CN108280880B - 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
- Publication number
- CN108280880B CN108280880B CN201810067398.9A CN201810067398A CN108280880B CN 108280880 B CN108280880 B CN 108280880B CN 201810067398 A CN201810067398 A CN 201810067398A CN 108280880 B CN108280880 B CN 108280880B
- Authority
- CN
- China
- Prior art keywords
- value
- bpoint
- bnet
- massif
- normarray
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/04—Indexing scheme for image data processing or generation, in general involving 3D image data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
Abstract
The present invention provides a kind of method of digital elevation data resolution that massif is improved using remote sensing image, the method that the digital elevation data resolution of massif is improved using remote sensing image can use the landform marginal information in massif remote sensing image by this method and refine the digital elevation information that the lower digital elevation data of original resolution ratio obtain high-resolution.This method can only obtain the digital elevation data of massif high-resolution with low resolution digital elevation data and remote sensing image data, meet the needs of mountainous region's natural environment three-dimensional modeling when not needing additionally to fly and survey and draw.
Description
Technical field
A kind of method that the present invention discloses digital elevation data resolution that massif is improved 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 technique
It is both needed to during the virtual reality applications such as Scene Teaching, history environment reproduction, film and electronic game production
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 the three-dimensional of specific massif directly can be quickly established using digital elevation data and is tied
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-dimensional space
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, these data, which construct three-dimensional mountain model, the three of bulk
Edged surface is constituted, the excessively coarse demand for being unable to satisfy 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 are unbearable
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 use the resolution ratio that some auxiliary datas improve digital elevation data to a certain extent, obtain better massif three
Dimension modeling effect.
Summary of the invention：
In view of the problems of the existing technology, the present invention provides a kind of digital elevation number that massif is improved using remote sensing image
According to the method for resolution ratio, by this method can use 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 the array of a WNUM column HNUM row, each element of the array is a base
Quasi- structure B Struct；
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 value is 0；
The number initial value of BID digital elevation point is 0；
TID agglomerate relationship number initial value is 0；
LYZD neighborhood minimum initial value is 0；
LYZG neighborhood peak initial value is 0；
It is 0 that CValue, which reconstructs height value initial value,；
S2 initializes the value of all BStruct in three-dimensional altitude datum net BNET according to digital elevation data DEM：
S201, the element B Point, BPoint taken out in BNET is the structural body of a BStruct type；
S202, BPoint behavior HH where in BNET, place is classified as LL；
S203 calculates place abscissa BPoint.BX1=X+LL × M of BPoint, place ordinate BPoint.BY1=
Y+HH×M；
S204 finds one with (BPoint.BX1, BPoint.BY1) apart from nearest point in DEM, takes out point volume
Number ID its height above sea level Elevation, is arranged BPoint.BID=ID, BPoint.BValue=in BPoint
Elevation；
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 relationship number BPoint2.TID=tcounter of BPoint2 is arranged in S208；
It is arranged for the number BID element identical with the BID of BPoint2 of digital elevation points all 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 is greater than MAXTID and so goes to S308；
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, establishing line number is 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 of Image pixel pixel, behavior HSS where the pixel, place is classified as LSS, counts
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, the element taken out in NormArray are stored into element variable NPixel；
S407 obtains row NHSS and column NLSS where NPixel；
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 sets variable Z1, Z2, Z3, and Z4, Z5, Z6, Z7, the value of Z8, Z9, their value is as follows：
The NHSS-1 row of Z1=NormArray, the value of the element of NLSS-1 column；
The NHSS-1 row of Z2=NormArray, the value of the element of NLSS column；
The NHSS-1 row of Z3=NormArray, the value of the element of NLSS+1 column；
The NHSS row of Z4=NormArray, the value of the element of NLSS-1 column；
The NHSS row of Z5=NormArray, the value of the element of NLSS column；
The NHSS row of Z6=NormArray, the value of the element of NLSS+1 column；
The NHSS+1 row of Z7=NormArray, the value of the element of NLSS-1 column；
The NHSS+1 row of Z8=NormArray, the value of the element of NLSS column；
The NHSS+1 row of Z9=NormArray, the value of the element of NLSS+1 column；
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 row NLSS column of bytd storage 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 net BNET
The reconstruct height value CValue of one element：
S501 takes out an element B Point in BNET；
Position is indulged and is sat in abscissa between BPoint.BX1-M/2 and BPoint.BX1+M/2 in S502, taking-up Image
All pixels being marked between BPoint.BY1-M/2 and BPoint.BY1+M/2, are put into Bpixellist；
S503 takes out the array element identical with pixel row and column positions all 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, processing terminate for this process；
S6 establishes massif high-resolution digital elevation data DEM2 according to massif three-dimensional elevation reference net BNET：
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, processing terminate for this process.
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,
It can use the landform marginal information in massif remote sensing image by this method and refine original lower digital elevation number of resolution ratio
According to the digital elevation information for obtaining high-resolution.This method can be when not needing additionally to 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.
Detailed description of the invention
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 exported after Fig. 9, the processing of 1 the method for the present invention of embodiment reproduces figure；
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 exported after Figure 11, the processing of 2 the method for the present invention of embodiment reproduces figure.
Specific embodiment
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 accomplish 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 biggish 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 initializes the value of all BStruct in three-dimensional altitude datum net BNET according to digital elevation data DEM；
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 net BNET
The reconstruct height value CValue of one element；
S6 establishes massif high-resolution digital elevation data DEM2 according to massif three-dimensional elevation reference net BNET；
After the method for the present invention processing, the result of output is as shown in Figure 9：
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 biggish 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 initializes the value of all BStruct in three-dimensional altitude datum net BNET according to digital elevation data DEM；
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 net BNET
The reconstruct height value CValue of one element；
S6 establishes massif high-resolution digital elevation data DEM2 according to massif three-dimensional elevation reference net BNET；
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 for 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 the array of a WNUM column HNUM row, each element of the array is a benchmark knot
Structure 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 value is 0；
The number initial value of BID digital elevation point is 0；
TID agglomerate relationship number initial value is 0；
LYZD neighborhood minimum initial value is 0；
LYZG neighborhood peak initial value is 0；
It is 0 that CValue, which reconstructs height value initial value,；
S2 initializes the value of all BStruct in three-dimensional altitude datum net BNET according to digital elevation data DEM：
S201, the element B Point, BPoint taken out in BNET is the structural body of a BStruct type；
S202, BPoint behavior HH where in BNET, place is classified as LL；
S203 calculates place abscissa BPoint.BX1=X+LL × M of BPoint, place ordinate BPoint.BY1=Y+HH
×M；
S204 finds one with (BPoint.BX1, BPoint.BY1) apart from nearest point in DEM, takes out point number ID
Its height above sea level Elevation, is 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 relationship number BPoint2.TID=tcounter of BPoint2 is arranged in S208；
Its TID is arranged for the number BID element identical with the BID of BPoint2 of digital elevation points all 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 is greater than MAXTID and so goes to S308；
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, establishing line number is Rows and columns Cols edge gradient array TDArray, 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 of Image pixel pixel, 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, the element taken out in NormArray are stored into element variable NPixel；
S407 obtains row NHSS and column NLSS where NPixel；
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 sets variable Z1, Z2, Z3, and Z4, Z5, Z6, Z7, the value of Z8, Z9, their value is as follows：
The NHSS-1 row of Z1=NormArray, the value of the element of NLSS-1 column；
The NHSS-1 row of Z2=NormArray, the value of the element of NLSS column；
The NHSS-1 row of Z3=NormArray, the value of the element of NLSS+1 column；
The NHSS row of Z4=NormArray, the value of the element of NLSS-1 column；
The NHSS row of Z5=NormArray, the value of the element of NLSS column；
The NHSS row of Z6=NormArray, the value of the element of NLSS+1 column；
The NHSS+1 row of Z7=NormArray, the value of the element of NLSS-1 column；
The NHSS+1 row of Z8=NormArray, the value of the element of NLSS column；
The NHSS+1 row of Z9=NormArray, the value of the element of NLSS+1 column；
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 row NLSS column of bytd storage 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 net 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.BX1-M/2 and BPoint.BX1+M/2, ordinate exists
All pixels between BPoint.BY1-M/2 and BPoint.BY1+M/2, are put into Bpixellist；
S503 takes out the array element identical with pixel row and column positions all 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, processing terminate for this process；
S6 establishes massif high-resolution digital elevation data DEM2 according to massif three-dimensional elevation reference net BNET：
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, processing terminate for this process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810067398.9A CN108280880B (en) | 2018-01-24 | 2018-01-24 | A method of improving the digital elevation data resolution of massif using remote sensing image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810067398.9A CN108280880B (en) | 2018-01-24 | 2018-01-24 | A method of improving the digital elevation data resolution of massif using remote sensing image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108280880A CN108280880A (en) | 2018-07-13 |
| CN108280880B true CN108280880B (en) | 2018-11-16 |
Family
ID=62804915
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810067398.9A Active CN108280880B (en) | 2018-01-24 | 2018-01-24 | A method of improving the digital elevation data resolution of massif using remote sensing image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108280880B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102324106B (en) * | 2011-06-02 | 2013-07-10 | 武汉大学 | SFS (Shape From Shading) three-dimensional reconstruction sparse-DEM (Digital Elevation Model) encrypting method considering surface spectral information |
| CN102339478B (en) * | 2011-09-14 | 2013-11-13 | 北京地拓科技发展有限公司 | Method and device for generating digital elevation model from contour map |
| CN102496185B (en) * | 2011-12-14 | 2013-09-25 | 南京大学 | Method for establishing dynamic effect model (DEM) based on multi-resolution remote sensing image discrete point fusion |
| CN102436679B (en) * | 2011-12-16 | 2014-04-30 | 南京大学 | Medium-resolution remote sensing image discrete point DEM (Digital Elevation Model) construction method based on medium value filtering |
| BR112014019836B1 (en) * | 2012-02-13 | 2022-03-08 | Stellenbosch University | METHOD OF PRODUCTION OF A DIGITAL ELEVATION MODEL OF IMPROVED RESOLUTION |
| CN103675790B (en) * | 2013-12-23 | 2016-01-20 | 中国国土资源航空物探遥感中心 | A kind of method improving InSAR technical monitoring Ground Deformation precision based on high accuracy DEM |
| CN106570936B (en) * | 2016-11-14 | 2019-07-02 | 河海大学 | A kind of equidistant weight interpolation encryption method based on gridded DEM data |
-
2018
- 2018-01-24 CN CN201810067398.9A patent/CN108280880B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN108280880A (en) | 2018-07-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Taylor et al. | An algorithm for computing Fekete points in the triangle | |
| CN105678757B (en) | A kind of ohject displacement measuring method | |
| CN106023302B (en) | Mobile communication terminal, server and method for realizing three-dimensional reconstruction | |
| CN108280880B (en) | A method of improving the digital elevation data resolution of massif using remote sensing image | |
| CN108776952B (en) | Sea chart coordinate conversion method for hydrological meteorological monitoring | |
| EP1457949A2 (en) | Map displaying apparatus | |
| CN109887073B (en) | Method and device for building three-dimensional digital model of rock core | |
| CN105388513B (en) | The method for building up and device of earthquake-capturing observation system | |
| Hoskins | Representation of the earth topography using spherical harmonies | |
| CN113158451B (en) | Large-area river three-dimensional simulation method based on one-dimensional flood evolution model | |
| CN108508463B (en) | Fourier-Hermite orthogonal polynomial based extended ellipsoid collective filtering method | |
| CN106556877B (en) | A kind of earth magnetism Tonghua method and device | |
| US20070211077A1 (en) | Fast gridding of irregular data | |
| CN112419499A (en) | Immersive situation scene simulation system | |
| CN109636912A (en) | Tetrahedron subdivision finite element interpolation method applied to three-dimensional sonar image reconstruction | |
| CN113111489A (en) | Dam overtopping and breaking process simulation method and simulation system for dam | |
| CN113175917A (en) | Method for measuring topography of coastal shallow water area by using low-altitude unmanned machine | |
| JP3218372B2 (en) | 3D graphic display method | |
| CN110147646B (en) | Over-current processing method for linear water retaining structure under numerical simulation framework | |
| CN115128605A (en) | Underwater terrain detection method, device and medium | |
| CN113552637B (en) | Collaborative three-dimensional inversion method for magnetic anomaly data in aviation-ground-well | |
| CN107589464B (en) | A kind of satellite-derived gravity data data and shipborne gravimetric data data fusion method | |
| CN110322424A (en) | High-definition picture processing method and device, VR image display method and VR equipment | |
| CN107357759B (en) | Seepage solving method based on seepage boundary and motion differential equation condition | |
| CN105157588A (en) | Multi-dimensional synchronous optimized measurement method for strain localization band interval evolution rule |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |