CN106909644A - A kind of multistage tissue and indexing means towards mass remote sensing image - Google Patents
A kind of multistage tissue and indexing means towards mass remote sensing image Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
A kind of multistage tissue and indexing means towards mass remote sensing image, comprises the following steps：Step A：Wide cut image raster data is obtained, is pre-processed；Step B：The caching pyramid of disk space is set up, the level indexing structure of the regular partition quaternary tree of sub-scene, connection ID and attribute database are added using scene pyramid；Step C：With reference to Method of Data Organization, realize that attribute data is carried, blocking organization completes multistage storage in HBase databases and sets up index；And step D：Resource of distributing according to need whenever and wherever possible is realized by virtualization, autgmentability and sharing capability storage pool.
Description
Technical field
The present invention relates to remote sensing image data process field, more particularly to a kind of multistage tissue towards mass remote sensing image
And indexing means.
Background technology
China obtains substantial amounts of remote sensing image data in recent years, and platform covers satellite platform, unmanned plane etc., resolution ratio from
Meter level is to Centimeter Level.Data have high resolution, the change of imaging breadth greatly, image data amount greatly, the spy such as imaging pattern is diversified
Point so that these data can be used for the object judgement, wider acquisition of information and the generation of more diversified information that more become more meticulous
Deng according to the combination of many kinds of parameters such as target type, image sensor type, time phase, matching degree, the related remote sensing shadow of acquisition
It is data as and according to the related object of target related time, position the attributive classification acquisition different to profile etc. and target
Excavate one of very important information source.Need to carry out the remote sensing image of magnanimity classification treatment, set up dividing for multiple resolution ratio
Tomographic image, realizes multi-level tissue, quick indexing and screening, improves mass data processing speed and Image Intelligence analysis efficiency.
At present, the achievement in research both at home and abroad to remotely-sensed data organization and administration is reported for work seldom, existing remotely-sensed data organizer
Method be mostly data-oriented storage organization and management method, be mostly solve the problems, such as remotely-sensed data how efficient storage, adopt more
Storage organization is carried out with database with the mode that file system is combined, metadata is stored in the database table of static structure,
Data entity is stored in file system, remotely-sensed data is retrieved and is read by Retrieval Interface and data access access interface
Write access.For example, Wang Mi et al. (Wang Mi, Gong Jianya, Li Deren, the seamless data tissue in space of large-scale Remote Sensing Image Database,
Wuhan University Journal information science version, 2001,26 (5)：419-423) it is directed to orthography, it is proposed that using a point band memory module
Realize the seamless organization management of image data.(such as Song Shuhua, Cheng Chengqi is based on the remote sensing shadow of EMD for Song Shuhua, Cheng Chengqi et al.
As new data organization model geography and Geographical Information Sciences .2013,29 (3)：21-25), propose based on the complete of sheet line system expansion
Ball subdivision model EMD, burst storage and tissue are carried out by the code identification of subdivision dough sheet by remotely-sensed data.Lai Jibao, Luo Xiao
It is beautiful et al. that (such as Lai Jibao, Luo Xiaoli is a kind of to support the remote sensing image data tissue scale-model investigation computer science of cloud computing
.2013,40 (7)：80-84).The remote sensing image data tissue model RSC-DOM of support cloud computing is proposed, with reference to image pyramid
The thought of piecemeal storage, completes the remote sensing image data storage under cloud computing framework and tissue.However, current pyramid level mould
Type can not reflect difference of the image data on source sensor and time dimension, largely limit global image data
Effective utilization.Do not take into full account residing geographical position, be concerned grade and data accumulation degree, increase non-hot region
Access pressure.
Google releases Google Earth software platforms, and it uses together with Google Map platforms and fixes 18 numbers of plies
According to organizational structure, with that can show vector data terrestrial reference, geometric type, grating image are superimposed and three-dimensional including point, line, surface etc.
Modeling virtual functions.In terms of remote sensing image data Organization And Management, and use previously generates map tile pyramid (Tile
Map Image) and each Tile is 256 × 256 pixel PNG files, and image data is managed.Wherein, it uses map
It, in order to data quickly show, is in order to calculator memory is efficient using the image file of 256 × 256 pixels that tile pyramid is
Management, and be then in order to computer network is efficiently transmitted using PNG file formats.WorldWind is by NASA (American Nationals
Aviation and space travel office) release a satellite photo for checking the earth virtually globe software, using plane Carree
Image data is expressed using rectangle, length-width ratio is 2：1.Using etc. four points of the partition patterns in the longitude and latitude whole world, the whole world is divided into
The Tile of multilayer different scale, the image data (512 × 512 pixel) in each Tile is identified and by floor group by ranks number
Knit and management.WorldWind builds linear quadtree to manage tile index and tile data on the basis of tile pyramid.Number
During according to retrieval, by the relation between longitude and latitude and the ranks number of Tile, retrieve ranks number to realize SkyLine companies of the U.S.
For user devises TerraBuilder instruments, can be by aerophotograph, satellite image, digital elevation model and various vectorial geographicals
Data, create into a single optimization compressed file, are easy to issue 3-D data set in a streaming manner.Reduced according to the order of magnitude
Run time.Even so, there is certain defect because the file access mode of standard obtains remote sensing image data, single width is distant
The data volume for feeling image is larger, and the I/O abilities of storage system and the network bandwidth limit file access speed, cause these soft
Part reads and the time delay of one width product image of displaying quite big, the serious efficiency and reliability that limit later stage application.
Additionally, existing indexed links parser, the hyperlink analysis with the PageRank of Google as representative, it is impossible to
Understanding content, also cannot just accomplish personalization.Indexed mode of the country based on bibliographic structure is according to the direct rope of classification for having divided
Draw, but the document index with a certain customizing messages cannot be met.And the document index mode for being based on query string can be than calibrated
Text message required for really finding out user, but because remotely-sensed data dimension is more, more complicated, the data scale of construction is bigger, this
A little Indexing Mechanisms cannot all adapt to the efficient access of magnanimity multi-source multiattribute data.
The content of the invention
In view of the problem that existing scheme is present, in order to overcome the shortcomings of above-mentioned prior art, the present invention proposes one
Plant the multistage tissue and indexing means towards mass remote sensing image.
According to an aspect of the invention, there is provided a kind of multistage tissue and indexing means towards mass remote sensing image,
Comprise the following steps：Step A：Wide cut image raster data is obtained, is pre-processed；Step B：Set up the caching gold of disk space
Word tower, the level indexing structure of the regular partition quaternary tree of sub-scene, connection ID and attribute database are added using scene pyramid；
Step C：With reference to Method of Data Organization, realize that attribute data is carried, blocking organization completes multistage storage in HBase databases
Indexed with setting up；And step D：Realized whenever and wherever possible by virtualization, autgmentability and sharing capability storage pool
Distribution according to need resource.
From above-mentioned technical proposal as can be seen that the invention has the advantages that：
The present invention has the Local Characteristic for strengthening raster data, reduces the visit capacity of data, reduces the access of disk
Span, total visit capacity reduction by 40%；Rational data-block cache mechanism is built, completes to need the efficient process of large scale data
Ask, for the real-time display of each image processing and interpretation tool operation provides guarantee.Rational data are built in different scale
Block caching mechanism, greatly improves the access efficiency of raster data.
The deficiency for being to make up traditional pyramid hierarchical model of the invention, the non-homogeneous compound pyramid hierarchical model of proposition,
Sensor and time dimension are anisotropically added in the other block data of each stage resolution ratio, global image data is fully demonstrated
Multidimensional characteristic, preferably organize and utilize image data.The dynamic image pyramid for setting up different accuracy, reduces data
The visit capacity in non-hot region.The atural object data tissue of each sub-scene, employs rule-based stroke in scene pyramid
Point quaternary tree index structure, it is to avoid the multiple storage of atural object entity identification, and greatly reduce the spatial relationship of complexity
Calculate.
The present invention devises multiple index mechanism, can according to sensor, image resolution, imagery zone is different with the time builds
Lithol draws, and stores in HBase databases.Distribution according to need resource, realizes magnanimity multi-source, multiple dimensioned, multi_temporal images data
High efficiency of transmission and access.
Brief description of the drawings
Fig. 1 is embodiment of the present invention multistage pyramid construction principle schematic；
Fig. 2 is flow chart of the embodiment of the present invention towards the multistage tissue and indexing means of mass remote sensing image；
Fig. 3 is the flow chart of Fig. 2 steps A；
Fig. 4 is the flow chart of Fig. 2 steps B；
Fig. 5 is the flow chart of Fig. 4 steps B3；
Fig. 6 is the flow chart of Fig. 2 steps C；
Fig. 7 is self-adapting compressing and progressive transmission schematic diagram in step D；
Fig. 8 is the flow chart of Fig. 2 steps D.
Specific embodiment
Certain embodiments of the invention will be done with reference to appended accompanying drawing in rear and more comprehensively describe to property, some of them but not complete
The embodiment in portion will be illustrated.In fact, various embodiments of the present invention can be realized in many different forms, and should not be construed
To be limited to this several illustrated embodiment；Relatively, there is provided these embodiments cause that the present invention meets applicable legal requirement.
In this manual, following is explanation for describing the various embodiments of the principle of the invention, should not be with any
Mode is construed to the scope of limitation invention.Referring to the drawings described below is used to help comprehensive understanding by claim and its equivalent
The exemplary embodiment of the invention that thing is limited.It is described below to help understand including various details, but these details should
Think what is be merely exemplary.Therefore, it will be appreciated by those of ordinary skill in the art that not departing from scope and spirit of the present invention
In the case of, embodiment described herein can be made various changes and modifications.Additionally, for clarity and brevity,
Eliminate the description of known function and structure.Additionally, running through accompanying drawing, same reference numerals are used for identity function and operation.
To make the object, technical solutions and advantages of the present invention become more apparent, below in conjunction with specific embodiment, and reference
Accompanying drawing, the present invention is described in more detail.
In order to support rapid tissue and the retrieval of remote sensing image data, the embodiment of the present invention provides a kind of towards magnanimity remote sensing
The multistage tissue and indexing means of image, specially a kind of non-homogeneous compound Groupe de la Pyramide of dynamic and indexing means are using multistage
Pyramid Optimization Mechanism, is effectively improved image processing efficiency, and multistage pyramid construction principle is as shown in figure 1, including image
Index, attribute and data (are write on one by pretreatment, the sampling of special tune ginseng bilinear interpolation, self-defined pyramid data file
Rise) and Method of Data Organization based on goal systems etc..
Fig. 2 is flow chart of the embodiment of the present invention towards the multistage tissue and indexing means of mass remote sensing image, referring to attached
A kind of Fig. 2, multistage tissue and indexing means towards mass remote sensing image specifically includes following steps：
Step A：Wide cut image raster dataset is obtained, and completes the pretreatment of image raster data.
Wherein image raster dataset includes coordinate, projection, wave band, image data, altitude data, the grating map of quantization
Deng.
Step A specifically includes following sub-step, as shown in Figure 3：
Sub-step A1：Image raster data breadth is larger, it is necessary to be classified piecemeal to it, carrying out coding to block data retouches
State；
By the pyramid method using classification piecemeal, efficiency is exchanged for space, and buffering area is set up in internal memory, according to number
Made according to geocoding piecemeal, the four kinds of piecemeals for generally using make form to be included：NASA WorldWind、GoogleEarth、
MIP, sentence leveling platform, unified description can be carried out to a variety of block forms by a set of TilesetConfig parameters,
It is defined as follows：
Title
Geographic unit (degree, rice, pixel ...)
Geographic range (northern Nan Xidong)
Coordinate system
0th layer of block size (unit used with " geographic range ")
Top level number
Bottom level number
The Pixel Dimensions of data block
The file format of data block
Multi-zone supervision
Sub-step A2：Under global geographic space frame to Coding and description after block data cut and merged.
With 360 degree of longitudes of digital earth and 180 degree latitude as standard, plane is pressed into different scale and divides stratification, with ranks
Mode stores latitude and longitude value, and each layer of block-by-block is encoded.The level that block data is currently located is first determined whether, then according to certainly
The upward row arrangement mode in bottom recurrence layer by layer, the row order after image block according to layer is cut, and it is suitable that each block is assigned
Block number, row number and the number of plies.
Sub-step A3：Loading processing is read out to altitude data, wherein altitude data is that image raster data is concentrated
Data；
Caching process is carried out to altitude data block, elevation is superimposed to image, inquired about to global different resolution level not
With the elevation put.Specifically, if elevation array of the elevation of point not in block, the point height is calculated by bilinear interpolation.
Scheduling to altitude data is by multithread scheduling, while maintaining a buffering on altitude data in internal memory
Area, realizes the quick access to altitude data..
Sub-step A4：Realize the visualization that tags of block data.
Using view label function, the topography and geomorphology in the visual in image display highlighting region of real-time visual technology, to screen
Curtain window viewing area is quickly positioned, and be directly targeted to for map window by the longitude and latitude or information of place names according to user input
Correspondence position.
Step B：The caching pyramid of disk space is set up, the regular partition quaternary tree of sub-scene is added using scene pyramid
Level indexing structure, connection ID and attribute database.
Split and simplified treatment to image raster data, by reasonable efficient coding, set up the slow of disk space
Deposit pyramid.Add the level indexing structure of the regular partition quaternary tree of sub-scene atural object using scene pyramid, can quickly determine
Position and the convenient scene pyramid for calculating neighbouring relations, accelerate remotely-sensed data storage organization, and connection ID and attribute database.
Step B specifically includes following sub-step, as shown in Figure 4：
Sub-step B1：To the deblocking coding for having loaded, buffer area is set up in Installed System Memory according to buffer memory principle, profit
It is the data of the bilinear interpolation sampling processing proximate region of experience adjustments parameter with special tune ginseng, realizes dynamic pyramid weight
Structure.
Sub-step B2：Define remote sensing metadata and derive model, data storage attribute and data are distinguished in database table ranks
Entity, wherein database list row are classified as the database table structure of storage image database.
Meta data category device is constructed in training set, when new remotely-sensed data is introduced, existing remotely-sensed data classification is included into,
If any remotely-sensed data classification cannot be included into, new metadata type will be derived by calculating the method for information gain.Define S
It is metadata sample training collection, defines c different class Ci(i=1 ... c), CI, sIt is the tuple-set of class in S, S-phase is for c
The entropy of individual attribute is defined as：
Wherein, piIt is that tuple belongs to classification C in SiProbability,Represent the summation operation from 1 to c, log2Represent and be with 2
The logarithm operation at bottom.
Information gain Gains (S, A) of the defined attribute A with respect to sample set S is defined as：
Where it is assumed that dividing the tuple in S by attribute A, A has v different value { a according to the observation of training data1,
a2... av, S is divided into v subset { S by attribute A1, S2... Sv, SjTuple in representative sample training set S, on attribute A
Value be aj。The summation operation from 1 to v is represented, | S | represents the number of tuple in sample training collection S, | Sj| represent set Sj
The number of middle tuple,Represent j-th weight of Attribute transposition.Select the attribute A of highest information gain to divide as classification to belong to
Property, i.e., new classification state is formed on original sample training collection S.
Sub-step B3：Define remotely-sensed data correlation model, utilize " regular quaternary tree " representation object between be subordinate to,
The relation such as arranged side by side, sets up customized pyramid data file, and data, attribute and index are associated together.
Sub-step B3 specifically includes following sub-step, as shown in Figure 5：
Secondary sub-step B3.1：Define the five-tuple of the incidence relation of remotely-sensed data；
Using the time, space, object of observation classification as associated data base attribute, the incidence relation of remotely-sensed data can be with
It is defined as such as the five-tuple of formula (3),
S=<ARt, ARs, ARo, TF, SF> (3)
Wherein, ARt, ARs, ARo are one group of correlation rules, one group of logical deduction by reasoning rule can be expressed as, for example, by condition
X is derived and is asserted Y, represented with X → Y.ARt is the correlation rule of time dimension, and ARs is the correlation rule of Spatial Dimension, and ARo is
Correlation rule based on object of observation.TF is temporal characteristics, such as the life cycle of data, time marking etc., and SF is space characteristics,
Such as the locus of data, regional extent etc..
Secondary sub-step B3.2：Set up data correlation tissue model；
Data correlation tissue model is represented using formula (4), it represents two tuples for containing vertex set and side collection composition
Θ=<V, E> (4)
Wherein,
V={ v1, v2... E={ e1, e2... (5)
V is the data vertex set of non-NULL, and each element representation meets remotely-sensed data classification or the remote sensing number of correlation rule
According to.E is the set of incidence relation, and the association that meets correlation rule of each element representation in certain or certain several dimensions is closed
System, E is equivalent to the S in formula (3).If E is empty set, any dimension between expression remotely-sensed data in definition does not all associate pass
System, is isolated unrelated between data.If E non-NULLs, then it represents that between remotely-sensed data in certain dimension, under certain correlation rule
There is incidence relation.
Sub-step B3.3：By Bayesian learning, the renewal of associated group organization model is completed, closed with adapting to real world association
It is the present situation of dynamic change, it is achieved thereby that the autonomous learning of model.
Sequence number n is introduced with ΘnThe state of incidence relation between the remotely-sensed data of expression current procedures n, with Xn={ x1, x2…xn}
Current data set is represented, with P (Θ0) represent correlation model original state probability measure, it is assumed that XnData are separate in collection,
Then there is posterior probability
Wherein, P (Θn) represent the prior probability without training data, P (Xn) represent the training data to be observed
Prior probability, P (Xn|Θn) represent and assume ΘnData X is observed in the case of establishmentnProbability.In view of remote sensing number
According to mutual independence, then have P (Xn|Xn-1, Θn)=P (xn, Θn), P (xn|Xn-1) be normaliztion constant, then haveFollowing formula can be obtained：
Conditional probability P (xn|Θn) represent Current observation data XnLikelihood score, P (Θn|Xn-1) represent current learning procedure
Prior probability, and as n=1 the prior probability be probability P (Θ0).Aposterior knowledge according to each study is obtained down
The priori for once learning, so as to be made up of the state renewal process of Infinite Cyclic Knowledge delivery.It is expressed as：
A transfer process for recursive form is formed, study and the renewal process of incidence relation state is illustrated.
Step C：With reference to Method of Data Organization, realize that attribute data is carried, blocking organization is completed in HBase databases
Multistage storage and foundation index.
HBase is a kind of framework in the big data data storage storehouse based on Hadoop.Here the framework has been used as number
According to hoc solutions.
Select suitable resolution match strategy, it is to avoid unordered search procedure, multistage is completed in HBase databases and is deposited
Storage and secondary index, it is to avoid substantial amounts of memory headroom by temporarily without index data take.With the data based on goal systems
Organizational form and interrelational form are combined closely, and realize the access of Large Volume Data.
Step C specifically includes following steps, as shown in Figure 6：
Sub-step C1：With reference to Method of Data Organization, realize that attribute data is carried, blocking organization；
Image, with unique ID identification informations, is connected as independent spatial entities with the attribute data of the image, and
Set up and associating with reference to objects system.All of entity is managed using entity table in attribute database.Each entity exists
One record of unique correspondence in entity table, the essential information of storage entity, multistage storage is in HBase databases.For task
Yardstick, there is provided multiple dimensioned remote sensing image data, multi-scale data tissue, realizes the inspection of related objective adequate resolution data
Rope, plotting, positioning.
Sub-step C2：Multistage storage is completed in HBase databases and index is set up.
For multi-scale view data blocking organization, there is provided different multi-stage data scheduling schemes.One is with file
The data-selected scheme of folder form storage, two is using big file index mode data storage, while providing data to both data
Server.Under the support of HBase databases, according to sensor, image resolution, imagery zone is different with the time sets up two grades
Index.Space based on object of reference-attribute interactive query, realizes corresponding point coordinates position, sits by referring to thing physical model
Mark scope meets fast data search in entity ID range of attributes.
Step D：Realize distributing according to need whenever and wherever possible by virtualization, autgmentability and sharing capability storage pool
Resource, wherein referring to storage pool for data warehouse, i.e. memory space.
Image raster data and other data need online migration in classification storage, and this is accomplished by considering data movement to preceding
The performance impact of platform I/O loads.Data Autonomic Migration Framework adjusts Data Migration speed according to the change that foreground I/O is loaded so that
Influence of the Data Migration action to the service quality of storage system in itself is very small, while enabling data migration task as early as possible
Complete.The major technique that data Autonomic Migration Framework storage is related to has：The speed control of Data Migration is with scheduling, Data Migration to application
Latency hiding, file access block position sequence prediction etc..Data Migration needs to solve data compression and progressive transmission, such as schemes
Shown in 7.In order to support that geospatial intelligence data realize high efficiency of transmission on all types of networks, it is ensured that the change of the network bandwidth with
Delay will not make user stagnate or wait as long for data.
Step D includes following sub-step, as shown in Figure 8：
Sub-step D1：According to the bandwidth in request source, suitable spatial data compression and expression way are selected, by intelligence
The adaptive strategy of change, the transmission mode to data is allocated.
Broadband uses Fast Compression and long data block transmission mode, and arrowband uses quick static compression and streaming transmission mode.
According to the result that terminal request task is parsed, bandwidth, loading condition based on real transmission network, concurrent access situations, terminal
The multiple-factor tale quale such as data volume size cases of request, designs efficient network transmission channel and estimates with unreliable channel bandwidth
Meter and bandwidth control, are comprehensively transmitted the intelligent adaptive strategy study of network.
Sub-step D2：Transmission data reasonably divide by different level, in batches, overall transmission data are divided into some
Relatively independent elementary cell, is compressed to elementary cell and encodes and continuously transmit, and elementary cell is carried out again in client
Receive, decoding and initial data synthesize.
For generally for raster data, most ripe processing mode is exactly the treatment of Pyramid technology piecemeal, compression algorithm
Compressed using JPEG2000.For vector data, data volume is relatively fewer, the principle of multi-scale expression can be followed, using space
Information form general model and node LOD models are processed different scale map, comprehensive Web Service, Plug-in and
The Technology design progressive transmission architecture such as Script, coding tissue is carried out using the SVG of W3C, empty as being issued on browser
Between Vector Message carrier.
Sub-step D3：Support that the data structure and efficient index design of Streaming Media expression ensure network progressive streaming.
RTP is a kind of agreement for providing end-to-end real-time multimedia data transmission, and its supporting agreement is RTCP (Real-
Time Transfer Control Protocol), it is the transmission for monitoring real time data.They both ensure the effect of transmission
Rate, can limitedly ensure the quality of transmission as needed again.To transmitting each the minimum delivery unit for coming, will be by solution
Code, index are extracted, the process of reconstruct.Resolution ratio and the size of indication range that component shows according to terminal, automatically determine terminal
The best information load capacity of display screen, the information data to transmitting carries out automatic Reconstruction on backstage.
So far, the present invention is finished towards the multistage tissue of mass remote sensing image and indexing means introduction.
The process or method described in accompanying drawing above can be by including hardware (for example, circuit, special logic etc.), solid
Part, software (for example, the software being carried in non-transient computer-readable media), or both the treatment logic of combination hold
OK.Although describing process or method according to some order operations above, however, it is to be understood that some described operation energy
Performed with different order.Additionally, concurrently rather than certain operations can be sequentially performed.
It should be noted that in accompanying drawing or specification text, the implementation for not illustrating or describing is affiliated technology
Form known to a person of ordinary skill in the art, is not described in detail in field.Additionally, the above-mentioned definition to each element and method is simultaneously
Various concrete structures, shape or the mode mentioned in embodiment are not limited only to, those of ordinary skill in the art can carry out letter to it
Singly change or replace, for example：
(1) bilinear interpolation calculates the point height and can also use other Interpolations in sub-step B1；
(2) compression algorithm in step D is compressed using JPEG2000；
(3) SVG of the W3C that step D is used carries out coding tissue, can also be replaced with other Vector Message carriers.
Particular embodiments described above, has been carried out further in detail to the purpose of the present invention, technical scheme and beneficial effect
Describe in detail bright, it should be understood that the foregoing is only specific embodiment of the invention, be not intended to limit the invention, it is all
Within the spirit and principles in the present invention, any modification, equivalent substitution and improvements done etc. should be included in protection of the invention
Within the scope of.
Claims (8)
1. it is a kind of to be organized and indexing means towards the multistage of mass remote sensing image, it is characterised in that to comprise the following steps：
Step A：Wide cut image raster data is obtained, is pre-processed；
Step B：The caching pyramid of disk space is set up, the two of the regular partition quaternary tree of sub-scene are added using scene pyramid
Level index structure, connection ID and attribute database；
Step C：With reference to Method of Data Organization, realize that attribute data is carried, blocking organization completes multistage in HBase databases
Storage and foundation index；And
Step D：Money of distributing according to need whenever and wherever possible is realized by virtualization, autgmentability and sharing capability storage pool
Source.
2. multistage according to claim 1 is organized and indexing means, it is characterised in that step A includes：
Sub-step A1：Piecemeal is classified to image raster data, and Coding and description is carried out to block data；
Sub-step A2：Block data after Coding and description is cut and merged；
Sub-step A3：Loading processing is read out to altitude data, wherein altitude data is the data that image raster data is concentrated；
And
Sub-step A4：Realize the visualization that tags of block data.
3. multistage according to claim 2 is organized and indexing means, it is characterised in that step B includes：
Sub-step B1：Deblocking to having loaded is encoded into Mobile state Pyramid Reconstruction；
Sub-step B2：Define remote sensing metadata and derive model, data storage attribute and data entity are distinguished in database table ranks；
And
Sub-step B3：Remotely-sensed data correlation model is defined, the relation between object is represented using regular quad-tree structure, set up certainly
The pyramid data file of definition, data, attribute and index are associated together.
4. multistage according to claim 3 is organized and indexing means, it is characterised in that sub-step B3 includes：
Secondary sub-step B3.1：Define the five-tuple of the incidence relation of remotely-sensed data；
Secondary sub-step B3.2：Set up data correlation tissue model；And
Sub-step B3.3：By Bayesian learning, the renewal of associated group organization model is completed.
5. multistage according to claim 4 is organized and indexing means, it is characterised in that the five-tuple is with following formula table
Show：
S=＜ ARt, ARs, ARo, TF, SF ＞
Wherein, ARt, ARs, ARo are one group of correlation rules, can be expressed as one group of logical deduction by reasoning rule, and ARt is time dimension
Correlation rule, ARs is the correlation rule of Spatial Dimension, and ARo is the correlation rule based on object of observation, and TF is temporal characteristics, SF
It is space characteristics.
6. multistage tissue according to claim 4 and indexing means, it is characterised in that the data correlation tissue model is
Contain two tuples of vertex set and side collection composition
Θ=<V, E>
Wherein,
V={ v1, v2... E={ e1, e2...
V is the data vertex set of non-NULL, and each element representation meets remotely-sensed data classification or the remotely-sensed data of correlation rule, E
It is the set of incidence relation, the incidence relation that meets correlation rule of each element representation in certain or certain several dimensions.
7. multistage according to claim 1 is organized and indexing means, it is characterised in that step C includes：
Sub-step C1：With reference to Method of Data Organization, realize that attribute data is carried, blocking organization；And
Sub-step C2：Multistage storage is completed in HBase databases and index is set up.
8. multistage according to claim 1 is organized and indexing means, it is characterised in that step D includes：
Sub-step D1：According to the bandwidth in request source, suitable spatial data compression and expression way are selected, by intelligentized
Adaptive strategy, the transmission mode to data is allocated；
Sub-step D2：Transmission data reasonably divide by different level, in batches, overall transmission data are divided into some relative
Independent elementary cell, is compressed to elementary cell and encodes and continuously transmit, client carry out again elementary cell reception,
Decoding and initial data synthesis；And
Sub-step D3：Support that the data structure and efficient index design of Streaming Media expression ensure network progressive streaming.
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