CN104077411B - remote sensing satellite data processing method and system - Google Patents
remote sensing satellite data processing method and system Download PDFInfo
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Abstract
The invention provides a kind of remote sensing satellite data processing method and system, method therein includes：List processing (LISP) and image characteristics extraction are carried out according to 0 DBMS file, wherein, list processing (LISP) is used for the cataloguing meta data file and catalogue and browse map file for generating remote sensing satellite data, image characteristics extraction carries out the image characteristics extraction for including color and texture by the sampling image data file to 0 DBMS file, to generate the image feature information of 0 DBMS file；Cataloguing meta data file, catalogue and browse map file and image feature information association are preserved to database；The remote sensing satellite data retrieval that progress text attribute and contents attribute are combined in database.The retrieval to satellite data text attribute and contents attribute can be completed simultaneously by the present invention, improve the precision of retrieval, the defects of single use text attribute carries out data management in remote sensing satellite data management is compensate for, adds the method retrieved and managed to satellite data.
Description
Technical field
The present invention relates to remote sensing satellite technical field of data administration, more specifically, is related at a kind of remote sensing satellite data
Manage method and system.
Background technology
The down-link satellite remotely-sensed data of remote sensing satellite needs to dock after the reception of data, record is completed by reception system
Receive, record remote sensing satellite data carry out decompression conciliate format analysis processing generate 0 DBMS file, in order to be saved in disk or
Achieved in the media such as tape.Retrieval and acquisition in order to facilitate remote sensing satellite data is, it is necessary to 0 series before being achieved
List processing (LISP) is carried out according to file, generates the metadata information of remote sensing satellite, while metadata information is entered into relational database.
These metadata informations include：Imaging time, sensor, cloud amount information, spatial dimension geography information, ranks number etc..So, use
Family, can be by the metadata information in searching database, so as to obtain corresponding remote sensing satellite number when needing satellite data
According to.
Remote sensing satellite data are different from other types of view data, and their individual data file is big, data tub of tissue
Reason mode is unique.Individual data file is big, determines that the time of required cost is longer when carrying out image characteristics extraction, so as to
Influence treatment effeciency.Therefore it is main that satellite is returned using the retrieval mode based on text attribute in remote sensing satellite data management
File data is managed.
Technology based on text attribute, being retrieved using meta data file information to remote sensing image data is developed more
Maturation, there is the characteristics of automaticity is high, matching precision is high.But also there is its limitation, be mainly reflected in following three aspects：
1st, retrieval mode is single, the fully content of sharp remote sensing satellite image data in itself useless；
2nd, for color, texture this category information, the mode based on text attribute can not be used to be inquired about；
3rd, requirement of the system for user is higher, it is necessary to which user understands sensor type, ground grid, image acquisition
The information such as time.
These above-mentioned limitations undoubtedly constrain the application of remote sensing satellite data.
Research to Image Retrieval is started in twentieth century eighties, has arrived the nineties that network technology is gradually popularized
Larger breakthrough is just obtained, a variety of achievements start to be converted into application technology, and growing application demand promotes it in turn
Study to higher level development.In recent years, CBIR has become a very active research field.
The fast development of computer technology, also solves the performance issue of remote sensing image processing to a certain extent so that base
Engineer applied is able in the search method of remote sensing satellite data.But in the management of remote sensing satellite data, view data has it
Specific tissue, management and acquisition methods, it is thus evident that single use content retrieval can not meet the needs of some application-specifics.
The content of the invention
In view of the above problems, it is an object of the invention to provide a kind of remote sensing satellite data processing method and system, especially
The remote sensing satellite data processing method and system that a kind of text attribute and contents attribute are combined, preferably to meet remote sensing satellite
The demand of data management.
According to an aspect of the invention, there is provided a kind of remote sensing satellite data processing method, including：
List processing (LISP) and image characteristics extraction are carried out according to 0 DBMS file, wherein, the list processing (LISP) is distant for generating
Feel the cataloguing meta data file and catalogue and browse map file of satellite data, described image feature extraction passes through to 0 DBMS
The sampling image data file of file carries out the image characteristics extraction for including color and texture, to generate the 0 DBMS file
Image feature information；
The cataloguing meta data file, catalogue and browse map file and the association of described image characteristic information are preserved to data
Storehouse；
The remote sensing satellite data retrieval that progress text attribute and contents attribute are combined in the database, wherein, institute
The search condition for stating contents attribute is the image data information of the retrieval result of the text attribute or the picture number inputted in addition
It is believed that breath.
According to another aspect of the present invention, there is provided a kind of remote sensing satellite data handling system, including：
List processing (LISP) unit, for carrying out list processing (LISP) wherein according to 0 DBMS file, to generate remote sensing satellite data
Cataloguing meta data file and catalogue and browse map file；
Image characteristics extraction unit, for by including to the sampling image data file of the 0 DBMS file
The image characteristics extraction of color and texture, to generate the image feature information of the 0 DBMS file；
Memory cell, for by the cataloguing meta data file, catalogue and browse map file and described image characteristic information
Association is preserved to database；
Retrieval unit, for the remote sensing satellite data for carrying out text attribute in the database and contents attribute is combined
Retrieval, wherein, the search condition of the contents attribute for the retrieval result of the text attribute image data information or in addition
The image data information of input.
Using above-mentioned remote sensing satellite data processing method and system according to the present invention, can complete simultaneously to satellite data
The retrieval of text attribute and contents attribute, the precision of retrieval is improved, make up the single use text in remote sensing satellite data management
Attribute carries out the defects of data management, increases the method retrieved and managed to satellite data.
In order to realize above-mentioned and related purpose, one or more aspects of the invention include will be explained in below and
The feature particularly pointed out in claim.Some illustrative aspects of the present invention are described in detail in following explanation and accompanying drawing.
However, some modes in the various modes of the principle that the present invention only can be used of these aspect instructions.It is in addition, of the invention
It is intended to include all these aspects and their equivalent.
Brief description of the drawings
By reference to the explanation and the content of claims below in conjunction with accompanying drawing, and with to the present invention more comprehensively
Understand, other purposes and result of the invention will be more apparent and should be readily appreciated that.In the accompanying drawings：
Fig. 1 shows the schematic flow sheet of remote sensing satellite data processing method according to embodiments of the present invention；And
Fig. 2 shows the logic diagram of remote sensing satellite data handling system according to embodiments of the present invention.
Identical label indicates similar or corresponding feature or function in all of the figs.
Embodiment
In the following description, for purposes of illustration, in order to provide the comprehensive understanding to one or more embodiments, explain
Many details are stated.It may be evident, however, that these embodiments can also be realized in the case of these no details.
In other examples, for the ease of describing one or more embodiments, known structure and equipment are shown in block form an.
The specific embodiment of the present invention is described in detail below with reference to accompanying drawing.
Fig. 1 shows the flow chart of remote sensing satellite data processing method according to embodiments of the present invention.
Remote sensing satellite data processing method provided by the invention, by the text attribute of remote sensing satellite data and contents attribute phase
With reference to break through the limitation of the simple remote sensing satellite data processing method based on text attribute, easily facilitating and remote sensing defended
Star filing data is managed.
Specifically, as shown in figure 1, remote sensing satellite data processing method provided by the invention mainly includes list processing (LISP), compiled
Mesh storage, image characteristics extraction, characteristics of image typing, text retrieval and content retrieval amount to six steps.
After reception system is completed the reception of remote sensing satellite data, recorded, the remote sensing satellite data for receiving, recording are entered
Row decompression conciliates format analysis processing and generates 0 DBMS file, and these 0 DBMS files then are carried out with list processing (LISP) and image is special
Sign extraction, i.e., the image characteristics extraction step shown in list processing (LISP) step and step S120 shown in step S110.
Wherein, list processing (LISP) is mainly completed by reading these 0 DBMS files, by being carried out to 0 DBMS file
Processing is carried out at sampling with generating the cataloguing meta data file of remote sensing satellite data to the view data in 0 DBMS file
Reason and wave band composition, generate the catalogue and browse map file of remote sensing satellite data.
Cataloguing meta data file generally comprises following information：Sensor information, scape data obtaining time information, scape central point
Longitude and latitude, the angle point longitude and latitude of scape four, scape center sun altitude and azimuth, scape cloud amount information, the starting of each scape data and knot
Beam line number.
In one embodiment of the invention, sensor information and scape are obtained by the receive information of satellite data
Data obtaining time information, scape central point longitude and latitude and scape corner longitude and latitude are calculated as follows method and calculated：
Temporal information and the posture of satellite corresponding to each row data central point are obtained from the assistance data in satellite data
Information；The ground intersection point of satellite sight and the earth is calculated, to calculate the latitude information of ground point corresponding to view data；Base area
The geographical feature of surface grids, obtain scape central point longitude and latitude and the angle point latitude and longitude information of scape four.
According to scape central point longitude and latitude and scape data obtaining time information, by correspondingly geometrical relationship model, calculate
Scape center sun altitude and azimuth and each scape data starting, terminate line number.
According to automatic cloud amount assessment algorithm, scape cloud amount information is calculated.
During the cataloguing meta data file of generation satellite data, logic is used to divide scape to generate the member of each scape data
Data file, so-called logic divide scape to refer to the definition according to satellite data ground grid, by the pixel in view data and ground
The longitude and latitude of face position is converted, so that it is determined that starting of the original position of ground grid in satellite data is with terminating line number
Process.Logic divide the process of scape pertain only to scape starting with terminate line number calculating, without by view data cut into using scape as
The data cell of size.Divide the unit that the satellite data of whole rail can be logically divided into units of scape by scape by logic.
Complete logic to divide after scape, then physics is carried out to the view data in 0 DBMS file and divides scape, so-called physics divides scape to refer to basis
View data is cut with terminating line number and exports into the process using scape as the data cell of size by the starting of scape.Complete physics
Divide after scape, the data sheet metaset that whole rail data are had been divided into units of scape.Each scape data cell is carried out again afterwards
To generate the catalogue and browse map file of each scape data, band combination refers to the difference of remote sensing satellite data for sampling and band combination
Wave band is mapped to R, G, B color space of view data, to generate coloured image.
Image characteristics extraction mainly by reading the DBMS file of satellite 0, is entered to the view data in 0 DBMS file
Row sampling processing is extracted in sampling image data file with obtaining the sampling image data file of the 0 DBMS file
The image feature information of view data, in the present embodiment, the color characteristic and textural characteristics of view data are make use of, in addition also
The characteristics of image of the feature as the view data such as spectral signature in view data, shape facility, spatial relationship can be extracted
Information.
Because color characteristic calculates simple and has very strong robustness, translation, rotation and dimensional variation for image
Insensitive, therefore, color characteristic is always one of principal character that CBIR uses.At one of the present invention
In embodiment, the extracting method of used color characteristic is as follows：
First, by the RGB color of the view data in sampling image data file be converted into HSV (Hue,
Saturation, Value, i.e. form and aspect, saturation degree, lightness) color space.Hsv color space is by form and aspect, saturation degree, brightness group
Into the calculation formula that RGB color is converted into hsv color space is as follows：
Then, the color histogram in the hsv color space after conversion is obtained.Color histogram needs to divide color space
For several small color intervals, each minizone turns into a bin of histogram.For example, in hsv color space, by H
(Hue, form and aspect) are quantified as 16 parts, and S (Saturation, saturation degree) and V (Value, lightness) are quantified as 4 equal portions respectively, so
HSV histograms just have 256 interval Bin.
Finally, the pixel quantity fallen by statistical color in color histogram in each minizone obtains hsv color sky
Between Color Statistical histogram.
Textural characteristics are an important features of remote sensing satellite image data, are to reflect that homogeneity phenomenon regards in image
Feel feature, generally refer to image in putting in order and the relation present in surrounding environment in structure, texture information for
Remote sensing images are distinguished to have very important significance.
In one embodiment of the invention, the extraction of textural characteristics is carried out using gray level co-occurrence matrixes (GLCM).Gray scale
Co-occurrence matrix is a kind of conventional textural characteristics statistical method, is proposed by Haralick in 1973.To the image of a width M × N
I, if including G gray level, then the gray level co-occurrence matrixes of diagram picture are G × G square formation MD, θ。MD, θIn element
Represent be respectively at a distance of d pixel, angle θ, gray value i and j pixel pair probability distribution：
MD, θ=prob { I (p1)=i, I (p2)=j }
Wherein, p1, p2For pixel i and j position, distance d and angle theta have following formula definition accordingly：
θ(p1, p2)=arctan ((y2-y1)/(x2-x1))
Usual MD, θIt is a symmetrical matrix, this symmetrical matrix can be by by a pair of pixels i, j are positive, reverse each system
Meter once obtains.MD, θValue on diagonal represents the probability distribution situation of the pixel with same grayscale value, away from cornerwise
Place reflects the probability distribution situation of high-contrast pixel pair in image；On the contrary, then reflect figure close to cornerwise place
The probability distribution situation of low contrast pixel pair as in.D and θ different values determine different gray level co-occurrence matrixes
(GLCM), usual selected parameter is d=1,2,3,4；θ=0 °, 45 °, 90 °, 135 °.
Textural characteristics based on GLCM represent it is to represent image, conventional statistics spy using some statistical natures of matrix
Sign have average (Mean), variance (Variance), angular second moment (Energy), entropy (Entropy), contrast (Contrast),
Correlation (Correlation), uniformity (Homogeneity) etc..
After list processing (LISP) and image characteristics extraction are completed, cataloguing meta data file, catalogue and browse picture and text acquisition
Part associates preservation with image feature information to database, so that the later stage carries out data retrieval.
Specifically, after list processing (LISP) completion, into step S130, that is, storing step of cataloguing, by reading cataloguing member
Data file and catalogue and browse map file, by the metadata information in meta data file of cataloguing and the path of catalogue and browse map file
Preserved in deposit database.
After image characteristics extraction completion, into step S140, i.e. characteristics of image storing step, by the color of generation,
Texture eigenvalue Data Enter is preserved into database, and by image feature information and 0 DBMS file, inventory information
It is associated with map file is browsed.
During cataloguing meta data file, catalogue and browse map file and image feature information is preserved, it is necessary to by these
Information is associated preservation, can fast and effeciently to obtain required inspection in the remote sensing satellite data retrieval process in later stage
Rope data.Because each scape data have it uniquely to number, referred to as scape ID.By taking Landsat-8 satellites as an example, scape ID is typically
Identified by satellite, scape ground grid numbering, data receipt time form, receiving station's information etc. forms, such as：LC81340402014132KHC00, its Satellite identification number, 134 and 040 is that scape ground grid is numbered, and 2014132 when being data
Between, KHC is reception station identifications.Therefore cataloguing meta data file, catalogue and browse map file and image feature information can use scape ID
To associate.
In addition, cataloguing meta data file, catalogue and browse map file and image feature information in units of scape in database
Preserved, Various types of data keeps the association to 0 DBMS, after retrieving required data, can get correspondingly
Satellite data file, i.e. 0 DBMS file.Similar to scape ID, 0 DBMS file has its unique data ID, referred to as 0 series
According to ID.0 DBMS file is stored in file system, in the information such as its file path and scape ID deposit database.Catalogue first number
0 DBMS ID can be associated according to file, catalogue and browse map file and image feature information.At the data for completing 0 DBMS file
After reason and preservation, it is possible to targetedly 0 DBMS file in database is retrieved.
Remote sensing satellite data processing method provided by the invention can support text attribute and contents attribute be combined it is distant
Satellite data retrieval is felt, to overcome existing single use content retrieval can not meet the defects of the needs of some application-specifics.
Wherein, the retrieval of text attribute, can complete it is based on text attribute, utilize meta data file information carry out satellite
Data retrieval, specifically include base attribute retrieval and geographic range retrieval.Search condition includes：When satellite designation, data acquisition
Between, base attribute information and the spatial dimension geography information such as cloud amount, ranks number.
Base attribute is retrieved, for according to the basic text inspection such as data time, sensor name, ranks number and cloud amount information
Rope condition, complete the retrieval to remote sensing satellite data.Each search condition can be retrieved individually, and multiple search conditions also can be combined
Retrieved.Retrieval result returns to the metadata information and browse graph information for the satellite data for meeting search condition.
Geographic range is retrieved, for according to ground range information, completing the inquiry to satellite data, being mainly to be used to examine
The satellite scape data that rope intersects with specified geographic range region.Satellite scape data form a matrix area mesh by its four angle point
Mark, can also regard a point target represented by its central point as.Spatial dimension geography information is a kind of spatial information, uses number
Stored according to the spatial relationship data library module in storehouse.Retrieval for geographical range information is needed by spatial relationship number
Completed according to library module.Retrieval result returns and the metadata information of specifying satellite scape data that geographic range region intersects and clear
Look at figure information.The API that geographic range searching step utilization space Relation DB module provides is completed to the geographical letter of spatial dimension
The retrieval of breath.
The retrieval of contents attribute, the retrieval to characteristics of image can be completed, extract the characteristics of image of input image data, carry
The image feature data taken includes color, texture, and is characterized as that condition is retrieved to image feature data information with this, carries out
Similarity mode, to obtain required data.The retrieval flow of contents attribute specifically include image characteristics extraction, similarity-rough set and
Sort result.
Wherein, image characteristics extraction, the image feature information of the image data for extracting input.
Similarity-rough set, for the characteristic information of the image data according to input, the image with image data in database
Characteristic information carries out similarity-rough set, and search backout feature compares 0 similar DBMS information.
Sort result, for according to feature weight, arranging retrieval result (aspect ratio is to 0 similar DBMS information)
Sequence.
The input of the retrieval of contents attribute can be the retrieval result of text attribute view data or in addition it is defeated
The image data entered.
When the input of the retrieval of contents attribute is the view data of the retrieval result of text attribute, contents attribute is being carried out
Retrieving in, to 0 DBMS carry out sampling processing and export sampling after image data file, afterwards to the figure after sampling
As data file, the image characteristics extraction of color, Texture eigenvalue is carried out, is then returned by similarity-rough set and sort result
With input image data similar in 0 DBMS information.
When the input of the retrieval of contents attribute is the image data inputted in addition, first with image characteristics extraction step pair
The image data of input carries out image characteristics extraction and then returned and input image data by similarity-rough set and sort result
Similar 0 DBMS information.
Corresponding with above-mentioned remote sensing satellite data processing method, the present invention also provides a kind of remote sensing satellite data processing system
System.Fig. 2 shows the logic diagram of remote sensing satellite data handling system according to embodiments of the present invention.
As shown in Fig. 2 processing system includes list processing (LISP) unit 210, image at remote sensing satellite data provided by the invention
Feature extraction unit 220, memory cell 230 and retrieval unit 240.
Wherein, list processing (LISP) unit 210 is used to carry out list processing (LISP) according to 0 DBMS file, to generate remote sensing satellite number
According to cataloguing meta data file and catalogue and browse map file；Image characteristics extraction unit 220, for by 0 DBMS
The sampling image data file of file carries out the image characteristics extraction for including color and texture, to generate the 0 DBMS file
Image feature information；Memory cell 230, for meta data file, catalogue and browse map file and the described image feature of cataloguing
Information association is preserved to database；Retrieval unit 240, mutually tied with contents attribute for carrying out text attribute in the database
The remote sensing satellite data retrieval of conjunction, wherein, the search condition of the contents attribute is the figure of the retrieval result of the text attribute
The image data information inputted as data message or in addition.
Further, retrieval unit 240 can also include text attribute retrieval unit 241 and contents attribute retrieval unit 242,
Text attribute retrieval unit 241 specifically includes base attribute retrieval unit 2411 and geographic range retrieval unit 2412；Content category
Property retrieval unit 242 specifically includes image characteristics extraction unit 2421, similarity-rough set unit 2422 and sort result unit
2423。
Specifically, base attribute retrieval unit 2411, for according to basic text retrieval condition, completing to remote sensing satellite number
According to retrieval, the basic text retrieval condition includes data time, sensor name, ranks number and cloud amount information；Geographical model
Retrieval unit 2412 is enclosed, for according to spatial dimension geography information, completing to specifying the intersecting satellite data in geographic range region
Retrieval.
Image characteristics extraction unit 2421, the characteristic information of the image data for extracting input；Similarity-rough set unit
2422, for the characteristic information of the image data according to input, similarity-rough set is carried out with the image feature information in database,
Search for backout feature and compare 0 similar DBMS information；Sort result unit 2423, for according to feature weight, being returned to described
The aspect ratio returned is ranked up to 0 similar DBMS information.
Remote sensing satellite data processing method and system provided by the invention can be seen that by the statement of above-described embodiment,
The scientific processing to remote sensing satellite data message is passed through, association preserves corresponding cataloguing meta data file, catalogue and browse text
The image feature information of part and sampling image data, can effectively overcome existing single use content retrieval not meet some
The defects of demand of application-specific, while the retrieval to remote sensing satellite data text attribute and contents attribute is completed, effectively improve
The precision of retrieval.
Remote sensing satellite data processing method and system according to the present invention is described in an illustrative manner above with reference to accompanying drawing.But
It is, it will be appreciated by those skilled in the art that the remote sensing satellite data processing method and system that are proposed for the invention described above, also
Various improvement can be made on the basis of present invention is not departed from.Therefore, protection scope of the present invention should be by appended
The content of claims determines.
Claims (10)
1. a kind of remote sensing satellite data processing method, including：
List processing (LISP) and image characteristics extraction are carried out according to 0 DBMS file, wherein, the list processing (LISP) is defended for generating remote sensing
The cataloguing meta data file and catalogue and browse map file of sing data, described image feature extraction pass through to the 0 DBMS file
Sampling image data file carry out the image characteristics extraction for including color and texture, to generate the figure of the 0 DBMS file
As characteristic information；Wherein,
During the cataloguing meta data file of generation remotely-sensed data, logic is used to divide scape to generate the metadata of each scape data
File, logic divide scape to be that whole rail and satellite data are logically divided into unit in units of scape, and logic divides scape to pertain only to scape
Starting and the calculating for terminating line number, after completing logic and dividing scape, physics point is carried out to the view data in 0 DBMS file
Scape, physics divide scape to be to cut view data and export into the data cell using scape as size；
Each scape data cell is sampled the catalogue and browse map file and band combination is generated；
The cataloguing meta data file, catalogue and browse map file and the association of described image characteristic information are preserved to database；
The remote sensing satellite data retrieval that progress text attribute and contents attribute are combined in the database, wherein, it is described interior
Hold the search condition of attribute for image data information or the picture number that inputs in addition of the retrieval result of the text attribute it is believed that
Breath.
2. remote sensing satellite data processing method as claimed in claim 1, wherein,
The cataloguing meta data file includes following information：Sensor information, scape data obtaining time information, scape central point longitude and latitude
Degree, the angle point longitude and latitude of scape four, scape center sun altitude and azimuth, scape cloud amount information, the starting and ending row of each scape data
Number.
3. remote sensing satellite data processing method as claimed in claim 2, wherein,
The sensor information and scape data obtaining time information are obtained by the receive information of remote sensing satellite data.
4. remote sensing satellite data processing method as claimed in claim 3, wherein, the scape central point longitude and latitude and scape corner warp
Method acquisition is calculated as follows in latitude：
The attitude information of temporal information and satellite corresponding to each row data central point is obtained from the assistance data in satellite data；
The ground intersection point of satellite sight and the earth is calculated, to calculate the latitude information of ground point corresponding to view data；
The geographical feature of base area surface grids, obtain scape central point longitude and latitude and the angle point latitude and longitude information of scape four.
5. remote sensing satellite data processing method as claimed in claim 1, wherein, during color feature extracted is carried out,
The RGB color of view data in sampling image data file is converted into hsv color space；
Obtain the color histogram in the hsv color space after conversion；
The pixel quantity fallen by statistical color in the color histogram in each minizone obtains the hsv color space
Color Statistical histogram.
6. remote sensing satellite data processing method as claimed in claim 1, wherein,
The extraction of textural characteristics is carried out using gray level co-occurrence matrixes.
7. remote sensing satellite data processing method as claimed in claim 1, wherein, the retrieval of the text attribute includes：
Base attribute is retrieved, for according to basic text retrieval condition, completing the retrieval to remote sensing satellite data, the text substantially
This search condition includes data time, sensor name, ranks number and cloud amount information；
Geographic range is retrieved, for according to spatial dimension geography information, completing to specifying the intersecting satellite number in geographic range region
According to retrieval.
8. remote sensing satellite data processing method as claimed in claim 1, wherein, it is defeated in addition in the search condition of contents attribute
During the image data information entered, the retrieval of the contents attribute includes：
Image characteristics extraction, the characteristic information of the image data for extracting input；
Similarity-rough set, for the characteristic information of the image data according to input, carried out with the image feature information in database
Similarity-rough set, search backout feature compare 0 similar DBMS information；
Sort result, for according to feature weight, being ranked up to the aspect ratio of the return to 0 similar DBMS information.
9. a kind of remote sensing satellite data handling system, including：
List processing (LISP) unit, for carrying out list processing (LISP) according to 0 DBMS file, to generate the cataloguing member number of remote sensing satellite data
According to file and catalogue and browse map file；Wherein,
During the cataloguing meta data file of generation remotely-sensed data, logic is used to divide scape to generate the metadata of each scape data
File, logic divide scape to be that whole rail and satellite data are logically divided into unit in units of scape, and logic divides scape to pertain only to scape
Starting and the calculating for terminating line number, after completing logic and dividing scape, physics point is carried out to the view data in 0 DBMS file
Scape, physics divide scape to be to cut view data and export into the data cell using scape as size；
Each scape data cell is sampled the catalogue and browse map file and band combination is generated；Image characteristics extraction
Unit, for carrying out the characteristics of image for including color and texture by the sampling image data file to the 0 DBMS file
Extraction, to generate the image feature information of the 0 DBMS file；
Memory cell, for cataloguing meta data file, catalogue and browse map file and the described image characteristic information to be associated
Preserve to database；
Retrieval unit, for the remote sensing satellite data inspection for carrying out text attribute in the database and contents attribute is combined
Rope, wherein, the search condition of the contents attribute is the image data information or defeated in addition of the retrieval result of the text attribute
The image data information entered.
10. remote sensing satellite data handling system as claimed in claim 9, wherein, the retrieval unit further comprises：
Base attribute retrieval unit, for according to basic text retrieval condition, completing the retrieval to remote sensing satellite data, the base
This text search condition includes data time, sensor name, ranks number and cloud amount information；
Geographic range retrieval unit, for according to spatial dimension geography information, completing to specifying geographic range region is intersecting to defend
The retrieval of sing data；
Image characteristics extraction unit, the characteristic information of the image data for extracting input；
Similarity-rough set unit, for the characteristic information of the image data according to input, with the image feature information in database
Similarity-rough set is carried out, search backout feature compares 0 similar DBMS information；
Sort result unit, for according to feature weight, arranging 0 similar DBMS information the aspect ratio of the return
Sequence.
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| CN104516975B (en) * | 2014-12-29 | 2019-03-22 | 中国科学院电子学研究所 | Automatic correlation method towards multivariate data |
| CN104900248B (en) * | 2015-05-27 | 2017-05-24 | 中国资源卫星应用中心 | Method for rapidly extracting land observing satellite scene data from magnetic tape |
| CN104899282B (en) * | 2015-06-02 | 2018-11-27 | 北京博阳世通信息技术有限公司 | A kind of processing method and search method of multi-source multi-temporal satellite remote sensing tile data |
| CN106294705B (en) * | 2016-08-08 | 2017-12-15 | 长安大学 | A kind of batch remote sensing image preprocess method |
| CN106845452B (en) * | 2017-02-23 | 2020-07-14 | 中国公路工程咨询集团有限公司 | Beidou-based high-resolution remote sensing data processing method |
| CN110019879B (en) * | 2017-07-31 | 2021-03-05 | 清华大学 | Satellite remote sensing image searching method and device |
| CN107680094A (en) * | 2017-10-17 | 2018-02-09 | 南昌工程学院 | A kind of water environment Remote Sensing Data Processing method and device |
| CN107944503A (en) * | 2017-12-13 | 2018-04-20 | 北京数洋智慧科技有限公司 | A kind of Urban Data acquisition method and system based on image recognition |
| CN109299298A (en) * | 2018-09-19 | 2019-02-01 | 北京悦图遥感科技发展有限公司 | Construction method, device, application method and the system of image fusion model |
| CN109947700B (en) * | 2019-01-30 | 2021-08-27 | 上海卫星工程研究所 | Satellite-borne imaging data management method based on multi-level file symbols |
| CN110059216B (en) * | 2019-04-27 | 2020-12-22 | 西安科技大学 | Remote sensing image retrieval method and system |
| CN110413828B (en) * | 2019-07-31 | 2021-08-31 | 中国电子科技集团公司第五十四研究所 | Remote sensing mass image automatic screening method based on optimized genetic algorithm |
| CN116634167B (en) * | 2023-07-24 | 2023-11-07 | 中国科学院空天信息创新研究院 | Satellite imaging data storage and extraction method |
| CN116668435B (en) * | 2023-08-01 | 2023-11-10 | 中国科学院空天信息创新研究院 | Interactive real-time remote sensing product generation method, device and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101241504A (en) * | 2008-01-23 | 2008-08-13 | 武汉大学 | Remote sense image data intelligent search method based on content |
| CN103530409A (en) * | 2013-10-25 | 2014-01-22 | 中国科学院对地观测与数字地球科学中心 | Searching method and searching system for remote sensing images |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040158832A1 (en) * | 2003-01-28 | 2004-08-12 | Gal Chechik | Method and system for scheduling image acquistion events based on dynamic programming |
-
2014
- 2014-07-14 CN CN201410334555.XA patent/CN104077411B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101241504A (en) * | 2008-01-23 | 2008-08-13 | 武汉大学 | Remote sense image data intelligent search method based on content |
| CN103530409A (en) * | 2013-10-25 | 2014-01-22 | 中国科学院对地观测与数字地球科学中心 | Searching method and searching system for remote sensing images |
Non-Patent Citations (4)
| Title |
|---|
| "基于内容的遥感影像检索";杨永明;《中国优秀硕士学位论文全文数据库 信息科技辑》;20111215(第S2期);I140-1292 * |
| "基于内容遥感影像检索技术研究";乔荣华;《中国优秀硕士学位论文全文数据库 信息科技辑》;20071115(第05期);第5页第第2章2.1节-第36页第2章第2.7节，第50页第4章第4.1节-第55页第4张第4.6节，图2.1-2.15，图4.1-4.4 * |
| "遥感卫星数据编目存档系统的研制";汪超亮;《地球信息科学》;20050630;第7卷(第2期);第116-119页 * |
| "遥感卫星数据获取与处理关键技术概述";冯钟葵等;《遥感信息》;20081104(第4期);第92页右栏倒数第2段-第94页左栏第7段，第95页右栏第1段-第96页右栏第1段 * |
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