CN106683059A - Night light data sequence construction method and apparatus - Google Patents
Night light data sequence construction method and apparatus Download PDFInfo
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T5/50—Image enhancement or restoration by the use of more than one image, e.g. averaging, subtraction
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Abstract
The invention discloses a night light data sequence construction method and apparatus. The night light data sequence construction method includes the steps: receiving a remote sensing image to be corrected, and acquiring the corresponding night light data to be corrected; based on the remote sensing image to be corrected, determining a training remote sensing image acquired by a remote sensing satellite which acquires the remote sensing image to be corrected, and obtaining a target correction model corresponding to the training remote sensing image; utilizing the target correction model to correct the night light data to be corrected, and obtaining the target night light data; and according to the target night light data, constructing a night light data sequence. The night light data sequence construction method can save the time of determining a ground calibration field, can improve the processing efficiency of the target correction model, and is convenient for computation during the process of correcting the night light data to be corrected, and the computing result is accurate and reliable.
Description
Technical field
The present invention relates to remote sensing application technical field, more particularly to a kind of nighttime light data sequence construct method and dress
Put.
Background technology
Night lights Remote Sensing Study comes from the project that U.S. national defense meteorological satellite plan in 1972 starts, but data compilation
Archive then originates in 1992.At present, nighttime light data is respectively derived from 6 different satellites, F10, F12, F14, F15,
F16, F18.Although up to the present, the satellite grandfather cycle reach more than 20 years, current research be still limited to use it is a certain
The data of individual satellite, or the data of certain several satellite, it is then less using nighttime light data sequence research.Its reason is, one
Individual aspect is that, because satellite sensor is degenerated, still further aspect is then, because the sensor differences of different satellites are larger, to cause number
It is difficult according to generation is compared across the time.Based on night lights sequence data, in economic research, energy research, urbanization extension
The directions such as research are respectively provided with very important effect, therefore it is significant to build night lights sequence data.
The relative detector calibration between different platform satellite data, continuous consistent sequence data is obtained in that.Existing night
Between light data sequence construct method include it is following several：
The first, is an effective relative detector calibration based on the relative radiometric correction method of constant atural object characteristic point
Method, DMSP (Defense Meteorologi-cal Satellite Program, Defence Meteorological Satellite Project)/OLS
(Operational LinescanSystem, linear scanning operation system) is due to lacking corresponding invariant features atural object, it is impossible to
Obtain high-quality constant atural object characteristic point.In the relative radiometric correction method based on constant atural object characteristic point, people can be passed through
Work obtains constant atural object characteristic point or obtains constant atural object characteristic point automatically, there is that personal error is big, amount of calculation is huge, process effect
The low problem of rate.
It two is, the relative radiometric correction method based on dark pixel method.On optical remote sensing image, dark pixel method is a kind of
With extensive relative radiometric correction method, its main thought is, as dark pixel, then to deduct this using deep water, high mountain shade
Value, obtains correction result.Relative radiometric correction method of dark pixel method should be based on, it is more to there is manual intervention, be vulnerable to subjective dry
In advance, and time and effort consuming, the low problem for the treatment of effeciency.
It three is, the crestal line Return Law.The crestal line Return Law is that effective relative radiometric calibration method is answered in a kind of letter, and it assumes
In one short period, two width images major part pixel can keep constant, therefore based on both two-dimensional distributions, by structure
Build regression model to carry out relative correction.But the existing crestal line Return Law has that amount of calculation is huge, treatment effeciency is low.
The content of the invention
The technical problem to be solved in the present invention is there is amount of calculation for existing nighttime light data sequence construct method
A kind of big and low defect for the treatment of effeciency, there is provided nighttime light data sequence construct method and device.
The technical solution adopted for the present invention to solve the technical problems is：
The present invention has the advantage that compared with prior art：Nighttime light data sequence construct side provided by the present invention
In method and device, remote sensing image to be corrected can be based on, it is determined that the remote sensing satellite of the remote sensing image to be corrected is gathered, and then determination should
The corresponding target correction model of training remote sensing image of remote sensing satellite collection, without the need for predefining ground calibration field mesh can be obtained
Calibration positive model, to save ground calibration field the time is determined, improves the treatment effeciency for determining target correction model；Also, utilize
Target correction model is corrected process to nighttime light data to be corrected, convenience of calculation and result of calculation is accurately and reliably, error
It is less.Target nighttime light data is based on again, nighttime light data sequence is built, due to having between target nighttime light data
Comparability, can make the nighttime light data sequence that it builds more accurately and reliably.
Description of the drawings
Below in conjunction with drawings and Examples, the invention will be further described, in accompanying drawing：
Fig. 1 is a flow chart of nighttime light data sequence construct method in the embodiment of the present invention 1.
Fig. 2 is the schematic diagram that nighttime light data is trained in the embodiment of the present invention 1.
Fig. 3 is a schematic diagram of Two dimensional Distribution scatterplot in the embodiment of the present invention 1.
Fig. 4 is a schematic diagram of different remote sensing images to be corrected and target remote sensing image in the embodiment of the present invention 1.
Fig. 5 is a nighttime light data sequence schematic diagram related to GDP in the embodiment of the present invention 1.
Fig. 6 is a theory diagram of nighttime light data sequence construct device in the embodiment of the present invention 2.
Specific embodiment
In order to be more clearly understood to the technical characteristic of the present invention, purpose and effect, now compare accompanying drawing and describe in detail
The specific embodiment of the present invention.
Embodiment 1
Fig. 1 illustrates the flow chart of nighttime light data sequence construct method in the present embodiment.The nighttime light data sequence
Construction method, by the night collected based on DMSP/OLS to different remote sensing satellites (such as F10, F12, F14, F15, F16, F18)
Between light data be corrected process so that different remote sensing satellite is collected have between nighttime light data comparability, will
The nighttime light data that different remote sensing satellites are collected carries out nighttime light data sequence construct, is economic research, energy research
Foundation is provided with the direction such as urbanization patulous research.
As shown in figure 1, the nighttime light data sequence construct method comprises the steps：
S10：Remote sensing image to be corrected is received, corresponding nighttime light data to be corrected is obtained.
Wherein, remote sensing image to be corrected is that needing of collecting of arbitrary remote sensing satellite is corrected the remote sensing image of process.
Specifically, each remote sensing image to be corrected includes that correcting image identifies and correct satellite mark.Wherein, correcting image is identified is used for
Unique identification remote sensing image to be corrected；Correction satellite identifies the remote sensing satellite for gathering the remote sensing image to be corrected for identification, such as
F10, F12, F14, F16 and F18 etc..Nighttime light data to be corrected can be the visible ray-near red obtained by DMSP/OLS
(VNIR) ripple and section thermal infrared (TIR) wave band outward, it is also possible to gray value.
S20：Based on remote sensing image to be corrected, it is determined that gathering the training remote sensing of the remote sensing satellite collection of remote sensing image to be corrected
Image, and obtain the corresponding target correction model of training remote sensing image.
Specifically, the satellite mark for being carried based on remote sensing image to be corrected determines the remote sensing for gathering the remote sensing image to be corrected
Satellite；The training remote sensing image for being gathered based on the satellite for gathering the remote sensing image to be corrected again, and obtain training remote sensing image pair
The target correction model answered.Wherein, each training remote sensing image also carries training image mark and trains satellite mark.It is based on
Remote sensing image to be corrected, it is determined that when gathering the training remote sensing image of remote sensing satellite collection of remote sensing image to be corrected so that treat school
The training satellite mark that positive Remote Sensing Image Correction and training remote sensing image are carried is identical, i.e., remote sensing image to be corrected with train remote sensing
Image is collected using same remote sensing satellite, to guarantee that remote sensing image to be corrected can be based on the corresponding target of training remote sensing image
Calibration model is corrected.
S30：Process is corrected to nighttime light data to be corrected using target correction model, target night lights are obtained
Data.
In the present embodiment, target correction model includes y=ax2+ bx+c, in target correction model construction process, will instruct
Practice the training nighttime light data of remote sensing image as x values, carry out as y values with reference to the reference nighttime light data of remote sensing image
Correction process, to determine the value of parameter a, b and c.Nighttime light data to be corrected is being corrected using target correction model
When, using the nighttime light data to be corrected of arbitrary pixel in remote sensing image to be corrected as the x values of target correction model, will obtain
Y values as target nighttime light data.Using the different target calibration model formed based on same reference remote sensing image to not
Process is corrected with nighttime light data to be corrected, with the target night for obtaining with there is comparability with reference to nighttime light data
Light data, trimming process convenience of calculation, accurately and reliably, error is less for result of calculation.
S40：According to target nighttime light data, nighttime light data sequence is built.
Specifically, target nighttime light data is that arbitrary invariant features atural object that different remote sensing satellites are collected is (such as Chinese
Or other regions) night got after different target calibration model is corrected is based in different annual remote sensing images to be corrected
Between light data, be that different training remote sensing images are corrected shape after process with same reference remote sensing image with target correction model
Into so that the target nighttime light data in the invariant features atural object different years has comparability.Therefore, can be based on not the same year
The target nighttime light data of degree builds nighttime light data sequence.The nighttime light data sequence is to arbitrary invariant features ground
Thing carries out the aspects such as economic research, energy research and Urban Expansion research to have great importance.
In a specific embodiment, target correction model is associated with training satellite mark and reference satellite mark.Appoint
One target correction model is distant based on the training remote sensing image for carrying training satellite mark and the reference for carrying reference satellite mark
Sense image is formed, and can embody the difference of training remote sensing image and the nighttime light data with reference to remote sensing image.Based on target school
The training satellite mark of positive model, it may be determined that the remote sensing image to be corrected that the target correction model can be corrected, i.e., it is to be corrected
The correction satellite mark of remote sensing image need to be consistent with the training satellite of target correction model mark, just using the target correction mould
Type is corrected.Reference satellite based on object reference model is identified, it may be determined that be corrected rear shape based on target correction model
Into target nighttime light data whether there is comparability, i.e., only identical different target straightening die is identified using reference satellite
Type is corrected process to different nighttime light datas to be corrected, and the target nighttime light data that it is obtained just has comparability.
I.e. in step S20, the correction satellite based on remote sensing image to be corrected is identified, and obtains the correction satellite mark of remote sensing image to be corrected
The training satellite mark of consistent training remote sensing image, then determine the mesh being associated with the training satellite mark of training remote sensing image
Calibration positive model.In step S30, school is treated to different using the different target calibration model associated from same reference satellite mark
Positive model is corrected process, obtains the different target nighttime light data with comparability.
In the nighttime light data sequence construct method, remote sensing image to be corrected can be based on, it is determined that it is to be corrected distant to gather this
The remote sensing satellite of sense image, and then determine the corresponding target correction model of training remote sensing image of the remote sensing satellite collection, without the need for
Predetermined ground calibration field can obtain target correction model, and to save ground calibration field the time is determined, improve and determine target
The treatment effeciency of calibration model；Also, process is corrected to nighttime light data to be corrected using target correction model, is calculated
Accurately and reliably, error is less for convenient and result of calculation.Target nighttime light data is based on again, builds nighttime light data sequence,
Due to having comparability between target nighttime light data, the nighttime light data sequence that it builds can be made more accurately and reliably.
If it is to be appreciated that being based on remote sensing image to be corrected, it is impossible to determine the remote sensing satellite collection of remote sensing image to be corrected
Training remote sensing image, or when not existing with the training corresponding target correction model of remote sensing image, need to be using training remote sensing
Image and with reference to remote sensing image build target correction model.Therefore, the nighttime light data sequence construct method also includes as follows
Step：
S50：Receive training remote sensing image and refer to remote sensing image, obtain corresponding training nighttime light data and refer to night
Between light data.
In the present embodiment, train remote sensing image and with reference to remote sensing image using different remote sensing satellites collect comprising night
The remote sensing image of light data.Wherein, the remote sensing image of marker is for use as with reference to remote sensing image.It is each with reference to distant
Sense image includes being identified with reference to image mark and reference satellite, wherein, identify with reference to image and refer to remote sensing shadow for unique identification
Picture, reference satellite is identified and gathers the remote sensing satellite with reference to remote sensing image for identification.Training remote sensing image is need to be based on reference
Remote sensing image is corrected process, so that itself and the remote sensing image with reference to remote sensing image with comparability.Training remote sensing image bag
Include training image mark and train satellite mark, wherein, training image is identified trains remote sensing image, training to defend for unique identification
Asterisk knows the remote sensing satellite for gathering the training remote sensing image for identification.
To make training remote sensing image and with reference to having comparability between remote sensing image, different remote sensing images is gathered same simultaneously
One invariant features atural object (as China or other same areas) training remote sensing image and refer to remote sensing image.It is to be appreciated that
In the case where other influences factor is ignored, while gathering the training nighttime light data and ginseng of same invariant features atural object
Examine nighttime light data to have differences mainly by collection training nighttime light data and the remote sensing satellite with reference to nighttime light data
Difference cause.It is to be appreciated that need to only make training remote sensing image and point to same invariant features atural object with reference to remote sensing image,
Target correction model is built based on training nighttime light data and with reference to the difference of nighttime light data, you can it is determined that collection training
The difference of remote sensing image and the remote sensing satellite gathered data with reference to remote sensing image, without the need for predefining ground calibration field, can be effective
Save ground calibration field and determine the time, improve treatment effeciency.
Specifically, train nighttime light data and with reference to nighttime light data can be by DMSP/OLS obtain it is visible
Light-near-infrared (VNIR) ripple and section thermal infrared (TIR) wave band, it is also possible to gray value.DMSP/OLS is set exclusively for cloud layer monitoring
The oscillatory scanning radiometer of meter, is provided with altogether two wave bands：Visible ray-near-infrared (VNIR) wave band, 0.4-1 μm, spectral resolution 6
Bit, intensity value ranges 0-63；Thermal infrared (TIR) wave band, 10-13 μm, the bit of spectral resolution 8, intensity value ranges 0-255.
Wherein visible light wave range has two sets of detectors again, uses daytime optical telescope head, night to use optical multiplication pipe.Night optics
The entrance pupil per wavelength spoke brightness of multiplier tube allows as little as 10-9watts/cm2/sr/ μm, this than OLS visible channels on daytime or
About low 4 orders of magnitude of the respective channel of other sensors such as NOAA/AVHRR, LANDSAT/TM radiation to be detected.Light
It is initially meteorological purpose design to learn multiplier tube, for detecting moon light irradiation under cloud, later because it has very strong photoelectricity
Amplifying power, therefore be gradually applied to detect cities and towns light, aurora, lightning, lights on fishing boats, fire etc. earth's surface activity.
S60：According to training remote sensing image and referring to remote sensing image, Two dimensional Distribution scatterplot is obtained.
Wherein, Two dimensional Distribution scatterplot includes pre-set space coordinate system and is arranged on many in the pre-set space coordinate system
Individual scatterplot, each scatterplot is with the training nighttime light data of a pixel in training remote sensing image and with reference to a pixel in remote sensing image
Reference nighttime light data be associated.In the present embodiment, train remote sensing image and be respectively provided with M*N picture with reference to remote sensing image
Unit, trains the pixel on remote sensing image to correspond with the pixel referred on remote sensing image；And, it is each in training remote sensing image
Pixel correspondence one trains nighttime light data, and with reference to each pixel in remote sensing image corresponding a nighttime light data is referred to.Can
To understand ground, each scatterplot can clearly illustrate different remote sensing satellites simultaneously to same invariant features ground in Two dimensional Distribution scatterplot
Thing trains nighttime light data and with reference to the difference between nighttime light data.
Step S60 specifically includes following steps：
S61：According to training remote sensing image and referring to remote sensing image, multigroup corresponding pixel group is obtained.
In the present embodiment, if training remote sensing image and being respectively provided with M*N pixel with reference to remote sensing image, remote sensing image is trained
There is the corresponding pixel group of M*N groups with reference to remote sensing image, the quantity that can be based on pixel group determines that the Two dimensional Distribution for getting dissipates
The scatterplot of point diagram.If setting with reference to each pixel in remote sensing image as Ai,j, wherein, i ∈ M, j ∈ N；Correspondingly, if training remote sensing shadow
Each pixel is B as ini,j, wherein, i ∈ M, j ∈ N；If i and j all sames, pixel Ai,jWith pixel Bi,jForm one group of correspondence
Pixel group, each pixel Ai,jCorrespondence one refers to nighttime light data, each pixel Bi,jCorrespondence one trains nighttime light data.
Wherein, train nighttime light data and with reference to nighttime light data include but is not limited in the present embodiment for gray value.
S62：The corresponding training of each pixel group and is referred to into nighttime light data at nighttime light data, respectively as default
The x coordinate value and y-coordinate value of corresponding goal pels in space coordinates.
In the present embodiment, pixel Ai,jWith pixel Bi,jOne group of corresponding pixel group is formed, is obtained pre- based on the pixel group
If corresponding goal pels C in space coordinatesi,j, so that pixel Ai,jCorrespondence one is with reference to nighttime light data as target picture
First Ci,jX coordinate value, pixel Bi,jCorrespondence one trains nighttime light data as goal pels Ci,jY-coordinate value, to determine mesh
Mark pixel Ci,jPosition in pre-set space coordinate system.
S63：In pre-set space coordinate system, the x coordinate value and y-coordinate value based on multiple goal pels, it is determined that with it is each
The one-to-one scatterplot of goal pels, to obtain Two dimensional Distribution scatterplot.
Pre-set space coordinate system is created i.e. in Two dimensional Distribution scatterplot, the x coordinate axle and instruction of pre-set space coordinate system is made
Practice nighttime light data be associated, y-coordinate axle be associated with reference to nighttime light data.In the Two dimensional Distribution scatterplot, make
Each goal pels one scatterplot of correspondence, according to the x coordinate value of goal pels and the position of the y-coordinate value corresponding scatterplot of determination.Due to
Train remote sensing image and be respectively provided with M*N pixel with reference to remote sensing image, obtain based on training remote sensing image and with reference to remote sensing image
Pixel group have a M*N groups, the scatterplot in correspondence Two dimensional Distribution scatterplot has M*N.
Further, in the nighttime light data sequence construct method, after step S60, also include：Two dimensional Distribution is dissipated
Point diagram is carried out except process of making an uproar；The process except making an uproar includes：By in Two dimensional Distribution scatterplot along X-direction horizontal distribution pixel and/or
Pixel along Y direction vertical distribution is removed.
It is to be appreciated that carrying out, except process of making an uproar, can remove because the factors such as sun glare cause to Two dimensional Distribution scatterplot
Random noise, with obtain optimization after Two dimensional Distribution scatterplot, random noise can be avoided to cause data redundancy, also can avoid with
The accuracy of the target correction model that machine influence of noise is obtained based on Two dimensional Distribution scatterplot.
S70：According to Two dimensional Distribution scatterplot, corresponding target correction model is obtained.
In the corresponding pre-set space coordinate system of Two dimensional Distribution scatterplot, the x coordinate value of each scatterplot and y-coordinate value are distinguished
Correspondence is trained nighttime light data and refers to nighttime light data so that the Two dimensional Distribution scatterplot can intuitively show training remote sensing
The difference of the nighttime light data of image and the same invariant features atural object collected with reference to remote sensing image.It is to be appreciated that
The difference of training remote sensing image and the nighttime light data gathered with reference to remote sensing image is represented using linear model, with can shape
Into corresponding target correction model.The target correction model can be applicable to gather the remote sensing satellite of collection training remote sensing image
To other images to be corrected be corrected process so that remote sensing image to be corrected and with reference between remote sensing image have it is comparable
Property.
If it is to be appreciated that collection is with reference to the remote sensing satellite of remote sensing image and the remote sensing satellite phase of collection training remote sensing image
Together, then the reference nighttime light data that it is collected is identical with training nighttime light data, then pixel Ai,jCorrespondence one refers to night
Light data and pixel Bi,jCorrespondence one trains nighttime light data identical；So that based on the training remote sensing image and referring to remote sensing
The Two dimensional Distribution scatterplot that image is formed is in y=x linear distributions.In the present embodiment, remote sensing satellite of the collection with reference to remote sensing image
With collection training remote sensing image remote sensing satellite differ, then the reference nighttime light data that it is collected and training night lights
Data are differed, and cause the Two dimensional Distribution scatterplot formed based on training remote sensing image and with reference to remote sensing image not to be in y=x linear
Distribution.
Step S70 specifically includes following steps：
S71：Crestal line is determined according to Two dimensional Distribution scatterplot.
Wherein, the scatterplot in Two dimensional Distribution scatterplot is distributed in crestal line, and a crestal line is manually determined in crestal line distribution, should
Crestal line tentatively embodying nighttime light data and the relation with reference to nighttime light data of train in Two dimensional Distribution scatterplot.
S72：Initial calibration model is determined according to crestal line, initial calibration model includes y=ax2+bx+c。
Wherein, based in Two dimensional Distribution scatterplot determine crestal line, according to crestal line route selection be associated with crestal line at the beginning of
Beginning calibration model, the initial calibration model includes y=ax2+bx+c。
S73：Using least-squares algorithm, the parameter of initial calibration model is resolved, to obtain corresponding target school
Positive model.
Wherein, initial calibration model y=ax2+ bx+c is associated with crestal line trend, parameter a, b in initial calibration model
It is unknown number with c, therefore least-squares algorithm need to be adopted, initial calibration Model Parameter a, b and c is resolved, based on solution
Parameter a, b and c after calculation, to obtain corresponding target correction model.The target correction model is associated with training remote sensing image,
So that the night lights number to be corrected of other remote sensing images to be corrected that the remote sensing satellite for gathering the training remote sensing image is collected
According to process can be corrected using the target correction model, have with the reference nighttime light data with reference to remote sensing image to obtain
There is the target nighttime light data of comparability.
Further, step S73 specifically includes following steps：
S731：Some sample scatterplots are obtained along crestal line direction, the corresponding x coordinate value of sample scatterplot and y-coordinate value is determined.
Some sample scatterplots are uniformly chosen along the crestal line direction of Two dimensional Distribution scatterplot, the quantity of sample scatterplot can basis
Demand independently determines that the quantity of sample scatterplot is more, when being resolved to the parameter of initial calibration model based on sample scatterplot, solution
Calculate result more accurate.In the present embodiment, the quantity of sample scatterplot is 64.
S732：According to the corresponding x coordinate value of sample scatterplot and y-coordinate value, using least-squares algorithm, to initial calibration mould
The parameter of type is resolved, to obtain corresponding target correction model.
Wherein, method of least square (also known as least square method) is a kind of mathematical optimization techniques.It is by minimizing error
Quadratic sum finds the optimal function matching of data.Unknown data can be easily tried to achieve using method of least square, and causes this
The quadratic sum of error is minimum between the data tried to achieve a bit and real data.Method of least square can be additionally used in curve matching.Using
Least-squares algorithm is resolved to the parameter of initial calibration model so that the target correction model of acquisition is more accurate.
In the present embodiment, the target correction model of acquisition is associated with training satellite mark and reference satellite mark.It is arbitrary
Target correction model is based on the reference remote sensing for carrying the training satellite training remote sensing image for identifying and carry reference satellite mark
What image was formed, the difference of training remote sensing image and the nighttime light data with reference to remote sensing image can be embodied.Based on target correction
The training satellite mark of model, it may be determined that the remote sensing image to be corrected that the target correction model can be corrected, i.e., it is to be corrected distant
The correction satellite mark of sense image need to be consistent with the training satellite of target correction model mark, just using the target correction model
It is corrected.Reference satellite based on object reference model is identified, it may be determined that formed after being corrected based on target correction model
Target nighttime light data whether there is comparability, i.e., only identical different target calibration model is identified using reference satellite
Process is corrected to different nighttime light datas to be corrected, the target nighttime light data that it is obtained just has comparability.
Fig. 2 illustrates the training that the remote sensing satellites such as F10, F12, F14, F15, F16 and F18 are gathered during 1992-2002
Nighttime light data, in Fig. 2, DN is gray value.In Fig. 3, a figures are the instructions in the training remote sensing image collected with F142003
Practice nighttime light data (DN) as the x coordinate value of scatterplot, with the reference night in the reference remote sensing image that F15 2000 is collected
Between light data (DN) as scatterplot y-coordinate value acquired in Two dimensional Distribution scatterplot.In Fig. 3, b figures are that a figures are removed
Make an uproar process, remove the Two dimensional Distribution scatterplot optimized after random noise, white point is represented and manually chosen not along crestal line direction in figure
Become feature atural object.In Fig. 4, the remote sensing image collected using F15 2000 is collected as remote sensing image is referred to different satellites
The result that is corrected of remote sensing image different to be corrected, different remote sensing images to be corrected are corrected into process, with
The target remote sensing image for obtaining and there is comparability with reference to remote sensing image.Fig. 5 illustrates that nighttime light data sequence is related to GPD
Schematic diagram, in Fig. 5, GDP is dotted line, and first is classified as that the remote sensing image to be corrected collected based on different remote sensing satellites is corresponding to be treated
The nighttime light data sequence that correction nighttime light data is formed；Second is classified as based on the target nighttime light data shape after correction
Into nighttime light data sequence；3rd is classified as the nighttime light data sequence built using existing algorithm.As shown in figure 5, this
The nighttime light data sequence construct method that embodiment is provided is compared to existing algorithm, the nighttime light data sequence that it gets
In, error is less between target nighttime light data, as a result more accurately and reliably.
In the nighttime light data sequence construct method that the present embodiment is provided, first based on training remote sensing image and with reference to distant
The corresponding target correction model of sense image capturing, recycles target correction model pair to defend using same remote sensing with training remote sensing image
The nighttime light data to be corrected of the remote sensing image to be corrected that star is collected is corrected process, to obtain target night lights number
According to so that there is comparability using the nighttime light data to be corrected of different remote sensing satellite collections, nighttime light data can be built
Sequence.In the nighttime light data sequence construct method, target correction model can be obtained without the need for predefining ground calibration field,
Determine the time to save ground calibration field, improve the treatment effeciency for determining target correction model；Also, using target correction model
It is corrected process to nighttime light data to be corrected, convenience of calculation and result of calculation is accurately and reliably, error is less.
Embodiment 2
Fig. 6 illustrates the theory diagram of nighttime light data sequence construct device in the present embodiment.The nighttime light data sequence
Row construction device, by what is collected based on DMSP/OLS to different remote sensing satellites (such as F10, F12, F14, F15, F16, F18)
Nighttime light data is corrected process, so that different remote sensing satellites is collected have between nighttime light data comparability, with
The nighttime light data that different remote sensing satellites are collected carries out nighttime light data sequence construct, is that economic research, the energy grind
Study carefully and provide foundation with the direction such as urbanization patulous research.
As shown in fig. 6, the nighttime light data sequence construct device is obtained including the first data acquisition module 10, calibration model
Delivery block 20, data correction processing module 30 and data sequence construct module 40.
First data acquisition module 10, for receiving remote sensing image to be corrected, obtains corresponding night lights number to be corrected
According to.
Wherein, remote sensing image to be corrected is that needing of collecting of arbitrary remote sensing satellite is corrected the remote sensing image of process.
Specifically, each remote sensing image to be corrected includes that correcting image identifies and correct satellite mark.Wherein, correcting image is identified is used for
Unique identification remote sensing image to be corrected；Correction satellite identifies the remote sensing satellite for gathering the remote sensing image to be corrected for identification, such as
F10, F12, F14, F16 and F18 etc..Nighttime light data to be corrected can be the visible ray-near red obtained by DMSP/OLS
(VNIR) ripple and section thermal infrared (TIR) wave band outward, it is also possible to gray value.
Calibration model acquisition module 20, for based on remote sensing image to be corrected, it is determined that gathering the distant of remote sensing image to be corrected
The training remote sensing image of sense satellite collection, and obtain the corresponding target correction model of training remote sensing image.
Specifically, the satellite mark for being carried based on remote sensing image to be corrected determines the remote sensing for gathering the remote sensing image to be corrected
Satellite；The training remote sensing image for being gathered based on the satellite for gathering the remote sensing image to be corrected again, and obtain training remote sensing image pair
The target correction model answered.Wherein, each training remote sensing image also carries training image mark and trains satellite mark.It is based on
Remote sensing image to be corrected, it is determined that when gathering the training remote sensing image of remote sensing satellite collection of remote sensing image to be corrected so that treat school
The training satellite mark that positive Remote Sensing Image Correction and training remote sensing image are carried is identical, i.e., remote sensing image to be corrected with train remote sensing
Image is collected using same remote sensing satellite, to guarantee that remote sensing image to be corrected can be based on the corresponding target of training remote sensing image
Calibration model is corrected.
Data correction processing module 30, for being corrected place to nighttime light data to be corrected using target correction model
Reason, obtains target nighttime light data.
In the present embodiment, target correction model includes y=ax2+ bx+c, in target correction model construction process, will instruct
Practice the training nighttime light data of remote sensing image as x values, carry out as y values with reference to the reference nighttime light data of remote sensing image
Correction process, to determine the value of parameter a, b and c.Nighttime light data to be corrected is being corrected using target correction model
When, using the nighttime light data to be corrected of arbitrary pixel in remote sensing image to be corrected as the x values of target correction model, will obtain
Y values as target nighttime light data.Using the different target calibration model formed based on same reference remote sensing image to not
Process is corrected with nighttime light data to be corrected, with the target night for obtaining with there is comparability with reference to nighttime light data
Light data, trimming process convenience of calculation, accurately and reliably, error is less for result of calculation.
Data sequence builds module 40, for according to target nighttime light data, building nighttime light data sequence.
Specifically, target nighttime light data is that arbitrary invariant features atural object that different remote sensing satellites are collected is (such as Chinese
Or other regions) night got after different target calibration model is corrected is based in different annual remote sensing images to be corrected
Between light data, and different target calibration model to be different training remote sensing images be corrected process with same reference remote sensing image
Formed afterwards so that the target nighttime light data in the invariant features atural object different years has comparability.Therefore, can be based on not
Nighttime light data sequence is built with the target nighttime light data in year.The nighttime light data sequence is to arbitrary constant spy
Expropriation of land thing carries out the aspects such as economic research, energy research and Urban Expansion research to have great importance.
In a specific embodiment, target correction model is associated with training satellite mark and reference satellite mark.Appoint
One target correction model is distant based on the training remote sensing image for carrying training satellite mark and the reference for carrying reference satellite mark
Sense image is formed, and can embody the difference of training remote sensing image and the nighttime light data with reference to remote sensing image.Based on target school
The training satellite mark of positive model, it may be determined that the remote sensing image to be corrected that the target correction model can be corrected, i.e., it is to be corrected
The correction satellite mark of remote sensing image need to be consistent with the training satellite of target correction model mark, just using the target correction mould
Type is corrected.Reference satellite based on object reference model is identified, it may be determined that be corrected rear shape based on target correction model
Into target nighttime light data whether there is comparability, i.e., only identical different target straightening die is identified using reference satellite
Type is corrected process to different nighttime light datas to be corrected, and the target nighttime light data that it is obtained just has comparability.
Specifically, calibration model acquisition module 20, identifies for the correction satellite based on remote sensing image to be corrected, obtains remote sensing to be corrected
The correction satellite of image identifies the training satellite mark of consistent training remote sensing image, then determines the training with training remote sensing image
The associated target correction model of satellite mark.Data correction processing module 30, for identifying phase using with same reference satellite
The different target calibration model of association is corrected process to different models to be corrected, obtains the different target night with comparability
Between light data.
In the nighttime light data sequence construct device, remote sensing image to be corrected can be based on, it is determined that it is to be corrected distant to gather this
The remote sensing satellite of sense image, and then determine the corresponding target correction model of training remote sensing image of the remote sensing satellite collection, without the need for
Predetermined ground calibration field can obtain target correction model, and to save ground calibration field the time is determined, improve and determine target
The treatment effeciency of calibration model；Also, process is corrected to nighttime light data to be corrected using target correction model, is calculated
Accurately and reliably, error is less for convenient and result of calculation.Target nighttime light data is based on again, builds nighttime light data sequence,
Due to having comparability between target nighttime light data, the nighttime light data sequence that it builds can be made more accurately and reliably.
If it is to be appreciated that being based on remote sensing image to be corrected, it is impossible to determine the remote sensing satellite collection of remote sensing image to be corrected
Training remote sensing image, or when not existing with the training corresponding target correction model of remote sensing image, need to be using training remote sensing
Image and with reference to remote sensing image build target correction model.Therefore, the nighttime light data sequence construct device also includes second
Data acquisition module 50, scatterplot acquisition module 60, calibration model build module 70 and except processing module 80 of making an uproar.
Second data acquisition module 50, for receiving training remote sensing image and referring to remote sensing image, obtains corresponding training
Nighttime light data and refer to nighttime light data.
In the present embodiment, train remote sensing image and with reference to remote sensing image using different remote sensing satellites collect comprising night
The remote sensing image of light data.Wherein, the remote sensing image of marker is for use as with reference to remote sensing image.It is each with reference to distant
Sense image includes being identified with reference to image mark and reference satellite, wherein, identify with reference to image and refer to remote sensing shadow for unique identification
Picture, reference satellite is identified and gathers the remote sensing satellite with reference to remote sensing image for identification.Training remote sensing image is need to be based on reference
Remote sensing image is corrected process, so that itself and the remote sensing image with reference to remote sensing image with comparability.Training remote sensing image bag
Include training image mark and train satellite mark, wherein, training image is identified trains remote sensing image, training to defend for unique identification
Asterisk knows the remote sensing satellite for gathering the training remote sensing image for identification.
To make training remote sensing image and with reference to having comparability between remote sensing image, different remote sensing images is gathered same simultaneously
One invariant features atural object (as China or other same areas) training remote sensing image and refer to remote sensing image.It is to be appreciated that
In the case where other influences factor is ignored, while gathering the training nighttime light data and ginseng of same invariant features atural object
Examine nighttime light data to have differences mainly by collection training nighttime light data and the remote sensing satellite with reference to nighttime light data
Difference cause.It is to be appreciated that need to only make training remote sensing image and point to same invariant features atural object with reference to remote sensing image,
Target correction model is built based on training nighttime light data and with reference to the difference of nighttime light data, you can it is determined that collection training
The difference of remote sensing image and the remote sensing satellite gathered data with reference to remote sensing image, without the need for predefining ground calibration field, can be effective
Save ground calibration field and determine the time, improve treatment effeciency.
Specifically, train nighttime light data and with reference to nighttime light data can be by DMSP/OLS obtain it is visible
Light-near-infrared (VNIR) ripple and section thermal infrared (TIR) wave band, it is also possible to gray value.DMSP/OLS is set exclusively for cloud layer monitoring
The oscillatory scanning radiometer of meter, is provided with altogether two wave bands：Visible ray-near-infrared (VNIR) wave band, 0.4-1 μm, spectral resolution 6
Bit, intensity value ranges 0-63；Thermal infrared (TIR) wave band, 10-13 μm, the bit of spectral resolution 8, intensity value ranges 0-255.
Wherein visible light wave range has two sets of detectors again, uses daytime optical telescope head, night to use optical multiplication pipe.Night optics
The entrance pupil per wavelength spoke brightness of multiplier tube allows as little as 10-9watts/cm2/sr/ μm, this than OLS visible channels on daytime or
About low 4 orders of magnitude of the respective channel of other sensors such as NOAA/AVHRR, LANDSAT/TM radiation to be detected.Light
It is initially meteorological purpose design to learn multiplier tube, for detecting moon light irradiation under cloud, later because it has very strong photoelectricity
Amplifying power, therefore be gradually applied to detect cities and towns light, aurora, lightning, lights on fishing boats, fire etc. earth's surface activity.
Scatterplot acquisition module 60, for according to training remote sensing image and referring to remote sensing image, obtaining Two dimensional Distribution scatterplot
Figure.
Wherein, Two dimensional Distribution scatterplot includes pre-set space coordinate system and is arranged on many in the pre-set space coordinate system
Individual scatterplot, each scatterplot is with the training nighttime light data of a pixel in training remote sensing image and with reference to a pixel in remote sensing image
Reference nighttime light data be associated.In the present embodiment, train remote sensing image and be respectively provided with M*N picture with reference to remote sensing image
Unit, trains the pixel on remote sensing image to correspond with the pixel referred on remote sensing image；And, it is each in training remote sensing image
Pixel correspondence one trains nighttime light data, and with reference to each pixel in remote sensing image corresponding a nighttime light data is referred to.Can
To understand ground, each scatterplot can clearly illustrate different remote sensing satellites simultaneously to same invariant features ground in Two dimensional Distribution scatterplot
Thing trains nighttime light data and with reference to the difference between nighttime light data.
Scatterplot acquisition module 60 specifically includes pixel group acquiring unit 61, cell coordinate determining unit 62 and scatterplot is true
Order unit 63.
Pixel group acquiring unit 61, for according to training remote sensing image and referring to remote sensing image, obtaining multigroup corresponding picture
Tuple.
In the present embodiment, if training remote sensing image and being respectively provided with M*N pixel with reference to remote sensing image, remote sensing image is trained
There is the corresponding pixel group of M*N groups with reference to remote sensing image, the quantity that can be based on pixel group determines that the Two dimensional Distribution for getting dissipates
The scatterplot of point diagram.If setting with reference to each pixel in remote sensing image as Ai,j, wherein, i ∈ M, j ∈ N；Correspondingly, if training remote sensing shadow
Each pixel is B as ini,j, wherein, i ∈ M, j ∈ N；If i and j all sames, pixel Ai,jWith pixel Bi,jForm one group of correspondence
Pixel group, each pixel Ai,jCorrespondence one refers to nighttime light data, each pixel Bi,jCorrespondence one trains nighttime light data.
Wherein, train nighttime light data and with reference to nighttime light data include but is not limited in the present embodiment for gray value.
Cell coordinate determining unit 62, for nighttime light data and the corresponding training of each pixel group to be referred to into night lamp
Light data, respectively as the x coordinate value and y-coordinate value of corresponding goal pels in pre-set space coordinate system.
In the present embodiment, pixel Ai,jWith pixel Bi,jOne group of corresponding pixel group is formed, is obtained pre- based on the pixel group
If corresponding goal pels C in space coordinatesi,j, so that pixel Ai,jCorrespondence one is with reference to nighttime light data as target picture
First Ci,jX coordinate value, pixel Bi,jCorrespondence one trains nighttime light data as goal pels Ci,jY-coordinate value, to determine mesh
Mark pixel Ci,jPosition in pre-set space coordinate system.
Scatterplot determining unit 63, for x coordinate value and y in pre-set space coordinate system, based on multiple goal pels
Coordinate figure, it is determined that with the one-to-one scatterplot of each goal pels, to obtain Two dimensional Distribution scatterplot.
Pre-set space coordinate system is created i.e. in Two dimensional Distribution scatterplot, the x coordinate axle and instruction of pre-set space coordinate system is made
Practice nighttime light data be associated, y-coordinate axle be associated with reference to nighttime light data.In the Two dimensional Distribution scatterplot, make
Each goal pels one scatterplot of correspondence, according to the x coordinate value of goal pels and the position of the y-coordinate value corresponding scatterplot of determination.Due to
Train remote sensing image and be respectively provided with M*N pixel with reference to remote sensing image, obtain based on training remote sensing image and with reference to remote sensing image
Pixel group have a M*N groups, the scatterplot in correspondence Two dimensional Distribution scatterplot has M*N.
Further, in the nighttime light data sequence construct device, also include except processing module 80 of making an uproar, for two dimension
Distribution scatterplot is carried out except process of making an uproar；The process except making an uproar includes：By in Two dimensional Distribution scatterplot along the pixel of X-direction horizontal distribution
And/or along the pixel removal of Y direction vertical distribution.
It is to be appreciated that carrying out, except process of making an uproar, can remove because the factors such as sun glare cause to Two dimensional Distribution scatterplot
Random noise, with obtain optimization after Two dimensional Distribution scatterplot, random noise can be avoided to cause data redundancy, also can avoid with
The accuracy of the target correction model that machine influence of noise is obtained based on Two dimensional Distribution scatterplot.
Calibration model builds module 70, for according to Two dimensional Distribution scatterplot, obtaining corresponding target correction model.
In the corresponding pre-set space coordinate system of Two dimensional Distribution scatterplot, the x coordinate value of each scatterplot and y-coordinate value are distinguished
Correspondence is trained nighttime light data and refers to nighttime light data so that the Two dimensional Distribution scatterplot can intuitively show training remote sensing
The difference of the nighttime light data of image and the same invariant features atural object collected with reference to remote sensing image.It is to be appreciated that
The difference of training remote sensing image and the nighttime light data gathered with reference to remote sensing image is represented using linear model, with can shape
Into corresponding target correction model.The target correction model can be applicable to gather the remote sensing satellite of collection training remote sensing image
To other images to be corrected be corrected process so that remote sensing image to be corrected and with reference between remote sensing image have it is comparable
Property.
If it is to be appreciated that collection is with reference to the remote sensing satellite of remote sensing image and the remote sensing satellite phase of collection training remote sensing image
Together, then the reference nighttime light data that it is collected is identical with training nighttime light data, then pixel Ai,jCorrespondence one refers to night
Light data and pixel Bi,jCorrespondence one trains nighttime light data identical；So that based on the training remote sensing image and referring to remote sensing
The Two dimensional Distribution scatterplot that image is formed is in y=x linear distributions.In the present embodiment, remote sensing satellite of the collection with reference to remote sensing image
With collection training remote sensing image remote sensing satellite differ, then the reference nighttime light data that it is collected and training night lights
Data are differed, and cause the Two dimensional Distribution scatterplot formed based on training remote sensing image and with reference to remote sensing image not to be in y=x linear
Distribution.
Calibration model builds module 70 and specifically includes crestal line determining unit 71, initial model determining unit 72 and initial model
Solving unit 73.
Crestal line determining unit 71, for determining crestal line according to Two dimensional Distribution scatterplot.
Wherein, the scatterplot in Two dimensional Distribution scatterplot is distributed in crestal line, and a crestal line is manually determined in crestal line distribution, should
Crestal line tentatively embodying nighttime light data and the relation with reference to nighttime light data of train in Two dimensional Distribution scatterplot.
Initial model determining unit 72, for determining initial calibration model according to crestal line, initial calibration model includes y=
ax2+bx+c。
Wherein, based in Two dimensional Distribution scatterplot determine crestal line, according to crestal line route selection be associated with crestal line at the beginning of
Beginning calibration model, the initial calibration model includes y=ax2+bx+c。
Initial model solving unit 73, for adopting least-squares algorithm, resolves to the parameter of initial calibration model,
To obtain corresponding target correction model.
Wherein, initial calibration model y=ax2+ bx+c is associated with crestal line trend, parameter a, b in initial calibration model
It is unknown number with c, therefore least-squares algorithm need to be adopted, initial calibration Model Parameter a, b and c is resolved, based on solution
Parameter a, b and c after calculation, to obtain corresponding target correction model.The target correction model is associated with training remote sensing image,
So that the night lights number to be corrected of other remote sensing images to be corrected that the remote sensing satellite for gathering the training remote sensing image is collected
According to process can be corrected using the target correction model, have with the reference nighttime light data with reference to remote sensing image to obtain
There is the target nighttime light data of comparability.
Further, initial model solving unit 73 specifically includes sample scatterplot and obtains subelement 731 and parametric solution operator
Unit 732.
Sample scatterplot obtains subelement 731, for obtaining some sample scatterplots along crestal line direction, determines sample scatterplot correspondence
X coordinate value and y-coordinate value.
Some sample scatterplots are uniformly chosen along the crestal line direction of Two dimensional Distribution scatterplot, the quantity of sample scatterplot can basis
Demand independently determines that the quantity of sample scatterplot is more, when being resolved to the parameter of initial calibration model based on sample scatterplot, solution
Calculate result more accurate.In the present embodiment, the quantity of sample scatterplot is 64.
Parameter calculation subelement 732, for according to the corresponding x coordinate value of sample scatterplot and y-coordinate value, using least square
Algorithm, resolves to the parameter of initial calibration model, to obtain corresponding target correction model.
Wherein, method of least square (also known as least square method) is a kind of mathematical optimization techniques.It is by minimizing error
Quadratic sum finds the optimal function matching of data.Unknown data can be easily tried to achieve using method of least square, and causes this
The quadratic sum of error is minimum between the data tried to achieve a bit and real data.Method of least square can be additionally used in curve matching.Using
Least-squares algorithm is resolved to the parameter of initial calibration model so that the target correction model of acquisition is more accurate.
In the present embodiment, the target correction model of acquisition is associated with training satellite mark and reference satellite mark.It is arbitrary
Target correction model is based on the reference remote sensing for carrying the training satellite training remote sensing image for identifying and carry reference satellite mark
What image was formed, the difference of training remote sensing image and the nighttime light data with reference to remote sensing image can be embodied.Based on target correction
The training satellite mark of model, it may be determined that the remote sensing image to be corrected that the target correction model can be corrected, i.e., it is to be corrected distant
The correction satellite mark of sense image need to be consistent with the training satellite of target correction model mark, just using the target correction model
It is corrected.Reference satellite based on object reference model is identified, it may be determined that formed after being corrected based on target correction model
Target nighttime light data whether there is comparability, i.e., only identical different target calibration model is identified using reference satellite
Process is corrected to different nighttime light datas to be corrected, the target nighttime light data that it is obtained just has comparability.
Fig. 2 illustrates the training that the remote sensing satellites such as F10, F12, F14, F15, F16 and F18 are gathered during 1992-2002
Nighttime light data, in Fig. 2, DN is gray value.In Fig. 3, a figures are the instructions in the training remote sensing image collected with F14 2003
Practice nighttime light data (DN) as the x coordinate value of scatterplot, with the reference night in the reference remote sensing image that F15 2000 is collected
Between light data (DN) as scatterplot y-coordinate value acquired in Two dimensional Distribution scatterplot.In Fig. 3, b figures are that a figures are removed
Make an uproar process, remove the Two dimensional Distribution scatterplot optimized after random noise, white point is represented and manually chosen not along crestal line direction in figure
Become feature atural object.In Fig. 4, the remote sensing image collected using F15 2000 is collected as remote sensing image is referred to different satellites
The result that is corrected of remote sensing image different to be corrected, different remote sensing images to be corrected are corrected into process, with
The target remote sensing image for obtaining and there is comparability with reference to remote sensing image.Fig. 5 illustrates that nighttime light data sequence is related to GPD
Schematic diagram, in Fig. 5, GDP is dotted line, and first is classified as that the remote sensing image to be corrected collected based on different remote sensing satellites is corresponding to be treated
The nighttime light data sequence that correction nighttime light data is formed；Second is classified as based on the target nighttime light data shape after correction
Into nighttime light data sequence；3rd is classified as the nighttime light data sequence built using existing algorithm.As shown in figure 5, this
The nighttime light data sequence construct device that embodiment is provided is compared to existing algorithm, the nighttime light data sequence that it gets
In, error is less between target nighttime light data, as a result more accurately and reliably.
In the nighttime light data sequence construct device that the present embodiment is provided, first based on training remote sensing image and with reference to distant
The corresponding target correction model of sense image capturing, recycles target correction model pair to defend using same remote sensing with training remote sensing image
The nighttime light data to be corrected of the remote sensing image to be corrected that star is collected is corrected process, to obtain target night lights number
According to so that there is comparability using the nighttime light data to be corrected of different remote sensing satellite collections, nighttime light data can be built
Sequence.In the nighttime light data sequence construct device, target correction model can be obtained without the need for predefining ground calibration field,
Determine the time to save ground calibration field, improve the treatment effeciency for determining target correction model；Also, using target correction model
It is corrected process to nighttime light data to be corrected, convenience of calculation and result of calculation is accurately and reliably, error is less.
The present invention is illustrated by several specific embodiments, it will be appreciated by those skilled in the art that, without departing from
In the case of the scope of the invention, various conversion and equivalent substitute can also be carried out to the present invention.In addition, for particular condition or tool
Body situation, can make various modifications, without deviating from the scope of the present invention to the present invention.Therefore, the present invention is not limited to disclosed
Specific embodiment, and whole embodiments for falling within the scope of the appended claims should be included.
Claims (10)
1. a kind of nighttime light data sequence construct method, it is characterised in that include：
Remote sensing image to be corrected is received, corresponding nighttime light data to be corrected is obtained；
Based on the remote sensing image to be corrected, it is determined that the training remote sensing of the remote sensing satellite collection of the collection remote sensing image to be corrected
Image, and obtain the corresponding target correction model of the training remote sensing image；
Process is corrected to the nighttime light data to be corrected using the target correction model, target night lights are obtained
Data；
According to the target nighttime light data, nighttime light data sequence is built.
2. nighttime light data sequence construct method according to claim 1, it is characterised in that also include：
Receive training remote sensing image and refer to remote sensing image, obtain corresponding training nighttime light data and with reference to night lights number
According to；
According to the training remote sensing image and the reference remote sensing image, Two dimensional Distribution scatterplot is obtained；
According to the Two dimensional Distribution scatterplot, the corresponding target correction model of the training remote sensing image is obtained.
3. nighttime light data sequence construct method according to claim 2, it is characterised in that described according to the training
Remote sensing image and the reference remote sensing image, obtain Two dimensional Distribution scatterplot, including：
According to the training remote sensing image and the reference remote sensing image, multigroup corresponding pixel group is obtained；
By the corresponding training nighttime light data of each pixel group and the reference nighttime light data, respectively as
The x coordinate value and y-coordinate value of corresponding goal pels in pre-set space coordinate system；
In pre-set space coordinate system, the x coordinate value and y-coordinate value based on multiple goal pels, it is determined that described with each
The one-to-one scatterplot of goal pels, to obtain the Two dimensional Distribution scatterplot.
4. nighttime light data sequence construct method according to claim 3, it is characterised in that described according to the training
Remote sensing image and the reference remote sensing image, after obtaining Two dimensional Distribution scatterplot, also include：
The Two dimensional Distribution scatterplot is carried out except process of making an uproar；
It is described except process of making an uproar includes：By in the Two dimensional Distribution scatterplot along the pixel of X-direction horizontal distribution and/or along Y-axis
The pixel of direction vertical distribution is removed.
5. the nighttime light data sequence construct method according to claim 3 or 4, it is characterised in that described in the basis
Two dimensional Distribution scatterplot, obtains corresponding target correction model, including：
Crestal line is determined according to the Two dimensional Distribution scatterplot；
Initial calibration model is determined according to the crestal line, the initial calibration model includes y=ax2+bx+c；
The parameter of the initial calibration model is resolved using least-squares algorithm, it is relative with the remote sensing image to obtain
The target correction model answered.
6. a kind of nighttime light data sequence construct device, it is characterised in that include：
First data acquisition module, for receiving remote sensing image to be corrected, obtains corresponding nighttime light data to be corrected；
Calibration model acquisition module, for based on the remote sensing image to be corrected, it is determined that gathering the remote sensing image to be corrected
The training remote sensing image of remote sensing satellite collection, and obtain the corresponding target correction model of the training remote sensing image；
Data correction processing module, for being corrected to the nighttime light data to be corrected using the target correction model
Process, obtain target nighttime light data；
Data sequence builds module, for according to the target nighttime light data, building nighttime light data sequence.
7. nighttime light data sequence construct device according to claim 6, it is characterised in that also include：
Second data acquisition module, for receiving training remote sensing image and referring to remote sensing image, obtains corresponding training night lamp
Light data and refer to nighttime light data；
Scatterplot acquisition module, dissipates for according to the training remote sensing image and the reference remote sensing image, obtaining Two dimensional Distribution
Point diagram；
Calibration model builds module, for according to the Two dimensional Distribution scatterplot, obtaining the corresponding mesh of the training remote sensing image
Calibration positive model.
8. nighttime light data sequence construct device according to claim 7, it is characterised in that the scatterplot obtains mould
Block includes：
Pixel group acquiring unit, for according to the training remote sensing image and the reference remote sensing image, obtaining multigroup corresponding
Pixel group；
Cell coordinate determining unit, for by the corresponding training nighttime light data of each pixel group and the reference
Nighttime light data, respectively as the x coordinate value and y-coordinate value of corresponding goal pels in pre-set space coordinate system；
Scatterplot determining unit, in pre-set space coordinate system, the x coordinate value and y based on multiple goal pels to be sat
Scale value, it is determined that with the one-to-one scatterplot of each goal pels, to obtain the Two dimensional Distribution scatterplot.
9. nighttime light data sequence construct device according to claim 8, it is characterised in that also include：Except process of making an uproar
Module, for carrying out to the Two dimensional Distribution scatterplot except process of making an uproar；
It is described except process of making an uproar includes：By in the Two dimensional Distribution scatterplot along the pixel of X-direction horizontal distribution and/or along Y-axis
The pixel of direction vertical distribution is removed.
10. the nighttime light data sequence construct device according to claim 7 or 8, it is characterised in that the calibration model
Building module includes：
Crestal line determining unit, for determining crestal line according to the Two dimensional Distribution scatterplot；
Initial model determining unit, for determining initial calibration model according to the crestal line, the initial calibration model includes y=
ax2+bx+c；
Initial model solving unit, for being resolved to the parameter of the initial calibration model using least-squares algorithm, with
Obtain the target correction model corresponding with the remote sensing image.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109102489A (en) * | 2018-06-14 | 2018-12-28 | 河海大学 | A method of the acquisition DMSP/OLS long-term sequence based on ridge regression method |
| CN110176019A (en) * | 2019-05-13 | 2019-08-27 | 中国科学院遥感与数字地球研究所 | A kind of night pure light extracting method |
| CN110675448A (en) * | 2019-08-21 | 2020-01-10 | 深圳大学 | Ground light remote sensing monitoring method, system and storage medium based on civil aircraft |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109102489A (en) * | 2018-06-14 | 2018-12-28 | 河海大学 | A method of the acquisition DMSP/OLS long-term sequence based on ridge regression method |
| CN110176019A (en) * | 2019-05-13 | 2019-08-27 | 中国科学院遥感与数字地球研究所 | A kind of night pure light extracting method |
| CN110675448A (en) * | 2019-08-21 | 2020-01-10 | 深圳大学 | Ground light remote sensing monitoring method, system and storage medium based on civil aircraft |
| CN110675448B (en) * | 2019-08-21 | 2023-05-02 | 深圳大学 | Ground lamplight remote sensing monitoring method, system and storage medium based on civil airliner |
| CN111192298A (en) * | 2019-12-27 | 2020-05-22 | 武汉大学 | Relative radiation correction method for luminous remote sensing image |
| CN112926532A (en) * | 2021-04-01 | 2021-06-08 | 深圳前海微众银行股份有限公司 | Information processing method, device, equipment, storage medium and computer program product |
| CN115565085A (en) * | 2022-12-05 | 2023-01-03 | 中国科学院空天信息创新研究院 | Method for constructing dense time series data of remote sensing image |
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