CN106845452A - High score Remote Sensing Data Processing method based on the Big Dipper - Google Patents
High score Remote Sensing Data Processing method based on the Big Dipper Download PDFInfo
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- CN106845452A CN106845452A CN201710099302.2A CN201710099302A CN106845452A CN 106845452 A CN106845452 A CN 106845452A CN 201710099302 A CN201710099302 A CN 201710099302A CN 106845452 A CN106845452 A CN 106845452A
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- 238000003672 processing method Methods 0.000 title claims abstract description 26
- 238000007781 pre-processing Methods 0.000 claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 17
- 238000000034 method Methods 0.000 claims abstract description 15
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/332—Query formulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration by the use of more than one image, e.g. averaging, subtraction
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20221—Image fusion; Image merging
Abstract
The invention discloses a kind of high score Remote Sensing Data Processing method based on the Big Dipper, including：0 grade of remote sensing satellite data is obtained, the remote sensing satellite data of acquisition are divided into by N number of classification according to atural object characteristic；The remote sensing satellite data of each classification are assigned to a calculate node；Remote sensing satellite data of each calculate node to being assigned to the calculate node carry out list processing (LISP) and image characteristics extraction, list processing (LISP) is used to generate the cataloguing meta data file of remote sensing satellite data, image characteristics extraction is used to extract the image feature information that remote sensing satellite data carry out color and texture, and cataloguing meta data file and image feature information are associated；Each calculate node carries out image preprocessing according to the cataloguing meta data file and image feature information that obtain；The retrieval of remote sensing satellite data is carried out in database according to text attribute, main controlled node transfers the image preprocessing result of retrieval, and merged and shown.This method can be rapidly processed to remote sensing satellite data.
Description
Technical field
The invention belongs to the technical field of data administration of remote sensing satellite, more particularly to a kind of high score remote sensing number based on the Big Dipper
According to processing method.
Background technology
Modern Remote Sensing Technical is developed rapidly, the particularly development of various countries' earth observation technology, satellite remote sensing sensor point
The rapid raising of resolution, the ability that remote sensing obtains data is more and more stronger, and the precision and acquisition frequency of the data for being obtained are also more next
It is higher.All kinds of high spatial resolutions, high spectral resolution, high time resolution satellite launch, and are remote sensing technique application
In transportation industry there is provided basic data source guarantee.The down-link satellite remotely-sensed data of remote sensing satellite completes data by reception system
Reception, record after, need to receive, record remote sensing satellite data carry out decompression conciliate format analysis processing generate 0 DBMS
File, is achieved in order to be saved in the media such as disk.For convenience remote sensing satellite data retrieval and acquisition, it is necessary to
Carry out list processing (LISP) before being achieved to 0 DBMS file, generate the metadata information of remote sensing satellite, while by metadata information
It is entered into relational database.Because remote sensing satellite data individual data file is very big, allow for being carried to Characteristics of The Remote Sensing Images
When taking, the time of cost is especially long, so as to have impact on the efficiency of data processing.
The content of the invention
In order to overcome the deficiencies in the prior art, the invention provides a kind of high score Remote Sensing Data Processing side based on the Big Dipper
Method, the method can be processed rapidly remote sensing satellite data, with expeditiously to display remote sensing satellite image.
The present invention provide technical scheme be：
A kind of high score Remote Sensing Data Processing method based on the Big Dipper, including：
0 grade of remote sensing satellite data is obtained, the 0 grade of remote sensing satellite data for obtaining are divided into by N number of classification, N according to atural object characteristic>
=2；
0 grade of remote sensing satellite data of each classification are assigned to a calculate node；
0 grade remote sensing satellite data of each calculate node to being assigned to the calculate node carry out list processing (LISP) and image is special
Extraction is levied, wherein, the list processing (LISP) is used to generate the cataloguing meta data file of remote sensing satellite data, described image feature extraction
It is for being extracted to the image feature information that 0 grade of remote sensing satellite data carry out color and texture and the cataloguing is first
Data file and described image characteristic information are associated；
It is pre- that each calculate node carries out image according to the cataloguing meta data file and described image characteristic information that obtain
Treatment, and image preprocessing result is stored into database；
Carry out the retrieval of remote sensing satellite data in the database according to text attribute, main controlled node is from the database
In transfer the image preprocessing result of retrieval, and merged and shown.
Preferably, the described high score Remote Sensing Data Processing method based on the Big Dipper, the cataloguing meta data file includes：
Sensor information, scape data obtaining time information, scape central point longitude and latitude, the angle point longitude and latitude of scape four, scape center altitude of the sun and
Azimuth, scape cloud amount information, the starting and ending line number of each scape data.
Preferably, the described high score Remote Sensing Data Processing method based on the Big Dipper, the atural object characteristic includes：Building
Thing, vegetation, mountain range, water body, wherein, the water body includes ocean water body and Inland Water.
Preferably, the described high score Remote Sensing Data Processing method based on the Big Dipper, the main controlled node is from the data
The image preprocessing result of retrieval is transferred in storehouse, and is merged and is shown, it is described to be shown as：Based on cataloguing metadata text
The layering remote sensing images from earth's surface to underground of part show and the earth's surface remote sensing images based on cataloguing meta data file show.
Preferably, the described high score Remote Sensing Data Processing method based on the Big Dipper, it is described based on cataloguing meta data file
The layering remote sensing images from earth's surface to underground show and specifically also include：Carried out from earth's surface to underground according to the circuit that user selects
Layering manifestation remote sensing images.
Preferably, the described high score Remote Sensing Data Processing method based on the Big Dipper, described image pretreatment includes successively：
The standardization cutting of the smart correction of the registration of image, image, Atmospheric Correction and image.
Preferably, the described high score Remote Sensing Data Processing method based on the Big Dipper, the main controlled node is from the data
The image preprocessing result of retrieval is transferred in storehouse, and is merged and is shown, the fusion process includes：Geometric correction
Journey, Fusion Edges process and output procedure.
Preferably, the described high score Remote Sensing Data Processing method based on the Big Dipper, described to carry out the figure of the color
During as feature information extraction, the RGB color of the view data in sampling image data file is converted into hsv color empty
Between；Obtain the color histogram in the hsv color space after conversion；Being fallen in the color histogram by statistical color, each is small
Pixel quantity in area obtains the Color Statistical histogram in the hsv color space；Line is carried out by using gray level co-occurrence matrixes
Manage the extraction of feature.
Preferably, the described high score Remote Sensing Data Processing method based on the Big Dipper, the sensor information and scape data
Temporal information is obtained to be obtained by the receive information of remote sensing satellite data.
Preferably, the described high score Remote Sensing Data Processing method based on the Big Dipper, 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 base
Presents 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 defending of specifying geographic range region intersecting
The retrieval of sing data.
The present invention at least includes following beneficial effect：Due to the 0 grade of remote sensing satellite data point that will be obtained according to atural object characteristic
Into N number of classification, 0 grade of remote sensing satellite data of each classification are assigned to a calculate node；Allow each calculate node to assigning
0 grade of remote sensing satellite data to the calculate node carry out list processing (LISP) and image characteristics extraction, it is ensured that the treatment of remotely-sensed data
Efficiency.And because each calculate node carries out image according to the cataloguing meta data file and described image characteristic information that obtain
Pretreatment, and image preprocessing result is stored into database, the preprocessing process of remote sensing satellite image is separately located
Reason so that image can be processed in time, while also improving main controlled node carries out the speed of image co-registration, accelerate figure
The display speed of picture.
Further advantage of the invention, target and feature embody part by following explanation, and part will also be by this
The research and practice of invention and be understood by the person skilled in the art.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the high score Remote Sensing Data Processing method based on the Big Dipper of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation is described, it is clear that described embodiment is only a part of embodiment of the invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is all other that those of ordinary skill in the art are obtained under the premise of creative work is not made
Embodiment, belongs to the scope of protection of the invention.
To make the advantage of technical solution of the present invention clearer, the present invention is made specifically with reference to the accompanying drawings and examples
It is bright.
As shown in figure 1, the high score Remote Sensing Data Processing method based on the Big Dipper provided in an embodiment of the present invention, including：
The 0 grade of remote sensing satellite data for obtaining are divided into N number of class by S101,0 grade of remote sensing satellite data of acquisition according to atural object characteristic
Not, N>=2.
Wherein, the atural object includes：Building, vegetation, mountain range, water body, wherein, the water body includes ocean water body and interior
Lu Shuiti.
It should be noted that the reception of remote sensing satellite data is completed in reception system, after record, to what is received, record
Remote sensing satellite data carry out decompression and conciliate format analysis processing 0 grade of remotely-sensed data file of generation.According to atural object characteristic to obtain 0 grade
Remote sensing satellite data are classified, and this needs processing system to be divided remote sensing satellite data according to the characteristics of atural object data
Class, each atural object has the data parameters of oneself.Atural object is not limited to the several types enumerated listed by the embodiment of the present invention,
This needs according to actual conditions or focuses on to need the species studied to be divided.
S102,0 grade of remote sensing satellite data of each classification are assigned to a calculate node.
Respective calculate node is respectively sent to after classifying to 0 grade of remote sensing satellite data, each classification has respectively
Oneself corresponding calculate node, on the whole for, improve data processing speed.
The 0 grade of remote sensing satellite data of S103, each calculate node to being assigned to the calculate node carry out list processing (LISP) and
Image characteristics extraction, wherein, the list processing (LISP) is used to generate the cataloguing meta data file of remote sensing satellite data, and described image is special
Extraction is levied for being extracted to the image feature information that 0 grade of remote sensing satellite data carry out color and texture, and will be described
Cataloguing meta data file and described image characteristic information are associated.
Wherein, the cataloguing meta data file includes：Sensor information, scape data obtaining time information, scape central point warp
Latitude, the angle point longitude and latitude of scape four, scape center altitude of the sun and azimuth, scape cloud amount information, the starting and ending row of each scape data
Number.The sensor information and scape data obtaining time information are obtained by the receive information of remote sensing satellite data.
It is described to be carried in the image feature information for carrying out the color in the another embodiment of the embodiment of the present invention
When taking, the RGB color of the view data in sampling image data file is converted into hsv color space；After obtaining conversion
Hsv color space color histogram；Fallen the pixel count in the color histogram in each cell by statistical color
Measure the Color Statistical histogram in the hsv color space；By the extraction that textural characteristics are carried out using gray level co-occurrence matrixes.
It should be noted that image characteristics extraction is mainly by reading 0 grade of remote sensing satellite data file, to remote sensing satellite
View data in data file carries out sampling processing to obtain the sampling image data file of 0 grade of remote sensing satellite data, enters
And the image feature information of view data in sampling image data file is extracted, in the present embodiment, make use of view data
Color characteristic and textural characteristics, it can in addition contain pass through to extract the spectral signature in view data, shape facility etc..Texture
It is characterized in another important feature of remote sensing satellite image data, is the visual signature for reflecting homogeneity phenomenon in image, is
Refer to image putting in order and the relation existing for surrounding environment in structure.
The cataloguing meta data file and described image characteristic information are associated, are that remote sensing is defended for the ease of the later stage
Sing data can fast and effeciently obtain required retrieval data when retrieving.
S104, each calculate node carry out figure according to the cataloguing meta data file and described image characteristic information that obtain
As pretreatment, and image preprocessing result is stored into database.
Specifically, described image pretreatment includes successively：The smart correction of the registration of image, image, Atmospheric Correction and image
Standardization cutting.
S105, the retrieval for carrying out remote sensing satellite data in the database according to text attribute, main controlled node is from described
The image preprocessing result of retrieval is transferred in database, and is merged and is shown.
In another preferred embodiment of the embodiment of the present invention, the main controlled node transfers retrieval from the database
Image preprocessing result, and merged and shown, it is described to be shown as：Based on cataloguing meta data file from earth's surface to
The layering remote sensing images of underground show and the earth's surface remote sensing images based on cataloguing meta data file show.
Specifically, the layering remote sensing images from earth's surface to underground based on cataloguing meta data file show and specifically also wrap
Include：Layering manifestation remote sensing images are carried out from earth's surface to underground according to the circuit that user selects.
The setting of Layering manifestation facilitates the image information that user is checked below earth's surface, such as in transport development, understands earth's surface
Following data message can be planned more effectively transport development.
Specifically, the main controlled node transfers the image preprocessing result of retrieval from the database, and merged
In being shown, the fusion process includes：Geometric correction process, Fusion Edges process and output procedure.
It is more accurate that fusion treatment process causes image to show, soft, lines are not lofty.
Specifically, 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 base
Presents search condition includes data time, sensor name, ranks number and cloud amount information；Search condition can be individually to carry out
Retrieval, it is also possible to which the multiple search conditions of combination are retrieved.
Geographic range is retrieved, for according to spatial dimension geography information, completing to defending of specifying geographic range region intersecting
The retrieval of sing data.
Although embodiment of the present invention is disclosed as above, it is not restricted to listed in specification and implementation method
With, it can be applied to various suitable the field of the invention completely, for those skilled in the art, can be easily
Other modification is realized, therefore under the universal limited without departing substantially from claim and equivalency range, the present invention is not limited
In specific details and shown here as the legend with description.
Claims (10)
1. a kind of high score Remote Sensing Data Processing method based on the Big Dipper, it is characterised in that including：
0 grade of remote sensing satellite data is obtained, the 0 grade of remote sensing satellite data for obtaining are divided into by N number of classification, N according to atural object characteristic>=2；
0 grade of remote sensing satellite data of each classification are assigned to a calculate node；
0 grade remote sensing satellite data of each calculate node to being assigned to the calculate node carry out list processing (LISP) and characteristics of image is carried
Take, wherein, the list processing (LISP) is used to generate the cataloguing meta data file of remote sensing satellite data, and described image feature extraction is used for
The image feature information that 0 grade of remote sensing satellite data carry out color and texture is extracted, and by the cataloguing metadata
File and described image characteristic information are associated；
Each calculate node carries out image preprocessing according to the cataloguing meta data file and described image characteristic information that obtain,
And store into database image preprocessing result；
Carry out the retrieval of remote sensing satellite data in the database according to text attribute, main controlled node is adjusted from the database
The image preprocessing result of retrieval is taken, and is merged and is shown.
2. the high score Remote Sensing Data Processing method of the Big Dipper is based on as claimed in claim 1, it is characterised in that cataloguing unit number
Include according to file：Sensor information, scape data obtaining time information, scape central point longitude and latitude, the angle point longitude and latitude of scape four, scape center
Altitude of the sun and azimuth, scape cloud amount information, the starting and ending line number of each scape data.
3. the high score Remote Sensing Data Processing method of the Big Dipper is based on as claimed in claim 1, it is characterised in that the atural object characteristic
Including：Building, vegetation, mountain range, water body, wherein, the water body includes ocean water body and Inland Water.
4. the high score Remote Sensing Data Processing method of the Big Dipper is based on as claimed in claim 2, it is characterised in that the main controlled node
The image preprocessing result of retrieval is transferred from the database, and is merged and is shown, it is described to be shown as：Based on volume
The layering remote sensing images from earth's surface to underground of mesh meta data file show and the earth's surface remote sensing figure based on cataloguing meta data file
As display.
5. the high score Remote Sensing Data Processing method of the Big Dipper is based on as claimed in claim 4, it is characterised in that described based on cataloguing
The layering remote sensing images from earth's surface to underground of meta data file show and specifically also include：The circuit selected according to user is from earth's surface
Layering manifestation remote sensing images are carried out to underground.
6. the high score Remote Sensing Data Processing method of the Big Dipper is based on as claimed in claim 1, it is characterised in that described image is located in advance
Reason includes successively：The standardization cutting of the smart correction of the registration of image, image, Atmospheric Correction and image.
7. the high score Remote Sensing Data Processing method of the Big Dipper is based on as claimed in claim 4, it is characterised in that the main controlled node
The image preprocessing result of retrieval is transferred from the database, and is merged and is shown, the fusion process includes：
Geometric correction process, Fusion Edges process and output procedure.
8. the high score Remote Sensing Data Processing method of the Big Dipper is based on as claimed in claim 1, it is characterised in that described to carry out institute
When the image feature information for stating color is extracted, the RGB color of the view data in sampling image data file is converted into
Hsv color space；Obtain the color histogram in the hsv color space after conversion；Fallen in the color histogram by statistical color
Pixel quantity in figure in each cell obtains the Color Statistical histogram in the hsv color space；By using gray scale symbiosis
Matrix carries out the extraction of textural characteristics.
9. the high score Remote Sensing Data Processing method of the Big Dipper is based on as claimed in claim 2, it is characterised in that the sensor letter
Breath and scape data obtaining time information are obtained by the receive information of remote sensing satellite data.
10. the high score Remote Sensing Data Processing method of the Big Dipper is based on as claimed in claim 1, it is characterised in that the text category
The retrieval of property includes：
Base attribute is retrieved, for according to basic text retrieval condition, completing the retrieval to remote sensing satellite data, the text substantially
Part 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 the satellite number to specifying geographic range region intersecting
According to retrieval.
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| 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 |
| CN109657080A (en) * | 2018-09-29 | 2019-04-19 | 中国科学院上海高等研究院 | Distributed approach/the system and medium of high score satellite remote sensing date |
| CN111948687A (en) * | 2020-08-07 | 2020-11-17 | 上海卫星工程研究所 | Distributed multi-source information fusion system suitable for multi-satellite formation |
| CN112113544A (en) * | 2019-06-20 | 2020-12-22 | 四川轻化工大学 | Remote sensing data processing method and system based on unmanned aerial vehicle image |
| CN112560833A (en) * | 2021-03-01 | 2021-03-26 | 广州汇图计算机信息技术有限公司 | Information display system based on remote sensing image |
| CN115358486A (en) * | 2022-09-20 | 2022-11-18 | 中咨数据有限公司 | Port freight volume prediction method, system and application based on three-dimensional satellite image |
| CN116597013A (en) * | 2023-07-17 | 2023-08-15 | 山东产研卫星信息技术产业研究院有限公司 | Satellite image geometric calibration method based on different longitude and latitude areas |
| CN116634167A (en) * | 2023-07-24 | 2023-08-22 | 中国科学院空天信息创新研究院 | Satellite imaging data storage and extraction method |
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| CN112560833A (en) * | 2021-03-01 | 2021-03-26 | 广州汇图计算机信息技术有限公司 | Information display system based on remote sensing image |
| CN112560833B (en) * | 2021-03-01 | 2021-05-11 | 广州汇图计算机信息技术有限公司 | Information display system based on remote sensing image |
| CN115358486A (en) * | 2022-09-20 | 2022-11-18 | 中咨数据有限公司 | Port freight volume prediction method, system and application based on three-dimensional satellite image |
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| CN116597013A (en) * | 2023-07-17 | 2023-08-15 | 山东产研卫星信息技术产业研究院有限公司 | Satellite image geometric calibration method based on different longitude and latitude areas |
| CN116597013B (en) * | 2023-07-17 | 2023-09-26 | 山东产研卫星信息技术产业研究院有限公司 | Satellite image geometric calibration method based on different longitude and latitude areas |
| CN116634167A (en) * | 2023-07-24 | 2023-08-22 | 中国科学院空天信息创新研究院 | Satellite imaging data storage and extraction method |
| 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 |
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