CN109947884A - A kind of high-efficiency tissue querying method of whole world ICESat/GLAS point cloud - Google Patents
A kind of high-efficiency tissue querying method of whole world ICESat/GLAS point cloud Download PDFInfo
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Abstract
A kind of high-efficiency tissue querying method of whole world ICESat/GLAS point cloud carries out ICESat/GLAS point cloud data tissue using topographic sheet partition strategy, and carries out closest point inquiry, blocking organization and the closest retrieval of two dimension KD-Tree using two dimension KD-Tree.The GLAS point data of original download is first read, and carries out tissue piecemeal by global Topographic map framing, all block datas are carried out to centrally stored, the few LASzip file of the lossless memory space of compression generation using the scalability of LAS format.In data query, LAS point cloud within the scope of preparatory reading operation simultaneously constructs two-dimensional surface KD-Tree and carries out nearest neighbor searching.A large amount of ICESat data files are effectively organized into single LAS file with can not losing data precision by this method, reduce quantity of documents and effectively save memory space, guarantee the convenient management and format versatility of data.This method carries out tissue to ICESat/GLAS point cloud data with partitioned mode simultaneously, solves the problems, such as that initial data is mixed and disorderly unordered.In addition, efficiency data query can effectively be promoted by inquiring nearest neighbor point by KD-Tree.
Description
Technical field
The present invention relates to remote sensing fields, more particularly, to a kind of high-efficiency tissue issuer of whole world ICESat/GLAS point cloud
Method.
Background technique
ICESat (Ice, Cloud, Land Elevation Satellite) satellite is that U.S. NASA is sent out in January, 2003
The observation satellite of the gla-cier thickness variation at monitoring the two poles of the earth and Greenland for penetrating, full name ice, cloud and land elevation satellite.On satellite
Equipped with ground elevation laser measurement system GLAS (Geoscience Laser Altimeter System), can provide solely
Vertical high-precision laser surveys high point data, and has duplicate measurements ability, therefore measures science, atmospheric science, land in ice sheet
It is used widely in the fields such as vegetation science.
In photogrammetric, high data GLA14 is surveyed on the main Global land surface using in ICESat/GLAS data product
Auxiliary optical satellite image carries out block adjustment, provides high process control for Pillarless caving region.GLA14 data set record
From 2 months 2003 on October 11st, 20 days 1,86 ° of north latitude to 86 ° of south latitude, the land table of 180 ° to 180 ° of west longitude of east longitude
High data are surveyed in face.The original GLA14 data downloaded from American National snow ice data center NSIDC are temporally to store, and are caused
When handling big regional satellite data, the very big GLA14 file of large number of and occupied space is generally required.And due to data text
The geographic range of part is unknown, therefore carries out that GLA14 data can be reduced by file access to GLA14 data when cloud is inquired
Operating efficiency limits the practical application of data and software.
In order to improve data manipulation efficiency, some algorithms are pre-processed for original GLA14 data set, and data is taken to turn
Original GLA14 data set is carried out physical block (customized piecemeal) and is converted to certain user-defined format by the mode changed.Though
Right such methods can effectively promote operating efficiency, but for the point cloud data of global range, even if according to the whole world 1:
1000000 topographic map Standard division ranges carry out piecemeal, still have a file more than 2000, and due to a cloud substantial amounts, without appointing
What compressing file processing, therefore the point cloud data collection after piecemeal processing can still occupy biggish memory space, lead to data
Versatility is not strong.It is improved there are also some algorithms for data query, data query is carried out using three-dimensional KD-Tree.But
Be the research of the invention finds that, ICESat point cloud data is extremely sparse point cloud for global space, between every two point
Spacing distance is very big, this shows that two-dimentional nearest neighbor point inquiry can't be higher than from the three-dimensional precision for carrying out closest point inquiry, because
Although efficiency can be improved in this three-dimensional KD-Tree inquiry, improved place still in need.
In short, the currently processing to original GLA14 data set, it is had focused largely on using upper, and small part is for being promoted
The method of the operating efficiency of data still has problem.
Summary of the invention
The problem and point cloud data is inquired that present invention mainly solves original GLA14 document data set quantity is more, file is big
Inefficient problem.
Technical solution of the present invention provides a kind of high-efficiency tissue querying method of whole world ICESat/GLAS point cloud, using landform
Figure map sheet partition strategy carries out ICESat/GLAS point cloud data tissue, and carries out closest point inquiry using two dimension KD-Tree,
Realization process the following steps are included:
Step 1, original download GLA14 document data set is opened, the three-dimensional coordinate information of ICEsat/GLAS point is read；
Step 2, using topographic map Standard division range, global longitude and latitude range is subjected to piecemeal, it then successively will spatially
Unordered original GLAS point cloud is filled into each piecemeal；Quantity, coordinate and the grid starting point of each piecemeal record point
With longitude and latitude interval；
Step 3, the point cloud file for creating LAS format records the quantity and offset and every of piecemeal in LAS file header
Quantity, grid starting point and the longitude and latitude interval of the point of one piecemeal, LAS file header record the point cloud coordinate note of each piecemeal later
Record is got off；
Step 4, the point cloud file of LAS format step 3 generated, then passing through compressing and converting is LASzip file；
Step 5, the minimum outsourcing rectangular extent for calculating to be checked cloud, obtains rectangle Rect-Range；
Step 6, the file header of 4 gained LASzip file of read step traverses all piecemeals, and carries out with Rect-Range
It asks friendship to calculate, obtains all piecemeals of Rect-Range covering；
Step 7, all GLAS point clouds in step 6 gained piecemeal are read out, and establishes X, the two dimension in Y-direction
KD-Tree；Step 8, a point P to be checked is successively taken out1, the KD-Tree that is established in step 7 using plane coordinates (X, Y)
In carry out Nearest Neighbor Search, obtain GLAS point P2.Calculate P1And P2Distance dis_XY in the horizontal and vertical directions and
Dis_Z, if dis_XY is less than pre-set level thresholds Threshold_dXY, if dis_Z is less than pre-set horizontal threshold
Value Threshold_dZ then assert GLAS point P2It is point P to be checked1Closest point, being otherwise considered as in ICEsat point cloud does not have a little
P1Closest point.
Moreover, carrying out space separating by ten thousand topographic map Standard division range of whole world 1:100 for all GLAS point clouds in step 2.
Moreover, store by block by the point cloud file of LAS format in step 3, it is general to support.
Moreover, using big feature is spaced between GLAS laser point, the two-dimentional KD-Tree of point cloud is only established in step 7,
It reduces to calculate to transport and deposit, improve the speed and recall precision for establishing KD-Tree.
Moreover, first judging whether that dis_XY is less than pre-set level thresholds Threshold_dXY, if not in step 8
Then judge not being closest point, otherwise continues to determine whether that dis_Z is less than pre-set level thresholds Threshold_dZ, if
Otherwise judge not being closest point, only all meet just be considered as point to be checked closest GLAS point.
ICESat/GLAS point cloud data tissue is carried out using partition strategy the invention proposes a kind of, and uses two dimension KD-
It is not high can effectively to solve more, the mixed and disorderly unordered and search efficiency of raw data file for the method that Tree carries out closest point inquiry
The problem of, while this method operand is very small, treatment effeciency is high, and it is empty can to greatly reduce the storage that a cloud file occupies
Between, simultaneously because the destination file generated is a kind of general point cloud file format (LASZip), therefore universality is good, transplantability
It is high.
Compared with prior art, the present invention has the advantage that
Fully consider that original GLAS document data set is more, data volume is big and the uncurrent problem of format, for the first time special
Data set is converted for general compression LAS file in industry angle.By million map sheet partition strategies, make unordered original point
Cloud is spatially orderly.Meanwhile closest point is retrieved using two-dimensional surface KD-Tree, reduce query time, improves operation
Efficiency.Furthermore operand of the present invention is smaller, and whole efficiency is very high.
Detailed description of the invention
Fig. 1 is the overview flow chart of the embodiment of the present invention；
Fig. 2 is the GLAS point cloud piecemeal intersected in the embodiment of the present invention with rectangular extent；
Fig. 3 be in the embodiment of the present invention point to be checked and closest point that DK-Tree is retrieved in the horizontal direction compared with
Schematic diagram；
Fig. 4 is point to be checked in the embodiment of the present invention compared with the closest point that DK-Tree is retrieved is in vertical direction
Schematic diagram.
Specific embodiment
The inventive technique scheme is described in detail below in conjunction with drawings and examples.
The embodiment of the present invention is carried out all original GLA14 document data sets by ten thousand topographic map standard map sheet of whole world 1:100
Piecemeal, and be converted to single general format LAS file, improve the operating efficiency of data and there is very strong transplantability, and is logical
Over-voltage shortens LASzip file into, can greatly reduce memory space.Meanwhile it being carried out using global ten thousand topographic sheet of 1:100
Piecemeal is simultaneously inquired using two dimension KD-Tree, and search efficiency can be greatly promoted.
Referring to Fig. 1, in embodiment, the strategy of piecemeal is carried out using global ten thousand topographic sheet of 1:100, it will be all original
GLA14 data set carries out piecemeal, then makes full use of the scalability of LAS format that all block datas are stored in single LAS text
In part, finally this document is compressed, generates lossless LASzip file.In data query, preparatory reading operation range
Interior LAS point cloud simultaneously constructs two-dimensional surface KD-Tree progress nearest neighbor searching.Its core process the following steps are included:
Step 1, by the GLA14 document data set of File Open original download, other extra information are filtered out, are only read
The three-dimensional coordinate of GLAS point.
In embodiment, 2003 and 2004 original GLAS laser is downloaded from American National snow ice data center NSIDC
Point cloud data, 642 files, total 57.3G.Then original GLA14 document data set is read by file, it is extra filters out other
The coordinate of GLAS point is read into memory by information.
Step 2, using global ten thousand Topographic map framing principle of 1:100, by global GLAS point piecemeal, then will spatially without
The original GLAS point cloud of sequence is filled into each corresponding sub-block.Quantity, coordinate and the grid starting of each piecemeal record point
Point and longitude and latitude interval.The piecemeal is deleted if not having GLAS point in certain piecemeal.
When it is implemented, judgement can be compared with the coordinate of grid by the coordinate of point, it will be spatially unordered
Original GLAS point cloud is filled into each corresponding sub-block.
Self-delineating block is mainly pressed different from the art prior art and carries out physical block, and present invention firstly provides utilizations
The Topographic map framing mode of technical field of mapping carries out the piecemeal on sheet line system.When it is implemented, those skilled in the art can
With reference to " National Standard of the People's Republic of China: national fundamental GIS framing and number ", with 1:1000000 topographic map
Based on, it is divided by global longitude and latitude.
In embodiment, space separating is carried out by ten thousand Topographic map framing of 1:100 for all GLAS point clouds, by global GLAS point
It is divided into 2700 pieces, 1429 piecemeals of last residue.
Step 3, the point cloud file of LAS format is created.The quantity of piecemeal, offset and every are recorded in LAS file header
The information (i.e. the quantity, grid starting point at step 2 gained piecemeal midpoint and longitude and latitude interval) such as the range and points of one piecemeal.?
The point cloud coordinate record of each piecemeal is got off behind LAS file header.
The prior art is all that only considered internal use with customized format.And the present invention is considered on professional
Versatility, to the point cloud of piecemeal obtained in step 2, strong, general in the profession advantage using the scalability of LAS file, into
Row is stored by block.
Step 4, by step 3, a large amount of original GLA14 data is subjected to piecemeal and are converted to single LAS file.But
It is still to occupy a large amount of memory spaces since laser point cloud data amount is very huge.And LASzip is the lossless of LAS format
Compressed format, therefore this method is LASzip file after the point cloud file that step 3 generates LAS format, then through compressing and converting.
Step 4 of the present invention, to the lossless compression advantage of data, carries out point cloud data compressing file using LASzip format, can
To greatly reduce memory space.In embodiment, after step 3, the original GLA14 document data set of 642 total 57.3G is
By piecemeal and be converted to the LAS file of single about 13.8G.Then lossless compression is carried out again, is converted to LASzip file.Finally
File size is 1.34G.
Following steps carry out nearest neighbor searching, and embodiment is for covering to be checked cloud of Shandong Province whole area:
Step 5, the minimum outsourcing rectangular extent for calculating to be checked cloud, obtains rectangle Rect-Range.In Fig. 2
The corresponding rectangular extent of Rect-Range.
Step 6, the file header of the GLAS point cloud data of LASzip format, the i.e. text of step 4 gained LASzip file are read
Part head traverses all piecemeals, and carries out that friendship is asked to calculate with Rect-Range, obtains all GLAS point clouds of Rect-Range covering
Piecemeal.Four black overstriking blocks as where in Fig. 2.
Step 7, all GLAS point clouds in piecemeal that step 6 asks friendship to be calculated are read out, and establishes X, Y-direction
On two-dimentional KD-Tree.
In view of meaning of the three-dimensional KD-Tree to intensive laser point cloud is larger, but not for the effect of GLAS laser point
Obviously, therefore the present invention overcomes technology prejudice, gives up elevation in step 7, using being spaced big feature between GLAS laser point,
The two-dimentional KD-Tree for only establishing point cloud reduces calculating fortune and deposits, improves the speed and recall precision for establishing KD-Tree.
Step 8, a point P1 to be checked, the KD-Tree established in step 7 using plane coordinates (X, Y) are successively taken out
In carry out Nearest Neighbor Search, obtain corresponding GLAS point P2.Calculate the distance of P1 and P2 in the horizontal and vertical directions
Dis_XY and dis_Z, if being less than pre-set horizontal and vertical distance threshold (Threshold_dXY, Threshold_dZ),
Then assert that GLAS point P2 is the closest point of point P1 to be checked, being otherwise considered as in GLAS point cloud does not have the closest point of point P1.
The present invention proposes a kind of new inquiring technology scheme, in step 8, to closest GLAS point undetermined, check threshold value into
Horizontal distance and vertical range between row point to be checked and closest point undetermined, and progress and threshold value comparison, are only both less than
It is just considered as the closest GLAS point of point to be checked in preset threshold.
When it is implemented, I and P distance dis_XY in the horizontal direction can first be calculated, if it is greater than or equal to pre-set water
Flat threshold value Threshold_dXY (embodiment is set as 30 meters), then think I not and be the closest point of P (P2 in such as Fig. 3 and
P4), continue to retrieve next point to be checked.Otherwise, continue to calculate I and P in vertical direction distance dis_Z, be such as larger than equal to
Vertical threshold Threshold_dZ (embodiment is set as 15 meters), then it is assumed that I is not the closest point (P3 in such as Fig. 4) of P.Only
Have when horizontal distance and vertical range are respectively smaller than Threshold_dXY and Threshold_dZ, just thinks that I is the most adjacent of P
Near point GLAS point, such as the P1 point in Fig. 3 and Fig. 4.ICEsat point namely GLAS point in figure.
Software technology can be used when specific implementation and realize the above process of automatic running.
The actual service conditions of major applications is met without carrying out path planning to satellite in orbit using the above process.
The image that filtering algorithm obtains reduces the quantity of image under the premise of meeting single-coverage demand as far as possible, thus substantially
The workload for reducing subsequent processing, improves overall efficiency.Furthermore the present invention is suitable for the image of various satellite sensors, constraint
Condition can customize, is expansible, and operand is smaller, and whole efficiency is very high.
Specific embodiment described herein is only an example for the spirit of the invention.The neck of technology belonging to the present invention
The technical staff in domain can make various modifications or additions to the described embodiments or replace by a similar method
In generation, however, it does not deviate from the spirit of the invention or beyond the scope of the appended claims.
Claims (5)
1. a kind of high-efficiency tissue querying method of whole world ICESat/GLAS point cloud, it is characterised in that: use topographic sheet piecemeal
Strategy carries out ICESat/GLAS point cloud data tissue, and carries out closest point inquiry using two dimension KD-Tree, realizes process packet
Include following steps:
Step 1, original download GLA14 document data set is opened, the three-dimensional coordinate information of ICEsat/GLAS point is read；
Step 2, using topographic map Standard division range, global longitude and latitude range is subjected to piecemeal, it then successively will be spatially unordered
Original GLAS point cloud be filled into each piecemeal；Quantity, coordinate and the grid starting point and warp of each piecemeal record point
Latitude interval；
Step 3, the point cloud file for creating LAS format records the quantity and offset and each point of piecemeal in LAS file header
Quantity, grid starting point and the longitude and latitude interval of the point of block, LAS file header are recorded later under the point cloud coordinate record of each piecemeal
Come；
Step 4, the point cloud file of LAS format step 3 generated, then passing through compressing and converting is LASzip file；
Step 5, the minimum outsourcing rectangular extent for calculating to be checked cloud, obtains rectangle Rect-Range；
Step 6, the file header of 4 gained LASzip file of read step, traverses all piecemeals, and carry out asking friendship with Rect-Range
It calculates, obtains all piecemeals of Rect-Range covering；
Step 7, all GLAS point clouds in step 6 gained piecemeal are read out, and establishes X, the two-dimentional KD- in Y-direction
Tree；
Step 8, a point P to be checked is successively taken out1, carry out in the KD-Tree established in step 7 using plane coordinates (X, Y)
Nearest Neighbor Search obtains GLAS point P2.Calculate P1And P2Distance dis_XY and dis_Z in the horizontal and vertical directions, if
Dis_XY is less than pre-set level thresholds Threshold_dXY, if dis_Z is less than pre-set level thresholds
Threshold_dZ then assert GLAS point P2It is point P to be checked1Closest point, be otherwise considered as in ICEsat point cloud without point P1
Closest point.
2. the high-efficiency tissue querying method of the whole world as described in claim 1 ICESat/GLAS point cloud, it is characterised in that: step 2
In, space separating is carried out by ten thousand topographic map Standard division range of whole world 1:100 for all GLAS point clouds.
3. the high-efficiency tissue querying method of the whole world as described in claim 1 ICESat/GLAS point cloud, it is characterised in that: step 3
In, store by block by the point cloud file of LAS format, it is general to support.
4. the high-efficiency tissue querying method of the whole world as described in claim 1 ICESat/GLAS point cloud, it is characterised in that: step 7
In, using big feature is spaced between GLAS laser point, the two-dimentional KD-Tree of point cloud is only established, calculating fortune is reduced and deposits, raising is built
The speed and recall precision of vertical KD-Tree.
5. the high-efficiency tissue querying method of the whole world ICESat/GLAS point cloud as claimed in claim 1 or 2 or 3 or 4, special
Sign is: in step 8, first judging whether that dis_XY is less than pre-set level thresholds Threshold_dXY, if otherwise judging
It is not closest point, otherwise continues to determine whether that dis_Z is less than pre-set level thresholds Threshold_dZ, if otherwise sentencing
Disconnected is not closest point, only all meet just be considered as point to be checked closest GLAS point.
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