CN109711446A - A kind of terrain classification method and device based on multispectral image and SAR image - Google Patents
A kind of terrain classification method and device based on multispectral image and SAR image Download PDFInfo
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Abstract
The present invention relates to terrain classification fields, and in particular to a kind of terrain classification method and device based on multispectral image and SAR image, this method and device obtain the multispectral image of predeterminable area, and carry out multispectral image feature extraction to multispectral image；The time series SAR image of predeterminable area is obtained, and time series SAR image feature is carried out to time series SAR image and is extracted；Feature-based fusion is carried out to multispectral image feature and time series SAR image feature, obtains terrain classification result.The advantage that this method and device utilize synthetic aperture radar SAR round-the-clock, all weather operations, revisiting period short, obtains long-term sequence SAR image, increases the characteristic dimension of input；Feature-based fusion is carried out to multispectral and SAR image, while abundant benefit spectral information, ground object structure, texture and Electromagnetic Scattering Characteristics that binding time sequence SAR image is reflected assist atural object to interpret.
Description
Technical field
The present invention relates to terrain classification fields, in particular to a kind of atural object based on multispectral image and SAR image
Classification method and device.
Background technique
In recent years, with the continuous development of satellite and imaging technique, optical remote sensing image and satellite-borne synthetic aperture radar
(SAR) the acquisition channel of image is more and more, becomes the significant data source of a wide range of earth observation.Optical image is abundant due to it
Spectral information, the identification and detection of target are relatively easy to.But traditional atural object classification method is only with optical image, by
In the influence of weather, it is difficult to form time series data collection, greatly limit the dimension of input feature vector.Satellite-borne synthetic aperture radar
(SAR) it is used as a kind of active microwave remote sensing technology, the advantage with round-the-clock, all weather operations, penetration capacity is strong and texture is believed
Breath is abundant, but is influenced by factors such as the intrinsic coherent spots of geometric distortion and SAR imaging system, and the interpretation to atural object is limited
It is not high individually to carry out terrain classification precision using radar image for ability.Therefore, how optics and SAR remote sensing image are melted
It closes, realizes that multi-source data message complementary sense and comprehensive utilization have become a hot topic of research to improve the precision of terrain classification.
Prior art a part only with visual remote sensing data is influenced that long-term sequence feature can not be obtained by cloud desk,
Limit nicety of grading；In the research with SAR visual fusion, Speckle reduction effect of the prior art to SAR data itself
It is undesirable, due to being filtered using home town ruling window, the statistical property of neighboring pixel is not accounted for, the same of noise is being inhibited
Shi Jiyi obscures strong scatterer and periphery Low coherence region.
Summary of the invention
The terrain classification method and device based on multispectral image and SAR image that the embodiment of the invention provides a kind of, with
At least solve the poor technical problem of existing terrain classification classification precision.
An embodiment according to the present invention provides a kind of terrain classification method based on multispectral image and SAR image,
The following steps are included:
S101: the multispectral image of predeterminable area is obtained, and multispectral image feature extraction is carried out to multispectral image；
S102: the time series SAR image of predeterminable area is obtained, and time series SAR is carried out to time series SAR image
Image feature extracts；
S103: feature-based fusion is carried out to multispectral image feature and time series SAR image feature, obtains terrain classification
As a result.
Further, method further include:
S104: accuracy evaluation is carried out to the terrain classification result of acquisition.
Further, step S101 includes:
It chooses and covers same research multispectral image of area's cloud desk less than 10%；Multispectral image is pre-processed, so
The vegetation index NDVI and water body index NDWI in multispectral image are calculated using Top Of Atmosphere TOA reflectivity afterwards；
Vegetation index (NDVI) calculation formula is as follows:
Water body index (NDWI) calculation formula is as follows:
Wherein, BandGreen、BandNIR、BandRedRespectively green, infrared, red spectral band the big pneumatic jack of multispectral image
Portion's reflectivity.
Further, step S102 includes:
The time series SAR image for choosing the same research area of covering, pre-processes time series SAR image: taking
Adaptive filter method based on statistics homogeneity set of pixels calculates interference coherence factor, VV/VH POLARIZATION CHANNEL amplitude dispersion is estimated
Meter, gray level co-occurrence matrixes GLCM texture feature extraction.
Further, the adaptive filter method for being taken based on statistics homogeneity set of pixels calculates interference coherence factor and includes:
According to the statistical distribution of each pixel, choosing with the pixel collection being distributed is statistics homogeneity set of pixels；Haplopia is multiple
A large amount of superimposed scattering strength Z (P) of distributed diffusion body are that a multiple Gauss becomes at random in the SAR image of data SLC format
Amount, variance areAccording to SAR image distributed diffusion body statistical theory, the amplitude of the SLC image of haplopia SARRayleigh distributed, and it is expected to be respectively as follows: with variance
One measurement standard of SAR smudges noise level, the i.e. coefficient of variation are defined as
For the stochastic variable A (P) of rayleigh distributed, formula (3) are substituted into (4), coefficient of variation CVAIt is constant:
For multiple view picture, number is regarded as L, then above formula becomes:
Therefore variance may be expressed as: with desired relationship
Var (A (P))=[CVA*E(A(P))]2=[CVA*μ(P)]2(7)；
Wherein μ (P) indicates the expectation E (A (P)) of stochastic variable A (P)；
Assuming that there is N phase SAR image, for any pixel P, amplitude forms a N-dimensional vector along time shaft:
A (P)=[A1(P),A2(P),…AN(P)]T(8)；
The sample point estimation of the expectation E (P) of A (P) can be expressed asAccording to
Central-limit theorem,As the increase of sample number N gradually approaches Gaussian Profile；Assuming that N is sufficiently large, Gauss assumes to set up,Distribution may be expressed as:
It is hereby achieved thatConfidence interval:
WhereinIt is just being distributed very much for standardQuantile；Formula (6), (7) are substituted into (10), are had:
The statistics homogeneity set of pixels about P is obtained by formula (11)；
Traditional Lee filtering is modified, as unit of the statistics homogeneity set of pixels of acquisition, backscatter intensity is carried out
Speckle reduction obtains filtered N width SAR intensity map；
The interference coherence factor calculation formula of one pixel is as follows:
Wherein S1(i) and S2(i) complex values of principal and subordinate's image respective pixel i for interference are represented, K is represented where pixel i
Statistics homogeneity set of pixels in pixel number；The corresponding coherence factor of each sample is calculated using formula (12), obtains phase responsibility
One group of several estimation γ1,γ2,…,γR, using the coherence factor of these Sampling Estimations and the coherence factor γ of original estimation, lead to
Cross the unbiased esti-mator that following formula obtains the coherence factor:
Further, obtained by formula (11) include: about the statistics homogeneity set of pixels of P
Search window is set first, by the sample average of all pixels in search rangeAs the estimated value of μ (P),
Initial confidence interval is calculated with α=50%, the pixel between fallen with original area is temporarily determined as homologous pints；
Then it is iterated, with initial homogeneity point set ΩinitFor new range computationAs μ (P) estimating newly
Evaluation calculates new confidence interval with α=5%, is carried out again with updated confidence interval to the pixel in initial homogeneity point set
Secondary judgement, falling between new district is homologous pints；
After obtaining new homogeneity point set, the connectivity of pixel is assessed, only retain directly or indirectly with reference point P phase
Point even, to obtain the statistics homogeneity set of pixels about P.
Further, calculating the corresponding coherence factor of each sample using formula (12) includes:
Deviation caused by landform phase removal interferometric phase first using the simulation of reference number elevation；
Secondly coherence factor calculating is carried out as unit of the homogeneity collection where each pixel, is caused to remove image quality
Estimated bias；
Finally to all samples in a homogeneity set of pixels, using the method for sampling with replacement Bootstrap, repeatedly sample R
It is secondary, obtain one group of estimation γ of coherence factor1,γ2,…,γR。
Further, VV/VH POLARIZATION CHANNEL amplitude dispersion estimation include:
Using filtered time series amplitude, the amplitude dispersion of VV/VH POLARIZATION CHANNEL is respectively calculated, to appoint
One pixel:
Wherein amp dispersion is the amplitude of backscatter intensity conversion, denominator mean (amp) and molecule std
It (amp) is the amplitude mean value and standard deviation calculated along time series respectively；
Gray level co-occurrence matrixes GLCM texture feature extraction includes:
Using original haplopia SAR image, the GLCM variance of two POLARIZATION CHANNELs of VV/VH is calculated separately, to time series chart
As being calculated；
Step S103 includes:
Using random forests algorithm, multispectral image feature and time series SAR image feature input classifier are carried out
Fusion；
Step S104 includes:
The confusion matrix that classification results are calculated according to the sample point of acquisition calculates user's essence of each classification extraction accordingly
Degree generates precision and overall classification accuracy, Kappa coefficient.
According to another embodiment of the present invention, it provides a kind of based on the terrain classification of multispectral image and SAR image dress
It sets, comprising:
Multispectral image feature extraction unit, for obtaining the multispectral image of predeterminable area, and to multispectral image into
Row multispectral image feature extraction；
Time series SAR image feature extraction unit, for obtaining the time series SAR image of predeterminable area, and clock synchronization
Between sequence SAR image carry out time series SAR image feature extract；
Feature-based fusion unit melts for carrying out feature level to multispectral image feature and time series SAR image feature
It closes, obtains terrain classification result.
Further, device further include:
Accuracy evaluation unit, for carrying out accuracy evaluation to the terrain classification result of acquisition.
The terrain classification method and device based on multispectral image and SAR image in the embodiment of the present invention, utilizes synthesis
The short advantage of aperture radar (SAR) round-the-clock, all weather operations, revisiting period obtains long-term sequence SAR image, increases
The characteristic dimension of input；Feature-based fusion is carried out to multispectral and SAR image, while abundant benefit spectral information, in conjunction with
Ground object structure, texture and Electromagnetic Scattering Characteristics that time series SAR image is reflected assist atural object to interpret.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes part of this application, this hair
Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is that the present invention is based on the flow charts of the terrain classification method of multispectral image and SAR image；
Fig. 2 is that the present invention is based on the preferred flow charts of the terrain classification method of multispectral image and SAR image；
Fig. 3 is that the present invention is based on the module maps of multispectral image and the terrain classification device of SAR image；
Fig. 4 is that the present invention is based on the preferred module figures of multispectral image and the terrain classification device of SAR image；
Fig. 5 is that the present invention is based on maximum likelihood method atural object is used in the terrain classification method of multispectral image and SAR image
Classification results analogous diagram；
Fig. 6 is that the present invention is based on algorithm of support vector machine is used in the terrain classification method of multispectral image and SAR image
Terrain classification result analogous diagram；
Fig. 7 be the present invention is based in the terrain classification method of multispectral image and SAR image using random forests algorithm
Object classification results analogous diagram.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only
The embodiment of a part of the invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people
The model that the present invention protects all should belong in member's every other embodiment obtained without making creative work
It encloses.
It should be noted that description and claims of this specification and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should be understood that using in this way
Data be interchangeable under appropriate circumstances, so as to the embodiment of the present invention described herein can in addition to illustrating herein or
Sequence other than those of description is implemented.In addition, term " includes " and " having " and their any deformation, it is intended that cover
Cover it is non-exclusive include, for example, the process, method, system, product or equipment for containing a series of steps or units are not necessarily limited to
Step or unit those of is clearly listed, but may include be not clearly listed or for these process, methods, product
Or other step or units that equipment is intrinsic.
Commonly used with machine learning algorithm, the land cover classification precision based on remote sensing image, which has, significantly to be mentioned
Height, and the validity of input feature vector and diversity become one of the main factor for influencing machine learning classification precision.Classifying
In terms of algorithm, common method has nearest neighbor algorithm, Bayesian Method, decision tree, support vector machines (SVM), neural network algorithm, with
And random forest (RF) algorithm etc..The one kind of random forests algorithm as machine learning algorithm, in the atural object based on remote sensing image
Classify with the advantage that speed is fast, nicety of grading is high, robustness is high.Some researches show that random forests algorithm can handle higher-dimension
Data, for input characteristic dimension there is no limit, be advantageously implemented the feature-based fusion of multi-source data.
Random forests algorithm is substantially the combination to bagging method (Bagging) and decision Tree algorithms.Firstly, from original sample
It is concentrated use in bootstrapping (bootstrap) method and randomly selects n training sample, carry out k wheel altogether and extract, obtain k training set；Its
It is secondary, for k training set, k decision-tree model of training: for single decision-tree model, it is assumed that the number of training sample feature
For n, then every time division when best feature can be selected to be divided than/gini index according to information gain/information gain；
Each tree all and so on divisions are gone down, until all training examples of the node belong to same class；Certainly by more of generation
Plan tree forms random forest.Finally, by voting generation classification results.The introducing of randomness, so that random forest is not easy
Over-fitting, and there is good noise resisting ability, therefore the algorithm shows higher stability.
It is the main means for improving input feature vector dimension using multisensor source, the remote sensing image of time series.So
And, it is seen that light remote sensing image is influenced to be difficult to obtain long-term sequence image by cloud desk, and SAR image has round-the-clock, round-the-clock
The advantages of work, it is possible to provide long-term sequence image makes up this deficiency, and spectrum and SAR image are due to sensor and imager
The difference of reason, there is complementation in terms of atural object interpretation, and the present invention is by melting to multispectral and SAR image feature level
It closes, nicety of grading is further increased based on machine learning algorithm.
Embodiment 1
An embodiment according to the present invention provides a kind of terrain classification method based on multispectral image and SAR image, ginseng
See Fig. 1, comprising the following steps:
S101: the multispectral image of predeterminable area is obtained, and multispectral image feature extraction is carried out to multispectral image；
S102: the time series SAR image of predeterminable area is obtained, and time series SAR is carried out to time series SAR image
Image feature extracts；
S103: feature-based fusion is carried out to multispectral image feature and time series SAR image feature, obtains terrain classification
As a result.
The terrain classification method based on multispectral image and SAR image in the embodiment of the present invention, utilizes synthetic aperture thunder
The advantage short up to (SAR) round-the-clock, all weather operations, revisiting period obtains long-term sequence SAR image, increases input
Characteristic dimension；Feature-based fusion, while abundant benefit spectral information, binding time sequence are carried out to multispectral and SAR image
Ground object structure, texture and the Electromagnetic Scattering Characteristics that SAR image is reflected are arranged to assist atural object to interpret.
In as a preferred technical scheme, referring to fig. 2, method further include:
S104: accuracy evaluation is carried out to the terrain classification result of acquisition.
Below with specific embodiment, the present invention is described in detail.
The method of the invention advantage short using synthetic aperture radar (SAR) round-the-clock, all weather operations, revisiting period,
Long-term sequence SAR image is obtained, the characteristic dimension of input is increased；By random forests algorithm, to multispectral and SAR image
Feature-based fusion is carried out, while abundant benefit spectral information, ground object structure that binding time sequence SAR image is reflected,
Texture and Electromagnetic Scattering Characteristics assist atural object to interpret；And the adaptive filter method based on statistics homogeneity set of pixels is used, compared with
Good inhibits speckle noise, while increasing the new features such as the estimation of amplitude dispersion, statistics homogeneity pixel segmentation, in conjunction with SAR
Dual polarization channel, significantly increase input feature vector dimension, further improve terrain classification precision.It is divided into: multispectral image
Feature extraction；SAR image feature extracts；Feature-based fusion and classification；Accuracy evaluation.
1. multispectral image feature extraction
Firstly, multispectral image of the cloud desk less than 10% for choosing the same research area of covering carries out subsequent analysis.To mostly light
Spectrum image is pre-processed, including resampling, ortho-rectification, radiant correction, cloud removing.Then Top Of Atmosphere (TOA) is used
Reflectivity calculates vegetation index (NDVI) and water body index (NDWI)；
Vegetation index (NDVI) calculation formula is as follows:
Water body index (NDWI) calculation formula is as follows:
Wherein, BandGreen、BandNIR、BandRedRespectively green, infrared, red spectral band the big pneumatic jack of multispectral image
Portion's reflectivity.
The multispectral image feature that this step uses includes: all spectral bands, vegetation index, the water of more phase optical images
Body index.
2.SAR image feature extracts
The time series SAR image in the same research area of covering is chosen, it is preferential to select multipolarization image.SAR data is carried out
Pretreatment, comprising:
2.1 subpixel registration；
2.2 interference patterns generate；
The removal of 2.3 landform phases；
2.4 are taken based on the adaptive filter method of statistics homogeneity set of pixels, including the following steps:
2.4.1 the statistics homogeneity set of pixels based on complex covariance matrix determines
Firstly, the pixel chosen with distribution becomes statistics homogeneity set of pixels according to the statistical distribution of each pixel.SAR
Pixel, that is, resolution cell backscatter intensity in image, be belong to this resolution cell a large amount of scatterers it is backward
The superposition of scattering strength.For distributed diffusion body most generally existing in natural scene, haplopia complex data (SLC) format
SAR image in a large amount of superimposed scattering strength Z (P) of distributed diffusion body be a multiple Gauss stochastic variable, variance is
According to SAR image distributed diffusion body statistical theory, the amplitude of the SLC image of haplopia SAR Obey Rayleigh
Distribution, and it is expected to be respectively as follows: with variance
One measurement standard of SAR smudges noise level, the i.e. coefficient of variation are defined as
For the stochastic variable A (P) of rayleigh distributed, formula (3) are substituted into (4), coefficient of variation CVAIt is constant:
For multiple view picture, number is regarded as L, then above formula becomes:
Therefore variance may be expressed as: with desired relationship
Var (A (P))=[CVA*E(A(P))]2=[CVA*μ(P)]2(7)；
Wherein μ (P) indicates the expectation E (A (P)) of stochastic variable A (P)；
Assuming that there is N phase SAR image, for any pixel P, amplitude forms a N-dimensional vector along time shaft:
A (P)=[A1(P),A2(P),…AN(P)]T(8)；
The sample point estimation of the expectation E (P) of A (P) can be expressed asAccording to
Central-limit theorem,As the increase of sample number (time series image) N gradually approaches Gaussian Profile.Assuming that N is enough
Greatly, Gauss assumes to set up,Distribution may be expressed as:
It is hereby achieved thatConfidence interval:
WhereinIt is just being distributed very much for standardQuantile.Formula (6), (7) are substituted into (10), are had:
Therefore, it will be assumed that check problem is converted to Estimating Confidence Interval, sentences to whether each pixel counts with distribution
It is fixed, to be divided into several homogeneity pixel point sets.It is overall it is expected that μ (P) is not known, setting one first in actual treatment
Biggish search window (such as 32 × 32 pixels centered on reference pixel P), by the sample of all pixels in search range
This mean valueAs the estimated value of μ (P), initial confidence interval, the picture between fallen with original area are calculated with α=50%
It is plain to be temporarily determined as homologous pints；Then it is iterated, with initial homogeneity point set ΩinitFor new range computationMake
For the new estimated value of μ (P), new confidence interval is calculated with α=5%, with updated confidence interval in initial homogeneity point set
Pixel determined that falling between new district is homologous pints again.After obtaining new homogeneity point set, the connectivity of pixel is carried out
Assessment, only retains the point being directly or indirectly connected with reference point P, to obtain the statistics homogeneity set of pixels about P.
2.4.2 based on backscatter intensity (intensity) filtering of statistics homogeneity set of pixels
Traditional Lee filtering is modified, as unit of the statistics homogeneity set of pixels obtained in previous step, to back scattering
Intensity carries out Speckle reduction, obtains filtered N width SAR intensity map.
2.4.3 the interference Coherence Estimation based on statistics homogeneity set of pixels
The interference coherence factor calculation formula of one pixel is as follows:
Wherein S1(i) and S2(i) complex values of principal and subordinate's image respective pixel i for interference are represented, K is represented where pixel i
Statistics homogeneity set of pixels in pixel number.To obtain more accurate Coherence Estimation, firstly, using reference number height
The landform phase of journey simulation eliminates deviation caused by interferometric phase；Secondly, as unit of the homogeneity collection where each pixel into
Row coherence factor calculates, to remove estimated bias caused by image quality；Finally, to all samples in a homogeneity set of pixels
This, using the method for sampling with replacement (Bootstrap), it is corresponding relevant to calculate each sample using formula (12) for repeatedly sample R times
Coefficient obtains one group of estimation γ of coherence factor1,γ2,…,γR, coherence factor and original estimation using these Sampling Estimations
Coherence factor γ, the unbiased esti-mator of the coherence factor is obtained by following formula:
The estimation of 2.5VV/VH POLARIZATION CHANNEL amplitude dispersion
Using filtered time series amplitude, the amplitude dispersion of VV/VH POLARIZATION CHANNEL is respectively calculated, formula
It is as follows, to any pixel:
Wherein amp dispersion is the amplitude of backscatter intensity conversion, denominator mean (amp) and molecule std
It (amp) is the amplitude mean value and standard deviation calculated along time series respectively.
2.6 gray level co-occurrence matrixes (GLCM) texture feature extraction
Using original haplopia SAR image, the GLCM variance of two POLARIZATION CHANNELs of VV/VH is calculated separately, to time series chart
As being calculated one by one.
2.7 geocoding
The SAR image feature layer that this method is extracted includes: time series VV/VH polarization backscatter intensity, time series
VV/VH polarization interference coherence factor, time series GLCM variance, VV/VH POLARIZATION CHANNEL amplitude dispersion, VV/VH POLARIZATION CHANNEL
Count homogeneity pixel segmentation result.
3. feature-based fusion and classification
Using random forests algorithm, the optical image feature and SAR image feature that previous step is extracted input classifier into
Row fusion.
Visual interpretation is carried out using the high-resolution optical image of contemporaneity in Google Earth and obtains training sample,
Random forest disaggregated model is parameterized.Parameterized model is applied to the overall situation, obtains terrain classification result.
4. accuracy evaluation
Visual interpretation is carried out using the high-resolution optical image of contemporaneity in Google Earth and obtains verifying sample,
The confusion matrix that classification results are calculated according to the sample point of acquisition, calculates user's precision (User ' s of each classification extraction accordingly
Accuracy), generate precision (Producer ' s Accuracy) and overall classification accuracy (Overall Accuracy) and
Kappa coefficient.
Embodiment 2
Another embodiment according to the present invention provides a kind of terrain classification device based on multispectral image and SAR image,
Referring to Fig. 3, comprising:
Multispectral image feature extraction unit 201, for obtaining the multispectral image of predeterminable area, and to multispectral image
Carry out multispectral image feature extraction；
Time series SAR image feature extraction unit 202, for obtaining the time series SAR image of predeterminable area, and it is right
Time series SAR image carries out time series SAR image feature and extracts；
Feature-based fusion unit 203, for carrying out feature level to multispectral image feature and time series SAR image feature
Fusion obtains terrain classification result.
The terrain classification device based on multispectral image and SAR image in the embodiment of the present invention, utilizes synthetic aperture thunder
The advantage short up to (SAR) round-the-clock, all weather operations, revisiting period obtains long-term sequence SAR image, increases input
Characteristic dimension；Feature-based fusion, while abundant benefit spectral information, binding time sequence are carried out to multispectral and SAR image
Ground object structure, texture and the Electromagnetic Scattering Characteristics that SAR image is reflected are arranged to assist atural object to interpret.
In as a preferred technical scheme, referring to fig. 4, device further include:
Accuracy evaluation unit 204, for carrying out accuracy evaluation to the terrain classification result of acquisition.
Innovative technology point of the invention and beneficial effect at least that:
1. using dual polarization or multipolarization SAR data, and time series SAR data is used, to each pole of each phase
The SAR image for changing channel carries out feature extraction；
2. being pressed down using the adaptive filter method based on statistics homogeneity set of pixels to the coherent spot of backscatter intensity
System, the backscatter intensity time series after being denoised are reevaluated as characteristic layer, and based on statistics homogeneity set of pixels
Interference coherence factor as another feature layer；
3. the statistics homogeneity pixel image segmentation result of each POLARIZATION CHANNEL is included in characteristic layer；
4. using the SAR amplitude dispersion of each POLARIZATION CHANNEL as characteristic layer.
Compared with the conventional method, it is an advantage of the invention that by using machine learning algorithm, to optics and SAR image into
Row feature-based fusion, can make full use of the advantage of satellite-borne synthetic aperture radar (SAR) round-the-clock, all weather operations, and acquisition is covered
The long-term sequence image in lid research area overcomes the problems, such as that optical image is limited valid data scarcity by cloud desk；Utilize SAR image
The dual polarization that has, multipolarization feature, further increase input feature vector dimension；Using based on the adaptive of statistics homogeneity set of pixels
It answers filtering method effectively to denoise SAR back scattering feature and interference coherence factor, improves classification performance；Increase base
In the statistics image segmentation result of homogeneity set of pixels, SAR back scattering amplitude dispersion two indices as new characteristic layer, into
One step improves nicety of grading.
Referring to Fig. 5-7, Sentinel-1 time series SAR image, and the Sentinel- in the same research area of covering are utilized
2 multispectral images carry out terrain classification, test the validity of the method for the present invention.To only with optical signature, only with SAR spy
Sign, and nicety of grading, the computational efficiency of the various machine learning algorithms of optics and SAR feature is combined to compare.Wherein:
Fig. 5 is classification results and accuracy evaluation of the maximum likelihood method terrain classification result (a) only with SAR feature；(b) only
Using the classification results of optical signature；(c) classification results of feature-based fusion are carried out to optics and SAR image；
Fig. 6 is classification results and accuracy evaluation of the algorithm of support vector machine terrain classification result (a) only with SAR feature；
(b) only with the classification results of optical signature；(c) classification results of feature-based fusion are carried out to optics and SAR image.
Fig. 7 is classification results of the random forests algorithm terrain classification result (a) only with SAR feature；(b) only with optics
The classification results of feature；(c) classification results of feature-based fusion are carried out to optics and SAR image.
The overall classification accuracy comparison of three kinds of algorithms is as follows:
1. algorithms of different classification accuracy assessment of table
The time-consuming comparison that three kinds of algorithms complete a subseries is as follows:
The comparison of 2. different classifications algorithm time-consuming of table
Table 2 is as can be seen that complete a subseries under same input condition, RF time-consuming is most short, and ML takes second place, SVM time-consuming longest.
No matter using which kind of feature input, maximum likelihood (ML) method nicety of grading is all minimum.It is random gloomy when only with SAR feature
The nicety of grading highest of woods (RF) algorithm, is higher by 6.5% than SVM algorithm.When only with optical signature, point of SVM and RF algorithm
Class precision is similar, 93% or so.After carrying out feature-based fusion to optics and SAR, the nicety of grading of SVM and RF algorithm all compared with
It increases when only with optical signature or only with SAR feature.After Fusion Features, SVM and RF equally reached 95% with
On overall classification accuracy, but under equivalent feature input condition, it is the tens of RF algorithm that SVM algorithm, which completes a subseries time-consuming,
Times.Analysis shows, the method proposed by the present invention based on the classification of optics, SAR feature-based fusion and random forest has bright above
Aobvious superiority.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
In the above embodiment of the invention, it all emphasizes particularly on different fields to the description of each embodiment, does not have in some embodiment
The part of detailed description, reference can be made to the related descriptions of other embodiments.
In several embodiments provided herein, it should be understood that disclosed technology contents can pass through others
Mode is realized.Wherein, system embodiment described above is only schematical, such as the division of unit, can be one kind
Logical function partition, there may be another division manner in actual implementation, such as multiple units or components can combine or can
To be integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Coupling, direct-coupling or communication connection can be through some interfaces, the indirect coupling or communication connection of unit or module,
It can be electrical or other forms.
Unit may or may not be physically separated as illustrated by the separation member, shown as a unit
Component may or may not be physical unit, it can and it is in one place, or may be distributed over multiple units
On.It can some or all of the units may be selected to achieve the purpose of the solution of this embodiment according to the actual needs.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
It, can if integrated unit is realized in the form of SFU software functional unit and when sold or used as an independent product
To be stored in a computer readable storage medium.Based on this understanding, technical solution of the present invention substantially or
Say that all or part of the part that contributes to existing technology or the technical solution can embody in the form of software products
Out, which is stored in a storage medium, including some instructions are used so that a computer equipment
(can be personal computer, server or network equipment etc.) executes all or part of step of each embodiment method of the present invention
Suddenly.And storage medium above-mentioned includes: USB flash disk, read-only memory (ROM, Read-Only Memory), random access memory
The various media that can store program code such as (RAM, Random Access Memory), mobile hard disk, magnetic or disk.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (10)
1. a kind of terrain classification method based on multispectral image and SAR image, which comprises the following steps:
S101: the multispectral image of predeterminable area is obtained, and multispectral image feature extraction is carried out to multispectral image；
S102: the time series SAR image of predeterminable area is obtained, and time series SAR image is carried out to time series SAR image
Feature extraction；
S103: feature-based fusion is carried out to multispectral image feature and time series SAR image feature, obtains terrain classification knot
Fruit.
2. the method according to claim 1, wherein the method also includes:
S104: accuracy evaluation is carried out to the terrain classification result of acquisition.
3. according to the method described in claim 2, it is characterized in that, the step S101 includes:
It chooses and covers same research multispectral image of area's cloud desk less than 10%；Multispectral image is pre-processed, is then adopted
The vegetation index NDVI and water body index NDWI in multispectral image are calculated with Top Of Atmosphere TOA reflectivity；
Vegetation index (NDVI) calculation formula is as follows:
Water body index (NDWI) calculation formula is as follows:
Wherein, BandGreen、BandNIR、BandRedGreen, infrared, red spectral band the Top Of Atmosphere of respectively multispectral image is anti-
Penetrate rate.
4. according to the method described in claim 3, it is characterized in that, the step S102 includes:
The time series SAR image for choosing the same research area of covering, pre-processes time series SAR image: being taken based on
The adaptive filter method for counting homogeneity set of pixels calculates interference coherence factor, the estimation of VV/VH POLARIZATION CHANNEL amplitude dispersion, ash
Spend co-occurrence matrix GLCM texture feature extraction.
5. according to the method described in claim 4, it is characterized in that, the adaptive filter for being taken based on statistics homogeneity set of pixels
Wave method calculates interference coherence factor
According to the statistical distribution of each pixel, choosing with the pixel collection being distributed is statistics homogeneity set of pixels；Haplopia complex data
A large amount of superimposed scattering strength Z (P) of distributed diffusion body are a multiple Gauss stochastic variables in the SAR image of SLC format, side
Difference isAccording to SAR image distributed diffusion body statistical theory, the amplitude of the SLC image of haplopia SAR
Rayleigh distributed, and it is expected to be respectively as follows: with variance
One measurement standard of SAR smudges noise level, the i.e. coefficient of variation are defined as
For the stochastic variable A (P) of rayleigh distributed, formula (3) are substituted into (4), coefficient of variation CVAIt is constant:
For multiple view picture, number is regarded as L, then above formula becomes:
Therefore variance may be expressed as: with desired relationship
Var (A (P))=[CVA*E(A(P))]2=[CVA*μ(P)]2(7)；
Wherein μ (P) indicates the expectation E (A (P)) of stochastic variable A (P)；
Assuming that there is N phase SAR image, for any pixel P, amplitude forms a N-dimensional vector along time shaft:
A (P)=[A1(P),A2(P),…AN(P)]T(8)；
The sample point estimation of the expectation E (P) of A (P) can be expressed asAccording to center
Limit theorem,As the increase of sample number N gradually approaches Gaussian Profile；Assuming that N is sufficiently large, Gauss assumes to set up,Distribution may be expressed as:
It is hereby achieved thatConfidence interval:
WhereinIt is just being distributed very much for standardQuantile；Formula (6), (7) are substituted into (10), are had:
The statistics homogeneity set of pixels about P is obtained by formula (11)；
Traditional Lee filtering is modified, as unit of the statistics homogeneity set of pixels of acquisition, is concerned with to backscatter intensity
Spot inhibits, and obtains filtered N width SAR intensity map；
The interference coherence factor calculation formula of one pixel is as follows:
Wherein S1(i) and S2(i) complex values of principal and subordinate's image respective pixel i for interference are represented, K represents the system where pixel i
Count the number of pixel in homogeneity set of pixels；The corresponding coherence factor of each sample is calculated using formula (12), obtains coherence factor
One group of estimation γ1,γ2,…,γR, using the coherence factor of these Sampling Estimations and the coherence factor γ of original estimation, under
Formula obtains the unbiased esti-mator of the coherence factor:
6. according to the method described in claim 5, it is characterized in that, described obtain the statistics homogeneity picture about P by formula (11)
Element collects
Search window is set first, by the sample average of all pixels in search rangeAs the estimated value of μ (P), with α
=50% calculates initial confidence interval, and the pixel between fallen with original area is temporarily determined as homologous pints；
Then it is iterated, with initial homogeneity point set ΩinitFor new range computationThe estimated value new as μ (P),
New confidence interval is calculated with α=5%, and the pixel in initial homogeneity point set is sentenced again with updated confidence interval
Fixed, falling between new district is homologous pints；
After obtaining new homogeneity point set, the connectivity of pixel is assessed, only reservation is directly or indirectly connected with reference point P
Point, to obtain the statistics homogeneity set of pixels about P.
7. according to the method described in claim 5, it is characterized in that, described corresponding relevant using each sample of formula (12) calculating
Coefficient includes:
Deviation caused by landform phase removal interferometric phase first using the simulation of reference number elevation；
Secondly coherence factor calculating is carried out as unit of the homogeneity collection where each pixel, is estimated caused by image quality to remove
Count deviation；
Finally to all samples in a homogeneity set of pixels, using the method for sampling with replacement Bootstrap, repeatedly sample R times is obtained
To one group of estimation γ of coherence factor1,γ2,…,γR。
8. according to the method described in claim 4, it is characterized in that, VV/VH POLARIZATION CHANNEL amplitude dispersion estimation includes:
Using filtered time series amplitude, the amplitude dispersion of VV/VH POLARIZATION CHANNEL is respectively calculated, to any picture
Element:
Wherein amp dispersion is the amplitude of backscatter intensity conversion, denominator mean (amp) and molecule std (amp)
It is the amplitude mean value and standard deviation calculated along time series respectively；
The gray level co-occurrence matrixes GLCM texture feature extraction includes:
Using original haplopia SAR image, calculate separately the GLCM variance of two POLARIZATION CHANNELs of VV/VH, to time-series image into
Row calculates；
The step S103 includes:
Using random forests algorithm, multispectral image feature is merged with time series SAR image feature input classifier；
The step S104 includes:
The confusion matrix that classification results are calculated according to the sample point of acquisition, calculates user's precision, the life of each classification extraction accordingly
At precision and overall classification accuracy, Kappa coefficient.
9. a kind of terrain classification device based on multispectral image and SAR image characterized by comprising
Multispectral image feature extraction unit for obtaining the multispectral image of predeterminable area, and carries out multispectral image more
Spectrum image feature extraction；
Time series SAR image feature extraction unit, for obtaining the time series SAR image of predeterminable area, and to time sequence
It arranges SAR image and carries out the extraction of time series SAR image feature；
Feature-based fusion unit is obtained for carrying out feature-based fusion to multispectral image feature and time series SAR image feature
Take terrain classification result.
10. device according to claim 9, which is characterized in that described device further include:
Accuracy evaluation unit, for carrying out accuracy evaluation to the terrain classification result of acquisition.
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