is as follows:
Step1: sample data centralization, i.e., to by m n-dimensional vector xiThe matrix by rows of composition carries out zero averaging,
Step2: it calculatesCovariance matrix,
Step3: diagonal matrix Λ and eigenmatrix are arrived to covariance C feature decomposition, wherein Λ=Diag [λ1,
λ2..., λm], λiIt is characterized value, U=[U1, U2..., Um], UiFor feature vector；
K rank principal component before Step4 takes,Wherein Y is exactly the main feature information for needing to extract.
Comparison obtains 10 dimension instruction plant feature sets.
The 4.2 instruction plant Feature Dimension Reductions based on local wave band
Pass through instruction plant bloom Spectral characteristics analysis and statistical analysis technique is combined to realize, first to pretreated experiment
Spectroscopic data under room environmental is analyzed, so that selected characteristic parameter, is denoted as standard database.Afterwards in standard database
On the basis of to the high-spectral data selected characteristic parameter under field environment.
5. single eigenvalue support vector machines
Vector machine classification is supported to feature set first, is realized by following calculating formula:
Classification problem is nonlinear relevant issues in this research, needs to classify using Kernel Function Transformation.
Formula is as follows:
To basic function (RBF):
5.1 are based on full wave instruction plant feature
Feature set based on 1,2,3,4 is classified using support vector machines, thus the result obtained to principal component analysis
It is verified.
The 5.2 instruction plant features based on local wave band
Feature set based on 1,2,3,4 is classified using statistical analysis, to verify to the result that dimensionality reduction obtains.
6. optimization
Former 138 dimensional feature collection of data is optimized, using single feature set sort method according to master i.e. inside feature set
The descending sequence of the variance yields of ingredient, therefore above method is verified.
7. obtaining result
This method is feasible to the extraction of instruction plant characteristic value, and accuracy rate is high.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiments being understood that.
Claims (8)
1. a kind of instruction plant recognition methods based on EO-1 hyperion, it is characterised in that: the recognition methods includes the following steps:
Step 1. acquires the high spectrum image of the instruction plant under laboratory environment and under field environment respectively, is denoted as source sample；
Step 2. carries out rejecting abnormalities sample process to the instruction plant sample obtained under two kinds of environment, is denoted as and does not make pretreatment sample
This；
Step 3. respectively pre-processes the instruction plant data of two kinds of data sources, obtains corresponding instruction plant feature set,
It is denoted as primary features collection；Wherein the instruction plant preprocess method under laboratory environment include after smooth and first differential smoothly,
Instruction plant volume preprocess method under field environment includes not pre-processing, and 9 gliding smoothings, S-G is smooth, first differential,
Second-order differential, polynary scatter correction, standard normal variable convert, smooth (first differential+S-G is smooth), first standard after first differential
Normal variate transformation goes trend to handle nine kinds of preprocess methods again；
Step 4. carries out dimensionality reduction to the primary features collection of the instruction plant under laboratory environment, and obtained new feature collection is denoted as standard
Feature set library；
Step 5. carries out dimension-reduction treatment to the primary features collection of instruction plant under field environment with all band and local wave band, wherein
Dimensionality reduction is carried out using primary features collection of the principal component analysis to all band instruction plant；And to the first of the instruction plant of local wave band
Grade feature set dimensionality reduction is to pass through instruction plant bloom Spectral characteristics analysis and statistical analysis technique is combined to realize；The number obtained after dimensionality reduction
According to being denoted as stage data collection；
Step 6. respectively to all band of instruction plant mid-level features collection in the field environment and local wave band be supported to
The modeling of amount machine obtains optimal preprocess method by comparison；
Step 7. optimizes primary features collection.
2. a kind of instruction plant recognition methods based on EO-1 hyperion according to claim 1, it is characterised in that: the step
The instruction plant sample mahalanobis distance rejecting abnormalities sample process obtained under 2 pairs of two kinds of environment, to improve established model
Differentiate quality.
3. a kind of instruction plant recognition methods based on EO-1 hyperion according to claim 1, it is characterised in that: the step
The processing of primary features collection is to be carried out based on pretreated data, and its dimension-reduction treatment is by bloom spectrum signature point in 4
What analysis and statistical analysis technique obtained.
4. a kind of instruction plant recognition methods based on EO-1 hyperion according to claim 1, it is characterised in that: the step
The standard feature collection that dimensionality reduction obtains under laboratory environment in 4 be in order to research field environment under extract feature set do basis, and
Data under laboratory environment are only involved in the analysis of local wave band under field environment, and data are as standard set.
5. a kind of instruction plant recognition methods based on EO-1 hyperion according to claim 1, it is characterised in that: the step
During carrying out dimensionality reduction for the mid-level features collection of local wave band instruction plant in 5, referring to the standard feature collection in step 4
Library obtains corresponding mid-level features collection.
6. a kind of instruction plant recognition methods based on EO-1 hyperion according to claim 1, it is characterised in that: the step
5,6 be to be based respectively on the primary features collection that nine kinds of preprocess methods obtain to be handled.
7. a kind of instruction plant recognition methods based on EO-1 hyperion according to claim 1, it is characterised in that: the step
Instruction plant feature set is optimized in 7, i.e., the feature in instruction plant feature set carries out single feature ordering optimization.
8. a kind of instruction plant recognition methods based on EO-1 hyperion according to claim 1, it is characterised in that: the step
7 be that the primary features collection converted for the optimal quasi- normal variate of pretreatment optimizes.
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| CN111257242A (en) * | 2020-02-27 | 2020-06-09 | 西安交通大学 | High-spectrum identification method for pollutant components of insulator |
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