CN110162650B - Small image spot melting method considering local optimization and overall area balance - Google Patents
Small image spot melting method considering local optimization and overall area balance Download PDFInfo
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Abstract
The embodiment of the invention discloses a small image spot melting method considering local optimum and integral area balance, which comprises the following steps: A. acquiring full-coverage vector pattern spot data of an image to be processed; acquiring data information of adjacent pattern spots according to the data; B. carrying out area pre-allocation on the small image spots in the image according to the data information of the adjacent image spots to obtain the subdivision area of each adjacent image spot on the small image spot; C. counting the areas of the first-class land types of the small patches after the area pre-allocation, and calculating each change rate of each land area before and after the area pre-allocation; D. when each change rate is lower than a second specified threshold value, determining an internal skeleton line of the small image spots, and respectively splitting each small image spot into a plurality of fragments according to the skeleton line; merging the patches into the patches adjacent to the patches so that the small patches are thawed out to form merged full-coverage patch data. Thus, the present application can effectively maintain the consistency of the ground types before and after the combination of the pattern spots.
Description
Technical Field
The invention relates to the field of map making and map synthesis, in particular to a small-pattern-spot melting method giving consideration to local optimization and overall area balance.
Background
With the increase of the application depth and the application range of the geographical national situation census data, the requirement of comprehensively reflecting the regional land utilization condition in a multilevel manner from large-scale ground coverage data (map spots) to various small-scale data is more and more urgent. However, the land use topic data is a full-coverage, non-overlapping spatial overlay, and when the map is changed from a large scale to a small scale, the small patches must be thawed because the area is too small to be expressed on the map (altinghua et al, 2010). The basic idea of the Dissolving operation is to split a fine pattern spot into a plurality of 'fragments' according to a certain rule according to the condition of an adjacent surface element, and combine the 'fragments' into an adjacent pattern spot, so that the elements in the picture frame are more concise. The small pattern spots refer to finely-divided land surface coverage data which are distributed in a discrete mode in the land utilization data, are small in area, large in quantity, complex and diverse in shape and widely distributed in the region. When the land use data is integrated, the thawing processing result of the small pattern spots can directly influence the quality of the integrated result of the land use data.
The key of the split melting of the image spots is how to define split lines in small image spots. The Delaunay triangulation network has the advantages of "compasses rule" or "maximum and minimum angle rule", etc., and becomes a common method for integrating the speckle melting (Ware et al, 1997 a). Jones et al (1995) proposed a Delaunay triangulation-based split line extraction method using triangulation to dissect the interior of small patches and to thaw them. The general problem of the polygonal planar target is proposed by the methods of Argentina and the like (2000), and the operations of polygonal combination and the like can be realized by establishing a constraint Delaunay triangulation structure. Gao et al (2004) combined with thematic knowledge, extracted small spot split lines based on Delaunay triangulation network, and performed dimension reduction (Collapse) operation on land use data. However, the above operations are all based on the central axis subdivision of the triangulation network, and einhua et al (2002) indicate that the central axis subdivision method does not consider the strong and weak points of the spatial competitive power of adjacent patches in the process of extracting the skeleton line, which is not beneficial to maintaining the percentage of each type of land area before and after integration, and the division line of a small patch should be adjusted according to the importance degree of the adjacent patch, so a weighted skeleton line subdivision strategy is provided. By utilizing the method, the Liu-Yan forest and the like (2010) establish an improved algorithm for dividing the constraint Delaunay triangulation network by the weighted skeleton line considering spatial proximity and semantic proximity, and the comprehensive result well keeps the characteristics of land utilization. Also, Meijers et al propose SPLITAREA method for weighted split line extraction based on local features for triangulation.
However, the existing algorithm considers the local optimal constraint more, so that the small-image-spot fusion result still has the defects: for example, the similarity between the small-area image spots and the land classes adjacent to the small-area image spots and the shared boundary length are used as local optimization, so that the global land area change is large after the subdivision and the merging, and the output result does not meet the actual condition. For example, Cheng and Li (2006) respectively use two local optimal calculation methods of adjacent patch areas and shared boundary length to melt small patches, and find that 12.3% and 8.3% of land types are changed before and after merging.
Therefore, there is a need for a small speckle fusion method that combines local optimization and overall area balance to solve or partially solve the above technical problems and better maintain the consistency of the speckle and the land before and after the speckle is combined.
Disclosure of Invention
In view of the above, the main objective of the present invention is to provide a small speckle fusion method that combines local optimization and overall area balance, so as to better maintain the consistency of the speckle and the ground.
The application provides a speckle melting method considering local optimization and overall area balance, which comprises the following steps:
A. acquiring full-coverage vector pattern spot data of an image to be processed; acquiring data information of adjacent pattern spots according to the data; wherein, the data information of the adjacent pattern spots at least comprises one of the following data information: the area of the adjacent pattern spot, the length of the shared edge and the semantic distance;
B. according to the data information of the adjacent image spots, carrying out area pre-allocation on the small image spots in the image to obtain the subdivision area of each adjacent image spot on the small image spot; wherein, the image spot with the area smaller than the first designated threshold value is taken as a small image spot;
C. counting the area of the first-level land class of each small map spot after area pre-allocation, and calculating each change rate of each land class area before and after the pre-allocation;
D. when the change rates are lower than a second specified threshold value, determining an internal skeleton line of each small image spot, and respectively splitting each small image spot into a plurality of fragments according to the skeleton lines; and merging the patches into patches adjacent thereto so that the small patches are thawed to form merged full-coverage patch data.
From the above, the method for thawing the image spots provided by the application can maintain the balance of the overall ground area before and after the thawing while considering the optimal local spatial pattern of the image spots in the thawing process. Is favorable for better keeping the consistency of the front and the back ground types of the pattern spots.
Wherein, the first grade land is the first grade land distribution standard of national standard, include: cultivated land, woodland, grassland, water area, (urban and rural, working condition, inhabitant) land and unused land.
Preferably, the step C further comprises:
and when the change rate of each change rate is higher than a second specified threshold value, recording the difference value between all the land categories and the second specified threshold value, and performing iterative processing on the area pre-classification result according to an area balance iterative algorithm until each change rate is lower than the second specified threshold value.
Thereby, better distribution and further melting process are facilitated.
Preferably, the step a of obtaining the calculation model of the area of the adjacent spot is:
wherein i is the clockwise number of each node of the adjacent polygon, xiFor each node abscissa, y, of the adjacent polygoni+1、yi-1Is the ordinate of each node of the adjacent polygon.
Therefore, the area of the adjacent image spot can be acquired more accurately and effectively.
Preferably, the step a of obtaining the calculation model of the shared edge length includes:
wherein x isi、yiRespectively being the abscissa and the ordinate of an end point of the shared edge; x is the number ofi+1And yi+1Respectively representing the abscissa and the ordinate of the other end point of the shared edge.
Therefore, the shared edge length can be acquired more accurately and effectively.
Preferably, the step B includes:
b1, obtaining the subdivision capability of each adjacent image spot of each small image spot on the subdivision area for obtaining the small image spot;
and B2, acquiring the subdivision area of each adjacent image spot to the small image spot according to the proportion of the subdivision capacity.
Therefore, the method is beneficial to more accurately and effectively acquiring the subdivision area of each adjacent image spot on the small image spot.
Preferably, the subdivision capability calculation model in step B1 is:
wherein S isiWherein i is 1,2,3, S1、S2、S3Respectively representing three constraint indexes of adjacent spot area, shared edge length and semantic distance, wiIs the weight of each index; wherein a represents the small spot, biIs shown asi adjacent patches.
Therefore, the subdivision capability of each adjacent image spot can be acquired more accurately and effectively.
Preferably, the step B2 obtains a calculation model of the subdivision area of each adjacent image spot to the small image spot according to the proportion of the subdivision capacity, where the calculation model is as follows:
Areai＝SAF(a，bi)/SAF(a，b)*Area
wherein, AreaiDividing the small image spot for the ith adjacent image spot; SAF (a, b) is the sum of all adjacent plaque dissection capabilities; area is the Area of the small pattern spot; SAF (a, b)i) Showing the subdivision capability of the ith adjacent spot on the subdivision area for obtaining the small spot.
Therefore, the method is beneficial to more accurately and effectively acquiring the subdivision area of each adjacent image spot to the small image spot.
Preferably, the step D includes:
d1, determining adjacent image spots for forming subdivision on the small image spots according to a Delaunay triangulation method;
d2, respectively carrying out pairwise calculation on the small pattern spots and the adjacent pattern spots to obtain split points;
d3, generating a splitting line according to the splitting points, splitting the small image spots into a plurality of fragments according to the splitting line, merging the fragments with adjacent image spots so that the small image spots are fused to form merged full-coverage image spot data.
Therefore, the small image spots can be more reasonably melted to form the merged full-coverage image spot data.
Preferably, the calculation formula of the acquisition split point is as follows:
wherein a represents the small spot, b representsShowing one neighboring patch, c another, (x)b,yb)、(xc,yc) Respectively representing the coordinates of two end points of an edge in a triangular mesh, wherein xbAnd xcDenotes the abscissa, ybAnd ycRepresents the ordinate; area (a, b) and Area (a, c) are values of the division areas of the small spots a by the adjacent spots b and c, respectively.
Therefore, the method is beneficial to more accurately and effectively acquiring the split points.
By the scheme, the invention at least has the following advantages:
according to the method for melting the image spots, the local space pattern of the image spots is considered to be optimal in the melting process, and meanwhile the balance of the integral ground area before and after melting can be maintained. Is favorable for better keeping the consistency of the front and the back ground types of the pattern spots.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of a conventional small image spot fusion method taking consideration of competition subdivision capability of adjacent image spots: (a) original skeleton line, (b) adjusted skeleton line;
fig. 2 is a schematic diagram illustrating the shortcomings of the existing small-spot fusion method considering local optimization: (ii) raw land use data and small patches (darkened portions) therein, (b) split lines (darkened lines) extracted using a local optimization algorithm based on a Delaunay triangulation network, (c) melting results;
FIGS. 3a and b are flow charts of the small patch melting method considering local optimum and overall area balance provided by the present invention;
FIG. 4 is a schematic diagram illustrating a statistical adjustment value of the change rate of the area types according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an iterative adjustment of a small patch fusion area in consideration of global optimality according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating statistical analysis of speckle adjustment areas of lawns and artificial structures according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a small patch fusion result of land use paving data according to an embodiment of the present invention;
FIG. 8 is a schematic diagram illustrating a comparison of the melted areas of small pattern spots to surrounding ground pattern spots according to an embodiment of the present invention.
Detailed Description
Example one
The following describes a pattern spot merging method for maintaining the feature of the structured feature profile according to the present invention in detail with reference to the accompanying drawings.
As shown in fig. 1, a schematic diagram of a conventional small-spot fusion method considering competition subdivision capability of neighboring spots is shown: (a) original skeleton line, (b) adjusted skeleton line.
The fusion of the small patches of land use data comprises the simplification processing of the geometrical characteristics in space, the merging of semantic type levels and the adjacent relation of adjacent land parcels. The Elatinghua et al (2002) provides a better small-pattern-spot melting method based on a Delaunay triangulation network, namely a weighted skeleton line subdivision method considering the competition subdivision capability of adjacent pattern spots. The core steps are as follows:
step 1: identifying a merged patch candidate set, such as patch a in FIG. 1 (a), on a scale smaller than an area threshold;
step 2: calculating the space competition subdivision capacity under the constraints of space geometric characteristics, semantic distance and the like of adjacent patches b and c (assuming that the subdivision capacity of the adjacent patches b and c on small patches is 8 and 2 respectively);
and step 3: the subdivision points are changed from bisectors of original triangle sides into subdivision points formed according to subdivision capacity proportion, and as shown in fig. 1(b), small image spots a are subdivided and fused to adjacent land parcels b and c.
Fig. 2 is a schematic diagram illustrating the shortcomings of the existing small-spot fusion method considering local optimization: the method comprises the steps of (a) raw land utilization data and small map spots (deepened parts) in the raw land utilization data, (b) splitting lines (bold lines) extracted by a local optimization algorithm based on a Delaunay triangulation network, and (c) melting results.
When the Delaunay triangulation network is applied to thaw the small map spots of the land use data, the most important thing is to determine the split points of the small map spots. The existing small-pattern-spot melting method relies on the subdivision melting of small patterns by directly utilizing local optimal calculation results of adjacent patterns (Podrenek, 2002; Van Smalaen, 2003), for example, based on the areas, shared edges, local semantic distances and the like of the adjacent patterns (Aitonghua and the like, 2010), the maintenance of global land area balance is ignored, and the proportion change of various land classification areas in the whole area before and after melting operation is large (Lijing and the like, 2014; crown, 2009). Especially, under the conditions that land utilization data are broken and small image spots are distributed more, the small image spots are fused based on a local optimal method, and the defects exist in the aspect of keeping global land area balance:
as shown in fig. 2(a), the original land use data is shown, small image spots in the data are all displayed deepened, and a rectangular frame is assumed as the whole research area; fig. 2(b) shows a modified split line (bold line) based on the Delaunay triangulation network using a local optimization algorithm (taking into account the areas of adjacent patches, the shared side length, and the semantic distance); the final small-spot melting result is shown in fig. 2 (c).
The area ratio statistics is carried out on the primary terrain map patches before and after the thawing, and the results are shown in table 1, so that the absolute value of the change of the circular terrain area of the whole area is as high as 56% before and after the thawing, and the change of the global terrain area before and after the thawing is large.
TABLE 1 statistics of spot area ratios of primary terrain class before and after thawing
Note: the figure is only a schematic view and the land use types in the area do not contain all land types.
As shown in fig. 3a and b, the present application provides a method for melting a patch with both local optimization and global area balance, comprising:
s301, acquiring full-coverage vector pattern spot data of an image to be processed; acquiring data information of adjacent pattern spots according to the data; wherein the data information of the adjacent pattern spots comprises: the area of the adjacent pattern spot, the length of the shared edge and the semantic distance; specifically, the method comprises the following steps of through local optimal indexes and calculation: inputting a full-coverage vector image spot data, and providing three indexes of adjacent image spot area, shared edge length and semantic distance as a calculation basis of partial adjacent image spot subdivision competitive power in a small image spot melting process; specifically, the method comprises the following steps:
(1) area of adjacent pattern spot
The area size of the adjacent image spots is the most intuitive influence factor for determining the small image spot occupation capacity, and for the adjacent image spots of the small image spots, the image spots with smaller relative area of the larger image spots belong to the local optimal fusion object in principle. The method for calculating the area of the adjacent pattern spot generally adopts a coordinate analysis method, and the mathematical model of the method is as follows:
wherein, the invention takes the first order adjacent field as the area of space pattern constraint, i is the clockwise number of each node of the adjacent polygon, xiFor each node abscissa, y, of the adjacent polygoni+1、 yi-1Is the ordinate of each node of the adjacent polygon.
(2) Length of shared edge
The length of the shared edge is an important index for judging the spatial proximity degree between the image spots, and in view ecology, the larger the shared edge is, the better material energy circulation and transition capacity are provided between the image spots, so that the adjacent image spots with larger shared edges have better small image spot attribution competitiveness. The shared edge is identified by adding semantic information to the topological structure, and if the node of a certain arc segment has two different semantic information, the edge is the shared edge. The method for calculating the distance (d) between two nodes of the shared edge of the adjacent pattern spot and the small pattern spot generally adopts an Euclidean distance method, and the mathematical model of the Euclidean distance method is as follows:
(3) semantic distance
Semantic proximity is a core element for judging the attribution of small patches (Liu et al, 2002), and Van Oosteriom (1995) proposes the semantic similarity degree between a small-area patch and a neighboring patch in a classical iterative merging algorithm as a local optimal judgment basis for the neighboring patch. The invention establishes a conditional semantic proximity model based on a drawing knowledge correlation theory, and refines a semantic distance calculation method between land classes. The method can not only prevent unreasonable conversion among land types (Yangjun, etc., 2013), but also ensure the concurrent operation of the same type of pattern spots.
The land utilization data is concerned about the total amount of primary and secondary land types, but the original data takes a more refined tertiary land type as a pattern spot classification management unit. According to the classification in the general survey content and index of the geographical national conditions and the operation rule of combining the spot data of the geographical national conditions, the semantic proximity model is created as follows:
wherein, X, YiTwo geographical classes, Y, for participation in proximity calculationsiThe land type is a land type of a merged source land type, and X is a land type of a merged target land type; m is the number of the land category with semantic adjacent relation to X, the semantic Distance between adjacent elements is 1 unit, and Distance (X, Y)i) To merge the location of the source land class in the set of semantic proximity land classes (consider first the proximity relationship with its same parent land class and then the proximity relationship with the non-same parent land class).
S302, performing area pre-allocation on each small image spot according to the data information of the adjacent image spots to obtain the subdivision area of each adjacent image spot on the small image spot; wherein, the image spot with the area smaller than the first designated threshold value is taken as a small image spot; specifically, based on a CRITIC objective weighting method, a near plaque subdivision capability Function (SAF) is defined to calculate the space competition capability of a certain small plaque for the neighboring plaque, and a small plaque is subjected to area pre-division by extracting a plaque target skeleton line by using a Delaunay triangulation network.
Wherein the S302 comprises:
b1, obtaining the subdivision capability of each adjacent image spot of each small image spot on the subdivision area for obtaining the small image spot;
and B2, acquiring the subdivision area of each adjacent image spot to the small image spot according to the proportion of the subdivision capacity.
The subdivision capability calculation model in the step B1 is as follows:
wherein S isiWherein i is 1,2,3, S1、S2、S3Respectively representing three constraint indexes of adjacent spot area, shared edge length and semantic distance, wiIs the weight of each index; wherein a represents the small spot, biRepresenting the ith neighboring spot. If S1And S2If one is 0, then the SAF is 0. SAF (a, b)i) Showing the subdivision capability of the ith adjacent spot on the subdivision area for obtaining the small spot.
The step B2 is to obtain a calculation model of the subdivision area of each adjacent image spot to the small image spot according to the proportion of the subdivision capacity, where the calculation model is:
Areai＝SAF(a，bi)/SAF(a，b)*Area
wherein, AreaiDividing the small image spot for the ith adjacent image spot; SAF (a, b) is the sum of all adjacent plaque dissection capabilities; area is the Area of the small pattern spot; SAF (a, b)i) Showing the subdivision capability of the ith adjacent spot on the subdivision area for obtaining the small spot.
And S303, counting the area of the first-class land of each small map spot after area pre-allocation, and calculating each change rate of the area of each land before and after the pre-allocation. Specifically, the method comprises the following steps:
(1) statistics of area of each region
And (4) counting the area of the first-level land class after spatial pre-classification, and calculating the change rate of the area of each land class before and after pre-classification. Giving a second specified threshold V of the threshold, and recording the difference U between all the land types and the threshold if the change rate of at least one land type exceeds the thresholdiIteratively adjusting the area pre-distribution result; and if the division line does not exceed the threshold, determining and melting the division line directly according to the area pre-division result.
(2) Area balance iterative algorithm
The land types to be adjusted are divided into two types, namely a land type with an area increase exceeding a threshold value (denoted as a land type), and a land type with an area reduction exceeding a threshold value (denoted as B land type). The land class with the area pre-divided area change within the threshold value range is marked as a C land class. The land types after area pre-division mainly comprise the three land types. The total area of the small image spots is recorded as N, and the main flow of the iterative adjustment of the land area is as follows:
1) searching the spot information of the land class needing to be adjusted
For the land type needing to be adjusted, firstly traversing all the adjacent image spots of the land type image spot and the small image spot, and recording the information of the small image spot and the adjacent image spot;
2) determining valid adjustment patch information
Filtering the adjacent spots of the small spots only containing the spots of the A land class or the B land class to obtain the effective adjustable spots, and recording that the area of each effective adjustable small spot is MiThe total area of the effective small pattern spots is M;
3) statistical global scale terrain adjustable area
Suppose global A, B places need to be adjusted by areas of Ua、UbC type increased maximum area is Uc1The maximum area which can be reduced is Uc2；
4) Counting the local small spot size of each image and the adjustable area of the land
And (3) distributing the global adjustment area obtained in the step (3) to each effective small image spot i, and enabling the adjacent image spots to be adjacent:
the area of the A type needing to be adjusted is as follows: n is a radical ofai＝Ua/M*Mi
The area of the B type needing to be adjusted is as follows: n is a radical ofbi＝Ub/M*Mi
The maximum area that can be increased and adjusted in the C field is as follows: n is a radical ofc1i＝Uc1/N*Mi
The maximum area of the C-type land which can be reduced and adjusted is as follows: n is a radical ofc2i＝Uc2/N*Mi
5) Area adjustment
If any two land types of A, B, C land types exist in the adjacent small image spots, the area values adjusted by the two land types are both Min { N }ai,Nbi,Nci}；
If all three types of land exist, A, B, C land adjusted areas are shown in the following table:
TABLE 4
Wherein, when the adjacent pattern spots have two or more A types (such as type A)1Class A of land2) Then A is1The area to be adjusted is the local A land type adjustment area and A1The area of the land classes adjusts the product of the threshold occupancy. The treatment of the B land and the C land is the same.
6) Iterative adjustment
Adjusting each small image spot according to the step 5, correspondingly adding or subtracting the adjusted Area and the pre-divided Area to obtain the Area of the adjacent adjusted image spots subdividing the small image spotsi. And judging whether the product after adjustment is within the threshold range through the global area statistics, and continuously iterating and adjusting until all the land types meet the conditions. The algorithm of the invention sets the maximum iteration number to 10000 times, namely, the maximum 10000 times of iteration process can be executed.
S304 determines whether each of the change rates is lower than a second predetermined threshold, and if not, executes S305, and if so, executes S306.
S305, when the change rate of each change rate is higher than a second specified threshold, recording the difference value between all the land categories and the second specified threshold, and performing iterative processing on the area pre-classification result according to an area balance iterative algorithm until each change rate is lower than the second specified threshold.
S306, when the change rates are lower than a second specified threshold value, determining an internal skeleton line of each small map spot, and respectively splitting each small map spot into a plurality of fragments according to the skeleton lines; and merging the patches into neighboring patches such that the small patches are thawed to form merged full-coverage patch data.
The S306 includes:
d1, determining adjacent image spots for forming subdivision on the small image spots according to a Delaunay triangulation method;
d2, respectively carrying out pairwise calculation on the small pattern spots and the adjacent pattern spots to obtain split points; wherein, the calculation formula for obtaining the split point is as follows:
wherein a represents the small spot, b represents a neighboring spot, c represents another neighboring spot, (x)b,yb)、(xc,yc) Respectively representing the coordinates of two end points of an edge in a triangular mesh, wherein xbAnd xcDenotes the abscissa, ybAnd ycRepresents the ordinate; area (a, b) and Area (a, c) are values of the division areas of the small spots a by the adjacent spots b and c, respectively.
D3, generating a splitting line according to the splitting points, splitting the small image spots into a plurality of fragments according to the splitting line, merging the fragments into the image spots adjacent to the small image spots, so that the small image spots are fused to form merged full-coverage image spot data.
Example two
Further, in order to better explain the technical scheme of the application, the applicant also carries out the following experiments, and by relying on a WJ-III map workstation developed by the Chinese surveying and mapping scientific research institute, the small-pattern-spot fusion method which is provided by the invention based on the constrained Delaunay triangulation network and gives consideration to the local optimum and the whole area balance is embedded, and the small-pattern-spot fusion operation of the geographical national condition data is realized by utilizing OpenMP in the C + + environment, so that the rationality and the effectiveness of the method are verified. The experiment takes the geographic national conditions of a certain county in Guangdong province as an example, the scale of the original data is 1:1 ten thousand, and 2604 small map spots need to be melted. The land feature types mainly comprise natural land features such as forest lands, cultivated lands, water bodies and the like, the natural land features account for 49.6%, 26.2% and 7.0% respectively, land features such as gardens, grasslands, house buildings, structures and the like are scattered, and the quantity of deserts and bare earth surfaces is rare and only accounts for 0.01%. Wherein, the target scale of simplification is 1:10 ten thousand. The experimental environment is a Microsoft Win 764-bit operating system, a CPU is an Intel Core I7-4790, a single machine 8-Core 8-thread, a main frequency of 3.2GHz, a memory of 16GB and a solid state disk of 1024 GB.
According to the technical requirements of the general survey result diagram of the geographical national conditions, if the scale of the map is larger than 1: and if the minimum upper graph area of each land category is 50 ten thousand, the minimum upper graph area is shown in the table 2, the minimum upper graph area is used as the judgment standard of the small image spots, and the judgment standards of the small image spots of other scales are finely adjusted by referring to the table 2.
TABLE 2 minimum upper graph area table
(1) Superiority verification
The method of the invention firstly performs space pre-classification on small pattern spots in a test area by using local optimal constraint, and counts the pre-classified area, and the area change before and after the pre-classification of each local category space is shown in table 3.
TABLE 3 area statistics of spot area ratio of first-class map before and after area pre-division (unit: km)2)
Because the standard of the change range of the land areas before and after the small image spots are thawed is lacked, the experiment sets the threshold value of the change rate of the land areas before and after the small image spots are thawed to be 5% according to the characteristics of relevant documents (Liu Yan Lin, etc., 2009) and data of the experimental area. As can be seen from Table 3, the area change rates of the grassland and the artificial structure land types exceed the threshold value by 5% only by using the local optimal method to perform small-pattern melting. It shows that the balance of the whole ground area can not be ensured by only utilizing the local optimal algorithm. According to the algorithm of the invention, the pre-divided area needs to be iteratively adjusted.
After the experiment is subjected to 108 times of iterative adjustment, the area change rate of all the land types after the small image spots are melted is ensured to be smaller than a threshold value. According to the invention, the adjustment values of the change rate of each land class are counted when the algorithm is iterated for 10 th, 20 th, 30 th, 40 th, 50 th, 60 th, 70 th, 80 th, 90 th, 100 th and 108 th times, the statistical analysis is carried out on the area change rate of each land class after each iteration adjustment, and the results are respectively shown in fig. 4 and fig. 5.
Fig. 4 is a schematic diagram illustrating a statistical adjustment value of the change rate of the area types according to the embodiment of the present invention.
Fig. 5 is a schematic diagram of iterative adjustment of a small patch fusion area considering global optimization according to an embodiment of the present invention.
As can be seen from fig. 4 and 5, the area of each category adjustment gradually decreases as the number of iterations increases. For each iterative adjustment, the adjustment values of the grassland and the artificial structure are positive values, the negative values are most cultivated land and forest land, and building construction and land piling are carried out, but the reduction range is small, so that the adjustment values are not obvious in the figure. Each iteration of each land is a process of converging to a threshold value until the area change rate of all the land is smaller than a set threshold value after the adjustment is finished. The feasibility of the method is fully proved, and the superiority of the method for guaranteeing the global area balance is ensured. The reliability verification of the method is combined, so that the method can maintain the balance of the global area while ensuring the local space and the semantic characteristics, and the feasibility of the method is proved.
(2) Reliability verification
In order to verify the reliability of the method, namely the melting result can still keep local optimum, the actual small image spot is selected in the experimental area, and the adjusted melting result is compared with the preliminary pre-classified melting result according to the local optimum. And selecting the grasslands and the structures with the area change rate exceeding a threshold value as test land types.
Are each represented by xi、yiRepresenting the initial subdivision area of the lawn or structure pattern spot and the subdivision area adjusted by the algorithm of the invention, calculating the difference (x) of the twoi-yi) That is, the adjustment area of each effective patch, wherein i represents the number of all the adjusted patches of the land category, and the statistical curve result of the adjustment area of each patch is shown in fig. 6 (because the number of the adjusted patches is large, the invention only selects the patch with the adjustment change area located at the top 100 for statistics).
FIG. 6 is a statistical chart of the spot adjustment areas of the grass and artificial structures according to the embodiment of the present invention.
As can be seen from FIG. 6, the maximum value of the lawn pattern patch adjustment area is 283.3m2The maximum adjustment area of the structure pattern spot is 112.6m2The adjusted products have no significant change from the initial pre-distribution. In the adjusted spots of the grassland and the structures, the first three spots (within the ellipse) with the largest adjustment area are respectively selected, and the adjusted melting result is compared with the initial subdivision result, as shown in fig. 6. FIG. 7 is a diagram in which the rectangle A in FIG. 7 shows the result of the fusion of small patches of land use overlay data using the algorithm of the present invention, the rectangle B1, B2 and B3 show three patches with a larger grass adjustment area, and the rectangle C1, C2 and C3 show three patches with a larger adjustment area of the artificial structure; wherein the solid line shows the melting splitting line of the small image spot by using the algorithm of the invention, and the dotted line shows the melting splitting line of the small image spot only by using the local optimum extraction.
Fig. 7 is a schematic diagram of a small-pattern-spot fusion result of land use paving data according to an embodiment of the present invention.
FIG. 8 is a schematic diagram illustrating a comparison of the melted areas of small pattern spots to surrounding ground pattern spots according to an embodiment of the present invention.
Comparing and counting the initial melting area of each small map spot towards the periphery in the rectangular frames B1, B2, B3, C1, C2 and C3 with the melting area of each adjusted individual land map spot, wherein the result is shown in FIG. 8, wherein the columns filled with dots represent the initial subdivision area of the small map spot towards the periphery according to local optimum, and the columns filled with blanks represent the subdivision area adjusted according to the method of the present invention.
As can be seen from fig. 7 and 8, compared with the result obtained by considering only the local optimum, the small-spot fusion result iteratively adjusted by the method of the present invention has a small difference in the subdivision capability of the surrounding spots on the small spot, regardless of the spatial feature or the specific area difference, and the two results maintain high consistency in the local feature, which fully proves that the method of the present invention can still maintain the reliability of the local spatial proximity and the semantic distance feature.
In summary, the method for thawing the image spots provided by the present application can maintain the balance of the overall ground area before and after the thawing while considering the optimal local spatial pattern of the image spots during the thawing process. Is favorable for better keeping the consistency of the front and the back ground types of the pattern spots.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. A small image spot melting method considering local optimization and integral area balance is characterized by comprising the following steps:
A. acquiring full-coverage vector pattern spot data of an image to be processed; acquiring data information of adjacent pattern spots according to the data; wherein, the data information of the adjacent pattern spots at least comprises one of the following data information: the area of the adjacent pattern spot, the length of the shared edge and the semantic distance;
B. according to the data information of the adjacent image spots, carrying out area pre-allocation on the small image spots in the image to obtain the subdivision area of each adjacent image spot on the small image spot; wherein, the image spot with the area smaller than the first designated threshold value is taken as a small image spot;
C. counting the area of the first-level land class of each small map spot after area pre-allocation, and calculating each change rate of each land class area before and after the pre-allocation;
D. when the change rates are lower than a second specified threshold value, determining an internal skeleton line of each small image spot, and respectively splitting each small image spot into a plurality of fragments according to the skeleton lines; and merging the patches into patches adjacent thereto so that the small patches are thawed to form merged full-coverage patch data.
2. The method of claim 1, wherein step C further comprises:
and when the change rate of each change rate is higher than a second specified threshold value, recording the difference value between all the land categories and the second specified threshold value, and performing iterative processing on the area pre-classification result according to an area balance iterative algorithm until each change rate is lower than the second specified threshold value.
3. The method of claim 1, wherein the step a of obtaining the computational model of the area of the adjacent patches is:
wherein i is the clockwise number of each node of the adjacent polygon, xi is the abscissa of each node of the adjacent polygon, and yi +1 and yi-1 are the ordinates of each node of the adjacent polygon.
4. The method according to claim 1, wherein the step a of obtaining the calculation model of the shared edge length is:
wherein xi and yi are respectively the abscissa and the ordinate of an end point of the shared edge; xi +1 and yi +1 represent the abscissa and ordinate, respectively, of the other end point of the shared edge.
5. The method of claim 1, wherein step B comprises:
b1, obtaining the subdivision capability of each adjacent image spot of each small image spot on the subdivision area for obtaining the small image spot;
and B2, acquiring the subdivision area of each adjacent image spot to the small image spot according to the proportion of the subdivision capacity.
6. The method of claim 5, wherein the subdivision capability calculation model of step B1 is:
wherein, i in Si is 1,2,3, S1, S2, S3 respectively represent three constraint indexes of adjacent patch area, shared edge length, semantic distance, and wi is the weight of each index; where a represents the small spot and bi represents the ith neighboring spot.
7. The method according to claim 6, wherein the step B2 is to obtain a calculation model of the subdivision area of each adjacent plaque to the small plaque according to the ratio of the subdivision ability, wherein the calculation model comprises:
Areai＝SAF(a，bi)/SAF(a，b)*Area
wherein, the area is the subdivision area of the ith adjacent image spot to the small image spot; SAF (a, b) is the sum of all adjacent plaque dissection capabilities; area is the Area of the small pattern spot; SAF (a, bi) shows the subdivision capability of the ith adjacent pattern spot for acquiring the subdivision area of the small pattern spot.
8. The method of claim 1, wherein step D comprises:
d1, determining adjacent image spots for forming subdivision on the small image spots according to a Delaunay triangulation method;
d2, respectively carrying out pairwise calculation on the small pattern spots and the adjacent pattern spots to obtain split points;
d3, generating a splitting line according to the splitting points, splitting the small image spots into a plurality of fragments according to the splitting line, merging the fragments with adjacent image spots so that the small image spots are fused to form merged full-coverage image spot data.
9. The method of claim 8, wherein the calculation formula for obtaining the split point is:
wherein a represents the small patch, b represents a neighboring patch, c represents another neighboring patch, (xb, yb), (xc, yc) respectively represent coordinates of two end points of the side in the triangulation network, wherein xb and xc represent abscissa, yb and yc represent ordinate; area (a, b) and Area (a, c) are values of the division areas of the small spots a by the adjacent spots b and c, respectively.
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