CN114004278A - Multi-source night light data correction fusion method, terminal equipment and medium - Google Patents

Multi-source night light data correction fusion method, terminal equipment and medium Download PDF

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CN114004278A
CN114004278A CN202111162881.3A CN202111162881A CN114004278A CN 114004278 A CN114004278 A CN 114004278A CN 202111162881 A CN202111162881 A CN 202111162881A CN 114004278 A CN114004278 A CN 114004278A
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郑渊茂
何原荣
范孟琳
柴春芳
王晓荣
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Xiamen University of Technology
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Abstract

The invention relates to a multi-source night lamplight data correction fusion method, terminal equipment and a medium, wherein the method comprises the following steps: s1: acquiring DMSP-OLS and NPP-VIIRS night light data to be fused; s2: preprocessing night light data; s3: selecting an invariant target area, and sequentially performing mutual correction among sensors, intra-image-year fusion and inter-image-year continuity correction on the preprocessed DMSP-OLS night light data by adopting an invariant target method; s4: the night light data of the DMSP-OLS and the NPP-VIIRS are mutually corrected; s5: the inter-image continuity correction is performed on the data mutually corrected in step S4. The invention improves the continuity and comparability of the multi-source night light data set of the time sequence in a long period.

Description

Multi-source night light data correction fusion method, terminal equipment and medium
Technical Field
The invention relates to the field of data fusion, in particular to a multi-source night light data correction fusion method, terminal equipment and a medium.
Background
Currently, there are two types of sensors that are commonly used internationally to detect the brightness of the lamp light on the earth's surface at night: one is The service line scanning System of National Defense Meteorological Satellite project (The Defense Meta scientific Satellite Program, Operational Linescan System night time stable light data, DMSP-OLS), The other is The (S-NPP) Satellite Visible light Infrared Imaging Radiometer (VIIRS) Day and night Band (The Sun National Polar-tracking Partnership, Visible Infrared Imaging Radiometer Day-night Band, NPP-VIIRS DNB). The DMSP-OLS can detect the activity information of town lights, lightning, fire lights and the like on the surface of the earth during night work, and the activity information is clearly contrasted with the dark rural background on the image data. DMSP-OLS is used for acquiring night light image data at first, and has the limitations of rough radiometric precision, low spatial resolution, lack of satellite-borne calibration and the like; and due to lack of on-satellite calibration, variable atmospheric conditions, and sensor degradation, annual dynamic changes in DMSP-OLS time series night light data cannot be directly used for comparison. The later new generation of NPP-VIIRS night light remote sensing data with high resolution has the characteristics of strong availability, radiometric calibration and the like, overcomes some limitations and defects of DMSP-OLS data, has higher satellite-borne calibration radiometric measurement precision and ensures the capability of detecting extremely low-brightness night light.
At present, the fourth version of DMSP-OLS non-radiometric calibration average night light intensity image data is historical archived data, the data currently comprises 34-phase images, the time span is 1992-charge 2013, the DN value range of each phase is 0-63, the stable night light remote sensing data is generally adopted to carry out quantitative inversion research on urbanization monitoring, but continuity and comparability are lacked among the data of each phase, and the problem of data saturation exists; and NPP-VIIRS night light remote sensing data can be obtained from 2012 so far, and the continuity is good. Whereas DMSP-OLS archives long-term historical data, NPP-VIIRS data is older and less intersected.
Disclosure of Invention
In order to solve the problems, the invention provides a multi-source night light data correction and fusion method, terminal equipment and a medium.
The specific scheme is as follows:
a multi-source night light data correction and fusion method comprises the following steps:
s1: acquiring DMSP-OLS and NPP-VIIRS night light data to be fused;
s2: preprocessing DMSP-OLS and NPP-VIIRS night light data, wherein the preprocessing comprises image re-projection, re-sampling and cutting;
s3: selecting an invariant target area, and sequentially performing mutual correction among sensors, intra-image-year fusion and inter-image-year continuity correction on the preprocessed DMSP-OLS night light data by adopting an invariant target method;
s4: and (3) performing mutual correction on the DMSP-OLS and NPP-VIIRS night light data by adopting the following model:
DNr＝α×(LgVIIRS)2+β×(LgVIIRS)+γ
wherein DNrExpressing the value of the corrected night light image data, VIIRS expressing the value of the NPP-VIIRS night light image data before correction, LgVIIRS expressing the logarithm of VIIRS, and alpha, beta and gamma being regression parameters in a quadratic regression model;
s5: the inter-image continuity correction is performed on the data mutually corrected in step S4.
Further, the specific process of the pretreatment comprises: firstly, re-projecting night light data into Alberts equal-product conical projection, and re-sampling to 1km by adopting a bilinear fitting mode; and secondly, obtaining night light data with the spatial resolution of 1km by cutting.
Further, a quadratic regression model adopted for mutual calibration between the sensors is as follows:
DNc＝a×DN2+b×DN+c
wherein DN and DNcRespectively representing the gray values of the images before and after correction; a. and b and c both represent secondary regression parameters.
Further, the model adopted by the image intra-year fusion is as follows:
Figure BDA0003290429360000031
wherein,
Figure BDA0003290429360000032
respectively representing the pixel DN values of the ith pixel of night light data acquired by two different sensors a and b after the nth mutual correction; DN(n,i)And expressing the DN value of the ith pixel of the corrected night light data of the nth year.
Further, the model used for the inter-annual continuity correction of the images is as follows:
Figure BDA0003290429360000033
wherein DN(n-1,i)、DN(n,i)、DN(n+1,i)Respectively representing the pixel DN values of the ith pixel DN of the night light data fused in the image year in the (n-1) th year, the (n) th year and the (n + 1) th year'(n,i)And expressing the DN value of the ith pixel of the lighting data of the nth year night after the continuity correction of the image between the years.
A multi-source night light data correction and fusion terminal device comprises a processor, a memory and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the method of the embodiment of the invention.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as described above for an embodiment of the invention.
By adopting the technical scheme, the problem of incomparability of different annual data of the DMSP-OLS night light image data set and the problem of incomparability between the DMSP-OLS night light image data set and the NPP-VIIRS night light image data set are solved. The invention improves the continuity and comparability of the multisource night light data set of the long-time sequence, expands the application range of the night light remote sensing data and can strengthen the understanding of the long-time urbanization process and the influence of the long-time urbanization process on the ecological environment.
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Fig. 1 is a flowchart illustrating a first embodiment of the present invention.
FIG. 2 is a graph showing the average DN value and the total pel value of the DMSP-OLS night light data in this embodiment.
FIG. 3 is a diagram showing the statistical analysis results of the corrected DN values of the DMSP-OLS time-series night light data in the study area in this embodiment.
FIG. 4 is a diagram showing the statistical analysis results of the corrected night light data DN values of the NPP-VIIRS time series in the study area in this embodiment.
FIG. 5 shows the mean value and the total number of the corrected time-series night light data DN values of the two types of night light data in the study area in this example.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures.
The invention will now be further described with reference to the accompanying drawings and detailed description.
The first embodiment is as follows:
the embodiment of the invention provides a multi-source night light data correction and fusion method, which is a flow chart of the multi-source night light data correction and fusion method in the embodiment of the invention as shown in fig. 1, and the method comprises the following steps:
s1: and acquiring night light data of DMSP-OLS and NPP-VIIRS to be fused.
S2: and preprocessing the DMSP-OLS and NPP-VIIRS night light data.
The DMSP-OLS and NPP-VIIRS night light data comprise town and other types of stable light data, and after being strictly processed, the influences of factors such as instantaneous bright light, sunlight, moonlight, cloud, aurora and the like of transient events such as fire disasters and the like are removed. However, the method cannot be directly used for extracting the information of the built-up areas of the city due to the limitation of the irrelevability of the data of the time series data sets in different years. The specific limitations are mainly expressed in that: (1) the average intensity values of the night light data acquired by two satellites in the same year are different; (2) the average intensity value of the night light data acquired by the same satellite in different years has abnormal fluctuation; (3) the quantity of pixels for acquiring night light data by two satellites in the same year is different; (4) the number of pixels of the same satellite for acquiring night light data in different years is reduced abnormally. Therefore, before research work is carried out, relative radiometric calibration, intra-year data correction, and inter-year sequence correction of the night light data sets are required to improve the continuity and comparability of the time series night light data sets.
In this embodiment, image re-projection, re-sampling, clipping, and other preprocessing are performed on the night light remote sensing data. In the specific operation, firstly, the night light remote sensing image in 21 years in 1992-.
S3: selecting an invariant target area, and sequentially performing mutual correction among sensors, intra-image-year fusion and inter-image-year continuity correction on the preprocessed DMSP-OLS night light data by adopting an invariant target method.
And performing statistical analysis on the social and economic statistical data of GDP, urban population, urban built-up area and the like of the urban district of the main city within the region range of China in 1992 and 2012 to further determine the invariable target region. The invariant target regions in this embodiment should satisfy the following conditions: (1) the change of the main socioeconomic statistical data is small in the 20-year period, which indicates that the city is in a relatively stable development situation all the time in the period; (2) the stable night light data has DN values from low values to high values and has a wide range, so that the accuracy of the night light data mutual correction model is ensured; (3) the 1992 night light image acquired by the F12 satellite has a good linear correlation with the 2012 night light image acquired by the F18 satellite, which indicates that the night light intensity variation of the city during this period is relatively stable. In this embodiment, after determining the invariant target region, the corresponding regions of the 33-phase DMSP-OLS stabilized night light image data and the F162006 radiometric calibration DMSP-OLS night light image data are determined as the image data to be corrected and the reference image data, respectively.
(1) Mutual correction between sensors
The abnormal fluctuation of the DN values of the image pixels of the multi-sensor is the main reason for the discontinuity of the image data of the light at night. In order to improve the continuity of the night light data, the night light data needs to be mutually corrected among the sensors. In order to effectively reduce the difference between DN values among night light data, the method for mutually correcting the global night light remote sensing data based on the unchanged target area comprises the following steps: determining a reference area to construct a regression model, wherein the reference area needs a DN value with small annual change; performing regression analysis on the DN values of the other-year image data and the calibration areas determined in the reference data; and finally, correcting the long-time sequence data set by using the constructed regression model.
Due to the fact that the difference of urban development levels among different regions is large, the actual development condition of the city is considered. (1) Firstly, after GDP and urban built-up area data of main cities are analyzed, cities with stable social and economic development are selected as reference areas; (2) secondly, selecting 2007 data of an F16 satellite as a reference data set; (3) performing secondary regression model analysis on the DN values of the night light remote sensing data of the reference areas in other years and the DN values of the night light remote sensing data of the reference areas F162007, wherein the secondary regression model is shown as a formula 1; (4) and finally, obtaining regression parameters by using the constructed quadratic regression model, and mutually correcting the DMSP-OLS night light remote sensing data sets of the long-time sequence.
DNc＝a×DN2+b×DN+c (1)
In the formula, DN and DNcThe gray values of the images before and after correction are respectively; a. and b and c both represent secondary regression parameters.
In this embodiment, the long-time sequence DMSP-OLS night light remote sensing data of the reference region is subjected to quadratic regression model analysis to obtain a regression decision coefficient R2All above 0.83, and performing linear regression model analysis to obtain regression determination coefficient R2The values are all above 0.82, and the regression models have good precision. Therefore, the present embodiment utilizes the parameters of the quadratic regression model to perform mutual calibration on the DMSP-OLS night light data in the range region of china in 1992 and 2012, and the parameters of the quadratic regression model and the linear regression model are shown in table 1.
TABLE 1
Figure BDA0003290429360000071
Figure BDA0003290429360000081
The long-time sequence DMSP-OLS night light data sets after mutual correction among the sensors have comparability, and meanwhile, the saturation degree of pixel DN values of the night light data at each period is reduced; however, the discontinuity problem of the night light data sets after mutual correction is still not solved, and is represented as: meanwhile, the phenomenon that the multiple sensors acquire night light data of the same year and the abnormal fluctuation phenomenon of pixel DN values of the same area of night light data sets of different years acquired by the multiple sensors exists. Therefore, the DMSP-OLS night light image data set also needs to be subjected to intra-image-year fusion correction and inter-image-year continuity correction.
(2) Intra-year fusion of images
Due to the difference of different satellite sensors, the sensors can be influenced by various factors when acquiring night light data. Therefore, there is a difference between the multi-sensor acquisition of the same year night light data. Although mutual correction based on an invariant target correction method reduces this difference, it does not completely eliminate it. The two-phase night light data of the same year still exist and have difference. In order to fully utilize the multiple sensors to independently acquire the night light and shadow data of the same year, the problem of processing the pixel difference of the multiple sensors in the same area of the same year night light data is solved, and image intra-year fusion is needed. Therefore, in this embodiment, formula (2) is adopted to perform intra-image fusion on the night light data after mutual correction of multiple sensors in the same year, so as to form a unique night light image data set for each year.
Figure BDA0003290429360000091
In the formula,
Figure BDA0003290429360000092
respectively representing the pixel DN values of the ith pixel of night light data acquired by two different sensors a and b after the nth mutual correction; DN(n,i)Expressing the DN value of the ith pixel of the corrected night light data of the nth year; n-1994, 1997, 1998, …, 2007.
(3) Inter-annual continuity correction of images
According to the development law that the urbanization process of China is continuously intensified, it can be assumed that the bright pixel of the urban area obtained from the previous night light image data cannot disappear in the later night light data, and researches show that the possibility that the bright pixel with a higher DN value in the night light data is extracted as the urban area is higher. Therefore, the DN value of the bright pixel in the early-stage night light data is not greater than the DN value of the bright pixel in the same area in the later-stage night light data. In view of this, in the DMSP-OLS night light data set of the long-time sequence, if one pixel exists in the early night light image and the same area disappears in the late night light image, or if the pixel DN value of the early night light image data of one pixel is greater than that of the late night light image data, the pixel data can be considered as unstable pixel data, that is, the night light image data set has the phenomenon of pixel fluctuation in different years.
Due to mutual correction and intra-year fusion correction of the night light data sets, the phenomenon that pixel DN values of multiple sensors fluctuate abnormally when the multiple sensors acquire night light data of different years is not solved. Therefore, it is necessary to perform an inter-year correction of the multi-sensor acquisition of the night light data of different years to improve the continuity of the night light data set. The inter-annual correction is based as follows: (1) when the pixel DN value in the later night light data is 0, the pixel DN at the same position in the earlier night light data is also 0; (2) and when the pixel DN value of the later-stage night light data is not 0, the pixel DN value of the earlier-stage night light data is not more than the pixel DN value of the same position in the later-stage night light data. The specific correction formula is shown in formula 3.
Figure BDA0003290429360000101
In the formula, DN(n-1,i)、DN(n,i)、DN(n+1,i)Respectively representing the pixel DN values of the ith pixel DN of the night light data fused in the image year in the (n-1) th year, the (n) th year and the (n + 1) th year'(n,i)Expressing the DN value of the ith pixel of the lighting data of the nth year night after the continuity correction of the image between the years; n ═ 1992, 1993, …, 2013.
In the embodiment, after the three correction methods are used for correction, regression fitting analysis is performed on DMSP-OLS night lamplight data of each time series satellite in 1992-2The values are all above 0.83, and the accuracy of the regression model is good.
In order to verify the rationality of the night light remote sensing image data correction method in 1992-2013, a qualitative method and a quantitative method are respectively adopted in the embodiment for verification. And the qualitative method adopts a visual interpretation method, and verifies whether the corrected night light remote sensing data solves the problem existing before correction or not by comparing the pixel DN value change between the night light remote sensing data of the same year before and after correction. Therefore, a quantitative analysis method is also needed to verify the night light data after correction, so that the verification result is more accurate, objective and reliable. The quantitative verification is to compare the condition that the Total DN Value (TDN) of the bright Value pixels corresponding to the night light remote sensing data before and after correction and the Total number (TLP) of the bright Value pixels change along with the time. The calculation formula of the sum of the bright pixel DN values (TDN) of the night light remote sensing data is shown in formula 4.
Figure BDA0003290429360000111
In the formula, DNiRepresenting DN value of ith pixel of night light image; ciAnd the number of the ith pixel of the night light image is represented.
In this embodiment, the corrected night light image data is also verified, and firstly, the average DN value and the total pixel value of the DMSP-OLS night light data in 1992-2013 are changed, as shown in fig. 2, so that discontinuity characteristics such as a large difference existing in the same year obtained by different sensors before correction, abnormal reduction of pixels existing in the night light data in different years obtained by the same sensor, and the like can be obtained. Secondly, the average DN value and the total pixel value of the DMSP-OLS night light data of 1992-2013 are changed, as shown in FIG. 3, it can be obtained that the time series night light remote sensing data after correction has a unique value data value in each year, and the pixels of the night light data in different years do not decrease abnormally, the average DN value and the total pixel DN value of the pixels of the night light remote sensing data show a stable increasing trend, and the consistency of the DMSP-OLS time series night light data after correction is better, and the result is more accurate.
S4: and performing sensor mutual correction on the DMSP-OLS and NPP-VIIRS night light data.
The DMSP-OLS night light data and the NPP-VIIRS night light data are different in data source. Therefore, the embodiment also comprises the step of carrying out registration correction on the DMSP-OLS night light data and the NPP-VIIRS night light data.
First, in this embodiment, regression analysis of quadratic terms and logarithmic quadratic terms is performed on the NPP-VIIRS long-time sequence night light data in reference area 2013 and 2020 and the DMSP-OLS night light data in 2012, and the accuracy of the regression model is shown in tables 2 and 3, so that the accuracy of the regression model is better. Secondly, the regression model parameters are further used for mutually correcting NPP-VIIRS night light remote sensing data in the 2013-year 2020 reference region in the embodiment, and for convenience of expression, the corrected NPP-VIIRS night light data are called as DMSP-OLS-like night light data in the embodiment.
In this embodiment, the correction between the two types of sensors of the DMSP-OLS night light data and the NPP-VIIRS night light data is expressed by a formula shown in formula 5.
DNr＝α×(LgVIIRS)2+β×(LgVIIRS)+γ (5)
In the formula, DNrThe corrected night light image data value is shown, VIIRS is the NPP-VIIRS night light image data value before correction, LgVIIRS is the logarithm of VIIRS, and alpha, beta and gamma are regression parameters in a quadratic regression model.
TABLE 2
Figure BDA0003290429360000121
Figure BDA0003290429360000131
S3: and (4) performing image inter-year continuity correction on the data (DMSP-OLS-like night light data) subjected to mutual correction in the step (S4).
The image inter-year continuity correction of the DMSP-OLS-like night light data is based on the following steps: (1) when the pixel DN value in the late-corrected DMSP-OLS-like night light data is 0, the pixel DN at the same position in the early-stage night light data is also 0; (2) when the pixel DN value of the later-stage corrected DMSP-OLS-like night light data is not 0, the pixel DN value of the earlier-stage DMSP-OLS-like night light data is not greater than the pixel DN value of the same position in the later-stage DMSP-OLS-like night light data, and the formula is specifically corrected and shown in formula 6.
Figure BDA0003290429360000132
In the formula, DN(n-1,i)、DN(n,i)、DN(n+1,i)Respectively representing the pixel DN values of the ith night light image after the mutual correction and the correction between the images of the same year acquired by the multiple sensors in the (n-1) th, the (n) th and the (n + 1) th year; n is 2013, 2014, …, 2020.
TABLE 3
Figure BDA0003290429360000133
Figure BDA0003290429360000141
In this embodiment, quadratic regression fitting analysis is performed on the logarithmic night light remote sensing data of NPP-VIIRS in 2013 and 2020 and the DMSP-OLS night light remote sensing data in 2012 to obtain a regression decision coefficient R2The accuracy of the regression model is better when the values are all above 0.71.
In order to verify the long-time sequence multi-source night light data correction fusion result of DMSP-OLS and NPP-VIIRS. Firstly, the rationality of the NPP-VIIRS night light image correction fusion result and method in the years of 2013 and 2020 is verified. In this embodiment, a quantitative method of a night light data average value and a night light brightness total value of time series data is used for evaluation and verification. The light data of the NPP-VIIRS at night is corrected by using the logarithmized quadratic regression model method provided by the embodiment, and the quantitative evaluation result after the NPP-VIIRS is corrected is shown in FIG. 4. The corrected NPP-VIIRS night light data in 2013 and 2020 can be obtained to increase year by year and show a stable increasing trend, which is consistent with the socioeconomic development condition in the research area. Therefore, the NPP-VIIRS night light remote sensing data correction result is better.
And (4) carrying out correction quantitative evaluation on time series multi-source night light data of the DMSP-OLS and the NPP-VIIRS, and counting the time-varying trends of TDN and TLP of the brightness value pixels of the two corrected night light image data. And performing a series of correction processing based on the DMSP-OLS time sequence night light data correction and the multi-source night light data correction method of DMSP-OLS and NPP-VIIRS. Finally, obtaining the DN average value and the DN total value of the time-series night light data after the multi-source night light data correction of DMSP-OLS and NPP-VIIRS in the period of 1992-2020, as shown in FIG. 5; wherein, data in 1992 and 2012 are DMSP-OLS night light remote sensing image data, and data in 2013 and 2020 are "DMSP-OLS-like" night light image data.
Fig. 5 shows the TLP and TDN presentation increasing trend of the corrected long-term series nightlight image data. The statistical difference of the TLP in 2014-2020 is gradually reduced. TDN steadily increases in 1992-2006, sharply increases in 2007-2013, and the increase speed in 2014-2020 tends to be stable. The main reason is that the night light intensity of urban area centers tends to be stable when urban area cities in Guangdong, Hongkong, Australia and Bay areas develop to the middle and later stages, the night light image data is expressed as stable pixel data, the available urban area expansion is the continuous expansion and continuous development from the central area to the surrounding areas, and the available urban area expansion can also be obtained by observing NPP-VIIRS night light image floating point type data. Furthermore, it is clear from 5 that: each annual night light data of the corrected multi-source night light remote sensing data in long time periods of 1992-charge 2020 has a unique value; and the data value of the night light in the next year is not smaller than that in the previous year, but the trend of increasing year by year and stably increasing is presented, which shows that the correction and fusion result of the multi-source night light remote sensing data sets of DMSP-OLS and NPP-VIIRS is better.
The DMSP-OLS and NPP-VIIRS data corrected by the steps can be regarded as data fusion.
The embodiment of the invention corrects the DMSP-OLS-like night light data of the NPP-VIIRS night light image data by performing the processes of relative radiation calibration, intra-year image correction, inter-year sequence correction and the like on the DMSP-OLS night light image data, improves the continuity and comparability of a long-time sequence multi-source night light image data set, expands the application range of night light remote sensing data, can strengthen the urbanization process, human activities and understanding of the influence of the long-time sequence on the ecological environment, and is beneficial to development of subsequent long-time multi-source night light data research work.
The correction and fusion method for the multi-source night light image provided by the embodiment improves the continuity and comparability of the multi-source night light data for a long time. The urban land utilization/coverage type can be inverted by the change of the bright value area range of the night light remote sensing image. Because the night light remote sensing data is easy to obtain, the correction process provided by the embodiment has repeatability, the corrected night light image data has continuity and stability, the DN value saturation phenomenon and the overflow effect are greatly reduced, and the corrected night light remote sensing data is an important data source for researching urban land utilization/coverage change. Therefore, the embodiment can be applied to urbanization expansion and space-time evolution research of dynamically monitoring large-scale urban groups for a longer period.
The method of the embodiment has the following key points:
the key point is as follows: aiming at DMSP-OLS night light remote sensing data of a time sequence, a quadratic regression model method of an invariant region is adopted, and the problem of incomparability of time sequence DMSP-OLS night light data sets in data of different years is solved. The concrete points are as follows: the method solves the following limitations of the time sequence DMSP-OLS: (1) the average intensity values of the night light data acquired by two satellites in the same year are different; (2) the average intensity value of the night light data acquired by the same satellite in different years has abnormal fluctuation; (3) the quantity of pixels for acquiring night light data by two satellites in the same year is different; (4) the number of pixels of the same satellite for acquiring night light data in different years is reduced abnormally.
The key point II is as follows: aiming at DMSP-OLS and NPP-VIIRS night light remote sensing data, the invention provides a 'logarithmized quadratic regression model method' for correction, solves the problem of incomparability of a DMSP-OLS night light data set and an NPP-VIIRS night light data set, and adopts TDN to carry out quantitative evaluation after correcting the night light.
The key point is three: aiming at the limitation of the DMSP-OLS night light remote sensing data saturation phenomenon. The mutual correction between the sensors in the 'quadratic regression model method of invariant regions' constructed by the method can remove the saturation phenomenon of DMSP-OLS night lamplight remote sensing image data.
The key point is four: the inter-year continuity correction of the DMSP-OLS-like night light data is performed for the discontinuity of the DMSP-OLS-like night light data (the data corrected by the mutual correction in step S4). The calibration is based on: (1) when the pixel DN value in the late-corrected DMSP-OLS-like night light data is 0, the pixel DN at the same position in the early-stage night light data is also 0; (2) when the pixel DN value of the later-stage corrected DMSP-OLS-like night light data is not 0, the pixel DN value of the earlier-stage DMSP-OLS-like night light data is not greater than the pixel DN value of the same position in the later-stage DMSP-OLS-like night light data, as shown in formula (6).
In a word, the embodiment improves the continuity and comparability of the multi-source night light data set of the long-period time sequence, and provides important technical support for the application research of the long-period night light data. And the application research of long-time multi-source night lamplight remote sensing data is greatly expanded, and the understanding of long-time sequence urbanization process monitoring, human activities and ecological environment influence can be enhanced.
Example two:
the invention also provides multi-source night light data correction and fusion terminal equipment which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the method embodiment of the first embodiment of the invention.
Further, as an executable scheme, the multi-source night light data correction and fusion terminal device may be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The multi-source night light data correction fusion terminal equipment can comprise, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above-mentioned composition structure of the multi-source night light data correction fusion terminal device is only an example of the multi-source night light data correction fusion terminal device, and does not constitute a limitation on the multi-source night light data correction fusion terminal device, and may include more or less components than the above, or combine some components, or different components, for example, the multi-source night light data correction fusion terminal device may further include an input/output device, a network access device, a bus, and the like, which is not limited in this embodiment of the present invention.
Further, as an executable solution, the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general processor can be a microprocessor or the processor can also be any conventional processor and the like, the processor is a control center of the multi-source night light data correction and fusion terminal equipment, and various interfaces and circuits are utilized to connect all parts of the whole multi-source night light data correction and fusion terminal equipment.
The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the multi-source night light data correction fusion terminal equipment by running or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the mobile phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The invention also provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method of an embodiment of the invention.
The module/unit integrated by the multi-source night light data correction fusion terminal equipment can be stored in a computer readable storage medium if the module/unit is realized in the form of a software functional unit and is sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), software distribution medium, and the like.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (7)

1. A multi-source night light data correction and fusion method is characterized by comprising the following steps:
s1: acquiring DMSP-OLS and NPP-VIIRS night light data to be fused;
s2: preprocessing DMSP-OLS and NPP-VIIRS night light data, wherein the preprocessing comprises image re-projection, re-sampling and cutting;
s3: selecting an invariant target area, and sequentially performing mutual correction among sensors, intra-image-year fusion and inter-image-year continuity correction on the preprocessed DMSP-OLS night light data by adopting an invariant target method;
s4: and (3) performing mutual correction on the DMSP-OLS and NPP-VIIRS night light data by adopting the following model:
DNr＝α×(LgVIIRS)2+β×(LgVIIRS)+γ
wherein DNrExpressing the value of the corrected night light image data, VIIRS expressing the value of the NPP-VIIRS night light image data before correction, LgVIIRS expressing the logarithm of VIIRS, and alpha, beta and gamma being regression parameters in a quadratic regression model;
s5: the inter-image continuity correction is performed on the data mutually corrected in step S4.
2. The multi-source night light data correction fusion method of claim 1, characterized in that: the specific process of pretreatment comprises: firstly, re-projecting night light data into Alberts equal-product conical projection, and re-sampling to 1km by adopting a bilinear fitting mode; and secondly, obtaining night light data with the spatial resolution of 1km by cutting.
3. The multi-source night light data correction fusion method of claim 1, characterized in that: the quadratic regression model adopted for mutual correction among the sensors is as follows:
DNc＝a×DN2+b×DN+c
wherein DN and DNcRespectively representing the gray values of the images before and after correction; a. and b and c both represent secondary regression parameters.
4. The multi-source night light data correction fusion method of claim 1, characterized in that: the model adopted by the image intra-year fusion is as follows:
Figure FDA0003290429350000021
wherein,
Figure FDA0003290429350000022
respectively representing the pixel DN values of the ith pixel of night light data acquired by two different sensors a and b after the nth mutual correction; DN(n,i)And expressing the DN value of the ith pixel of the corrected night light data of the nth year.
5. The multi-source night light data correction fusion method of claim 1, characterized in that: the model used for correcting the continuity between the image years is as follows:
Figure FDA0003290429350000023
wherein DN(n-1,i)、DN(n,i)、DN(n+1,i)Respectively representing the pixel DN values of the ith pixel DN of the night light data fused in the image year in the (n-1) th year, the (n) th year and the (n + 1) th year'(n,i)And expressing the DN value of the ith pixel of the lighting data of the nth year night after the continuity correction of the image between the years.
6. The utility model provides a multisource night light data correction fuses terminal equipment which characterized in that: comprising a processor, a memory and a computer program stored in the memory and running on the processor, the processor implementing the steps of the method according to any of claims 1 to 5 when executing the computer program.
7. A computer-readable storage medium storing a computer program, characterized in that: the computer program when executed by a processor implementing the steps of the method as claimed in any one of claims 1 to 5.
CN202111162881.3A 2021-09-30 2021-09-30 Multi-source night light data correction fusion method, terminal equipment and medium Pending CN114004278A (en)

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CN115713691A (en) * 2022-11-21 2023-02-24 武汉大学 Pixel-level electric power popularity estimation method and device based on noctilucent remote sensing
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CN114529478A (en) * 2022-02-28 2022-05-24 暨南大学 DMSP/OLS data correction method, system and medium based on random forest algorithm
CN115713691A (en) * 2022-11-21 2023-02-24 武汉大学 Pixel-level electric power popularity estimation method and device based on noctilucent remote sensing
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