CN104182456B - Spatial entity increment extraction method based on MRS-MM (Multi-Rules Supported Matching Model) target matching model - Google Patents

Spatial entity increment extraction method based on MRS-MM (Multi-Rules Supported Matching Model) target matching model Download PDF

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CN104182456B
CN104182456B CN201410325399.0A CN201410325399A CN104182456B CN 104182456 B CN104182456 B CN 104182456B CN 201410325399 A CN201410325399 A CN 201410325399A CN 104182456 B CN104182456 B CN 104182456B
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杜震洪
张丰
刘仁义
曹洋洋
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Zhejiang University ZJU
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Abstract

The invention discloses a spatial entity increment extraction method based on an MRS-MM (Multi-Rules Supported Matching Model) target matching model. Data in two years are subjected overlapping analysis, an MRS-MM is adopted for target matching, the entity change types, including entity new addition, entity loss, entity pattern change and entity attribute change, shown in the temporal and spatial change process are judged in the aspect of the spatial entity change behavior, the increment information extraction of a spatial database is realized through a designed increment information extraction algorithm according to the change types, and the problem of obtaining difficulty of increment packets in the current land utilization and urban and rural cadastral database increment updating is solved. The spatial entity increment extraction method provided by the invention has the advantages that the realization method of the technology is simple, the spatial databases in the two years are effectively managed, the computer automatic processing is adopted, the man-machine interaction is little, the time is saved, the work efficiency is improved, and the application prospects in the increment updating fields of the spatial databases of land utilization spatial databases, cadastral databases and the like are wide.

Description

A kind of spatial entities increment extracting method based on MRS-MM object matching models
Technical field
The invention belongs to spatial data matching and incremental update field.More particularly to it is a kind of based on MRS-MM object matching moulds The spatial entities increment extracting method of type.
Background technology
With the development and popularization of spatial database, the construction of all kinds of spatial databases of China achieves very big achievement, But the Up-to-date state of types of databases cannot be guaranteed, Up-to-date state is the vitality of spatial database, spatial database main flow Update mode incremental update is the effective means for solving spatial database Up-to-date state, how fast and effectively from latest edition data Increment information is extracted in storehouse, the incremental update for completing spatial database is the key issue of spatial database development.
At present, the incremental update domestic and foreign scholars on spatial database have research, Bukauskas (2003), Effenberg (1996) and Lin M Y (1998) etc. have studied the object matching and incremental update problem of database, due to research Field is inconsistent, and the matching algorithm and increment updating method taken do not possess general character, and the present invention attempts to solve in the field of territory The problem that the object matching and increment information of spatial database are extracted.Zhou Xiaoguang (2005) discusses the land deeds based on topological relation Database incremental update problem, the renewal on ancestor ground, but this are realized by the topological relation for analyzing plot using various operation operators The method of kind is not processed well in terms of ancestor ground land parcel change trace mechanism, and hidden danger is brought to later ancestor ground dispute investigation. Li Su (2008) realizes the dynamic of cadastral information by foundation " three storehouses are integrally " update mode, i.e. trend of the times storehouse, history library, change storehouse State updates, but this kind of update mode have ignored the topological coherence of CADASTRAL DATA and the error problem of attribute logic uniformity, Easily cause data topology associated errors and data redundancy.Multi-scale modeling storehouse (the Multi- that Ye Y (2013) are proposed Scale Spatial Database) auto-increment update method can realize the incremental update of spatial database, but in space There is loose filter, leakage matching problem in the design of key element matching algorithm, matching accuracy is not high enough.
Traditional spatial data updates main using mapping acquisition data, manually carries out edit operation more in ArcMap Newly, do not support that computer integration is processed, and there is topological relation in a jumble, the defect such as consistency maintenance missing, after the completion of renewal, Need to re-start topological relation inspection, modification, artificial operation renewal in addition is wasted time and energy, and error-prone.
The content of the invention
The purpose of the present invention is to overcome the deficiencies in the prior art, proposes a kind of space based on MRS-MM object matching models Entity increment extracting method.
Sky of the one kind based on MRS-MM (Multi-Rules Supported Matching Model) object matching model Between entity increment extracting method the step of it is as follows：
1) map data mining platform of spatial entities is extracted respectively from two spatial databases of different year, in ArcGIS systems In call stacked function in spatial analysis module, the figure layer of two different years is laid out analysis, and to after stacked Result set is pre-processed, and eliminates logic error, and rendered；
2) MRS-MM more rules object matching models are built, the model includes semantic matching algorithm, overall identification code matching Algorithm and geometric match algorithm；Each block space entity object is traveled through successively, the space and attribute according to spatial entity Characteristic selects Corresponding matching algorithm,
Selection overall identification code matching algorithm judgement first,
Spatial Data Engine mechanism can distribute a globally unique identification code for each spatial entities, and the identification code will not Change, the identification code of the same spatial entities in different year database is constant, this is in former and later two time databases Data Matching offers convenience, i.e., by judging the change of the spatial entities by the overall identification code for comparing two spaces entity Behavior is to increase newly or loss, if judging the change behavior of spatial entities, carries out the judgement of next spatial entities；
If can not judge, selection semantic matching algorithm judges that the attributive character according to spatial entities is filtered, semantic Vacancy, the uniformity for judging spatial entities field value are fitted through, the similitude of critical field and the similarities and differences of metadata are judged；If Can not judge, then select geometric match algorithm to judge,
Geometric match is using shape similarity, three similar index of area similarity and direction similarity come weighted calculation two The overall matching degree of individual Space Elements, spatial data geometric similarity degree Measure Theory is to be weighed by the way of multi-angle measuring and calculating The matching similarity of extraterrestrial target；
3) change type of spatial entities is determined according to matching result, the increment information extraction algorithm using design is increased The extraction of amount information, the result that will be extracted is present in update step and renewal process layer, and compression is packed into spatial information Delta package.
Described geometric match algorithm is specially：It is to be surveyed using multi-angle to introduce spatial data geometric similarity degree Measure Theory The mode of calculation weighs the matching similarity of extraterrestrial target, introduces shape description factor ω and ν to measure two spaces figure Shape similarity；Using the stacked rate of area as index, two spaces figure is judged by the size of the stacked rate of reference area Area similarity degree；Outgoing direction similarity is calculated again, finally by weighted calculation two shape, the faces of the extraterrestrial target of matching Product, three, direction vector value draw overall matching degree；
2.1) shape similarity is calculated
The shape similarity of two spaces figure is measured using shape description factor ω and ν, figure M and figure N is defined ω and ν difference ωM, νM, ωN, νN, wherein：
νMWith νNIt is the coordinate of M and N,WithEach length of side vectors of respectively figure M, N, k is the less polygon edge number of side number, M Shape degree of ramification and N between is defined as：
Wherein, | νM-νN| andThe Euclidean distance of vector is referred to, by test of many times, u1And u2It is 0.5, ω and ν makees normalization and calculates, by being calculated 0 ＜ d (M, N)shape＜ 1, show that shape similarity is：
Sim (M, N)shape=1-d (M, N)shape, 0 ＜ sim (M, N)shape＜ 1 (2)；
2.2) area Similarity Measure
Area similarity, as index, two skies is judged by the size of the stacked rate of reference area using the stacked rate of area Between figure area similarity degree, the stacked rate CR of area refers to the intersection area of two spaces figure and the ratio of respective area Rate, is defined as area similarity sim (M, N)areaIt is CR,
Wherein, Δ S is two intersection areas of figure, and S (X) is the area of figure X, and CR is more than 0 and less than 1, when When CR is intended to 0, the figure at two moment is more similar, otherwise two graphic differences are bigger, and when CR is 1, two figures are complete It is equal, set the value of error e；
Make | S (A)-Δ S |≤e, show that area similarity is：
Wherein | S (A)-Δ S |≤e；
2.3) direction Similarity Measure
The direction of Space Elements is with the minimum external world's rectangle of figure M, N length of side axle more long, the angle formed with adjacent edge Deflection, defines d (M, N)directForThe respectively deflection of figure M, N, direction is similar Degree can be defined as：
2.4) overall matching degree is calculated
Total similarity is obtained by weighted calculation two shapes of the space diagram row of matching, area, three, direction vector values Go out, wherein, set [θ1, θ2, θ3]T, [C1, C2, C3]TShape, area and three, the direction vector of figure M, N, definition are represented respectively D (M, N) is the degree of ramification of M, N, and sim (M, N) is the similarity of M, N, then the geometric similarity of figure M, N can use the formula To represent：
Sim (M, N)=1-d (M, N) (5)
D (M, N) is measured using the Minkowski distances of weighting, then (5) can be converted into：
Wherein, f values 2, ujIt is weight coefficient, and|θj-Cj| it, by the value after normalization, is figure to be J-th degree of ramification of component in shape M and N, wherein j=1,2,3,0 ＜ | θj-Cj| ＜ 1, the phase of sample is calculated according to formula (6) Like being worth, filtering threshold is determined according to matching precision, by calculating, average value is, standard deviation isDefining filtering threshold is sim0,Above-mentioned shape, area, three, direction vector value are substituted into formula (6), extraterrestrial target is extrapolated total Geometric similarity degree, as sim (M, N) ＞ sim0When, then can determine whether that figure M, N are matched, as sim (M, N) ＜ sim0, judge figure M, N are mismatched.
Described step 3) include：
3.1) ancestor stratum CADAt1, CADAt2 of two different years are laid out analysis, generate overlapping layers, overlapping layers The graphical information and attribute information in two times are overlapped respectively, Attri (M), Attri (N) be figure M to be compared and The attribute of N；
3.2) the minimum unit figure spot in traversal overlapping layers, minimum unit refers to the feature object superposition between two time points The set of the minimal face object for being formed；
3.3) minimal face is set to as being Obj (D), D is minimal face to picture.First determine whether whether objective attribute target attribute Attri (N) deposits , if Attri (N) does not exist, the change behavior of spatial entities is to loss, by key element X be stored in renewal process layer file；
If 3.4) Attri (M), Attri (N) in D objects are present, calculate the stacked rate of area of figure M and N, if D with The stacked rate of Nj areas is in error e value, it is believed that two figures be it is identical be not changed in, Nj is j-th difference of component in N Degree；Whether the property value for contrasting Attri (M) and Attri (N) again is equivalent, if equivalent, graphic attribute does not change, Assert that two figures are unchanged；If property value is different, it is judged to that figure is unchanged, figure N is now write into update step file, will Figure M writes into renewal process layer file；
3.5) if the Attri (M), Attri (N) in D objects are present, but D and the stacked rate of Nj areas are outside error e value, Then it is judged to that figure is changed, N graphical informations is write into update step message file, figure M is write into renewal process layer file；
3.6) update step message file and renewal process layer message file are finally changed into VCT DIFs text respectively Part, finally encapsulation packing, forms CADASTRAL DATA delta package.
The present invention has the advantage that compared with prior art：
1) two contrasts of the spatial database of different historical junctures are realized, target information is carried out using Matching Model All standing is matched, and the increment information extracting method of design causes that increment information file is extracted, and the method has matching higher Accuracy rate, support towards various spatial entities increment information extract, be one it is transparent, automation, it is controllable, can entangle Wrong data abstraction techniques.
2) the technology of the present invention implementation method is simple, and Data Matching degree is high, and increment extraction error is small, and execution efficiency is high.
Brief description of the drawings
Fig. 1 is spatial entities increment information extraction thinking schematic diagram in the present invention；
Fig. 2 is spatial entities increment information extraction algorithm design drawing in the present invention；
Fig. 3 is MRS-MM more rules Matching Model figures in the present invention；
Fig. 4 is matching experiment flow figure.
Specific embodiment
As shown in Figure 1 to Figure 3, it is a kind of to be based on MRS-MM (Multi-Rules Supported Matching Model) mesh The step of marking the spatial entities increment extracting method of Matching Model is as follows：
1) map data mining platform of spatial entities is extracted respectively from two spatial databases of different year, in ArcGIS systems In call stacked function in spatial analysis module, the figure layer of two different years is laid out analysis (Union), and to folded The result set for postponing is pre-processed, and eliminates logic error, and rendered；
2) MRS-MM more rules object matching models are built, the model includes semantic matching algorithm, overall identification code matching Algorithm and geometric match algorithm；Each block space entity object is traveled through successively, the space and attribute according to spatial entity Characteristic selects Corresponding matching algorithm,
Selection overall identification code matching algorithm judgement first,
Spatial Data Engine mechanism can distribute a globally unique identification code for each spatial entities, and the identification code will not Change, the identification code of the same spatial entities in different year database is constant, this is in former and later two time databases Data Matching offers convenience, i.e., by judging the change of the spatial entities by the overall identification code for comparing two spaces entity Behavior is to increase newly or loss, if judging the change behavior of spatial entities, carries out the judgement of next spatial entities；
If can not judge, selection semantic matching algorithm judges that the attributive character according to spatial entities is filtered, semantic Vacancy, the uniformity for judging spatial entities field value are fitted through, the similitude of critical field and the similarities and differences of metadata are judged；If Can not judge, then select geometric match algorithm to judge,
Geometric match is using shape similarity, three similar index of area similarity and direction similarity come weighted calculation two The overall matching degree of individual Space Elements, spatial data geometric similarity degree Measure Theory is to be weighed by the way of multi-angle measuring and calculating The matching similarity of extraterrestrial target；
3) change type of spatial entities is determined according to matching result, the increment information extraction algorithm using design is increased The extraction of amount information, the result that will be extracted is present in update step and renewal process layer, and compression is packed into spatial information Delta package.
Described geometric match algorithm is specially：It is to be surveyed using multi-angle to introduce spatial data geometric similarity degree Measure Theory The mode of calculation weighs the matching similarity of extraterrestrial target, introduces shape description factor ω and ν to measure two spaces figure Shape similarity；Using the stacked rate of area as index, two spaces figure is judged by the size of the stacked rate of reference area Area similarity degree；Outgoing direction similarity is calculated again, finally by weighted calculation two shape, the faces of the extraterrestrial target of matching Product, three, direction vector value draw overall matching degree；
2.1) shape similarity is calculated
The shape similarity of two spaces figure is measured using shape description factor ω and ν, figure M and figure N is defined ω and ν difference ωM, νM, ωN, νN, wherein：
νMWith νNIt is the coordinate of M and N,WithEach length of side vectors of respectively figure M, N, k is the less polygon edge number of side number, M Shape degree of ramification and N between is defined as：
Wherein, | νM-νN| andThe Euclidean distance of vector is referred to, by test of many times, u1And u2It is 0.5, ω and ν makees normalization and calculates, by being calculated 0 ＜ d (M, N)shape＜ 1, show that shape similarity is：
Sim (M, N)shape=1-d (M, N)shape, 0 ＜ sim (M, N)shape＜ 1 (2)；
2.2) area Similarity Measure
Area similarity, as index, two skies is judged by the size of the stacked rate of reference area using the stacked rate of area Between figure area similarity degree, the stacked rate CR of area refers to the intersection area of two spaces figure and the ratio of respective area Rate, is defined as area similarity sim (M, N)areaIt is CR,
Wherein, Δ S is two intersection areas of figure, and S (X) is the area of figure X, and CR is more than 0 and less than 1, when When CR is intended to 0, the figure at two moment is more similar, otherwise two graphic differences are bigger, and when CR is 1, two figures are complete It is equal, set the value of error e；
Make | S (A)-Δ S |≤e, show that area similarity is：
Wherein | S (A)-Δ S |≤e；
2.3) direction Similarity Measure
The direction of Space Elements is with the minimum external world's rectangle of figure M, N length of side axle more long, the angle formed with adjacent edge Deflection, defines d (M, N)directForThe respectively deflection of figure M, N, direction is similar Degree can be defined as：
2.4) overall matching degree is calculated
Total similarity is obtained by weighted calculation two shapes of the space diagram row of matching, area, three, direction vector values Go out, wherein, set [θ1, θ2, θ3]T, [C1, C2, C3]TShape, area and three, the direction vector of figure M, N, definition are represented respectively D (M, N) is the degree of ramification of M, N, and sim (M, N) is the similarity of M, N, then the geometric similarity of figure M, N can use the formula To represent：
Sim (M, N)=1-d (M, N) (5)
D (M, N) is measured using the Minkowski distances of weighting, then (5) can be converted into：
Wherein, f values 2, ujIt is weight coefficient, and|θj-Cj| it, by the value after normalization, is figure to be J-th degree of ramification of component in shape M and N, wherein j=1,2,3,0 ＜ | θj-Cj| ＜ 1, the phase of sample is calculated according to formula (6) Like being worth, filtering threshold is determined according to matching precision, by calculating, average value is, standard deviation is, defining filtering threshold is sim0,Above-mentioned shape, area, three, direction vector value are substituted into formula (6), extraterrestrial target is extrapolated total Geometric similarity degree, as sim (M, N) ＞ sim0When, then can determine whether that figure M, N are matched, as sim (M, N) ＜ sim0, judge figure M, N are mismatched.
Described step 3) include：
3.1) ancestor stratum CADAt1, CADAt2 of two different years are laid out analysis, generate overlapping layers, overlapping layers The graphical information and attribute information in two times are overlapped respectively, Attri (M), Attri (N) be figure M to be compared and The attribute of N；
3.2) the minimum unit figure spot in traversal overlapping layers, minimum unit refers to the feature object superposition between two time points The set of the minimal face object for being formed；
3.3) minimal face is set to as being Obj (D), D is minimal face to picture.First determine whether whether objective attribute target attribute Attri (N) deposits , if Attri (N) does not exist, the change behavior of spatial entities is to loss, by key element X be stored in renewal process layer file；
If 3.4) Attri (M), Attri (N) in D objects are present, calculate the stacked rate of area of figure M and N, if D with The stacked rate of Nj areas is in error e value, it is believed that two figures be it is identical be not changed in, Nj is j-th difference of component in N Degree；Whether the property value for contrasting Attri (M) and Attri (N) again is equivalent, if equivalent, graphic attribute does not change, Assert that two figures are unchanged；If property value is different, it is judged to that figure is unchanged, figure N is now write into update step file, will Figure M writes into renewal process layer file；
3.5) if the Attri (M), Attri (N) in D objects are present, but D and the stacked rate of Nj areas are outside error e value, Then it is judged to that figure is changed, N graphical informations is write into update step message file, figure M is write into renewal process layer file；
3.6) update step message file and renewal process layer message file are finally changed into VCT DIFs text respectively Part, finally encapsulation packing, forms CADASTRAL DATA delta package.
Embodiment
This experiment chooses the 2012 and 2013 years ground data in Jiande City Datong District town city as experimental subjects.Experimental data By professional surveying and mapping company field survey, interior industry staff becomes according to newest situation of change in 2013 to 2012 annual data storehouses More, 2013 annual datas are that ancestor ground newest after changing reflects on the spot, analysis are overlapped to the data of 2 years, by MRS-MM Matching Model identifies each piece of change behavior on a ground, is that subsequent delta information extraction is laid the groundwork.The purpose of matching is to determine The change behavior on ancestor ground, is to do foundation for increment is extracted.
According to analysis and MRS-MM Matching Models above, experiment flow, such as Fig. 4 are formulated.Choose two ancestor ground numbers in time According to being pre-processed to reference projection system, matching area association attributes field values, it is ensured that the quality of data before matching. First, ArcToolBox is stacked the ancestor ground key element layer segment data after instrument is changed 2012 and 2013 in calling ArcCatalog It is laid out analysis.After ancestor ground key element layer is laid out operation, stacked layer data is produced.
According to matching experiment flow, first using codes match algorithm, increase newly with filtering out ancestor and loss two kinds and change class The ancestor ground of type.Ancestor ground data in overlapped layers have the two identical fields of row, uniqueness and figure number one according to identification code Cause property principle can be newly-increased with judging ancestor quickly or loss.As shown in table 1, plot 2 is in two time period items attributes Value does not change, is judged to unchanged, and the match is successful；Can be detected by the attribute in plot 4, be within 2012 not deposit Occurring by 2013, you can be newly-increased type with being judged as the block；The identification code of plot 5 and ancestor ground numbering change, Can not judge specifically to change behavior using identification code, this kind of data are accomplished by being detected using geometric match method.
Table 1
It is unmatched extraterrestrial target by overall identification code matching result, into geometric match flow, geometric match is led to Three similarity measure components of calculating extraterrestrial target are crossed to judge matching result.As shown in table 2, codes match result is randomly selected It is unmatched 10 pieces ground data, calculates overall similarity, calculates | θj-Cj| the standard deviation of (j=1,2,3) is respectively 0.0253,0.0725,0.0238, then ujValue is respectively 0.2431,0.0921,0.02412.Obtain sim standard deviationsFor 0.0238, average is 0.9604, then sim0It is 0.08968.By each plot similarity with threshold value sim0(0.08968) compare Compared with as shown in table 3, the only plot 5 in sample data does not meet matching and requires, 5 ground belong to figure and there occurs change, should The ancestor ground of type extracts link in increment needs further judgement to extract change information.After the completion of matching, according to matching result Judge the change type of spatial entities, using increment extraction algorithm, complete the extraction of increment information, eventually form delta package.
The matching threshold of table 2：0.08968
Table 3

Claims (2)

1. a kind of spatial entities increment extracting method based on MRS-MM object matching models, it is characterised in that it the step of such as Under：
1) extract the map data mining platform of spatial entities respectively from two spatial databases of different year, adjusted in ArcGIS systems With the stacked function in spatial analysis module, the figure layer of two different years is laid out analysis, and to being stacked after result Collection is pre-processed, and eliminates logic error, and rendered；
2) MRS-MM more rules object matching models are built, the model includes semantic matching algorithm, overall identification code matching algorithm With geometric match algorithm；Each block space entity object is traveled through successively, the characteristic in space and attribute according to spatial entity Selection Corresponding matching algorithm,
Selection overall identification code matching algorithm judgement first,
Spatial Data Engine mechanism can distribute a globally unique identification code for each spatial entities, and the identification code will not change Become, the identification code of the same spatial entities in different year database is constant, this is the number in former and later two time databases Offered convenience according to matching, i.e., by judging the change row of the spatial entities by the overall identification code for comparing two spaces entity To be newly-increased or lossing, if judging the change behavior of spatial entities, the judgement of next spatial entities is carried out；
If can not judge, selection semantic matching algorithm judges that the attributive character according to spatial entities is filtered, semantic matches By judging vacancy, the uniformity of spatial entities field value, the similitude of critical field and the similarities and differences of metadata are judged；If judging Do not go out, then select geometric match algorithm to judge,
Geometric match is using shape similarity, three similar index of area similarity and direction similarity come two skies of weighted calculation Between key element overall matching degree, spatial data geometric similarity degree Measure Theory is to weigh space by the way of multi-angle measuring and calculating The matching similarity of target；
3) change type of spatial entities is determined according to matching result, increment letter is carried out using the increment information extraction algorithm of design The extraction of breath, the result that will be extracted is present in update step and renewal process layer, and compression is packed into the increment of spatial information Bag；
Described step 3) include：
3.1) ancestor stratum CADAt1, CADAt2 of two different years are laid out analysis, generate overlapping layers, overlapping layers is by two The graphical information and attribute information in individual time are overlapped respectively, and Attri (M), Attri (N) are figure M and N to be compared Attribute；
3.2) the minimum unit figure spot in traversal overlapping layers, minimum unit refers to the feature object superposition institute shape between two time points Into minimal face object set；
3.3) set minimal face to as be Obj (D), D be minimal face to picture, first determine whether that objective attribute target attribute Attri (N) whether there is, If Attri (N) does not exist, the change behavior of spatial entities is to loss, and key element X is stored in into renewal process layer file；
If 3.4) Attri (M), Attri (N) in D objects are present, the stacked rate of area of figure M and N is calculated, if D and Nj faces The stacked rate of product is in error e value, it is believed that two figures be it is identical be not changed in, Nj is j-th degree of ramification of component in N； Whether the property value for contrasting Attri (M) and Attri (N) again is equivalent, if equivalent, graphic attribute does not change, assert Two figures are unchanged；If property value is different, it is judged to that figure is unchanged, figure N is now write into update step file, by figure M writes into renewal process layer file；
3.5) if the Attri (M), Attri (N) in D objects are present, but D is stacked rate outside error e value with Nj areas, then sentence It is set to figure to change, N graphical informations is write into update step message file, figure M is write into renewal process layer file；
3.6) update step message file and renewal process layer message file are finally changed into VCT DIF files respectively, most Post package is packed, and forms CADASTRAL DATA delta package.
2. a kind of spatial entities increment extracting method based on MRS-MM object matching models according to claim 1, its It is characterised by that described geometric match algorithm is specially：It is to use multi-angle to introduce spatial data geometric similarity degree Measure Theory The mode of measuring and calculating weighs the matching similarity of extraterrestrial target, introduces shape description factor ω and ν to measure two spaces figure Shape similarity；Using the stacked rate of area as index, two spaces figure is judged by the size of the stacked rate of reference area The area similarity degree of shape；Calculate outgoing direction similarity again, finally by the shapes of the extraterrestrial target of the matching of weighted calculation two, Area, three, direction vector value draw overall matching degree；
2.1) shape similarity is calculated
The shape similarity of two spaces figure is measured using shape description factor ω and ν, ω and ν point of figure M and figure N is defined Other ωM, νM, ωN, νN, wherein： νMAnd νNIt is the coordinate of M and N,WithEach length of side vectors of respectively figure M, N, k is the less polygon edge of side number Number, the shape degree of ramification between M and N is defined as：
Wherein, | νM-νN| andThe Euclidean distance of vector is referred to, by test of many times, u1And u2It is 0.5, ω Make normalization with ν to calculate, by being calculated 0 ＜ d (M, N)shape＜ 1, show that shape similarity is：
sim(M,N)shape=1-d (M, N)shape, 0 ＜ sim (M, N)shape＜ 1 (2)；
2.2) area Similarity Measure
Area similarity, as index, two spaces figure is judged by the size of the stacked rate of reference area using the stacked rate of area The area similarity degree of shape, the stacked rate CR of area refers to the intersection area of two spaces figure and the ratio of respective area, fixed Justice is area similarity sim (M, N)areaIt is CR,
Wherein, Δ S is two intersection areas of figure, and S (X) is the area of figure X, and CR is more than 0 and less than 1, when CR becomes To in 0 when, the figure at two moment is more similar, otherwise two graphic differences are bigger, when CR is 1, two complete phases of figure Deng the value of setting error e；
Make | S (A)-Δ S |≤e, show that area similarity is：
Wherein | S (A)-Δ S |≤e；
2.3) direction Similarity Measure
The direction of Space Elements is with the minimum external world's rectangle of figure M, N length of side axle more long and the angle of adjacent edge formation as direction Angle, defines d (M, N)directFor The respectively deflection of figure M, N, direction similarity can be with It is defined as：
2.4) overall matching degree is calculated
Total similarity is drawn by weighted calculation two shapes of the space diagram row of matching, area, three, direction vector values, its In, set [θ1, θ2, θ3]T, [C1, C2, C3]TRepresent shape, area and three, the direction vector of figure M, N respectively, define d (M, N) it is the degree of ramification of M, N, sim (M, N) is the similarity of M, N, then the geometric similarity of figure M, N can use the formula (5) To represent：
Sim (M, N)=1-d (M, N) (5)
D (M, N) is measured using the Minkowski distances of weighting, then (5) can be converted into：
Wherein, f values 2, ujIt is weight coefficient, and|θj-Cj| it, by the value after normalization, is figure M and N to be In j-th degree of ramification of component, wherein j=1,2,3,0 ＜ | θj-Cj| ＜ 1, the similar value of sample is calculated according to formula (6), Filtering threshold is determined according to matching precision, by calculating, average value isStandard deviation isDefinition filtering threshold is sim0,Above-mentioned shape, area, three vector values in direction are substituted into formula (6), total several of extraterrestrial target are extrapolated What similarity, as sim (M, N)>sim0When, then can determine whether that figure M, N are matched, as sim (M, N)<sim0, judge figure M, N not Matching.
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