CN109523125A - A kind of poor Measurement Method based on DMSP/OLS nighttime light data - Google Patents
A kind of poor Measurement Method based on DMSP/OLS nighttime light data Download PDFInfo
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Abstract
The invention discloses a kind of poor Measurement Methods based on DMSP/OLS nighttime light data comprising following steps: obtaining DMSP/OLS night lights remotely-sensed data；DMSP/OLS night lights remotely-sensed data is cut using polar plot；It chooses test block and the lamplight brightness value of each pixel is extracted according to the DMSP/OLS night lights remotely-sensed data for the test block for cutting acquisition, obtain the average optical index L of test block；Obtain the synthesis misery index IPI in test block；Average optical index L and comprehensive misery index IPI in test block is subjected to regression analysis, fitting establishes poverty and estimates inverse model；Inverse model is estimated into the poverty and is applied to all DMSP/OLS night lights remotely-sensed datas, determines the synthesis misery index IPI of each pixel in DMSP/OLS night lights remotely-sensed data.The present invention is capable of the spatial distribution of quick obtaining poverty-stricken area, improves the poor detection accuracy estimated.
Description
Technical field
The present invention relates to Remote Sensing Image Processing Technology fields more particularly to a kind of based on DMSP/OLS nighttime light data
Poor Measurement Method.
Background technique
Caring for the needy and those with extreme difficulties is economic development important aspect.The poor shape of diverse geographic location is known precisely in time
Condition facilitates accurate poverty alleviation.And the method estimated at present for poverty is all the socio-economic indicator parameter by collecting area
For statistical analysis, such method needs to investigate collection data in advance, spends the time for statistical analysis, and time-consuming and needs
Artificial on-the-spot investigation is employed, it is cumbersome, and influence of the poor precision estimated by data integrity.
Summary of the invention
The purpose of the present invention is to provide a kind of poor Measurement Method based on DMSP/OLS nighttime light data, the bases
It is capable of the spatial distribution of quick obtaining poverty-stricken area in the poor Measurement Method of DMSP/OLS nighttime light data, improves poor survey
The detection accuracy of degree.
The present invention is achieved by the following technical solutions:
A kind of poor Measurement Method based on DMSP/OLS nighttime light data, includes the following steps:
Obtain DMSP/OLS night lights remotely-sensed data；
Polar plot is drawn according to geographic area, cuts DMSP/OLS night lights remotely-sensed data using polar plot；
It is distant according to the DMSP/OLS night lights for the test block for cutting acquisition as test block to choose a wherein geographic area
Feel data, after removing zero and negative value pixel, extracts the lamplight brightness value of each pixel, obtain the average optical index L of test block；
Obtain the synthesis misery index IPI in test block；
Average optical index L and comprehensive misery index IPI in test block is subjected to regression analysis, fitting is established poverty and estimated
Inverse model；
Inverse model is estimated into the poverty and is applied to all DMSP/OLS night lights remotely-sensed datas, determines DMSP/
The synthesis misery index IPI of each pixel in OLS night lights remotely-sensed data.
Compared with the existing technology, the present invention is based on socioeconomic driving forces extracts comprehensive poverty using Principal Component Analysis
Index IPI, and select a test block, by the synthesis misery index IPI and DMSP/OLS of test block be averaged optical index establish it is poor
It is tired to estimate inverse model；Inverse model is estimated into the poverty of foundation and is applied to all poor spaces point of DMSP/OLS data progress
Cloth drawing, so that the poor Measurement Method proposed by the present invention based on DMSP/OLS nighttime light data being capable of quick obtaining
The spatial distribution of large area poverty improves the poor monitoring accuracy estimated in accurate poverty alleviation.
Further, after the step of acquisition DMSP/OLS night lights remotely-sensed data and a wherein geographic area is chosen
Zero and negative value pixel are removed according to the DMSP/OLS night lights remotely-sensed data for the test block for cutting acquisition as test block
Afterwards, the lamplight brightness value for extracting each pixel further includes following steps before the step of obtaining the average optical index L of test block:
The DMSP/OLS night lights remotely-sensed data that different satellite sensors obtain is corrected by calibration model；Institute
State calibration model are as follows: DNadjust=C0+C1*DN+C2*DN^2；Wherein, DNadjustFor the pixel DN value after correction；DN is to be corrected
Pixel DN value；C0, C1And C2For quadratic polynomial constant.
Further, described that polar plot is drawn according to geographic area, DMSP/OLS night lights remote sensing is cut using polar plot
The step of data includes drawing polar plot according to administrative region.
Further, described that polar plot is drawn including drawing polar plot by province, city, county's three-level according to administrative region.
Further, the average optical index L=B/N；Wherein, L is average optical index, and B is test block light total luminance value；
N is the summation that test block removes all pixels after zero and the number of negative value pixel.
Further, the step of synthesis misery index IPI obtained in test block specifically comprises the following steps:
Obtain 10 socio-economic indicator data in the statistical yearbook in test block；
Principal component analysis is carried out to 10 socio-economic indicator data, two most principal components of information content is chosen and replaces
10 socio-economic indicator data；It takes:
Z1=a00x0+a01x1+a02x2+a03x3+a04x4+a05x5+a06x6+a07x7+a08x8+a09x9
Z2=a10x0+a11x1+a12x2+a13x3+a14x4+a15x5+a16x6+a17x7+a18x8+a19x9
Wherein, Z1、Z2Respectively represent two principal components, xiRespectively represent 10 socio-economic indicator data, xiPreceding coefficient
It is to be got using principal component weight coefficient matrix and characteristic value calculating；
By two principal component Z1And Z2Fit comprehensive misery index IPI:IPI=λ1*Z1/(λ1+λ2)+λ2*Z2/(λ1+
λ2)；Wherein, λ1And λ2It is characterized value.
Further, the socio-economic indicator data include resident's per capita net income, per capita local revenue, per capita city
Township's savings deposits of urban and rural residents, per capita year end financial institution's items balance of deposits, per capita total industrial output value, per capita social consumer goods
Retail sales, per capita main business income, per capita profits tax total value, per capita total export and the per capita sum of investments in fixed assets used.
Further, it is IPI=a*L-b that inverse model is estimated in the poverty.
The present invention also provides a kind of computer-readable storage medias, store computer program thereon, the computer program
The step of poor Measurement Method as described above based on DMSP/OLS nighttime light data is realized when being executed by processor.
The present invention also provides a kind of computer equipment, including reservoir, processor and it is stored in the reservoir simultaneously
The computer program that can be executed by the processor, the processor realize base as described above when executing the computer program
In the poor Measurement Method of DMSP/OLS nighttime light data the step of.
In order to better understand and implement, the invention will now be described in detail with reference to the accompanying drawings.
Detailed description of the invention
Fig. 1 is the flow chart of the poor Measurement Method of the invention based on DMSP/OLS nighttime light data.
Fig. 2 is the sub-step of the step S40 of the poor Measurement Method of the invention based on DMSP/OLS nighttime light data
Flow chart.
Fig. 3 is that the average optical index and comprehensive misery index IPI in the embodiment of the present invention 1 are fitted scatter plot.
Fig. 4-1 to 4-5 is the poor spatial distribution map in the embodiment of the present invention 1.
Specific embodiment
At present satellite remote sensing technology large-scale application in the various aspects of the social life such as agricultural, navigation, bio-identification,
And night lights brightness is just able to reflect a local poverty and prosperity, the big place of lamplight brightness is common in big city,
The small place of lamplight brightness is common in rural area, thus by the discrimination of the lamplight brightness of different geographic regions in remote sensing image, it can
To realize estimating for poverty.In order in time and know precisely the spatial distribution of poverty status or the subsequent tracking of Poverty Alleviation Result
Investigation, the present invention propose a kind of poor Measurement Method based on DMSP/OLS nighttime light data, are on the one hand passed through by multiple societies
Ji statistical data dimension-reduction treatment extracts the synthesis misery index IPI of reflection poverty degree, on the other hand by remote sensing image subregion
Domain obtains night lights luminance information, establishes corresponding relationship between the two, then carry out poverty by this corresponding relationship and estimate.
Referring to Fig. 1, the poor Measurement Method of the invention based on DMSP/OLS nighttime light data, including walk as follows
It is rapid:
S10: DMSP/OLS night lights remotely-sensed data is obtained；
S20: polar plot is drawn according to geographic area, cuts DMSP/OLS night lights remotely-sensed data using polar plot；
S30: wherein a geographic area is as test block for selection, according to the DMSP/OLS night lamp for the test block for cutting acquisition
Light remotely-sensed data extracts the lamplight brightness value of each pixel, the average light for obtaining test block refers to after removing zero and negative value pixel
Number L；
S40: the synthesis misery index IPI in test block is obtained；
S50: the average optical index L and comprehensive misery index IPI in test block is subjected to regression analysis, poverty is established in fitting
Estimate inverse model；
S60: inverse model is estimated into the poverty and is applied to all DMSP/OLS night lights remotely-sensed datas, is determined
The synthesis misery index IPI of each pixel in DMSP/OLS night lights remotely-sensed data.
In the step S10, the DMSP/OLS night lights remotely-sensed data derives from DMSP/OLS (Version4)
The night lights image data collection of non-radiative calibration.The night lights image number of DMSP/OLS (Version4) the non-radiative calibration
According to collection by U.S.National Oceanic and Atmospheric Administration (National Oceanic and Atmospheric
Administration, NOAA) subordinate American National Geophysical Data Center (National Geophysical Data
Center, NGDC) publication.U.S. national defense meteorological satellite (Defense Meteorological Satellite Program,
DMSP) the business molded line scanning sensor (Operational Linescan System, OLS) carried is due to its unique light
Electrically amplified ability can detect the faint near-infrared radiation of earth's surface, therefore the night lamp shadow that the sensor obtains at night
As more and more being used to study mankind's activity.And the DMSP/OLS night lights image data collection of long-term sequence is when having both
Effect property and economy, and time span with higher and spatial coverage, were widely used in city sprawl, economy in recent years
Action evaluation, density of population estimation, power consumption and carbon emission, light pollution and Hazard Assessment etc..Existing image data collection
Including by 6 different DMSP satellite F10 (1992-1994), F12 (1994-1999), F14 (1997-2003), F15 (2000-
2007), the image from 1992-2013 that F16 (2004-2009), F18 (2010-2013) are obtained, all images can be
It downloads the website (http://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html) of NGDC.
But there are still some problems for the data set: satellite sensor is obtaining the process of surface data by many factors
Influence (absorption and scattering, solar elevation, topographic relief amplitude, the pick up calibration of such as atmosphere), so, difference sensing
Be between the image in the same year that device obtains it is discrepant, be mainly shown as the different annual shadows that more satellite sensors obtain
Characterizing the pixel DN value of intensity of light in discontinuous and image as between, there are saturated phenomenon, shortage year border comparativities.In order to improve
The relevance and year border comparativity of data, so that the DMSP/OLS night lights remotely-sensed data obtained is more suitable for poverty and estimates, this
Invention realizes that the DMSP/OLS night lights of long-term sequence are distant based on satellite data application immutable object field method in 1999
Feel the mutual correction of data.Further include following steps between the step S10 and step S20:
The DMSP/OLS night lights remotely-sensed data that different satellite sensors obtain is corrected by calibration model；Institute
State calibration model are as follows: DNadjust=C0+C1*DN+C2*DN^2；Wherein, DNadjustFor the pixel DN value after correction；DN is to be corrected
Pixel DN value；C0, C1And C2For quadratic polynomial constant.
Further, the step S20 is to draw polar plot according to administrative region.Particularly, described in Chinese range
Drawing polar plot according to administrative region includes drawing polar plot by province, city, county's three-level.It cuts and obtains each province, the county each city He Ge
DMSP/OLS night lights remotely-sensed data, the poverty for being easy to implement three-level administrative region are estimated and are statisticallyd analyze, and show more intuitive.
The present invention is to choose a test block, by the synthesis misery index IPI and the DMSP/OLS night that construct test block
The corresponding relationship namely model of light remotely-sensed data, then this corresponding relationship or model are applied to all (region of interest
Domain, can be nationwide, can be certain province, certain city or certain county) DMSP/OLS night lights remotely-sensed data, it is anti-release with
DMSP/OLS night lights remotely-sensed data integrates misery index IPI accordingly, realizes that poverty is estimated.
Obtain the average optical index L and comprehensive misery index IPI of test block respectively in step S30 and S40 as a result,.
In order to which the lamplight brightness for preferably representing test block is horizontal, using average optical index, average optical index reflects light
The average level of brightness.In the step S30, average optical index L=B/N；Wherein, L is average optical index, and B is test block lamp
Light total luminance value；N is the summation that test block removes all pixels after zero and the number of negative value pixel.DMSP/OLS night lights
The value range of remotely-sensed data is 0 to 63, and zero is that the background being identified is replaced in DMSP/OLS night lights remotely-sensed data
Noise.
Referring to Fig. 2, the step S40 specifically comprises the following steps:
S41: 10 socio-economic indicator data in statistical yearbook are obtained
S42: carrying out principal component analysis to 10 socio-economic indicator data, chooses two most principal components of information content
Instead of 10 socio-economic indicator data；It takes:
Z1=a00x0+a01x1+a02x2+a03x3+a04x4+a05x5+a06x6+a07x7+a08x8+a09x9
Z2=a10x0+a11x1+a12x2+a13x3+a14x4+a15x5+a16x6+a17x7+a18x8+a19x9
Wherein, Z1、Z2Respectively represent two principal components, xiRespectively represent 10 socio-economic indicator data, xiPreceding coefficient
It is to be got using principal component weight coefficient matrix and characteristic value calculating；
S43: by two principal component Z1And Z2Fit comprehensive misery index IPI:
IPI=λ1*Z1/(λ1+λ2)+λ2*Z2/(λ1+λ2)；
Wherein, λ1And λ2It is characterized value, characteristic value is the covariance matrix characteristic value point to 10 socio-economic indicator data
Solve the result obtained.
In step S41, the socio-economic indicator data include resident's per capita net income, per capita local revenue,
Savings deposits of urban and rural residents remaining sum, per capita year end financial institution's items balance of deposits, per capita total industrial output value, per capita society per capita
Retail sales of consumer goods, per capita main business income, per capita profits tax total value, per capita total export and per capita investment in fixed assets are completed
Volume.
10 socio-economic indicator data, the complex redundancy of data, it is difficult to quick and precisely hold poverty status, pass through step
Rapid S41~S42 carries out dimension-reduction treatment to 10 socio-economic indicator data application Principal Component Analysis, and fits one and can weigh
The synthesis misery index IPI of poverty degree is measured, data redudancy is eliminated in the convenient and accurate judge for carrying out poverty degree.
Further, the average optical index L and comprehensive misery index IPI in test block is carried out regression analysis by step S50,
Fitting establishes poverty and estimates inverse model, and average optical index L and comprehensive misery index IPI are positively correlated, and inverting mould is estimated in poverty
Type is IPI=a*L-b.
Finally, the DMSP/ after inverse model is used to correct is estimated in the poverty of the test block built in step S60
OLS night lights remotely-sensed data obtains poor spatial distribution map, realizes that poverty is estimated.
Embodiment 1
Below by way of by taking Shandong Province's poverty is estimated as an example, to the poverty of the invention based on DMSP/OLS nighttime light data
Measurement Method is illustrated.
1, DMSP/OLS night lights remotely-sensed data is obtained, is chosen having collected from 2001 to 2013 in this example year
5 years DMSP/OLS night lights remotely-sensed datas, see Table 1 for details.
The DMSP/OLS night lights remotely-sensed data that 1 example of table is collected
2, DMSP/OLS night lights remotely-sensed data is cut
Using the polar plot on Shandong Province boundary as area-of-interest, it is applied to the DMSP/OLS night lights remote sensing shadow in the whole world
Picture cuts the DMSP/OLS night lights remote sensing image of Shandong Province；On the remote sensing image of the Shandong Province cut, into one
Step draws polar plot and Shandong provincial, and municipal level administrative area using administrative areas at the county level, Shandong Province and draws polar plot, cut and obtain each city and
The DMSP/OLS night lights remote sensing image in each county.
3, the DMSP/OLS night lights remotely-sensed data that correction different sensors are collected
The data that this example uses come from 3 different sensors, in order to improve the relevance and year border comparativity of data, answer
The DMSP/OLS night lights remotely-sensed data collected with such as drag correction different sensors:
DNadjust=C0+C1*DN+C2*DN^2
4, average optical index is obtained
Selected County of Shandong Province is that the lamp of this county of each pixel is extracted on the basis of data radiant correction in test block
Brightness values, and remove zero or negative value pixel acquires County Scale and is averaged lamplight brightness, average light is acquired by L=B/N and is referred to
Number.
5, it is fitted comprehensive misery index IPI
The socio-economic indicator data for collecting test block analyze each counties and districts' economic indicator data principal component in Shandong Province,
The 73.48% of first principal component gross information content, Second principal component, accounts for the 13.57% of gross information content, and accumulative population variance accounts for about
87.06%, therefore 10 socio-economic indicator data are replaced with the first, second principal component, then:
Z1=0.2298x1+0.3372x2+0.3276x3+0.3534x4+0.3291x5+0.2973x6
+0.3261x7+0.3058x8+0.2973x9+0.3409x10
Z2=0.5363x1+0.1279x2+0.2677x3+0.0180x4-0.3681x5+0.3879x6
-0.3733x7-0.4402x8+0.0532x9-0.0420x10
Z1、Z2Respectively represent two principal components, xiRespectively represent 10 socio-economic indicator data, xiPreceding coefficient is benefit
It is got with principal component weight coefficient matrix and characteristic value calculating；The principal component weight coefficient matrix please refers to table 2；The feature
Value please refers to table 4.
Each ingredient weight coefficient matrix of table 3
The population variance of 4 principal component of table
Characteristic value is read by table 4, wherein λ1=7.348 and λ2=1.358, to be fitted the synthesis misery index of acquisition
IPI:
IPI=0.8440Z1+0.1560Z2
6, it establishes poverty and estimates inverse model
Referring to Fig. 3, the average optical index L and comprehensive misery index IPI in test block is carried out regression analysis, fitting is built
Inverse model is estimated in vertical poverty, and average optical index L and comprehensive misery index IPI are positively correlated, and the goodness of fit 0.645 is poor
Estimate inverse model are as follows:
IPI=1.25*L-5.4
7, estimate inverse model progress poverty using poverty to estimate
Fig. 4-1 to 4-5 is please referred to, the comprehensive poor measure model of the County Scale of building is used for pretreated 2001
To Shandong Province's DMSP/OLS image in 2013, Shandong Province's poverty spatial distribution map is obtained.
Compared to the prior art, the present invention is based on socioeconomic driving forces extracts comprehensive poverty using Principal Component Analysis
Index IPI, and select a test block, by the synthesis misery index IPI and DMSP/OLS of test block be averaged optical index establish it is poor
It is tired to estimate inverse model；Inverse model is estimated into the poverty of foundation and is applied to all poor spaces point of DMSP/OLS data progress
Cloth drawing, so that the poor Measurement Method proposed by the present invention based on DMSP/OLS nighttime light data being capable of quick obtaining
The spatial distribution of large area poverty improves the poor monitoring accuracy estimated in accurate poverty alleviation.
The present invention also provides a kind of computer-readable storage medias, store computer program thereon, the computer program
The poor Measurement Method based on DMSP/OLS nighttime light data as described in above-mentioned any one is realized when being executed by processor
The step of.
It wherein includes storage medium (the including but not limited to disk of program code that the present invention, which can be used in one or more,
Memory, CD-ROM, optical memory etc.) on the form of computer program product implemented.Computer-readable storage media packet
Permanent and non-permanent, removable and non-removable media is included, can be accomplished by any method or technique information storage.Letter
Breath can be computer readable instructions, data structure, the module of program or other data.The example packet of the storage medium of computer
Include but be not limited to: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM),
Other kinds of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory
(EEPROM), flash memory or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM), digital versatile disc
(DVD) or other optical storage, magnetic cassettes, tape magnetic disk storage or other magnetic storage devices or any other non-biography
Defeated medium, can be used for storage can be accessed by a computing device information.
The present invention also provides a kind of computer equipment, including reservoir, processor and it is stored in the reservoir simultaneously
The computer program that can be executed by the processor, the processor are realized when executing the computer program as above-mentioned any one
The step of poor Measurement Method based on DMSP/OLS nighttime light data described in item.
The invention is not limited to above embodiment, if not departing from the present invention to various changes or deformation of the invention
Spirit and scope, if these changes and deformation belong within the scope of claim and equivalent technologies of the invention, then this hair
It is bright to be also intended to encompass these changes and deformation.
Claims (10)
1. a kind of poor Measurement Method based on DMSP/OLS nighttime light data, which comprises the steps of:
Obtain DMSP/OLS night lights remotely-sensed data；
Polar plot is drawn according to geographic area, cuts DMSP/OLS night lights remotely-sensed data using polar plot；
Wherein a geographic area is as test block for selection, according to the DMSP/OLS night lights remote sensing number for the test block for cutting acquisition
According to extracting the lamplight brightness value of each pixel, obtain the average optical index L of test block after removing zero and negative value pixel；
Obtain the synthesis misery index IPI in test block；
Average optical index L and comprehensive misery index IPI in test block is subjected to regression analysis, fitting establishes poverty and estimates inverting
Model；
Inverse model is estimated into the poverty and is applied to all DMSP/OLS night lights remotely-sensed datas, determines DMSP/OLS night
Between in light remotely-sensed data each pixel synthesis misery index IPI.
2. the poor Measurement Method according to claim 1 based on DMSP/OLS nighttime light data, it is characterised in that: institute
It states the step of obtaining DMSP/OLS night lights remotely-sensed data and polar plot is drawn according to geographic area, cut using polar plot
Further include following steps between the step of DMSP/OLS night lights remotely-sensed data:
The DMSP/OLS night lights remotely-sensed data that different satellite sensors obtain is corrected by calibration model；The school
Positive model are as follows: DNadjust=C0+C1*DN+C2*DN^2；Wherein, DNadjustFor the pixel DN value after correction；DN is picture to be corrected
First DN value；C0, C1And C2For quadratic polynomial constant.
3. the poor Measurement Method according to claim 1 based on DMSP/OLS nighttime light data, it is characterised in that: institute
The step of stating and draw polar plot according to geographic area, cutting DMSP/OLS night lights remotely-sensed data using polar plot includes basis
Draw polar plot in administrative region.
4. the poor Measurement Method according to claim 3 based on DMSP/OLS nighttime light data, it is characterised in that: institute
Stating and drawing polar plot according to administrative region includes drawing polar plot by province, city, county's three-level.
5. the poor Measurement Method according to claim 1 based on DMSP/OLS nighttime light data, it is characterised in that: institute
State average optical index L=B/N；Wherein, L is average optical index, and B is test block light total luminance value；N is that test block removes zero
With the summation of all pixels after the number of negative value pixel.
6. the poor Measurement Method according to claim 1 based on DMSP/OLS nighttime light data, it is characterised in that: institute
The step of obtaining the synthesis misery index IPI in test block is stated to specifically comprise the following steps:
Obtain 10 socio-economic indicator data in the statistical yearbook in test block；
Principal component analysis is carried out to 10 socio-economic indicator data, two most principal components of information content is chosen and replaces 10
Socio-economic indicator data；It takes:
Z1=a00x0+a01x1+a02x2+a03x3+a04x4+a05x5+a06x6+a07x7+a08x8+a09x9
Z2=a10x0+a11x1+a12x2+a13x3+a14x4+a15x5+a16x6+a17x7+a18x8+a19x9
Wherein, Z1、Z2Respectively represent two principal components, xiRespectively represent 10 socio-economic indicator data, xiPreceding coefficient is benefit
It is got with principal component weight coefficient matrix and characteristic value calculating；
By two principal component Z1And Z2Fit comprehensive misery index IPI:IPI=λ1*Z1/(λ1+λ2)+λ2*Z2/(λ1+λ2)；Its
In, λ1And λ2It is characterized value.
7. the poor Measurement Method according to claim 6 based on DMSP/OLS nighttime light data, it is characterised in that: institute
State socio-economic indicator data include resident's per capita net income, per capita local revenue, per capita more than savings deposits of urban and rural residents
Volume, per capita year end financial institution's items balance of deposits, per capita total industrial output value, per capita amount of social consumption product retail manage mainly per capita
Health service revenue, per capita profits tax total value, per capita total export and the per capita sum of investments in fixed assets used.
8. the poor Measurement Method according to claim 1 based on DMSP/OLS nighttime light data, it is characterised in that: institute
Stating poverty to estimate inverse model is IPI=a*L-b.
9. a kind of computer-readable storage media, stores computer program thereon, which is characterized in that the computer program is located
It manages and realizes the poor survey based on DMSP/OLS nighttime light data as claimed in any of claims 1 to 8 in one of claims when device executes
The step of degree method.
10. a kind of computer equipment, which is characterized in that including reservoir, processor and be stored in the reservoir and can
The computer program executed by the processor, the processor realize such as claim 1 to 8 when executing the computer program
Any one of described in the poor Measurement Method based on DMSP/OLS nighttime light data the step of.
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