CN109544473A - A kind of optical satellite relative detector calibration coefficient calculation method, system and medium - Google Patents
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Abstract
A kind of optical satellite relative detector calibration coefficient calculation method includes the following steps: that the file attribute for obtaining the data after selecting for the first time indexes Step 1: select for the first time to data source；Step 2: classifying to the data after selecting for the first time, sorted data are obtained；Step 3: data list and the necessary execution information of each calculate node are written in standardization task inventory by the sorted data of step by the quantity mean allocation of investment calculate node；Step 4: standardization task inventory is sent in sequence in other each sub- calculate nodes by initial calculation node；Each sub- calculate node generates standardization statistical data file, then sends back initial calculation node；Step 5: obtaining relative detector calibration coefficient using histogram matching.The method of the present invention obtains the grey level probability density histogram of standard by the multi-disc CCD synthesis of satellite in orbit, can take into account the relative radiation consistency between single CCD itself and multi-disc CCD piece.
Description
Technical field
The present invention relates to a kind of optical satellite relative detector calibration coefficient calculation method, system and media, belong to remote sensing shadow
As process field.
Background technique
Remote sensing image relative detector calibration is a basic data processing work, is to improve remote sensing satellite image quality
One extremely important approach.Relative radiometric correction method is broadly divided into scaling method and statistic law, compared to scaling method, statistic law
Special training data is not needed, directly extracts correction coefficient from image by analyzing and counting, to carry out school to image
Just, therefore 0 grade of image data being distributed of current optical satellite image central station is suitble to carry out using statistic law
Relative detector calibration.
The gray scale spacing of image can be made to widen using histogram modification or keep intensity profile uniform, to make the details of image
It is more clear, achievees the purpose that enhancing.Histogram Matching (histogram specification) be by establishing histogram look-up table, will be original
On the Histogram Matching of image to desired histogram.In general, histogram matching requires selected image as more as possible
All kinds of atural objects of covering, sample size is sufficient, so that each intensity profile for visiting member is than more consistent.But it is this in practical application
In there is also certain limitations.Firstly, satellite in orbit generally has the CCD of multiple imagings, the response coefficient of each CCD is difficult to protect
It holds unanimously, the standard histogram counted using single CCD cannot take entire image into account well；Satellite in orbit image data
It is all producing daily, to cover enough representative regions can make the data volume of statistics very big, general single machine statistical disposition
Time-consuming, it is difficult to guarantee timely updating for relative detector calibration coefficient, be unable to satisfy the needs of actual production；In addition, by people
Work chooses typical photographic, and time-consuming, poor reliability, and cannot achieve the automation of entire relative detector calibration coefficient production.
Summary of the invention
The technical problem to be solved by the present invention is having overcome the deficiencies of the prior art and provide a kind of optical satellite with respect to spoke
Correction coefficient calculating method and system are penetrated, the imaging unit of comprehensive multiple imaging CCD is counted and obtained standard histogram, energy
It is enough that initial data is screened according to specified conditions, is classified, relative detector calibration system is obtained using multinode distributed computing
Number.
The object of the invention is achieved by the following technical programs:
A kind of optical satellite relative detector calibration coefficient calculation method, includes the following steps:
Step 1: being carried out for the first time according to satellite model, sensor type, imaging pattern, imaging time section to data source
It selects, obtains the file attribute index of the data after selecting for the first time；
Step 2: gain, series information in the file attribute index based on data described in step 1, to choosing for the first time
Data after choosing are classified, and sorted data are obtained；
Step 3: by sorted data described in step 2 by the quantity mean allocation of investment calculate node, by data
List and the necessary execution information of each calculate node are written in standardization task inventory；
Step 4: standardization task inventory described in step 3 is sent in sequence to other each sub- meters by initial calculation node
On operator node；Each sub- calculate node executes corresponding task list, generates standardization statistical data file, then sends back initial
Calculate node；
Step 5: summing up system after initial calculation node receives the standardization statistical data file of each sub- calculate node
Meter obtains standard grayscale probability density histogram；Then histogram matching is utilized, relative detector calibration coefficient is obtained.
Above-mentioned optical satellite relative detector calibration coefficient calculation method, sorted data described in step 2 include target
Gain and series data splitting, other gains and series data splitting.
Above-mentioned optical satellite relative detector calibration coefficient calculation method, each necessary execution of calculate node described in step 3
Information includes satellite type number, sensor type, imaging pattern, imaging time section, single scape outgoing route, comprehensive outgoing route.
Above-mentioned optical satellite relative detector calibration coefficient calculation method, is obtained described in step 5 using histogram matching
Obtain relative detector calibration coefficient method particularly includes: utilize histogram matching, establish a grayscale mapping function, make each
The grey level probability density histogram of the imaging unit of CCD complies with standard grey level probability density histogram, the grayscale mapping function
As relative detector calibration coefficient.
Above-mentioned optical satellite relative detector calibration coefficient calculation method, after the step 5, according to predetermined period weight
Step 1~step 5 is executed again, obtains the relative detector calibration coefficient under different condition.
A kind of optical satellite relative detector calibration coefficient computing system, including data sorting module, task allocating module, meter
Operator node module；
Then sorted data are sent to institute for data source to be selected and classified by the data sorting module
State task allocating module；The task allocating module is allocated sorted data by the quantity of investment calculate node, so
Data list and the necessary execution information of each calculate node are written in standardization task inventory afterwards, finally by standardization task
Inventory is sent to the calculate node module；The calculate node module includes initial calculation node and the sub- calculate node of Ta Ge；
Initial calculation node sends the standardization task inventory in other each sub- calculate nodes；Each sub- calculate node executes corresponding
Task list, generate standardization statistical data file, then send back initial calculation node；Initial calculation node receives each son
After the standardization statistical data file of calculate node, statistics is summed up, obtains standard grayscale probability density histogram；Then sharp
With histogram matching, relative detector calibration coefficient is obtained.
Above-mentioned optical satellite relative detector calibration coefficient computing system, the sorted data include target gain and grade
Number data splitting, other gains and series data splitting.
Above-mentioned optical satellite relative detector calibration coefficient computing system, each necessary execution information of calculate node include
Satellite model, sensor type, imaging pattern, imaging time section, single scape outgoing route, comprehensive outgoing route.
Above-mentioned optical satellite relative detector calibration coefficient computing system, it is described to obtain opposite spoke using histogram matching
Penetrate correction coefficient method particularly includes: utilize histogram matching, establish a grayscale mapping function, make each CCD at
As the grey level probability density histogram of unit complies with standard grey level probability density histogram, the grayscale mapping function is as opposite
Radiant correction coefficient.
A kind of computer readable storage medium, is stored thereon with computer program, realization when which is executed by processor
Step described in above-mentioned optical satellite relative detector calibration coefficient calculation method.
The present invention has the following beneficial effects: compared with the prior art
(1) the grey level probability density histogram of standard is obtained by the multi-disc CCD synthesis of satellite in orbit, can be good at taking into account
Relative radiation consistency between single CCD itself and multi-disc CCD piece；
(2) it can automatically search for, screen and classify in data source according to condition of selecting and obtain the data that needs count, phase
Than improving a lot in efficiency and stability in traditional artificial searching data mode；
(3) generation of relative detector calibration coefficient requires image to cover enough representative regions, the image to combine
Than more consistent, this makes the data volume for participating in counting very huge for each DN distribution for visiting member, and traditional single machine processing is difficult to do
To the generation of real-time results, and statistics task automatic Partitioning is distributed to more estrade nodes and carries out distributed Statistics Division by the present invention
Reason, each node retransmit back the subsequent integrated treatment of start node progress after generating statistical result, substantially increase operation efficiency；
(4) relative detector calibration coefficient processing program daily operation mechanism is established, the needs for concentrating statistics are consumed
Time, computing resource are further distributed into daily, and can day data be carried out with the integrated treatment of specified conditions, meet difference and defend
The demand of the relative detector calibration coefficient production of star, sensor, imaging pattern, imaging time section and gain, series combination etc..
Detailed description of the invention
Fig. 1 is the step flow chart of the method for the present invention；
Fig. 2 is the flow diagram of the embodiment of the present invention；
Fig. 3 is that the daily normalization of relative detector calibration coefficient of the embodiment of the present invention automatically generates schematic diagram of mechanism；
Fig. 4 is that the standardization statistical data file of the embodiment of the present invention is illustrated；
Fig. 5 is that the adduction of the embodiment of the present invention counts signal；
Fig. 6 is that the imaging unit grey level probability density histogram of the embodiment of the present invention is illustrated；
Fig. 7 is that the standard grayscale probability density histogram of the embodiment of the present invention is illustrated；
Fig. 8 is the relative detector calibration coefficient files of the embodiment of the present invention；
Fig. 9 is the standardization document administrative directory of the embodiment of the present invention.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached drawing to implementation of the invention
Mode is described in further detail.
A kind of optical satellite relative detector calibration coefficient calculation method, as shown in Figure 1, including the following steps:
Step 101, according to satellite model, sensor type, imaging pattern, imaging time section, first is carried out to data source
It is secondary to select, obtain the file attribute index of the data after selecting for the first time；
Step 102, the gain in the file attribute index based on data described in step 101, series information, to for the first time
Data after selecting are classified, and sorted data are obtained；The sorted data include target gain and series combination
Data, other gains and series data splitting；
Step 103, the quantity mean allocation that sorted data described in step 102 are pressed to investment calculate node, will count
It is written in standardization task inventory according to list and the necessary execution information of each calculate node；Each calculate node is necessary to be held
Row information includes satellite model, sensor type, imaging pattern, imaging time section, single scape outgoing route, comprehensive outgoing route；
Standardization task inventory described in step 103 is sent in sequence to other each sons by step 104, initial calculation node
In calculate node；Each sub- calculate node executes corresponding task list, generates standardization statistical data file, then sends back just
Beginning calculate node；
After step 105, initial calculation node receive the standardization statistical data file of each sub- calculate node, system is summed up
Meter obtains standard grayscale probability density histogram；Then histogram matching is utilized, a grayscale mapping function is established, makes
The grey level probability density histogram of the imaging unit of each CCD complies with standard grey level probability density histogram, the grey scale mapping
Function is relative detector calibration coefficient.
After the step 105, step 1~step 5 is repeated according to predetermined period, is obtained under different condition
Relative detector calibration coefficient.
A kind of optical satellite relative detector calibration coefficient computing system, including data sorting module, task allocating module, meter
Operator node module；
Then sorted data are sent to institute for data source to be selected and classified by the data sorting module
State task allocating module；The task allocating module is allocated sorted data by the quantity of investment calculate node, so
Data list and the necessary execution information of each calculate node are written in standardization task inventory afterwards, finally by standardization task
Inventory is sent to the calculate node module；The calculate node module includes initial calculation node and the sub- calculate node of Ta Ge；
Initial calculation node sends the standardization task inventory in other each sub- calculate nodes；Each sub- calculate node executes corresponding
Task list, generate standardization statistical data file, then send back initial calculation node；Initial calculation node receives each son
After the standardization statistical data file of calculate node, statistics is summed up, obtains standard grayscale probability density histogram；Then sharp
With histogram matching, a grayscale mapping function is established, makes the grey level probability density histogram of the imaging unit of each CCD
Grey level probability density histogram is complied with standard, the grayscale mapping function is relative detector calibration coefficient.
A kind of computer readable storage medium, is stored thereon with computer program, realization when which is executed by processor
The step of optical satellite relative detector calibration coefficient calculation method.
Embodiment:
A kind of optical satellite relative detector calibration coefficient calculation method, includes the following steps, as shown in Figures 2 and 3.
Step 1, data are slightly selected.Select condition according to given, as satellite model, sensor type, imaging pattern, at
As period etc., corresponding order information file (XML) is searched for and read, obtains the store path of file, establishes and is slightly selected
The file attribute of data afterwards indexes；
Step 2, secondary classification.According to recorded in file property index in step 1 gain, series information, to slightly selecting
Data afterwards are classified, and are divided into target gain, series combination and other gains, series and are combined two class data；
Step 3, standardization task inventory generate.Data sorted in step 2 are put down by the quantity of investment calculate node
It distributes, by data list and the necessary execution information of each node (when such as satellite model, sensor type, imaging pattern, imaging
Between section, single scape outgoing route, comprehensive outgoing route etc.) be written in standardization task inventory；
If the number of nodes of investment is N (Node), needing to handle 0 grade of image data file number altogether is M, and int () is to be rounded
Function, then node i data file number n to be treatediAre as follows:
Step 4, start node, will using high-performance message passing library OpenMPI by the standardized task list of generation
Inventory is sent to each child node；After child node receives task list, start to carry out 0 grade of image data of the label in inventory
Effectively parsing, parsing thinking are as follows:
(1) to certain a piece of CCD, the gray value of each imaging unit record of the piece CCD recorded in 0 grade of data is obtained,
Count the number (frequency) that each gray scale of each imaging unit occurs；
It (2) is row with imaging unit number, tonal range is column, and the gray scale frequency of each imaging unit of statistics is written to
It standardizes in statistical data file, every a line may be regarded as the gray scale frequency disribution that the row corresponds to imaging unit, think by above-mentioned parsing
Road obtains the gray scale frequency disribution of each imaging unit of each CCD, generates corresponding standardization statistical data, sends back
Start node；As shown in Figure 4.
The standardization statistical data file that the comprehensive each child node of step 5, start node (total node) is sent back, sums up
Statistics, as shown in figure 5, obtaining standard grayscale probability density histogram.
The calculation formula of gray probability are as follows:
If nkThe pixel number for being k for gray scale, MN are total pixel number, pr(rk) be gray scale be k pixel occur probability, then:
The purpose of Histogram Matching (histogram specification) is exactly to adjust the histogram of original image to be allowed to meet a certain rule
Determine the requirement of histogram.If pr(r) and pz(z) the intensity profile probability density of original-gray image and target image is respectively indicated
Function, as shown in Figure 6 and Figure 7.
The realization process of Histogram Matching (histogram specification) are as follows:
Step 5.1, the histogram pr (r) for calculating given image, and find the histogram equalization of formula (3) such as with it and convert.
SkThe integer being rounded in range [0, L-1].
Wherein MN is the pixel summation of image, njIt is with gray value rjPixel number, L is possible gray level in image
Number.
Step 5.2 calculates all values of transforming function transformation function G to q=0,1,2 ..., L-1 with formula (4), wherein pz(zi) it is regulation
Histogram value.The value of G is rounded to the integer in range [0, L-1].The value of G is stored in a table.
Step 5.3, to each value sk, k=0,1,2 ..., L-1 find corresponding z using the G value that step 5.2 storesq
Value, so that G (zq) closest to sk, and store these mapping F from s to z.S is given when meetingkZqWhen being worth more than one, by used
Example selects the smallest value.
Step 5.4 first carries out equilibrium to input picture, and the mapping then found using a step 3 is each in the image
Pixel value s after equalizationkCorresponding z in image after being mapped as histogram specificationqValue, after forming histogram specification
Image.
The mapping of gray value after the gray value to regulation for the original image established in above-mentioned process is relative radiation
Correction coefficient, in the way of similar standardization statistical data file, being stored as relative detector calibration coefficient, (i.e. gray scale is searched
Table) file, and establish standardization document administrative directory and carry out file management, as shown in Figure 8 and Figure 9.
Step 5, for the problem that statistics need data volume it is big, parsing and statistics time-consuming, establish relative radiation school
The daily normalization of positive coefficient automatically generates mechanism, and the daily clocked flip of setting program handles the same day or specified 0 grade of shadow a few days ago
As data, computing resource and calculate time-consuming that decentralized statistics needs to occupy.And the data for passing by have counted can be carried out special
The integrated treatment of fixed condition meets the phase of different satellites, sensor, imaging pattern, imaging time section and gain, series combination etc.
Demand to the production of radiant correction coefficient, flow chart such as Fig. 2.
The content that description in the present invention is not described in detail belongs to the well-known technique of those skilled in the art.
Claims (10)
1. a kind of optical satellite relative detector calibration coefficient calculation method, characterized by the following steps:
Step 1: choose for the first time to data source according to satellite model, sensor type, imaging pattern, imaging time section
Choosing obtains the file attribute index of the data after selecting for the first time；
Step 2: gain, series information in the file attribute index based on data described in step 1, after selecting for the first time
Data classify, obtain sorted data；
Step 3: by sorted data described in step 2 by the quantity mean allocation of investment calculate node, by data list
It is written in standardization task inventory with the necessary execution information of each calculate node；
Step 4: standardization task inventory described in step 3 is sent in sequence to other each sub- calculating sections by initial calculation node
Point on；Each sub- calculate node executes corresponding task list, generates standardization statistical data file, then sends back initial calculation
Node；
Step 5: summing up statistics after initial calculation node receives the standardization statistical data file of each sub- calculate node, obtaining
Obtain standard grayscale probability density histogram；Then histogram matching is utilized, relative detector calibration coefficient is obtained.
2. a kind of optical satellite relative detector calibration coefficient calculation method according to claim 1, it is characterised in that: step
Sorted data described in two include target gain and series data splitting, other gains and series data splitting.
3. a kind of optical satellite relative detector calibration coefficient calculation method according to claim 1, it is characterised in that: step
Each necessary execution information of calculate node described in three include satellite model, sensor type, imaging pattern, imaging time section,
Single scape outgoing route, comprehensive outgoing route.
4. a kind of optical satellite relative detector calibration coefficient calculation method according to claim 1, it is characterised in that: step
Relative detector calibration coefficient is obtained using histogram matching described in five method particularly includes: utilizes Histogram Matching side
Method establishes a grayscale mapping function, so that the grey level probability density histogram of the imaging unit of each CCD is complied with standard gray scale general
Rate density histogram, the grayscale mapping function are relative detector calibration coefficient.
5. a kind of optical satellite relative detector calibration coefficient calculation method according to claim 1, it is characterised in that: in institute
After stating step 5, step 1~step 5 is repeated according to predetermined period, obtains the relative detector calibration under different condition
Coefficient.
6. a kind of optical satellite relative detector calibration coefficient computing system, it is characterised in that: including data sorting module, task point
With module, calculate node module；
Then sorted data are sent to described appoint for data source to be selected and classified by the data sorting module
Business distribution module；The task allocating module is allocated sorted data by the quantity of investment calculate node, then will
Data list and the necessary execution information of each calculate node are written in standardization task inventory, finally by standardization task inventory
It is sent to the calculate node module；The calculate node module includes initial calculation node and the sub- calculate node of Ta Ge；Initially
Calculate node sends the standardization task inventory in other each sub- calculate nodes；Each sub- calculate node executes corresponding appoint
Business inventory generates standardization statistical data file, then sends back initial calculation node；Initial calculation node receives each sub- calculating
After the standardization statistical data file of node, statistics is summed up, obtains standard grayscale probability density histogram；Then using straight
Square figure matching process obtains relative detector calibration coefficient.
7. a kind of optical satellite relative detector calibration coefficient computing system according to claim 6, it is characterised in that: described
Sorted data include target gain and series data splitting, other gains and series data splitting.
8. a kind of optical satellite relative detector calibration coefficient computing system according to claim 6, it is characterised in that: described
Each necessary execution information of calculate node includes satellite model, sensor type, imaging pattern, imaging time section, single scape output
Path, comprehensive outgoing route.
9. a kind of optical satellite relative detector calibration coefficient computing system according to claim 6, it is characterised in that: described
Relative detector calibration coefficient is obtained using histogram matching method particularly includes: is utilized histogram matching, is established one
A grayscale mapping function makes the grey level probability density histogram of the imaging unit of each CCD comply with standard grey level probability density straight
Fang Tu, the grayscale mapping function are relative detector calibration coefficient.
10. a kind of computer readable storage medium, is stored thereon with computer program, it is characterised in that: the program is by processor
The step of one of Claims 1 to 5 the method is realized when execution.
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| CN111044076B (en) * | 2019-12-11 | 2022-03-04 | 中国资源卫星应用中心 | Geometric calibration method for high-resolution first-number B satellite based on reference base map |
| CN111967127A (en) * | 2020-06-30 | 2020-11-20 | 航天东方红卫星有限公司 | Small satellite delivery reliability problem screening rate calculation method |
| CN112184570A (en) * | 2020-09-10 | 2021-01-05 | 中国人民解放军61646部队 | Method for detecting response state change of probe element based on relative radiation correction coefficient |
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