CN107909039A - The ground mulching sorting technique of high-resolution remote sensing image based on parallel algorithm - Google Patents

The ground mulching sorting technique of high-resolution remote sensing image based on parallel algorithm Download PDF

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CN107909039A
CN107909039A CN201711138873.9A CN201711138873A CN107909039A CN 107909039 A CN107909039 A CN 107909039A CN 201711138873 A CN201711138873 A CN 201711138873A CN 107909039 A CN107909039 A CN 107909039A
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CN107909039B (en
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钟燕飞
赵�济
吕鹏远
王晶
马爱龙
刘艳飞
伍丝琪
张良培
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Wuhan University WHU
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/13—Satellite images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/187—Segmentation; Edge detection involving region growing; involving region merging; involving connected component labelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30184—Infrastructure

Abstract

The invention discloses a kind of ground mulching sorting technique of the high-resolution remote sensing image based on parallel algorithm, including：S1 carries out cutting according to the number of computer to high-definition remote sensing image data, obtains the high-resolution remote sensing image block after cutting；S2 is based on the parallel frames of OpenMP and all high-resolution remote sensing image blocks is distributed to m processor, concurrent execution ground mulching classification processing；S3, by all high-resolution remote sensing image block numbers according to merging, obtains final ground mulching classification results according to data segmentation principle.The method of the present invention carries out cutting to data automatically according to size of data and using calculator memory situation, use configuration file tissue typing algorithm flow, realize parallel classification algorithm, so as to adapt to the great scale of data volume, the high-resolution ground mulching that finely divides of object space chart task.

Description

The ground mulching sorting technique of high-resolution remote sensing image based on parallel algorithm
Technical field
The invention belongs to Remote Sensing Image Processing Technology field, the more particularly to high-resolution remote sensing image based on parallel algorithm Ground mulching sorting technique.
Background technology
Land resource is basis for the survival of mankind, with the hair of urbanization process concerning national society's economic construction Exhibition, determines the natural quality of reflection soil surface type of ground objects and ground mulching/utilization of social property, for earth system mould Formula, global environmental change, territory protection, urban development decision-making, hydraulic engineering construction etc. all play an important roll.For this reason, in mistake Go in decades, the research of international community and each tissue to ground mulching/utilization and its change is paid much attention to, research project It is commonplace, such as International Geosphere Program (IGBP) and the soil of International Human Dimensions Programme on Global Environmental Change (IHDP) Changeement project and National 863 plan are utilized using/ground mulching changes the ground mulching of core project, U.S. NASA/ Key project " global seismic cover remote sensing mapping and key technology research " etc., the corresponding earth's surface of achievement in research successively issue are covered Lid product, according to described in pertinent literature, ground mulching product there are about 21 sets on Global Scale at present, at least 43 on regional scale Set.Meanwhile in order to which the needs built and developed, country have successively carried out second national land investigation and first time on a large scale National conditions generally investigate project, and the acquisition of wherein ground mulching/utilize is the vital task of land investigation and geographical national conditions monitoring.To sum up institute State, extensive global or zonal ground mulching drawing has important research meaning and application value.
The global seismic covering product of highest resolution is the whole world 30 of the China based on Landsat TM data creatings at present The ground mulching product of rice spatial resolution.The Landsat TM data that 30 meter table covering products use are compared to, in rice The great feature of high-resolution remote sensing image in the ground mulching drawing application of level spatial resolution is high in order to cover the same area Resolution remote sense image needs the size of bigger, more number of pixels, thus the great scale of data volume, object space finely draw Divide and require higher so that the global seismic covering drawing task based on high-resolution remote sensing image becomes extremely difficult.The present invention Purpose be to be directed to high-resolution remote sensing image, there is provided the full-automatic extensive earth's surface cover classification method based on parallel computation.
The content of the invention
For high-resolution remote sensing image ground mulching drawing application in data volume greatly and it is computationally intensive the problem of, the present invention Parallel processing mode is calculated using data parallel and algorithm, extracts the spectral signature of remote sensing image, by existing maximum seemingly So merged with algorithm of support vector machine, reuse connected component labeling algorithm and intermediate result is post-processed, so as to Quickly to obtain high-resolution ground mulching classification results.
To reach above-mentioned purpose, the technical scheme is that a kind of high-resolution remote sensing image based on parallel algorithm Ground mulching sorting technique, includes the following steps：
Step 1, carries out cutting to high-definition remote sensing image data according to the number of computer, obtains the height after cutting Resolution remote sense image block, while the coordinate of each high-resolution remote sensing image block start-stop position is recorded, implementation is as follows,
If h is the length of image, w is the width of the width, then the high-resolution remote sensing image block after cutting of imageLength H=W*r, wherein ratio r=h/w, m are memory number, StFor data type, SsTo be safely Number；
All high-resolution remote sensing image blocks are distributed to m processor, concurrently by step 2 based on the parallel frames of OpenMP The classification of execution ground mulching processing；
Step 3, according to the coordinate of each high-resolution remote sensing image block start-stop position, by all high-definition remote sensing shadows As block number is according to merging, final ground mulching classification results are obtained.
Further, ground mulching classification processing includes the following steps in the step 2：
Step 1, high-resolution remote sensing image block feature extracts, including following sub-step；
Step 1.1, the spectral signature of high-resolution remote sensing image block is extracted；
Step 1.2, feature of the normalized differential vegetation index as extraction vegetation is calculated；
Step 1.3, feature of the normalization water body index as extraction water body is calculated；
Step 1.4, the textural characteristics based on gray scale symbiosis square are calculated as local spatial feature；
Step 1.5, by the spectral signature of extraction, normalized differential vegetation index, normalization water body by way of vector is superimposed Index and Texture Feature Fusion, the feature as subsequent classification input；
Step 2, the sorting algorithm of structure fusion maximum likelihood and support vector machines, in high-resolution remote sensing image block Table covering classification is classified, and obtains classification chart picture, including following sub-step：
Step 2.1, the membership probability of ground mulching classification is obtained based on maximum likelihood classification algorithm；
Step 2.2, the atural object class membership probabilities based on acquisition, according to the accuracy of the differentiation of threshold decision atural object, its Further comprise：
Step 2.2.1, the optimal mark classification of current pixel is determined by maximum a posteriori probability；
Step 2.2.2, differentiates the accuracy of pixel classification, directly using step if classification accuracy is more than given threshold value The classification results that label corresponding to maximum probability forms in rapid 2.1 maximum likelihood classification algorithm, otherwise, jump into step 2.3；
Step 2.3, using Nonlinear Support Vector Machines sorting algorithm to the picture that is difficult to accurately differentiate in maximum likelihood classification Member distinguishes in non-linear space；
Step 3, cutting object is produced on classification chart picture based on connected component labeling algorithm, passes through region merging technique strategy pair Classification results in step 2 are post-processed, and obtain ground mulching classification results in high-resolution remote sensing image block, specific implementation Mode is as follows,
Step 3.1, on the basis of classification results in step 2, obtained using classical eight neighborhood connected component labeling algorithm To cutting object O；
Step 3.2, according to the spatial resolution of high-definition remote sensing and atural object characteristic, the cutting object of acquisition is defined For Oi, wherein i is classification number；Pass through threshold value T1Judge OiWhether belong to noise object, work as OiMore than given threshold value T1It is then direct Retain, be otherwise merged into neighbouring cutting object.
Further, ground mulching classification processing further includes step 4 in the step 2, according to Kappa coefficients to earth's surface Cover classification result carries out quality evaluation, and embodiment is as follows：
Step 4.1, confusion matrix is calculated,
Wherein m represents the other number of target class, pijRepresent that the i-th row jth shows pijThe pixel of the i-th class of a actual ownership Number is predicted to be jth class；Numerical value on matrix leading diagonal is expressed as, certain one kind and corresponding classification are attributed in earth's surface true picture It is attributed to the number of of a sort pixel, i.e., the number for the pixel correctly classified in figure；
Step 4.2, producer's precision, formula are calculated based on confusion matrixWherein i, j are represented respectively For the atural object classification shared by the i-th class in confusion matrix and concrete class jth class, AjjRepresent the element on diagonal in confusion matrix Value；
Calculate overall classification accuracy, formulaWherein N represents authentic specimen sum；
Kappa coefficients are calculated, formula kappa=(d-q)/(N-q), wherein N are authentic specimen sum, and d is confusion matrix The number of sample on middle diagonal, C be classification number, AijRepresent and i is corresponded in confusion matrix, the element value of j positions, q's Expression formula is
Step 4.3, when Kappa values are more than α, the uniformity between presentation class result and ground reference information is very big or smart Degree is very high, and Kappa values represent that uniformity is medium in β-α, and Kappa values then represent that uniformity is very poor less than β.
Further, the calculation formula of normalized differential vegetation index is in step 1.2Wherein ρnirIt is The reflectance value of the near infrared band of high-resolution multi-spectral remote sensing image, ρredIt is the reflectance value of red band.
Further, water body index calculation formula is normalized in step 1.3 isWherein ρnirIt is The reflectance value of the near infrared band of high-resolution multi-spectral remote sensing image, ρgreenIt is the reflectance value of image green band.
Further, the textural characteristics in step 1.4 include entropy and homogeney based on gray scale symbiosis square, and implementation is such as Under,
It is horizontal direction spatial domain to make Kx={ 1,2 ..., Nx }, and Ky={ 1,2 ..., Ny } is vertical direction spatial domain, figure As gray scale pixel section Kx*Ky=k, wherein entropy can be definedHomogeneyP in formulaijRepresent the element value that the i-th row j is arranged in the gray level co-occurrence matrixes after normalization.
Further, the value of β and α is respectively 0.4 and 0.8 in step 4.3.
Compared with prior art, the invention has the advantages that and beneficial effect：
(1) adaptability is good, can carry out cutting to data automatically according to size of data and using calculator memory situation, So as to be adapted to different configuration of running environment；
(2) there is computation capability, add the parallel algorithm of adaptability, can be carried out according to the CPU quantity of machine Parallelization is handled；
(3) there is ease for use, without auxiliary information and manual intervention, calculating speed is fast, can automatic business processing.
(4) there is reusability, the global seismic covering drawing application system based on High Resolution Remote Sensing Satellites uses configuration File organization sorting algorithm flow, so that different user can repeat to obtain corresponding ground mulching by identical configuration file Charting results.
The method of the present invention is by way of vector is superimposed by the spectral signature of extraction, normalized differential vegetation index, normalization water Body index and Texture Feature Fusion, the feature for obtaining fusion as subsequent classification input, and utilize maximum likelihood and supporting vector The sorting algorithm of machine classifies high-resolution remote sensing image, and locates after being carried out by region merging technique strategy to classification results Reason, finally calculates Kappa coefficients and carries out quality evaluation to classification chart picture, realize the judgement to nicety of grading.
Brief description of the drawings
Fig. 1 is ground mulching of the present invention classification process chart；
Fig. 2 is hyperplane schematic diagram in Nonlinear Support Vector Machines of the embodiment of the present invention.
Embodiment
Technical solution for a better understanding of the present invention, is the present invention below in conjunction with drawings and examples further in detail Describe in detail bright.
Step 1, parallel computation frame is established.
Step 1.1, data parallel, since high-definition remote sensing image data amount is big, before ground mulching classification is carried out, Need high-definition remote sensing image data carrying out cutting.Data parallel is before data operation, according to certain strategy certainly It is dynamic that cutting is carried out to data, a larger specification data is divided into multiple small data modules, is calculated by multiple core cpus etc. Resource is calculated, calculate complete after the automatic of data carried out according to data segmentation principles merge, return to requirement format Data.Data parallel can be calculated with formula below.
Automatically after carrying out cutting to data, the width of each small data moduleRatio r= H/w, wherein, h is the length of image, and w is the width of image, and m is memory number, StFor data type, for example it is integer, floating-point Type, SsFor safety coefficient, safety coefficient takes 4 in the present embodiment；The length H=W*r of each small data template；Record decimal at the same time According to the coordinate of the start-stop position of module.
Step 1.2, algorithm is parallel.Algorithm is to be directed to specific algorithm parallel, in actual calculating process, based on OpenMP Etc. parallel frame distribute to multiple processors it is concurrent perform calculating.The present invention is realized automatically by the way of flow custom Table cover classification, user can select extracting method, disaggregated model and the post-classification comparison method of feature by configuration file.
Step 2, feature extraction is carried out for the high-resolution remote sensing image block after cutting, this step further comprises following Step.
Step 2.1 directly carries out Spectra feature extraction using the multi light spectrum hands of high-resolution remote sensing image block, by general Thang-kng spectrum band overlapping, which provides, differentiates the basic of atural object.
Step 2.2, feature of the normalized differential vegetation index (NDVI) as extraction vegetation is calculated, calculation formula isWherein ρnirIt is the reflectance value of the near infrared band of high-resolution multi-spectral remote sensing image, ρredIt is The reflectance value of red band.
Step 2.3, feature of the normalization water body index (NDWI) as extraction water body is calculated, calculation formula isWherein ρnirIt is the reflectance value of the near infrared band of high-resolution multi-spectral remote sensing image, ρgreen It is the reflectance value of image green band.
Step 2.4, calculate based on the textural characteristics of gray level co-occurrence matrixes (GLCM) as local spatial feature, described with this Spatial relationship between each pixel between image, it is horizontal direction spatial domain to make Kx={ 1,2 ..., Nx }, Ky=1,2 ..., Ny } it is vertical direction spatial domain, for given image, Nx, Ny are known quantity, and image can be defined gray scale pixel section Kx*Ky=k, with entropyAnd homogeneyAs local sky Between feature, estimate structure textural characteristics Data-Statistics, wherein, p in formulaijRepresent that the i-th row j is arranged in the gray level co-occurrence matrixes after normalization Element value.Gray level co-occurrence matrixes are pixel distance and the matrix function of angle, it is by calculating certain distance and one in image The correlation between 2 gray scales in direction is determined, to reflect comprehensive letter of the image on direction, interval, amplitude of variation and speed Breath, its calculation is the prior art, for details, reference can be made to document [1], and the present invention not writes.
[1] elevation journey, texture feature extraction [J] the computer system applications of Hui Xiaowei based on gray level co-occurrence matrixes, 2010,19(6):195-198.
Step 2.5, by the spectral signature of extraction, normalized differential vegetation index, normalization water body by way of vector is superimposed Index and Texture Feature Fusion, the feature for obtaining fusion as subsequent classification input.
Step 3, the sorting algorithm of structure fusion maximum likelihood and support vector machines, carries out high-resolution remote sensing image block Classification, obtains classification chart picture, and the embodiment of this step is as follows：
Step 3.1, the membership probability of ground mulching classification is provided based on maximum likelihood classification algorithm (MLC), to obtain most Whole ground mulching type provides decision-making foundation.Maximum likelihood classification is by the distribution of remote sensing multi-wavelength data as multidimensional normal state It is distributed to construct discriminant function.Its basic thought is：The data of all kinds of known pixels form certain in a plane or in space Point group；As soon as forming a normal distribution on the number axis of oneself per a kind of every one-dimensional data, such multidimensional data forms this One multiple normal distribution of class, there is all kinds of multiple dimensional distribution models, for the data vector of any one unknown classification, It can be reversely asked to belong to all kinds of probability；Compare the size of these probability, see that the probability which kind of belongs to is big, just this data Vector or this pixel are classified as such, are represented by：
In formula, m is wave band number；p(wi) be the i-th class m dimension Density Function of Normal Distribution, by it can be seen that in kth class There is the probability size of various probable values in middle m n-dimensional random variable ns x.The m dimension data vectors of pixel are represented by：
With miRepresent class ωiThe average of each wave bandThe mean vector formed, such as following formula,
CiRepresent the covariance matrix of class, formula isWherein, nkIt is the pixel number of kth class；WkIt is kth Mean dispersion error matrix in the class of class, such as following formula,
In formula, ωk11,ωk22,…ωkmmIt is the variance within clusters of kth class；And ωk12,…,ωk1mAnd ωk21,…,ωkm1Deng It is covariance in the class of kth class, this makes it possible to obtain the covariance matrix of class.
Can be following formula by full scale equation formula abbreviation by abbreviation,
Wherein p (wi) represent class ωiProbability, f represents corresponding class ωiProbability density.Based on maximum likelihood decision function Value, passes through Pi(l)=exp (Di(f)), l=1 ..., K obtains the membership probability of atural object classification, and wherein K is ground mulching classification Number.Maximum likelihood classification algorithm (MLC) algorithm is the prior art, for details, reference can be made to document [2].
[2] soup Guoan process in remote sensing digital image processing [M] Science Presses, 2004.
Step 3.2, the atural object class membership probabilities based on acquisition, according to the accuracy of the differentiation of threshold decision atural object, its Further comprise：
Step 3.2.1, the optimal mark of current pixel, formula are determined by maximum a posteriori probability Wherein k represents the optimal mark of current pixel；
Step 3.2.2, differentiates the accuracy of pixel classification, directly using step if classification accuracy is more than given threshold value The classification results that label corresponding to maximum probability forms in rapid 2.1 maximum likelihood classification algorithm, otherwise, jump into step 2.3.Formula Pi(k) ＞ T, wherein T are the acquiescence accuracy threshold values set, and usual value is 0.8, represent the probably acquirement pair of current pixel The mark answered.
Step 3.3, using Nonlinear Support Vector Machines (SVM) sorting algorithm to being difficult to accurately differentiate in maximum likelihood classification Pixel distinguished in non-linear space.Different from maximum likelihood sorting technique based on probability, support vector machines is one Machine learning algorithm of the kind based on Statistical Learning Theory, using structural risk minimization principle, is minimizing the same of sample error When reduce model extensive error, so as to improve the generalization ability of model.Formula f (x)=w Φ (x)+b represents hyperplane Discriminant function, wherein w are weight vector, and b is biasing, and Φ (x) is the vector for being related to parameter x.As shown in fig. 2, there are one two Dimensional plane, has two kinds of different data in plane, is represented respectively with circle and fork.Since these data are linear separabilities, This two classes data can be separated with straight line, in two-dimensional space, the straight line is equivalent to hyperplane.Nonlinear Support Vector Machines Sorting algorithm is the prior art, for details, reference can be made to document [3].
[3] publishing house of Li Hang statistical learning methods [M] Tsinghua University, 2012.
Step 4, cutting object is produced based on connected component labeling algorithm, by region merging technique strategy to point in step 2 Class result is post-processed, and the embodiment of this step is as follows：
Step 4.1, on the basis of classification results in step 2, connected using the classical eight neighborhood of Union-find Sets data structure Logical zone marker algorithm can obtain cutting object O；
Step 4.2, on the basis of classification results in step 2, according to the spatial resolution of high-resolution remote sensing image And atural object characteristic, the cutting object of acquisition is defined as Oi, wherein i is classification number.Pass through threshold value T1Judge whether it belongs to Noise object (T1General value is 0.8) such as larger than given threshold value T1Then directly retain, be otherwise merged into neighbouring cutting object, In units of object, classification results are obtained by maximum temporal voting strategy, the classification results in step 2 are improved, so as to obtain Take finer ground mulching classification results.
Step 5, according to the process shown in step 1, the start-stop position coordinates of the recorded small data module of use, inversely Data merging is carried out, obtains final classification results.
Step 6, quality evaluation is carried out to classification chart picture, the embodiment of this step is as follows：
Step 6.1, confusion matrix is calculated, confusion matrix is by by the position and classification chart of the true pixel of each earth's surface The classification pixel of relevant position be relatively calculated, the row of confusion matrix represent pixel in classification chart in each classification In number, row represents the true belonging kinds of data.Confusion matrix is as follows：
Wherein m represents the other number of target class, pijRepresent that the i-th row jth shows pijThe pixel quilt of the i-th class of a actual ownership It is predicted as jth class.Therefore, the numerical value on matrix leading diagonal is expressed as, certain one kind and corresponding point are attributed in earth's surface true picture It is attributed to the number of of a sort pixel, i.e., the number for the pixel correctly classified in class figure.By upper, in matrix on leading diagonal Numerical value is more big, and the pixel number correctly classified is more, and the precision of classification is higher.
Step 6.2, producer's precision (Producer ' s Accuracy, PA), formula are calculated based on confusion matrixWherein i, j are expressed as the i-th class and the atural object classification shown in concrete class jth class in confusion matrix, AjjRepresent the element value on diagonal in confusion matrix；Calculate overall classification accuracy (Overall Accuracy, OA), formulaWherein N represents the authentic specimen sum being pre-selected.Calculate the injustice between Kappa coefficients reaction classification Weighing apparatus property, wherein formula kappa=(d-q)/(N-q), N are the number of total authentic specimen, and d is on diagonal in confusion matrix The number of sample, C be classification number (can be according to User Defined), AijRepresent and i is corresponded in confusion matrix, the member of j positions Element value, the expression formula of q areKappa values>0.80 is one between classification chart and ground reference information Cause property is very big or precision is very high, and Kappa values represent that uniformity is medium in 0.40-0.80, and Kappa values are less than 0.40 and represent consistent Property is very poor.
Specific embodiment described herein is only that spirit of the present invention is illustrated.Technology belonging to the present invention is led The technical staff in domain can make described specific embodiment various modifications, supplement or replace in a similar way Generation, but not deviate the spirit or beyond the scope of the appended claims of the present invention.

Claims (7)

1. the ground mulching sorting technique of the high-resolution remote sensing image based on parallel algorithm, it is characterised in that including following step Suddenly：
Step 1, carries out cutting to high-definition remote sensing image data according to the number of computer, obtains the high-resolution after cutting Rate remote sensing image block, while the coordinate of each high-resolution remote sensing image block start-stop position is recorded, implementation is as follows,
If h is the length of image, w is the width of the width, then the high-resolution remote sensing image block after cutting of imageLength H=W*r, wherein ratio r=h/w, m are memory number, StFor data type, SsTo be safely Number；
All high-resolution remote sensing image blocks are distributed to m processor by step 2 based on the parallel frames of OpenMP, and concurrent holds The classification of row ground mulching is handled；
Step 3, according to the coordinate of each high-resolution remote sensing image block start-stop position, by all high-resolution remote sensing image blocks Data merge, and obtain final ground mulching classification results.
2. the ground mulching sorting technique of the high-resolution remote sensing image based on parallel algorithm as claimed in claim 1, it is special Sign is, in the step 2 ground mulching classification processing include the following steps：
Step 1, high-resolution remote sensing image block feature extracts, including following sub-step；
Step 1.1, the spectral signature of high-resolution remote sensing image block is extracted；
Step 1.2, feature of the normalized differential vegetation index as extraction vegetation is calculated；
Step 1.3, feature of the normalization water body index as extraction water body is calculated；
Step 1.4, the textural characteristics based on gray scale symbiosis square are calculated as local spatial feature；
Step 1.5, by the spectral signature of extraction, normalized differential vegetation index, normalization water body index by way of vector is superimposed And Texture Feature Fusion, the feature as subsequent classification input；
Step 2, the sorting algorithm of structure fusion maximum likelihood and support vector machines, covers earth's surface in high-resolution remote sensing image block Lid classification is classified, and obtains classification chart picture, including following sub-step：
Step 2.1, the membership probability of ground mulching classification is obtained based on maximum likelihood classification algorithm；
Step 2.2, the atural object class membership probabilities based on acquisition, according to the accuracy of the differentiation of threshold decision atural object, wherein into One step includes：
Step 2.2.1, the optimal mark classification of current pixel is determined by maximum a posteriori probability；
Step 2.2.2, differentiates the accuracy of pixel classification, and step is directly used if classification accuracy is more than given threshold value The classification results that label corresponding to maximum probability forms in 2.1 maximum likelihood classification algorithms, otherwise, jump into step 2.3；
Step 2.3, existed using Nonlinear Support Vector Machines sorting algorithm to the pixel for being difficult to accurately differentiate in maximum likelihood classification Distinguished in non-linear space；
Step 3, cutting object is produced on classification chart picture based on connected component labeling algorithm, by region merging technique strategy to step Classification results in 2 are post-processed, and obtain ground mulching classification results in high-resolution remote sensing image block, embodiment It is as follows,
Step 3.1, on the basis of classification results in step 2, divided using classical eight neighborhood connected component labeling algorithm Cut object O；
Step 3.2, according to the spatial resolution of high-definition remote sensing and atural object characteristic, the cutting object of acquisition is defined as Oi, Wherein i is classification number；Pass through threshold value T1Judge OiWhether belong to noise object, work as OiMore than given threshold value T1Then directly retain, Otherwise it is merged into neighbouring cutting object.
3. the ground mulching sorting technique of the high-resolution remote sensing image based on parallel algorithm as claimed in claim 2, it is special Sign is, in the step 2 ground mulching classification processing further include step 4, according to Kappa coefficients to earth's surface cover classification knot Fruit carries out quality evaluation, and embodiment is as follows：
Step 4.1, confusion matrix is calculated,
<mrow> <mi>M</mi> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>p</mi> <mn>11</mn> </msub> </mtd> <mtd> <msub> <mi>p</mi> <mn>21</mn> </msub> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <msub> <mi>p</mi> <mrow> <mi>m</mi> <mn>1</mn> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>p</mi> <mn>12</mn> </msub> </mtd> <mtd> <msub> <mi>p</mi> <mn>22</mn> </msub> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <msub> <mi>p</mi> <mrow> <mi>m</mi> <mn>2</mn> </mrow> </msub> </mtd> </mtr> <mtr> <mtd> <mo>...</mo> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <mo>...</mo> </mtd> </mtr> <mtr> <mtd> <msub> <mi>p</mi> <mrow> <mn>1</mn> <mi>m</mi> </mrow> </msub> </mtd> <mtd> <msub> <mi>p</mi> <mrow> <mn>2</mn> <mi>m</mi> </mrow> </msub> </mtd> <mtd> <mo>...</mo> </mtd> <mtd> <msub> <mi>p</mi> <mrow> <mi>m</mi> <mi>m</mi> </mrow> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow>
Wherein m represents the other number of target class, pijRepresent that the i-th row jth shows pijThe pixel number of the i-th class of a actual ownership is pre- Survey as jth class；Numerical value on matrix leading diagonal is expressed as, certain one kind is attributed in earth's surface true picture and is returned with corresponding classification chart In the number of of a sort pixel, i.e., the number for the pixel correctly classified；
Step 4.2, producer's precision, formula are calculated based on confusion matrixWherein i, j are expressed as mixing Confuse the i-th class and the atural object classification shared by concrete class jth class, A in matrixjjRepresent the element value on diagonal in confusion matrix；
Calculate overall classification accuracy, formulaWherein N represents authentic specimen sum；
Kappa coefficients are calculated, formula kappa=(d-q)/(N-q), wherein N are authentic specimen sum, and d is right in confusion matrix The number of sample on linea angulata, C be classification number, AijRepresent and i is corresponded in confusion matrix, the element value of j positions, the expression of q Formula is
Step 4.3, when Kappa values are more than α, uniformity between presentation class result and ground reference information is very big or precision very Height, Kappa values represent that uniformity is medium in β-α, and Kappa values then represent that uniformity is very poor less than β.
4. the ground mulching sorting technique of the high-resolution remote sensing image based on parallel algorithm as claimed in claim 3, it is special Sign is：The calculation formula of normalized differential vegetation index is in step 1.2Wherein ρnirIt is that high-resolution is more The reflectance value of the near infrared band of spectral remote sensing image, ρredIt is the reflectance value of red band.
5. the ground mulching sorting technique of the high-resolution remote sensing image based on parallel algorithm as claimed in claim 4, it is special Sign is：Water body index calculation formula is normalized in step 1.3 isWherein ρnirIt is that high-resolution is more The reflectance value of the near infrared band of spectral remote sensing image, ρgreenIt is the reflectance value of image green band.
6. the ground mulching sorting technique of the high-resolution remote sensing image based on parallel algorithm as claimed in claim 5, it is special Sign is：Textural characteristics in step 1.4 include entropy and homogeney based on gray scale symbiosis square, and implementation is as follows,
It is horizontal direction spatial domain to make Kx={ 1,2 ..., Nx }, and Ky={ 1,2 ..., Ny } is vertical direction spatial domain, and image can be with It is defined gray scale pixel section Kx*Ky=k, wherein entropyHomogeney P in formulaijRepresent the element value that the i-th row j is arranged in the gray level co-occurrence matrixes after normalization.
7. the ground mulching sorting technique of the high-resolution remote sensing image based on parallel algorithm as claimed in claim 6, it is special Sign is：The value of β and α is respectively 0.4 and 0.8 in step 4.3.
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