CN108280880A - A method of improving the digital elevation data resolution of massif using remote sensing image - Google Patents
A method of improving the digital elevation data resolution of massif using remote sensing image Download PDFInfo
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Abstract
The present invention provides a kind of method for the digital elevation data resolution improving massif using remote sensing image, the method that the digital elevation data resolution of massif is improved using remote sensing image can utilize the landform marginal information in massif remote sensing image to refine the digital elevation information that the lower digital elevation data of original resolution ratio obtain high-resolution by this method.This method need not additionally can fly and survey and draw, and only use low resolution digital elevation data and remote sensing image data to obtain the digital elevation data of massif high-resolution, meet the needs of mountainous region's natural environment three-dimensional modeling.
Description
Technical field
The present invention discloses a kind of method for the digital elevation data resolution improving massif using remote sensing image, utilizes massif
Landform marginal information in remote sensing image refines the number that the lower digital elevation data of original resolution ratio obtain high-resolution
Elevation information belongs to Geoprocessing field.
Background technology
It is both needed to during the virtual reality applications such as Scene Teaching, history environment reproduction, film and electronic game making
Establish the threedimensional model of a wide range of natural environment.For mountainous region, massif three-dimensional structure quality for entire work
Cheng Jianmo mass has crucial effect.And it directly can quickly establish the three-dimensional of specific massif using digital elevation data and tie
Structure, the altitude data that spatial resolution is higher, sampled point is more careful have highly important in terms of massif three dimensional type modeling
Use value.
In the application and research of the three-dimensional modeling of massif, main three established using digital elevation data in three dimensions
Edged surface, and pass through triangle surface construction massif three-dimensional structure.There are two types of the acquisition channels of Contemporary Digital altitude data：One is direct
Digital elevation data are obtained by downloading, buying.Since mountain area is typically more remote, so the data that can be directly obtained are differentiated
Rate is usually 30 meters even high-resolution data of 100 meters of resolution ratio, and the three-dimensional mountain model of these data structure has the three of bulk
Edged surface is constituted, the excessively coarse demand that cannot be satisfied current virtual reality；Second is that directly mounting is corresponding sharp aboard
Light mapping equipment directly obtains high-resolution digital elevation data, the higher energy of such data resolution in desired zone flight
Enough meet the needs of virtual reality, however funds in need are very huge (it is generally necessary to millions of members), most units can not be born
The expense.
Therefore a kind of method is needed, can on the one hand makes full use of the lower digital elevation data of existing resolution ratio, it is another
Aspect can utilize some auxiliary datas to improve the resolution ratio of digital elevation data to a certain extent, obtain better massif three
Dimension modeling effect.
Invention content：
In view of the problems of the existing technology, the present invention provides a kind of digital elevation number improving massif using remote sensing image
According to the method for resolution ratio, can be utilized by this method the landform marginal information in massif remote sensing image refine original resolution ratio compared with
Low digital elevation data obtain the digital elevation information of high-resolution.
A kind of method of digital elevation data resolution improving massif using remote sensing image of the present invention, including with
Lower step：
S1 inputs the digital elevation data DEM of low resolution, inputs the resolution ratio M of massif high-resolution altitude data,
Input generates the abscissa X and ordinate Y of data, lateral length W and longitudinal length H；Construct massif three-dimensional altitude datum net
BNET：
The default resolution of digital elevation data DEM is 30 meters；
The default value of the resolution ratio M of body high-resolution altitude data is 1 meter；
S101, massif high-resolution altitude data transverse direction element number WNUM=Round (W/M+0.5)；
S102, massif high-resolution altitude data longitudinal direction element number HNUM=Round (H/M+0.5)；
Wherein, Round is to round up；
S103 establishes BNET, and BNET is a WNUM row HNUM row, each element is a benchmark architecture body
BStruct；
BStruct includes following field contents, and the initial value of all fields is 0；
Abscissa initial value where BX1 is 0；
Ordinate initial value where BY1 is 0；
BValue altitude datum value initial values are 0；
The number initial value of BID digital elevation points is 0；
TID agglomerate relationship number initial values are 0；
LYZD neighborhood minimum initial values are 0；
LYZG neighborhood peak initial values are 0；
It is 0 that CValue, which reconstructs height value initial value,；
S2, according to the value of all BStruct in the three-dimensional altitude datum net BNET of digital elevation data DEM initialization：
S201, the element B Point, BPoint taken out in BNET is the structure of a BStruct type；
S202, BPoint behavior HH where in BNET, place is classified as LL；
S203 calculates place abscissa BPoint.X1=X+LL × M of BPoint, place ordinate BPoint.Y1=Y+
HH ×M；
S204 finds one with (BPoint.X1, BPoint.Y1) apart from nearest point in DEM, takes out point number
ID its height above sea level Elevation, are arranged BPoint.BID=ID, BPoint.BValue=Elevation in BPoint；
Otherwise S205 goes to S201 if all data are taken and so go to S206 in BNET；
S206, agglomerate counter tcounter=1；
S207 takes out the element B Point2 that a TID is 0 in BNET；
The agglomerate number BPoint2.TID=tcounter of BPoint2 is arranged in S208；
It is arranged for the number BID elements identical with the BID of BPoint2 of all digital elevation points in BNET in S209
The value of TID is tcounter；
S210, tcounter=tcounter+1；
S211, if going to S207 there are the element that TID is 0 in BNET, otherwise going to S212；
S212, agglomerate maximum number value MAXTID=tcounter-1；
S3, according to the agglomerate relationship code T ID of all elements in BNET, the neighborhood for calculating each element in BNET is minimum
Value, neighborhood peak：
S301, number counter ITID=1；
S302, all elements for taking out TID=ITID in BNET are put into list PLIST；
S303 takes out TID ≠ ITID and the element adjacent with any one element in PLIST in BNET, is put into NLIST
In；
S304 counts element height value BValue all in NLIST, obtains maximum value MaxBValue and minimum value
MinBValue；
S305, for all elements in list PLIST, neighborhood minimum LYZD=MinBValue, neighborhood peak
LYZG=MaxBValue；
S306, ITID=ITID+1；
Otherwise S307 goes to S302 if ITID so goes to S308 more than MAXTID；
S308, the step process terminate；
S4, input remote sensing image Image calculate image edge gradient array TDArray：
S401 obtains the line number Rows and columns Cols of remote sensing image Image；
S402 establishes line number as Rows and columns Cols edge gradient array TDArray, each element is first in array
Initial value is 1.0；
S403 establishes the normalization array NormArray that line number is Rows and columns Cols；
S404, for each pixel pixel in Image, the behavior HSS where the pixel, place is classified as LSS, meter
Calculate all band values of the pixel and pixelsum, pixelsum is stored to the behavior HSS of NormArray to the member for being classified as LSS
In element；
S405 normalizes between section [0,1] all elements in NormArray；
S406 takes out in the element storage to element variable NPixel in NormArray；
S407, the row NHSS where acquisition NPixel and column NLSS；
S408, if NHSS>=2 and NHSS<(Rows-2) S409 is gone to, S413 is otherwise gone to；
S409, if NLSS>=2 and NLSS<(Cols-2) S410 is gone to, S413 is otherwise gone to；
S410, the value of setting variable Z1, Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9, their value are as follows：
The NHSS-1 rows of Z1=NormArray, the value of the element of NLSS-1 row；
The NHSS-1 rows of Z2=NormArray, the value of the element of NLSS row；
The NHSS-1 rows of Z3=NormArray, the value of the element of NLSS+1 row；
The NHSS rows of Z4=NormArray, the value of the element of NLSS-1 row；
The NHSS rows of Z5=NormArray, the value of the element of NLSS row；
The NHSS rows of Z6=NormArray, the value of the element of NLSS+1 row；
The NHSS+1 rows of Z7=NormArray, the value of the element of NLSS-1 row；
The NHSS+1 rows of Z8=NormArray, the value of the element of NLSS row；
The NHSS+1 rows of Z9=NormArray, the value of the element of NLSS+1 row；
S411, calculates the value of edge gradient operator bytd, and formula is as follows：
Temp1=tanh ((Z7+2 × Z8+Z9)-(Z1+2 × Z2+Z3))；
Temp2=tanh ((Z3+2 × Z6+Z9)-(Z1+2 × Z4+Z7))；
Wherein, tanh is hyperbolic tangent function；
S412, among the element of the NHSS rows NLSS row of bytd storages to TDArray；
S413 goes to S406 if still having data untreated in NormArray, otherwise goes to S414；
S414, this step process terminate；
S5, it is every in conjunction with remote sensing image Image and edge gradient array TDArray adjustment massif three-dimensional altitude datum nets BNET
The reconstruct height value CValue of one element：
S501 takes out an element B Point in BNET；
S502, take out Image in position in abscissa between BPoint.X1-M/2 and BPoint.X1+M/2, ordinate
All pixels between BPoint.Y1-M/2 and BPoint.Y1+M/2, are put into Bpixellist；
S503 takes out the array element identical with all pixel row and columns position in Bpixellist in TDArray, puts
Enter in TDList；
S504, the mean value for calculating all elements in TDList are put into mean value variable avg；
S505, sets the reconstruct height value Cvalue of BPoint, and formula is as follows：
BPoint.Cvalue=BPoint.Bvalue+ (avg-1) × (BPoint.LYZG-BPoint.LYZD)
Otherwise S506 goes to S501 if all elements are disposed and so go to S507 in BNET；
S507, the processing of this process terminate；
S6, according to massif three-dimensional elevation reference net BNET, massif high-resolution digital elevation data DEM2：
S601 establishes empty high-resolution digital elevation data DEM2；
S602, number counter IDCounter=0；
S603 takes out an element B Point in BNET；
S604, by number counter IDCounter, place abscissa BPoint.BX1, place ordinate BPoint.BY1,
Reconstruct height value BPoint.CValue is added among DEM2；
S605, IDCounter=IDCounter+1；
Otherwise S606 goes to S603 if all elements are disposed and so go to S607 in BNET；
S607, the processing of this process terminate.
Beneficial effects of the present invention are as follows：The method that the digital elevation data resolution of massif is improved using remote sensing image,
The landform marginal information in massif remote sensing image can be utilized to refine original lower digital elevation number of resolution ratio by this method
According to the digital elevation information for obtaining high-resolution.This method need not additionally can fly and survey and draw, only with low
Resolution digital altitude data and remote sensing image data obtain the digital elevation data of massif high-resolution, meet mountainous region
The needs of natural environment three-dimensional modeling.
Description of the drawings
Fig. 1, overview flow chart of the present invention；
Fig. 2, the corresponding figure of step 1 of the present invention；
Fig. 3, the corresponding figure of step 2 of the present invention；
Fig. 4, the corresponding figure of step 3 of the present invention；
Fig. 5, the corresponding figure of step 4 of the present invention；
Fig. 6, the corresponding figure of step 5 of the present invention；
Fig. 7, the corresponding figure of step 6 of the present invention；
One in 16 peak of Changbai Mountain for the digital elevation data configuration that the original resolution ratio of Fig. 8, embodiment 1 is 30 meters
The slope view on mountain peak；
The three-dimensional scenic reproduction figure that Fig. 9, the processing of 1 the method for the present invention of embodiment export later；
The three grotesque peak slope of Changbai Mountain for the digital elevation data configuration that Figure 10, embodiment 2 are 30 meters based on original resolution ratio
Face figure；
The three-dimensional scenic reproduction figure that Figure 11, the processing of 2 the method for the present invention of embodiment export later.
Specific implementation mode
By following embodiment further illustrate description the present invention, do not limit the invention in any way, without departing substantially from
Under the premise of technical solution of the invention, easy to implement any of those of ordinary skill in the art made for the present invention changes
Dynamic or change is fallen within scope of the presently claimed invention.
Embodiment 1
It is a mountain peak in 16 peak of Changbai Mountain of 30 meters of digital elevation data configuration based on original resolution ratio
Slope surface is as shown in Figure 8：
It can be seen that whole be made of larger triangular facet, the more coarse needs for being difficult to meet scene reproduction.
The method that the present invention improves the digital elevation data resolution of massif using remote sensing image, includes the following steps：
S1 inputs the digital elevation data DEM of low resolution, inputs the resolution ratio M of massif high-resolution altitude data,
Input generates the abscissa X and ordinate Y of data, lateral length W and longitudinal length H.Construct massif three-dimensional altitude datum net
BNET；
S2, according to the value of all BStruct in the three-dimensional altitude datum net BNET of digital elevation data DEM initialization；
S3, according to the agglomerate relationship code T ID of all elements in BNET, the neighborhood for calculating each element in BNET is minimum
Value, neighborhood peak；
S4, input remote sensing image Image calculate image edge gradient array TDArray；
S5, it is every in conjunction with remote sensing image Image and edge gradient array TDArray adjustment massif three-dimensional altitude datum nets BNET
The reconstruct height value CValue of one element；
S6, according to massif three-dimensional elevation reference net BNET, massif high-resolution digital elevation data DEM2；
After the method for the present invention processing, the results are shown in Figure 9 for output：
It can be seen that the more apparent needs that can reach three-dimensional scenic reproduction of the structure on mountain peak.
Embodiment 2：
It is as shown in Figure 10 for the three grotesque peak slope surface of Changbai Mountain of 30 meters of digital elevation data configuration based on original resolution ratio：
It can be seen that whole be made of larger triangular facet, the more coarse needs for being difficult to meet scene reproduction.
The method that the present invention improves the digital elevation data resolution of massif using remote sensing image, includes the following steps：
S1 inputs the digital elevation data DEM of low resolution, inputs the resolution ratio M of massif high-resolution altitude data,
Input generates the abscissa X and ordinate Y of data, lateral length W and longitudinal length H；Construct massif three-dimensional altitude datum net
BNET；
S2, according to the value of all BStruct in the three-dimensional altitude datum net BNET of digital elevation data DEM initialization；
S3, according to the agglomerate relationship code T ID of all elements in BNET, the neighborhood for calculating each element in BNET is minimum
Value, neighborhood peak；
S4, input remote sensing image Image calculate image edge gradient array TDArray
S5, it is every in conjunction with remote sensing image Image and edge gradient array TDArray adjustment massif three-dimensional altitude datum nets BNET
The reconstruct height value CValue of one element；
S6, according to massif three-dimensional elevation reference net BNET, massif high-resolution digital elevation data DEM2；
After the method for the present invention processing, the result of output is as shown in figure 11：
It can be seen that the more apparent needs that can reach three-dimensional scenic reproduction of the structure on mountain peak.
Claims (1)
1. a kind of method for the digital elevation data resolution being improved massif using remote sensing image, is included the following steps：
S1 inputs the digital elevation data DEM of low resolution, inputs the resolution ratio M of massif high-resolution altitude data, input
Generate the abscissa X and ordinate Y of data, lateral length W and longitudinal length H；Construct massif three-dimensional altitude datum net BNET：
S101, massif high-resolution altitude data transverse direction element number WNUM=Round (W/M+0.5)；
S102, massif high-resolution altitude data longitudinal direction element number HNUM=Round (H/M+0.5)；
Wherein, Round is to round up；
S103 establishes BNET, and BNET is a WNUM row HNUM row, each element is a benchmark architecture body BStruct；
BStruct includes following field contents, and the initial value of all fields is 0；
Abscissa initial value where BX1 is 0；
Ordinate initial value where BY1 is 0；
BValue altitude datum value initial values are 0；
The number initial value of BID digital elevation points is 0；
TID agglomerate relationship number initial values are 0；
LYZD neighborhood minimum initial values are 0；
LYZG neighborhood peak initial values are 0；
It is 0 that CValue, which reconstructs height value initial value,；
S2, according to the value of all BStruct in the three-dimensional altitude datum net BNET of digital elevation data DEM initialization：
S201, the element B Point, BPoint taken out in BNET is the structure of a BStruct type；
S202, BPoint behavior HH where in BNET, place is classified as LL；
S203, calculates place abscissa BPoint.X1=X+LL × M of BPoint, and place ordinate BPoint.Y1=Y+HH ×
M；
S204, found in DEM one with (BPoint.X1, BPoint.Y1) apart from nearest point, take out point number ID its
BPoint.BID=ID, BPoint.BValue=Elevation is arranged in height above sea level Elevation in BPoint；
Otherwise S205 goes to S201 if all data are taken and so go to S206 in BNET；
S206, agglomerate counter tcounter=1；
S207 takes out the element B Point2 that a TID is 0 in BNET；
The agglomerate number BPoint2.TID=tcounter of BPoint2 is arranged in S208；
Its TID is arranged for the number BID elements identical with the BID of BPoint2 of all digital elevation points in BNET in S209
Value be tcounter；
S210, tcounter=tcounter+1；
S211, if going to S207 there are the element that TID is 0 in BNET, otherwise going to S212；
S212, agglomerate maximum number value MAXTID=tcounter-1；
S3, according to the agglomerate relationship code T ID of all elements in BNET, calculate the neighborhood minimum of each element in BNET,
Neighborhood peak：
S301, number counter ITID=1；
S302, all elements for taking out TID=ITID in BNET are put into list PLIST；
S303 takes out TID ≠ ITID and the element adjacent with any one element in PLIST in BNET, is put into NLIST；
S304 counts element height value BValue all in NLIST, obtains maximum value MaxBValue and minimum value
MinBValue；
S305, for all elements in list PLIST, neighborhood minimum LYZD=MinBValue, neighborhood peak LYZG
=MaxBValue；
S306, ITID=ITID+1；
Otherwise S307 goes to S302 if ITID so goes to S308 more than MAXTID；
S308, the step process terminate；
S4, input remote sensing image Image calculate image edge gradient array TDArray：
S401 obtains the line number Rows and columns Cols of remote sensing image Image；
S402, it is Rows and columns Cols edge gradient array TDArray to establish line number, the initial value of each element in array
It is 1.0；
S403 establishes the normalization array NormArray that line number is Rows and columns Cols；
S404, for each pixel pixel in Image, the behavior HSS where the pixel, place is classified as LSS, and calculating should
All band values of pixel and pixelsum, the behavior HSS of pixelsum storage to NormArray are classified as the element of LSS and worked as
In；
S405 normalizes between section [0,1] all elements in NormArray；
S406 takes out in the element storage to element variable NPixel in NormArray；
S407, the row NHSS where acquisition NPixel and column NLSS；
S408, if NHSS>=2 and NHSS<(Rows-2) S409 is gone to, S413 is otherwise gone to；
S409, if NLSS>=2 and NLSS<(Cols-2) S410 is gone to, S413 is otherwise gone to；
S410, the value of setting variable Z1, Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9, their value are as follows：
The NHSS-1 rows of Z1=NormArray, the value of the element of NLSS-1 row；
The NHSS-1 rows of Z2=NormArray, the value of the element of NLSS row；
The NHSS-1 rows of Z3=NormArray, the value of the element of NLSS+1 row；
The NHSS rows of Z4=NormArray, the value of the element of NLSS-1 row；
The NHSS rows of Z5=NormArray, the value of the element of NLSS row；
The NHSS rows of Z6=NormArray, the value of the element of NLSS+1 row；
The NHSS+1 rows of Z7=NormArray, the value of the element of NLSS-1 row；
The NHSS+1 rows of Z8=NormArray, the value of the element of NLSS row；
The NHSS+1 rows of Z9=NormArray, the value of the element of NLSS+1 row；
S411, calculates the value of edge gradient operator bytd, and formula is as follows：
Temp1=tanh ((Z7+2 × Z8+Z9)-(Z1+2 × Z2+Z3))；
Temp2=tanh ((Z3+2 × Z6+Z9)-(Z1+2 × Z4+Z7))；
Wherein, tanh is hyperbolic tangent function；
S412, among the element of the NHSS rows NLSS row of bytd storages to TDArray；
S413 goes to S406 if still having data untreated in NormArray, otherwise goes to S414；
S414, this step process terminate；
S5, in conjunction with remote sensing image Image and edge gradient array TDArray adjustment massif three-dimensional altitude datum nets BNET each
The reconstruct height value CValue of element：
S501 takes out an element B Point in BNET；
S502, taking out position in Image, in abscissa between BPoint.X1-M/2 and BPoint.X1+M/2, ordinate exists
All pixels between BPoint.Y1-M/2 and BPoint.Y1+M/2, are put into Bpixellist；
S503 takes out the array element identical with all pixel row and columns position in Bpixellist in TDArray, is put into
In TDList；
S504, the mean value for calculating all elements in TDList are put into mean value variable avg；
S505, sets the reconstruct height value Cvalue of BPoint, and formula is as follows：
BPoint.Cvalue=BPoint.Bvalue+ (avg-1) × (BPoint.LYZG-BPoint.LYZD)
Otherwise S506 goes to S501 if all elements are disposed and so go to S507 in BNET；
S507, the processing of this process terminate；
S6, according to massif three-dimensional elevation reference net BNET, massif high-resolution digital elevation data DEM2：
S601 establishes empty high-resolution digital elevation data DEM2；
S602, number counter IDCounter=0；
S603 takes out an element B Point in BNET；
S604, by number counter IDCounter, place abscissa BPoint.BX1, place ordinate BPoint.BY1, reconstruct
Height value BPoint.CValue is added among DEM2；
S605, IDCounter=IDCounter+1；
Otherwise S606 goes to S603 if all elements are disposed and so go to S607 in BNET；
S607, the processing of this process terminate.
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| CN112348951A (en) * | 2020-11-30 | 2021-02-09 | 长春工程学院 | Digital elevation data reconstruction method for heterogeneous remote sensing image content |
| CN114677485A (en) * | 2022-04-07 | 2022-06-28 | 辽宁大学 | Damaged mountain modeling, stability and design integrated analysis method based on remote sensing and GIS |
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| WO2013121340A1 (en) * | 2012-02-13 | 2013-08-22 | Stellenbosch University | Digital elevation model |
| ZA201404917B (en) * | 2012-02-13 | 2015-09-30 | Univ Stellenbosch | Digital elevation model |
| CN103675790A (en) * | 2013-12-23 | 2014-03-26 | 中国国土资源航空物探遥感中心 | Method for improving earth surface shape change monitoring precision of InSAR (Interferometric Synthetic Aperture Radar) technology based on high-precision DEM (Digital Elevation Model) |
| CN106570936A (en) * | 2016-11-14 | 2017-04-19 | 河海大学 | Grid DEM (digital elevation model) data-based equidistant weight interpolation encryption method |
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| CN112348951A (en) * | 2020-11-30 | 2021-02-09 | 长春工程学院 | Digital elevation data reconstruction method for heterogeneous remote sensing image content |
| CN112348951B (en) * | 2020-11-30 | 2022-08-26 | 长春工程学院 | Digital elevation data reconstruction method for heterogeneous remote sensing image content |
| CN114677485A (en) * | 2022-04-07 | 2022-06-28 | 辽宁大学 | Damaged mountain modeling, stability and design integrated analysis method based on remote sensing and GIS |
| CN114677485B (en) * | 2022-04-07 | 2024-05-07 | 辽宁大学 | Damaged mountain modeling, stability and design integrated analysis method based on remote sensing and GIS |
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