CN107229913A - Density of population analysis system based on high score satellite remote sensing date combination building height - Google Patents
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Abstract
The present invention provides a kind of density of population analysis system based on high score satellite remote sensing date combination building height, including：Residential area extraction module, building height determining module, grid partition module, grid space computing module and density of population determining module.The application calculates the density of population respectively by the way that target area is divided into multiple grid in units of grid, more accurate compared to prior art so as to calculate population dispersal more specific in target area；In addition, the application can accurately determine the settlement place region in target area based on remotely-sensed data, so as to using settlement place as with reference to the distribution situation that the density of population is determined more accurately；Further, the application calculates the height of building according to remotely-sensed data, associating for resident living space and the density of population is set up from space angle, so as to embody the density of population difference of different height building, and then the distribution situation of the density of population can be more accurately determined.
Description
Technical Field
The invention relates to the technical field of population density analysis, in particular to a population density analysis system based on combination of high-resolution satellite remote sensing data and building height.
Background
The population density is the number of population living on a unit area of land, is an index representing the density degree of population in an area, can be used for measuring the economic development level, the urban construction level and the like of one area, and can also provide data support for the macroscopic regulation and control of the country and the place and the urban development planning, and in addition, accurate population density distribution data is beneficial to making decisions such as reasonable site selection, industrial layout and the like for enterprises and public institutions and entrepreneurs.
At present, the population density is generally calculated by taking administrative divisions as a unit, for example, the population density of a certain county and city is determined by dividing the total population number of the county and city by the area, the accuracy is poor, and the specific distribution of the population in the county and city is unknown.
In summary, there is a strong need for a population density analysis system with higher accuracy.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a population density analysis system based on high-resolution satellite remote sensing data combined with building height, so as to improve the accuracy of population density calculation, provide data support for national and local macro regulation and control and urban development planning, and provide data support for site selection and industrial layout of enterprises and public institutions and entrepreneurs.
The invention provides a population density analysis system based on high-resolution satellite remote sensing data combined with building height, which comprises: the system comprises a residential area extraction module, a building height determination module, a grid division module, a grid space calculation module and a population density determination module; wherein,
the residential area extraction module is used for extracting a residential area contained in a target area according to first remote sensing data corresponding to the target area;
the building height determining module is used for determining the height of each building in the residential area extracted by the residential area extracting module according to second remote sensing data;
the grid dividing module is used for dividing the target area into a plurality of grids;
the grid space calculation module is used for calculating residential housing spaces corresponding to the grids according to the areas of residential area in the grids and the height of the building;
and the population density determining module is used for calculating the population density of each grid according to the residential housing space and the spatial population coefficient corresponding to each grid so as to determine the population density distribution condition of the target area, wherein the spatial population coefficient is the population number in the residential housing space of a unit.
Optionally, the first remote sensing data includes radar remote sensing data, and the residential area extraction module includes:
and the radar data residential area extracting unit is used for extracting a residential area from the target area according to radar remote sensing data corresponding to the target area based on the reflection and scattering characteristics of different ground object types to radar signals.
Optionally, the first remote sensing data includes multispectral remote sensing data, and the residential area extraction module includes:
and the multi-spectral data residential area extracting unit is used for extracting a residential area from the target area according to the multi-spectral remote sensing data corresponding to the target area based on the difference of different ground object types to the spectral reflectances of different wave bands.
Optionally, the multi-spectral data residential area extracting unit includes:
the land feature classification subunit is used for classifying the land feature types into blue top buildings, red top buildings, cement top buildings, bare land, lakes, rivers, farmlands and forest lands; wherein, the blue top building, the red top building and the cement top building belong to residential areas;
the surface feature determining and scheduling subunit is used for respectively calling the following extraction index construction subunit, the index value calculating subunit and the binarization processing subunit to extract an area corresponding to the surface feature type of the residential area from the target area according to the division result of the surface feature classification subunit on the surface feature type of the residential area so as to obtain the residential area;
the extraction index construction subunit is used for constructing a ground feature extraction index which can distinguish the type of the ground feature to be extracted from other ground features according to the difference of the type of the ground feature to be extracted and other types of the ground feature on the spectral reflectivity of different wave bands;
the index value calculating operator unit is used for calculating the index value of the surface feature extraction index corresponding to each pixel in the remote sensing data;
and the binarization processing subunit is used for carrying out binarization processing on the index value of the surface feature extraction index of each pixel, segmenting the remote sensing data according to a binarization result, and extracting an area corresponding to the surface feature type to be extracted.
Optionally, the second remote sensing data includes synthetic aperture radar remote sensing data, and the building height determining module includes:
and the radar data building height determining unit is used for calculating the height of each building in the residential area extracted by the residential area extracting module based on a backscattering model and by utilizing the multi-polarization information of the synthetic aperture radar image according to the synthetic aperture radar remote sensing data.
Optionally, the second remote sensing data includes stereopair remote sensing data, and the building height determining module includes:
and the stereopair building height determining unit is used for calculating the height of each building in the residential area extracted by the residential area extracting module according to the stereopair remote sensing data.
Optionally, the population density analysis system based on high-score satellite remote sensing data combined with building height further includes:
and the space population coefficient calculating module is used for calculating the space population coefficient by adopting a regression algorithm according to a plurality of groups of sample data corresponding to a plurality of grids for determining the living space and the population number of the residents.
Optionally, the population density analysis system based on high-score satellite remote sensing data combined with building height further includes:
and the population density optimization module is used for optimizing the population density of each grid calculated by the population density determination module according to the night light remote sensing data based on the corresponding relation between the night light intensity and the population density so as to optimize the population density distribution condition of the target area.
Optionally, the population density optimizing module includes:
a population density optimizing unit for optimizing the population density of each grid according to the following mathematical algorithm:
wherein, PiIndicating the population density corresponding to the ith grid obtained after optimization,the population density corresponding to the ith grid calculated and obtained by the population density determining module is represented; l isjIndicating the light intensity corresponding to the jth grid,representing the average light intensity of the target area; plRepresenting the number of people represented by unit light; s is adjustmentAnd (4) the coefficient.
Optionally, the population density analysis system based on high-score satellite remote sensing data combined with building height further includes:
and the population density distribution map generating module is used for filling the color corresponding to the grid population density into the position corresponding to each grid according to the mapping relation between the population density and different colors so as to draw the population density distribution map of the target area.
According to the technical scheme, the population density analysis system based on the combination of the high-score satellite remote sensing data and the building height comprises the following steps: the system comprises a residential area extraction module, a building height determination module, a grid division module, a grid space calculation module and a population density determination module. Compared with the prior art, the population density analysis system based on the high-score satellite remote sensing data combined with the building height provided by the application divides the target area into a plurality of grids, and then calculates the population density in each grid by taking the grids as units, so that a more specific population density distribution condition in the target area can be calculated, and the system is more accurate compared with the prior art; on the other hand, the residential area in the target area can be more accurately determined based on the remote sensing data, so that the population density distribution can be more accurately determined by taking the residential area as a reference; furthermore, the height of buildings in the residential area is calculated according to the remote sensing data, and the correlation between the residential space and the population density is established from the space perspective, so that the difference of the population densities of the buildings with different heights can be reflected, and the distribution condition of the population density can be more accurately determined. In conclusion, based on the application, the population density distribution condition in the target area can be determined more accurately and accurately, so that data support is provided for the macro regulation and control of the country and the place and the urban development planning, and data support is provided for the site selection and the industrial layout of enterprises and public institutions and entrepreneurs.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below.
FIG. 1 is a schematic diagram of a population density analysis system based on high-resolution satellite remote sensing data combined with building height according to a first embodiment of the invention;
FIG. 2 shows a schematic diagram of a first telemetry data acquisition module;
FIG. 3 is a graph showing the reflectance of various types of terrain for different wavelength band spectra;
FIG. 4 shows a schematic diagram of a building geometry model based on a backscatter model;
fig. 5 shows an effect diagram of population density distribution in a certain area according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
The invention provides a population density analysis system based on high-resolution satellite remote sensing data combined with building height. Embodiments of the present invention will be described below with reference to the drawings.
Fig. 1 is a schematic diagram illustrating a population density analysis system based on high-resolution satellite remote sensing data combined with building height according to a first embodiment of the present invention. As shown in fig. 1, a population density analysis system based on high-score satellite remote sensing data combined with building height according to a first embodiment of the present invention includes:
the system comprises a residential area extraction module 1, a building height determination module 2, a grid division module 3, a grid space calculation module 4 and a population density determination module 5; wherein,
the residential area extraction module 1 is used for extracting a residential area contained in a target area according to first remote sensing data corresponding to the target area;
the building height determining module 2 is used for determining the height of each building in the residential area extracted by the residential area extracting module 1 according to second remote sensing data;
the grid dividing module 3 is configured to divide the target region into a plurality of grids;
the grid space calculation module 4 is configured to calculate residential spaces corresponding to the grids according to the areas of residential areas in the grids and the heights of the buildings;
the population density determining module 5 is configured to calculate population densities of the grids according to residential housing spaces and spatial population coefficients corresponding to the grids to determine population density distribution conditions of the target area, where the spatial population coefficients are population numbers in unit residential housing spaces.
The ground cover types are classified according to different classifications of the ground cover, and can be flexibly classified according to actual requirements, for example, the ground cover can be classified into a blue top building, a red top building, a cement top building, bare land, a lake, a river, farmland, forest land and the like according to the reflection characteristics of the ground cover to different wave bands of light; the method analyzes the distribution situation of population density, and population is mainly distributed in residential areas, so that the residential area needs to be extracted from a target area according to remote sensing data, and in the types of the land features, blue-top buildings, red-top buildings and cement-top buildings can be regarded as residential areas.
With the development of remote sensing technology and high-resolution data acquisition technology, the resolution of remote sensing data is higher and higher, and the data types are richer and richer, so that the extraction of residential area by adopting high-resolution remote sensing data is possible, the identification accuracy is higher and higher, and on the basis, the grid can be divided into as small as possible, so as to more accurately and precisely determine the population density distribution condition of the target area.
Because different remote sensing satellites adopt different remote sensing modes and collected remote sensing data may also be different, for example, a high-resolution 3 satellite emitted by China and a Radarsat-2 satellite emitted by Canada adopt a synthetic aperture radar to collect remote sensing data in the form of radar data, and high-resolution 5 satellites and Landsat series satellites adopt instruments such as a full-spectrum imager to collect remote sensing data in the form of multispectral data, the radar remote sensing data and the multispectral remote sensing data can be used for determining the type of a ground object in a target area, and further extracting a residential area according to the type of the ground object, in the embodiment of the invention, the population density analysis system based on high-resolution satellite remote sensing data and building height further comprises a first remote sensing data acquisition module 6, and the first remote sensing data acquisition module 6 can selectively acquire corresponding first remote sensing data according to the working principle of the residential area extraction module 1, such as radar remote sensing data or multi-spectral remote sensing data.
In an embodiment provided by the present application, a method of selecting different first remote sensing data according to weather conditions is adopted, multispectral remote sensing data is adopted in a clear air area, and radar remote sensing data is adopted in a cloudy and rainy area, so that accuracy of subsequent calculation is ensured to the maximum extent from a data source, and a specific embodiment is as shown in fig. 2, which shows a schematic diagram of a first remote sensing data obtaining module 6, where the first remote sensing data obtaining module 6 includes: a weather determination unit 61, a radar data acquisition unit 62, and a multispectral data acquisition unit 63;
the weather judging unit 61 is used for selectively triggering the radar data acquiring unit 62 to acquire radar remote sensing data or triggering the multispectral data acquiring unit 63 to acquire multispectral remote sensing data according to the weather condition of the target area; specifically, the multispectral data acquisition unit 63 can be triggered to acquire multispectral remote sensing data under clear weather conditions, and the radar data acquisition unit 62 can be triggered to acquire radar remote sensing data under weather conditions such as cloud, rain, fog and snow;
the radar data acquisition unit 62 is configured to acquire radar remote sensing data of the target area under the trigger of the weather determination unit 61;
the multispectral data acquiring unit 63 is configured to acquire multispectral remote sensing data of the target area under the trigger of the weather determining unit 61.
The weather judging unit 61 may preset a database of correspondence between each region and a common weather condition according to a weather statistical result according to a judgment basis of the weather condition for the weather condition, and the weather judging unit 61 calls the database in real time as required; or a weather record of the appointed date of the target area is called, and the weather condition of the appointed date of the target area is determined according to the record; all of which are modifications of the present application and are within the scope of the present application.
According to the difference of the remote sensing data, the residential area extraction module 1 also adopts different modes to extract data, for example, for radar remote sensing data, due to the difference of the layout, the material, the structure and the surrounding environment of buildings, different texture characteristics are presented on SAR images (namely radar remote sensing data), such as the buildings in cities are distributed neatly, the intervals among the buildings are larger, most of the buildings are high-rise buildings with neat flat tops, most of the used materials have good reflectivity, and are represented as strong brightness areas on the images, and roads among the buildings, rough vegetation such as lawns and the like are represented as dark areas due to surface scattering, so that urban residential areas represent as textures with alternate light and shade on the images, and the similarity is smaller; rural residential areas are distributed relatively scattered without obvious rules, and areas such as roads are not obvious on images, so that the areas are irregular and spotty, and have high similarity.
Therefore, in an embodiment provided by the present application, the first remote sensing data includes radar remote sensing data, and the residential area extraction module 1 includes:
and the radar data residential area extracting unit is used for extracting a residential area from the target area according to radar remote sensing data corresponding to the target area based on the reflection and scattering characteristics of different ground object types to radar signals.
Specifically, the radar data residential area extraction unit may adopt a threshold determination method based on an iterative P parameter method based on a variation function theory on the basis of analyzing the geographic features of residential areas in the high-resolution SAR image, and assigns weights to pixel points satisfying a threshold range to increase the difference of variation functions between residential areas and non-residential areas, thereby extracting the residential areas, which not only can ensure a high detection rate, but also can significantly reduce a false alarm rate.
For multispectral remote sensing data, although the prior art discloses a method for partially extracting water surface and buildings, the inventor finds that the extraction precision and accuracy are not ideal in application, so that the application provides a more accurate and more precise mode, in one embodiment provided by the application, the first remote sensing data comprises multispectral remote sensing data, and the residential area extraction module 1 comprises:
and the multi-spectral data residential area extracting unit is used for extracting a residential area from the target area according to the multi-spectral remote sensing data corresponding to the target area based on the difference of different ground object types to the spectral reflectances of different wave bands.
Specifically, in an embodiment provided by the present application, the multispectral data residential area extracting unit includes:
the land feature classification subunit is used for classifying the land feature types into blue top buildings, red top buildings, cement top buildings, bare land, lakes, rivers, farmlands and forest lands; the blue top building, the red top building and the cement top building belong to residential areas and are main determination objects of the embodiment of the invention, other land types can be uniformly divided into non-residential areas, and the population number of the non-residential areas can be considered as zero, so that the residential areas only need to be extracted according to the remote sensing data (other areas are directly determined as the non-residential areas);
the surface feature determining and scheduling subunit is used for respectively calling the following extraction index construction subunit, the index value calculating subunit and the binarization processing subunit to extract the region corresponding to the surface feature type belonging to the residential area from the target region according to the division result of the surface feature classification subunit on the surface feature type, wherein the extraction index construction subunit, the index value calculating subunit and the binarization processing subunit are used for extracting the region corresponding to the surface feature type belonging to the residential area;
the extraction index construction subunit is used for constructing a ground feature extraction index which can distinguish the type of the ground feature to be extracted from other ground features according to the difference of the type of the ground feature to be extracted and other types of the ground feature on the spectral reflectivity of different wave bands;
the index value calculating operator unit is used for calculating the index value of the surface feature extraction index corresponding to each pixel in the remote sensing data;
and the binarization processing subunit is used for carrying out binarization processing on the index value of the surface feature extraction index of each pixel, segmenting the remote sensing data according to a binarization result, extracting an area corresponding to the surface feature type to be extracted, and obtaining the residential area.
In the above embodiment, the land feature classification subunit more finely and accurately classifies the land feature types into blue roof buildings (mainly plant sheds of enterprises), red roof buildings (mainly red roof houses, and parts of plant sheds of enterprises), cement roof buildings (mainly urban residential areas, roads, and the like), bare land, lakes (artificial lakes, reservoirs, and the like), rivers, farmlands (covered with crops), and forest lands according to the differences of the reflectivity of different types of land features to different wave band spectrums and the types of land features contained in the residential areas.
The extraction index constructing subunit, by comparing the reflectances of the types of the features to different wavelength band spectrums, further constructs a feature extraction index capable of distinguishing the types of the features to be extracted from other features according to the difference between the reflectances of the types of the features to be extracted and the reflectances of the different wavelength band spectrums of other features, please refer to fig. 3, which shows a schematic diagram of the reflectances of the types of the features to different wavelength band spectrums, in the diagram, a wavelength band 2 represents a blue light wavelength band, a wavelength band 3 represents a green light wavelength band, and a wavelength band 4 represents a red light wavelength band, it can be known from the diagram that the reflectivity of the blue top building in the blue light wavelength band is obviously higher than that of the green light wavelength band, while the reflectances of other feature types are basically equal or higher than that of the blue light wavelength band, so that if the reflectivity of the blue light wavelength band is subtracted from the reflectivity of the green light wavelength band, the corresponding value of the blue top, the numerical values corresponding to other ground object types are negative numbers or positive numbers close to zero, and accordingly the blue-top buildings can be extracted; by adopting the same theory, the reflectivity of the red top building and the cement top building (including bare soil) in a red light wave band is obviously higher than that of a green light wave band, and the reflectivity of the green light wave band is higher than that of other ground object types, so that if the reflectivity of the green light wave band is subtracted from the reflectivity of the red light wave band, the corresponding numerical value of the red top building and the cement top building (including bare soil) is a large positive number, and the corresponding numerical value of other ground object types is a negative number, and accordingly the red top building and the cement top building (including bare soil) can be extracted. After the blue-top building is extracted, bare soil can be extracted based on the reflectivity of the blue light wave band and the reflectivity of the green light wave band, and the red-top building and the cement-top building can be extracted more accurately by deducting the bare soil.
Since the actual area of the bare soil is small and can be ignored, in order to simplify the calculation, the embodiment of the present invention adopts a surface feature extraction method including bare soil for exemplary description, and a person skilled in the art can change the implementation on the basis of the above description, further extract the bare soil and deduct the bare soil to extract a more accurate region corresponding to the surface feature type, which is also within the protection scope of the present application.
Taking the feature extraction with bare soil as an example, the extraction index constructing subunit may construct, through the above calculation, a feature extraction index that can distinguish the feature to be extracted from other features according to the difference between the spectral reflectances of different wavelength bands of each feature type, for example, if the feature type to be extracted is a blue-top building, the extraction index constructing subunit may construct, according to the difference between the first reflectance difference corresponding to the blue-top building and the first reflectance difference corresponding to other feature types, the following feature extraction index for the blue-top building, where the first reflectance difference is the difference between the reflectance for the blue-light wavelength band spectrum and the reflectance for the green-light wavelength band spectrum:
in the formula, NDBIB2-B3Representing the ground extraction index, OLI, for blue-top buildings2Expressing the reflectance, OLI, for the blue band spectrum3Indicating the reflectivity for the green band spectrum.
For another example, if the types of features to be extracted are a red-top building and a cement-top building (which are not easy to distinguish and can be extracted together), the extraction index constructing subunit may construct the following feature extraction indexes for the red-top building and the cement-top building according to a difference between a second reflectance difference corresponding to the red-top building and the cement-top building and a second reflectance difference corresponding to other feature types, where the second reflectance difference is a difference between a reflectance for a red-light band spectrum and a reflectance for a green-light band spectrum:
in the formula, NDBIB4-B3Representing the ground object extraction index, OLI, for red-top and cement-top buildings4Indicating the reflectivity, OLI, for the spectrum in the red wavelength band3Indicating the reflectivity for the green band spectrum.
The two specific ground object extraction indexes are adopted, the difference of the indexes corresponding to the ground object to be extracted and other ground object types can be further amplified, so that the ground object to be extracted can be accurately extracted in the subsequent processing, and in the specific implementation, an adjusting parameter can be subtracted from the formula, so that a larger positive number and a smaller positive number are adjusted into a positive number and a negative number, and the noise or the error generated in the subsequent binarization processing process is reduced.
Correspondingly, after the binarization processing subunit performs binarization processing on the index value of the ground feature extraction index of each pixel, and segments the remote sensing data according to a binarization result, the blue-top building can be extracted according to the binarization processing result of the index value of the ground feature extraction index for the blue-top building, and the red-top building and the cement-top building can be extracted according to the binarization processing result of the index value of the ground feature extraction index for the red-top building and the cement-top building, so that a residential area is extracted from the target area.
According to the difference of the adopted second remote sensing data, the building height determining module 2 can determine the height of each building in the residential area in different modes.
In one embodiment provided by the present application, the second remote sensing data comprises synthetic aperture radar remote sensing data, and the building height determining module 2 comprises:
and the radar data building height determining unit is used for calculating the height of each building in the residential area extracted by the residential area extracting module 1 based on a backscattering model and by utilizing the multi-polarization information of the synthetic aperture radar image according to the synthetic aperture radar remote sensing data.
Specifically, the radar data building height determination unit may calculate the height of each building in the residential area extracted by the residential area extraction module 1 by using the following method:
synthetic Aperture Radar (SAR) is an active side-looking imaging remote sensing system operating in the microwave band and has a higher azimuth resolution than Real Aperture Radar (RAR). With the development of remote sensing technology, an SAR system has unique advantages compared with an optical image, so that the extraction of building height from the SAR image is an important embodiment of microwave remote sensing in urban application.
At the present stage, a method for extracting the height of a building by utilizing microwave remote sensing data (based on interference SAR and radar photogrammetry) is complex in process, strict limits are provided for the coherence of the data, the length of a base line and the like, and particularly in an area with complex terrain, a large number of ground control points are needed for geometric correction; in view of the defects, the method for extracting the building height by using the single-scene SAR image and combining certain prior parameters at home and abroad is gradually mature, and the field measurement workload is greatly reduced under the condition of meeting certain precision. Therefore, considering that the amplitude information of the SAR image is the most direct embodiment of the change of the radar backscattering echo signal and simultaneously reflects the backscattering characteristic of the ground feature, a standard geometric and electromagnetic characteristic model of an urban building is proposed, the influence of geometric parameters of the building on the scattering characteristic is discussed, and quantitative explanation is provided for the contribution of different scattering mechanisms in the overall backscattering; or extracting the overlapping and masking information in the SAR image through an edge ratio detector, and improving the overlapping and masking boundary precision of the building by combining the aerial image so as to invert the height of the building; meanwhile, the geometric and scattering characteristics of the high-resolution SAR image are calculated by using the known parameters, and the feasibility of the GO-PO model is verified. For the secondary scattering characteristics of buildings, the secondary scattering calculation formula provided by microwave darkroom experimental data is verified in the prior research, and the possibility of inverting the height of the building by using secondary scattering is pointed out; or the building height is extracted by calculating the secondary scattering intensity of the building and combining certain prior information by utilizing the backward scattering characteristics of the ground objects; some methods adopt a Lambert plane to simulate the earth surface, establish a backscattering model and solve a height increment expression of each point; some determine the position and the direction of the bottom profile of the building by analyzing the secondary scattering structure of the building, and invert the height of the building by a simulation image iteration matching method of distribution density function difference; some provide a method for extracting the roof size and height of a building from a single-scene SAR image by combining the building secondary scattering principle on the basis of analyzing the building overlap and shadow areas.
Through analysis, the existing method is complicated in process, high in human participation and high in requirements on data, or only single polarization information of a single-scene SAR image is considered, the height is inverted by analyzing a mechanism of building secondary scattering and combining prior knowledge, and the influence of other polarization information on height extraction is not comprehensively considered. Therefore, the embodiment of the invention provides a method for extracting the height of a building by utilizing multi-polarization information of an SAR (synthetic aperture radar) image based on a backscattering model by utilizing a GF-3 (high-resolution 3 satellite) remote sensing image with medium and high resolution, gives an optimal combination among different polarizations, and tries to obtain the height of the building in a large range by utilizing the method.
Based on the backscattering model, the building height extraction formula is as follows:
in the formula: theta is the radar incident angle (equal to the imaging angle of view at surface level); l is the main length of the building (the length of the side with an included angle of less than 90 degrees with the flying direction of the radar); phi is the building azimuth (the included angle between the main length of the building and the flying direction of the radar); spq1 element in the Sinclair polarization scattering matrix, wherein p and q are respectively a horizontal polarization component and a vertical polarization component; sigma0The contribution of the secondary scattering to RCS (RCS: Radar-Cross Section); l and σ are surface roughness parameters, representing correlation length and standard deviation, respectively. Referring to fig. 4, a schematic diagram of a geometric model of a building based on a backscattering model is shown, where in fig. 4, w is the building width; h is the draft of the building; l is the main length of the building; phi is the azimuth angle of the building; r is the dielectric constant of the asphalt pavement; w is the dielectric constant of the industrial wall; s is the earth surface dielectric constant; l and σ are surface roughness parameters, namely: correlation length and standard deviation.
Theoretically, if only secondary scattering is considered, RCS (RCS: Radar-Cross Section) is calculated as follows:
in the formula: r is the distance from the radar sensor to the target; esA magnetic field scattered for the surface S; e0Are amplitude values.
However, in actual calculation, the contribution σ of the secondary scattering to RCS0Are usually substituted by the equivalent of the formula, i.e.
σ0＝β0sinθ＝ks|DN|2sinθ
Wherein, Ks is a calibration constant (which cannot be directly obtained from source data and needs to be inverted through the actual height of the building); DN is the value of each pixel in the secondary scatter region obtained from the image amplitude map (averaging the gray values of the secondary scatter region).
According to the surface scattering characteristics, the standard deviation sigma of the ground roughness parameter can be obtained by the following formula:
in the formula: z is a radical ofiIs the height of a certain point on the earth's surface;is the average height of the N surface points.
The correlation length L is a measure describing the similarity of the height z (x) of a point x to the height z (x + x ') of another point x' offset from x, and is defined as:
observing the image characteristics and material composition of buildings in the target area, and reasonably estimating the rough characteristic parameters of the target to obtain the following surface characteristic parameters:
as previously mentioned, SpqDifferent polarization features in the SAR image are represented, namely:
in the formula: psi and zeta are incident angles of the radar waves irradiated on the wall surface and the ground surface of the building respectively; r is the Fresnel reflection coefficient, with different indices representing different planes of incidence and polarization modes (R)┴rFor vertical polarisation of the ground, R//rFor horizontal polarisation of the ground, R┴wFor vertical polarization of the wall, R//wHorizontally polarized for the wall).
The horizontal polarized wave and the vertical polarized wave can be calculated by the following formula respectively:
in the formula: complex dielectric constant for ground target (need to be replaced by correspondingrAndw) α is the incident angle of radar wave to the corresponding ground object (R can be obtained by replacing phi and ξ with those of corresponding wave length┴r(ζ)，R┴r(ψ),R┴w(psi) and R┴w(ζ)). From the above, the polarization scattering vector S can be obtainedpqAnd further calculate the height h of the building.
Through verification, the correlation coefficient of the height of the building calculated by the method provided by the embodiment of the invention and the actual height is as high as 0.9095, and the accuracy and precision are very high.
In an embodiment provided by the present application, the second remote sensing data includes stereopair remote sensing data, and the building height determining module 2 includes:
and the stereopair building height determining unit is used for calculating the height of each building in the residential area extracted by the residential area extracting module 1 according to the stereopair remote sensing data.
Specifically, the stereopair building height determining unit may calculate the height of each building in the residential area extracted by the residential area extracting module 1 by the following method:
a Digital Surface Model (DSM), is a model of a solid surface that represents the height of the ground in the form of a set of ordered arrays of values. The DSM contains height information of buildings, bridges, etc. on the ground in addition to ground elevation information. There are many methods for creating DSM, and the data sources and acquisition methods mainly include: obtaining the aerial or aerospace image through a photogrammetry way; field measurement acquisition, etc.
The method can quickly acquire large-range DSM data, and satellite remote sensing is a good technical means. And with the development of satellite sensors, the acquired DSM has higher and higher precision. For example, 0.41 m GeoEye-1, which is the highest resolution of the current commercial satellite, the vertical accuracy can be as medium as 0.5 m when high quality control data is used. The satellites that can be stereoscopically imaged are mainly ASTER, ALOSPRSM, CARTOSAT-1, FORMOSAT-2, IKOONOS, KOMPSAT-2, OrbView-3, Quickbird, RapidEye, GeoEye-1, WorldView-1/2, SPOT5/6, Pleiades, and the third and first domestic resources, 02C, etc.
The embodiment of the invention is based on the principle of photogrammetry, establishes a geometric optical model, and utilizes a stereopair to extract a research area DSM.
The stereo image pair of the high-resolution image has unique advantages when urban DSM is extracted in a large range, although the stereo image pair cannot fully show the detail outline of each building like the shadow of the building, when the research area is Beijing urban area with a large coverage area, the elevation distribution of the building reflected by the elevation extracted by the image pair can also meet certain precision requirement. According to the embodiment of the invention, the heights of the sampling points can be extracted by arranging the sampling points around the building area, then the basic potential surface of the research area is obtained by utilizing the spatial interpolation technology, and the building height is obtained by combining DSM calculation.
Through verification, the correlation coefficient of the height of the building calculated by the method provided by the embodiment of the invention and the actual height is as high as 0.8212, and the accuracy and precision are also very high.
In the embodiment of the present invention, the grid dividing module 3 is configured to divide the target region into a plurality of grids, where the grid division may be flexibly set according to actual requirements and the resolution of the remote sensing data, for example, the target region may be divided into a plurality of ten-meter grids, hundred-meter grids, or kilometer grids, which are all within the protection scope of the present application.
In the embodiment of the present invention, the grid space calculating module 4 is configured to calculate residential spaces corresponding to each grid according to the area of the residential area in each grid and the height of the building; specifically, the volume of each building in the residential area can be calculated by multiplying the area by the height, and the volume is the residential space, and then the residential spaces corresponding to the grid can be obtained by adding the volumes of the buildings in the grid.
In the embodiment of the invention, the space population coefficient is the population number in the residential space of the unit resident, and can be determined according to the priori knowledge, considering that the population densities of different areas are likely to have larger differences, for example, the population densities of Beijing and Qinghai have very large differences, therefore, the embodiment of the invention preferably adopts the sample data in the target area to determine the space population coefficient so as to ensure the accuracy of the space population coefficient. In one embodiment provided by the present application, the population density analysis system based on high-score satellite remote sensing data combined with building height further includes:
and the space population coefficient calculating module is used for calculating the space population coefficient by adopting a regression algorithm according to a plurality of groups of sample data corresponding to a plurality of grids for determining the living space and the population number of the residents.
For example, the spatial population coefficient calculation module may obtain sample data of a plurality of grids in the target region, where each sample data includes the population number and the residential dwelling space value corresponding to the grid, and based on the sample data, a regression model is established using the residential dwelling space as an independent variable and the population number as a dependent variable, and then the sample data is input to the regression model, and the spatial population coefficient is determined through data fitting. By adopting the regression algorithm, more accurate space population coefficients can be obtained, so that the final calculation is facilitated to obtain more accurate population density.
In the embodiment of the present invention, the population density determining module 5 is configured to calculate the population density of each grid according to the residential space and the spatial population coefficient corresponding to each grid to determine the population density distribution of the target area, specifically, the residential space of each grid may be multiplied by the spatial population coefficient to obtain the population number in the grid, and then the population number is divided by the area of the grid to obtain the population density of the grid.
Since the grids are obtained by dividing the target region, the population density of each grid is determined, and the population density distribution (i.e., the spatial population distribution) of the target region is determined.
Based on the above description of the embodiments, the population density analysis system based on high-score satellite remote sensing data and building height provided by the first embodiment of the present invention divides the target area into a plurality of grids, and then calculates the population density in each grid by taking the grids as a unit, so as to calculate a more specific population density distribution situation in the target area, which is more accurate than the prior art; on the other hand, the residential area in the target area can be more accurately determined based on the remote sensing data, so that the population density distribution can be more accurately determined by taking the residential area as a reference; furthermore, the height of buildings in the residential area is calculated according to the remote sensing data, and the correlation between the residential space and the population density is established from the space perspective, so that the difference of the population densities of the buildings with different heights can be reflected, and the distribution condition of the population density can be more accurately determined. In conclusion, based on the application, the population density distribution condition in the target area can be determined more accurately and accurately, so that data support is provided for the macro regulation and control of the country and the place and the urban development planning, and data support is provided for the site selection and the industrial layout of enterprises and public institutions and entrepreneurs.
In order to more intuitively represent the population density distribution, in an embodiment provided by the present application, the population density analysis system based on high-score satellite remote sensing data combined with building height further includes:
and the population density distribution map generating module is used for filling the color corresponding to the grid population density into the position corresponding to each grid according to the mapping relation between the population density and different colors so as to draw the population density distribution map of the target area.
As a modified embodiment of the above-mentioned embodiment, a gray-scale map may be used instead of a color map to represent the population density distribution diagram of the target area, as shown in fig. 5, which is an effect diagram of the population density distribution situation of a certain area provided in the embodiment of the present invention, and in the diagram, the whiter the color indicates that the population density is greater, so that it can be seen that the population density distribution situation of the target area can be determined more accurately by using the method provided in the embodiment of the present invention, compared to the conventional simple and rough method of calculating and representing the population density distribution situation by using an administrative division.
Considering that the residential area and the building height are important factors reflecting the population distribution, when the distribution of the population density distribution is analyzed by only using the residential area and the building height, the buildings with the same height may have different population densities due to the fact that the buildings may have various buildings such as factory buildings, office buildings, residential buildings and shopping buildings, and research shows that the night light data is highly related to the population densities. Therefore, in an embodiment provided by the present application, the population density analysis system based on high-score satellite remote sensing data combined with building height further includes:
and the population density optimization module is used for optimizing the population density of each grid calculated by the population density determination module 5 according to the night light remote sensing data based on the corresponding relation between the night light intensity and the population density so as to optimize the population density distribution condition of the target area.
The night light intensity can be obtained from night light remote sensing data corresponding to the target area, and the night light remote sensing data can be acquired through a remote sensing satellite with a staring panchromatic camera or a staring multispectral camera, for example, a high-grade 4 satellite emitted by China can acquire the night light remote sensing data at night, and according to the acquired night light remote sensing data of the target area and grid division of the target area, the night light intensity corresponding to each grid and the average light intensity of the target area can be determined, so that the population density of each grid calculated by the population density determination module 5 can be optimized.
Specifically, in an embodiment provided herein, the population density optimizing module includes:
a population density optimizing unit for optimizing the population density of each grid according to the following mathematical algorithm:
wherein, PiIndicating the population density corresponding to the ith grid obtained after optimization,indicating said population densityThe determining module 5 calculates the population density corresponding to the ith grid; l isjRepresenting the light intensity corresponding to the jth grid, wherein L represents the average light intensity of the target area; plRepresenting the number of people represented by unit light; s is the adjustment factor.
Those skilled in the art can make various reasonable changes to the specific mathematical algorithm based on the above description of the embodiments, and details are not described again, which should be within the scope of the present application.
The population density of the grid is optimized by adopting the night light data, and the population density difference between the same ground feature types can be represented by means of the night light data, so that the calculated population density is more accurate.
It is to be understood that, for the case of optimizing the population density of each of the grids calculated by the population density determination module 5, the population density distribution map generation module may map the population density distribution map of the target region by using the optimized population density of each of the grids.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
It should be noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The population density analysis system based on high-score satellite remote sensing data combined with building height provided by the embodiment of the invention can be a computer program product, and comprises a computer readable storage medium storing program codes, wherein instructions included in the program codes can be used for executing the method described in the foregoing method embodiment, and specific implementation can be referred to the method embodiment, and is not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, systems and units described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in this application, it should be understood that the disclosed system, and method may be implemented in other ways. The above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and there may be other divisions in actual implementation, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of systems or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (10)
1. A population density analysis system based on high-score satellite remote sensing data combined with building height is characterized by comprising: the system comprises a residential area extraction module, a building height determination module, a grid division module, a grid space calculation module and a population density determination module; wherein,
the residential area extraction module is used for extracting a residential area contained in a target area according to first remote sensing data corresponding to the target area;
the building height determining module is used for determining the height of each building in the residential area extracted by the residential area extracting module according to second remote sensing data;
the grid dividing module is used for dividing the target area into a plurality of grids;
the grid space calculation module is used for calculating residential housing spaces corresponding to the grids according to the areas of residential area in the grids and the height of the building;
and the population density determining module is used for calculating the population density of each grid according to the residential housing space and the spatial population coefficient corresponding to each grid so as to determine the population density distribution condition of the target area, wherein the spatial population coefficient is the population number in the residential housing space of a unit.
2. The system according to claim 1, wherein the first remote sensing data comprises radar remote sensing data, and the residential area extracting module comprises:
and the radar data residential area extracting unit is used for extracting a residential area from the target area according to radar remote sensing data corresponding to the target area based on the reflection and scattering characteristics of different ground object types to radar signals.
3. The system according to claim 1, wherein the first remote sensing data comprises multispectral remote sensing data, and the residential area extraction module comprises:
and the multi-spectral data residential area extracting unit is used for extracting a residential area from the target area according to the multi-spectral remote sensing data corresponding to the target area based on the difference of different ground object types to the spectral reflectances of different wave bands.
4. The population density analysis system based on high-score satellite remote sensing data combined with building height as claimed in claim 3, wherein the multispectral data residential area extracting unit comprises:
the land feature classification subunit is used for classifying the land feature types into blue top buildings, red top buildings, cement top buildings, bare land, lakes, rivers, farmlands and forest lands; wherein, the blue top building, the red top building and the cement top building belong to residential areas;
the surface feature determining and scheduling subunit is used for respectively calling the following extraction index construction subunit, the index value calculating subunit and the binarization processing subunit to extract an area corresponding to the surface feature type of the residential area from the target area according to the division result of the surface feature classification subunit on the surface feature type of the residential area so as to obtain the residential area;
the extraction index construction subunit is used for constructing a ground feature extraction index which can distinguish the type of the ground feature to be extracted from other ground features according to the difference of the type of the ground feature to be extracted and other types of the ground feature on the spectral reflectivity of different wave bands;
the index value calculating operator unit is used for calculating the index value of the surface feature extraction index corresponding to each pixel in the remote sensing data;
and the binarization processing subunit is used for carrying out binarization processing on the index value of the surface feature extraction index of each pixel, segmenting the remote sensing data according to a binarization result, and extracting an area corresponding to the surface feature type to be extracted.
5. The system according to claim 1, wherein the second remote sensing data comprises synthetic aperture radar remote sensing data, and the building height determination module comprises:
and the radar data building height determining unit is used for calculating the height of each building in the residential area extracted by the residential area extracting module based on a backscattering model and by utilizing the multi-polarization information of the synthetic aperture radar image according to the synthetic aperture radar remote sensing data.
6. The system for population density analysis based on high-score satellite remote sensing data combined with building height according to claim 1, wherein the second remote sensing data comprises stereopair remote sensing data, and the building height determination module comprises:
and the stereopair building height determining unit is used for calculating the height of each building in the residential area extracted by the residential area extracting module according to the stereopair remote sensing data.
7. The system for population density analysis based on high-score satellite remote sensing data combined with building height according to claim 1, further comprising:
and the space population coefficient calculating module is used for calculating the space population coefficient by adopting a regression algorithm according to a plurality of groups of sample data corresponding to a plurality of grids for determining the living space and the population number of the residents.
8. The system for population density analysis based on high-score satellite remote sensing data combined with building height according to claim 1, further comprising:
and the population density optimization module is used for optimizing the population density of each grid calculated by the population density determination module according to the night light remote sensing data based on the corresponding relation between the night light intensity and the population density so as to optimize the population density distribution condition of the target area.
9. The population density analysis system based on high-score satellite remote sensing data combined with building height according to claim 8, wherein the population density optimization module comprises:
a population density optimizing unit for optimizing the population density of each grid according to the following mathematical algorithm:
<mrow> <msub> <mi>P</mi> <mi>i</mi> </msub> <mo>=</mo> <msub> <mover> <mi>P</mi> <mo>&OverBar;</mo> </mover> <mi>i</mi> </msub> <mo>+</mo> <mrow> <mo>(</mo> <mi>L</mi> <mi>i</mi> <mo>-</mo> <mover> <mi>L</mi> <mo>&OverBar;</mo> </mover> <mo>)</mo> </mrow> <mo>*</mo> <msub> <mi>P</mi> <mi>l</mi> </msub> <mo>*</mo> <mi>s</mi> </mrow>
wherein, PiIndicating the population density corresponding to the ith grid obtained after optimization,the population density corresponding to the ith grid calculated and obtained by the population density determining module is represented; l isjIndicating the light intensity corresponding to the jth grid,representing the average light intensity of the target area; plRepresenting the number of people represented by unit light; s is the adjustment factor.
10. The population density analysis system based on high-score satellite remote sensing data combined with building height according to any one of claims 1-9, further comprising:
and the population density distribution map generating module is used for filling the color corresponding to the grid population density into the position corresponding to each grid according to the mapping relation between the population density and different colors so as to draw the population density distribution map of the target area.
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