CN113065481A - Urban built-up area extraction method fusing multi-source data in transportation and delivery environment - Google Patents

Urban built-up area extraction method fusing multi-source data in transportation and delivery environment Download PDF

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CN113065481A
CN113065481A CN202110382529.4A CN202110382529A CN113065481A CN 113065481 A CN113065481 A CN 113065481A CN 202110382529 A CN202110382529 A CN 202110382529A CN 113065481 A CN113065481 A CN 113065481A
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吴政
张成成
张扬奇
李成名
刘丽
戴昭鑫
王晓艳
朱立宁
肖学福
潘璠
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Abstract

The invention discloses a method for extracting a built-up area of a city by fusing multi-source data in a transportation and delivery environment, which belongs to the technical application field of remote sensing information in urban geography.

Description

Urban built-up area extraction method fusing multi-source data in transportation and delivery environment
Technical Field
The invention relates to the field of application of remote sensing information technology in urban geography, in particular to an urban built-up area extraction method fusing multi-source data in a transportation and delivery environment.
Background
Transportation and delivery are processes of transporting personnel, equipment and materials to a destination by using various transportation means, and are important means for completing strategic deployment, strategic actions and guarantee tasks. Generally, the specific scheme and form of transportation and delivery are often provided with a certain knowledge range, the quantity of transported personnel, equipment and materials is larger than that of conventional logistics, passenger transportation and the like, the appearance is more attractive, and therefore, the route range of the transportation and delivery is generally prevented from entering a built-up area of a city to ensure the safety of the transportation and delivery. Therefore, when a transportation and delivery scheme is determined, how to accurately and quickly acquire the range of the built-up area of the city is a more important problem.
At present, a plurality of urban built-up areas are extracted based on remote sensing satellite image data, such as traditional remote sensing images, high-resolution remote sensing images, night light images and the like. Compared with a common remote sensing satellite image, a night light image (NTL) is used as a remote sensing data source for objectively capturing night ground surface light radiation in real time, the recorded brightness information has great advantages in human activity area difference and change detection, the human activity area difference can be reflected more directly, in addition, the spectrum confusion of the traditional multispectral remote sensing can be effectively avoided, and the method is widely applied to the extraction of urban built-up areas. In the prior art, the urban built-up area extraction method based on night light mainly comprises the steps of extracting based on original light images, and fusing and extracting based on light images and other data, but the urban built-up area boundary is difficult to accurately extract based on the night light data, the sensitivity of a built-up area extraction method fusing natural elements is reduced in an area with higher vegetation coverage, and the built-up area extraction effect is poor;
although recent research considers that night light data is fused with other social elements to extract a built-up area, the new research focuses on a single element such as a POI or a road network, and has the problems of few considered factors, weak constraint conditions and the like. If the road network density of a certain region is small but the POI information is rich, or the POI information is less but the road network density is large, incomplete extraction of the built-up region is easily caused based on a single element; in addition, the single element has low constraint on night light data, so that the built-up area is easy to judge wrongly, and the built-up area extraction accuracy is low.
Therefore, how to provide a city built-up area extraction method based on night light fusion multi-source data in a transportation and delivery environment is a problem that needs to be solved urgently by technical personnel in the field.
Disclosure of Invention
In view of the above, the invention provides a built-up area extraction method based on night light data and integrating EVI, POI and road network multi-constraint conditions, which can effectively improve the accuracy of built-up area extraction and represent more real urban space morphology.
In order to achieve the purpose, the invention adopts the following technical scheme:
a city built-up area extraction method fusing multi-source data in a transportation and delivery environment comprises the following steps:
s100: acquiring NPP/VIIRS night light data, EVI data, POI data and road network data of a city;
s200: denoising the NPP/VIIRS night light data to obtain a brightness value of the NPP/VIIRS light data;
s300: performing kernel density estimation on the POI data and the road network data to obtain road network density and POI kernel density;
s400: constructing a night light brightness index PRE-ANI based on the EVI data, the road network density and the POI kernel density;
s500: and correcting the brightness value of the NPP/VIIRS night light data based on the constructed night light brightness index PRE-ANI, and performing the established area extraction on the corrected night light data.
Preferably, the step S200 includes:
s210: eliminating the pixel brightness value with the negative DN value in the NPP/VIIRS night light data and obtaining the national maximum brightness value of light intensity;
s220: and smoothing the abnormal highest value of the NPP/VIIRS night light data by using the national light intensity highest value to obtain a brightness value corresponding to the NPP/VIIRS night light data.
Preferably, the specific expression of the kernel density estimation in step S300 is:
Figure BDA0003013507780000021
in the formula, KjIs the weight of data point j; dijThe Euclidean distance between the space point i and the data point j; r is the bandwidth of the calculation rule area; n is the number of data points j within the calculation rule area.
Preferably, the step S400 includes:
s410: correcting the NPP/VIIRS night light data according to the EVI data;
s420: constructing a night light brightness index PRE-ANI, wherein the specific expression is as follows:
Figure BDA0003013507780000031
wherein EVI is an EVI index after standardization, P is POI kernel density after standardization, R is road network kernel density after standardization, and NTL is a brightness value of light at night after standardization.
Preferably, the step S500 includes:
s510: and correcting the NPP/VIIRS night light data according to the night light brightness index PRE-ANI to obtain the total extracted area of the urban area:
Figure BDA0003013507780000032
wherein D isiDenotes the threshold value, S (D)i) Indicating greater than threshold D within the regioniDmax represents the maximum value of the area lamp brightness, f (D)j) Light brightness D in the display areajThe area of (d);
s520: and (3) referring to the built-up area data, and extracting the total area according to the built-up area to obtain a difference value corresponding to the two: the specific expression is as follows:
E(Di)＝S(Di)-S
wherein S represents a regional reference built-up region area, E (D)i) And representing the difference value of the total area of the urban area extraction and the area of the reference built-up area.
S530: approximating the reference area by an iterative method, and determining whether the calculation result of the formula in S520 satisfies E (D)i-1)＞E(Di)＞E(Di+1) If the condition is met, outputting a result, wherein the output result is a threshold with the minimum error, namely an optimal threshold;
s540: and according to the optimal threshold value, performing built-up area extraction on the night light data after the night light brightness index PRE-ANI is corrected.
Preferably, if the formula calculation result in S520 does not satisfy E (D)i-1)＞E(Di-1)＞E(Di+1) If so, the process returns to step S510 to re-calculate until E (D) is satisfiedi-1)＞E(Di-1)＞E(Di+1) And (4) obtaining the optimal threshold value.
Preferably, before the correction index PRE-ANI, the method further includes: standardizing the road network density and the POI kernel density, wherein the concrete expression is as follows:
Figure BDA0003013507780000033
wherein x isiIs the ith element data, min (x)i)Is the minimum value of index i, max (x)i) Is the maximum value of index i.
According to the technical scheme, compared with the prior art, the invention discloses and provides the urban built-up area extraction method fusing multi-source data in the transportation and delivery environment, firstly, on the basis of corrected NPP/VIIRS night light data, a night light correction Index PREANI (POI & Road Density & EVI adjusted NTL Index) taking into account natural elements of an EVI Index (Enhanced vector Index) and multi-source social elements such as POI and Road is constructed; then, based on the correction index and the reference built-up area, a dynamic threshold dichotomy is used to extract the built-up area. The boundary accuracy and urban spatial structure identification degree of the built-up area extracted by the method are high, the more real urban spatial form is represented, and the method has good feasibility.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a main flow chart of a built-up area extraction method for night light fusion multi-source data provided by the invention;
FIG. 2 is a flow chart of the constructed area extraction provided in this embodiment;
fig. 3(a) is a diagram illustrating an extracted graph based on a road network density built-up area in the prior art provided by this embodiment;
FIG. 3(b) is a diagram illustrating the extraction of a region based on POI density in the prior art provided in this embodiment;
FIG. 3(c) is a drawing illustrating an extraction effect of a built-up area based on road network density according to the present invention;
FIG. 3(d) is a drawing illustrating the effect of extracting the constructed area based on POI density according to the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to the attached drawing 1, the embodiment of the invention discloses a method for extracting a built-up city area fused with multi-source data in a transportation and delivery environment, which comprises the following steps:
s100: acquiring NPP/VIIRS night light data, EVI data, POI data and road network data of a city;
s200: denoising the NPP/VIIRS night light data to obtain the brightness value of the NPP/VIIRS light data;
s300: performing kernel density estimation on the POI data and the road network data to obtain road network density and POI kernel density;
s400: constructing a night light brightness index PRE-ANI based on the EVI data, the road network density and the POI kernel density;
s500: and correcting the brightness value of the NPP/VIIRS night light data based on the constructed night light brightness index PRE-ANI, and performing the established area extraction on the corrected night light data.
In one embodiment, step S200 includes:
s210: eliminating the pixel brightness value with negative DN value in NPP/VIIRS night light data and obtaining the national maximum brightness value of light intensity;
wherein, the maximum brightness value of national light intensity is the maximum brightness value of pixels in the larger cities of Beijing, Shanghai, and the like in China.
S220: and carrying out eight-neighborhood smoothing treatment on the abnormal highest value of the NPP/VIIRS night light data by utilizing the national highest value of the light intensity to obtain the brightness value corresponding to the NPP/VIIRS night light data.
In one embodiment, the specific expression of the kernel density estimation of step S300 is:
Figure BDA0003013507780000051
in the formula, KjIs the weight of data point j; dijThe Euclidean distance between the space point i and the data point j; r is the bandwidth of the calculation rule area; n is the number of data points j within the calculation rule area.
In one embodiment, step S400 includes:
s410: correcting the NPP/VIIRS night light data according to the EVI data;
s420: constructing a night light brightness index PRE-ANI, wherein the specific expression is as follows:
Figure BDA0003013507780000061
wherein EVI is an EVI index after standardization, P is POI kernel density after standardization, R is road network kernel density after standardization, and NTL is a brightness value of light at night after standardization.
In one embodiment, referring to fig. 2, step S500 includes:
s510: and correcting the NPP/VIIRS night light data according to the night light brightness index PRE-ANI to obtain the total extracted area of the urban area:
Figure BDA0003013507780000062
wherein D isiDenotes the threshold value, S (D)i) Indicating greater than threshold D within the regioniDmax represents the maximum value of the area lamp brightness, f (D)j) Light brightness D in the display areajThe area of (d);
s520: and (3) referring to the built-up area data, and extracting the total area according to the built-up area to obtain a difference value corresponding to the two: the specific expression is as follows:
E(Di)＝S(Di)-S
wherein S represents a regional reference built-up region area, E (D)i) And representing the difference value of the total area of the urban area extraction and the area of the reference built-up area.
S530: approximating the reference area by an iterative method, and determining whether the formula calculation result in S520 satisfies E (D)i-1)＞E(Di)＞E(Di+1) If the condition is met, outputting a result, wherein the output result is a threshold with the minimum error, namely an optimal threshold;
s540: and according to the optimal threshold value, performing the established area extraction on the night light data after the night light brightness index PRE-ANI is corrected.
In one embodiment, if the formula in S520 does not satisfy E (D)i-1)＞E(Di-1)＞E(Di+1) If so, the process returns to step S510 to re-calculate until E (D) is satisfiedi-1)＞E(Di-1)＞E(Di+1) And (4) obtaining an optimal threshold value.
Referring to fig. 3(a), the yellow boundary is a real built-up region boundary, and the red boundary is a built-up region boundary extracted from the night light fusion road network. When the built-up area is extracted based on the night light fusion road network density, in some residential areas (area A1) with backward construction, the road network density is low, so that the building-up area is missed in comprehensive identification; the area A2 is a small-scale factory area (not belonging to a built-up area of a city), although the road network is relatively perfect, most of the areas are homogeneous facilities, the density of interest points is low, and the built-up area is prone to misjudgment based on a road network method.
Referring to fig. 3(B), in some newly built large-scale connected-slice industrial/development areas (area B), since most of the areas are homogeneous buildings, the POI density is low, and when building area extraction is performed based on night light fusion POI density, the area is missed in comprehensive identification.
Referring to fig. 3(c) and fig. 3(d), after the night light brightness index PRE-ANI is corrected, the existing defects can be obviously improved by using the boundary of the extracted built-up area extracted by the invention.
In one embodiment, before modifying the index PRE-ANI, the method further includes: standardizing the road network density and the POI core density, wherein the concrete expression is as follows:
Figure BDA0003013507780000071
standardizing the road network density and the POI nuclear density so as to eliminate the influence of element magnitude difference on the precision, wherein xiIs the ith element data, min (xi) is the minimum value of index i, max (x)i) Is the maximum value of index i.
In one embodiment, the selection of the bandwidth R has a critical impact on the result when performing the kernel density calculation, and the selection of the bandwidth needs to be well combined with the spatial distribution of the elements and the requirements of practical problems. Smaller bandwidth can generate more high-value or low-value areas, and the local characteristic of the nuclear density distribution is better reflected; the larger bandwidth can better reflect the core density distribution characteristics at the global scale.
In order to obtain a kernel density distribution map with smooth edges and detailed contents, the method selects 500m, 1000m and 2000m of bandwidth to perform tests respectively, randomly selects a certain section as a sample, and counts the kernel density values of different bandwidths under the section. Through multiple tests, 1000m is selected as a nuclear density bandwidth value, so that the nuclear density center keeps good stability.
According to the technical scheme, compared with the prior art, the invention discloses and provides the urban built-up area extraction method fusing multi-source data in the transportation and delivery environment, firstly, on the basis of corrected NPP/VIIRS night light data, a night light correction Index PREANI (POI & Road Density & EVI adjusted NTL Index) taking into account natural elements of an EVI Index (Enhanced vector Index) and multi-source social elements such as POI and Road is constructed; then, based on the correction index and the reference built-up area, a dynamic threshold dichotomy is used to extract the built-up area. The boundary accuracy and urban spatial structure identification degree of the built-up area extracted by the method are high, and the more real urban spatial form is represented.
Example 2
The method provided by the embodiment 1 of the invention is verified, and the specific process is as follows:
1. experimental data
Based on the urban built-up area extraction method fusing multi-source data in the transportation and delivery environment, the built-up area boundary extraction is carried out by taking Shandong province Ying city as an experimental sample area. The Dongying city is located at the mouth of the yellow river entering the sea in the northeast of Shandong province, and is adjacent to the Bohai sea in the east, and the northern part is close to the Jingjin Ji region, and is an important node of the economic area of the yellow river delta and the Bohai sea in the Ring. The Dongying city is composed of Dongying district, reclaiming district, three districts of estuary district, Guangxong county and Lijin county, and the total area is 8243 square kilometers. According to 2020 year city space layout planning of Dongying city, a 'two-core five-piece area, two horizontal, two vertical and multi-center pattern' is formed, city functional areas are guided to gather, the city space structure is optimized, and city health and sustainable development are promoted.
The test data adopted in this embodiment 2 mainly includes NPP/VIIRS night light data, EVI data, POI data, road network data, and reference built-up area boundary data, and specifically includes:
(1) NPP/VIIRS night light data come from the American national oceanic and atmospheric administration (NOAA/NGDC), the spatial resolution is 15', it needs to be noted that, because NPP/VIIRS data in high and medium latitude areas in China have data distortion condition and mostly appear in summer, the invention selects data in 1-3, 9-12 months and 7 months to synthesize annual light data;
(2) the EVI data is from MOD13A1 data published by NASA, the data precision is 250m in spatial resolution, the time resolution is 16d, the method selects data in 6-9 months, and vegetation is vigorous at the time, so that the vegetation condition can be effectively reflected;
(3) the POI data is from a Baidu map, and after the data is cleaned, the POI data totally contains 14 types of 15 ten thousand data, such as catering services, traffic services, shopping services, science and education culture services and the like;
(4) road network data is derived from basic geographic national condition monitoring data, including national roads, provincial roads, expressways, urban roads and the like;
(5) the reference built-up area boundary is derived from third-time homeland space survey data and is obtained through visual interpretation and field survey of high-resolution remote sensing images, and the reference built-up area boundary has high precision.
2. Global accuracy analysis
And (3) verifying the built-up area results extracted by three methods of singly based on the road network, singly based on the POI and fusing the road network and the POI, wherein the precision comparison results are shown in a table 1.
TABLE 1 comparison of accuracy of extraction results in built-up areas
Figure BDA0003013507780000091
Referring to table 1, the overall accuracy of the present invention is superior to the existing method, wherein the absolute error between the extracted area and the area of the reference built-up area is only 0.39%, which is reduced by 62.8% and 20% respectively compared with the method based on POI and road network alone. In general, the road network-based extraction method has the worst effect due to the POI-based established region extraction method. The recall ratio of the built-up area extraction method based on the method and the POI is more than 80%, and although the two methods can better learn the characteristics of the built-up area and effectively identify the characteristics, the method has more advantages. In the aspect of precision ratio, compared with the existing method, the double-constraint built-up area extraction method provided by the invention is more than 80%, which shows that the built-up area is more accurately described by the invention and can be divided with higher correct probability. In addition, the measurement value F1 of the invention is obviously higher than that of the two existing methods, and the invention fully proves that the invention not only can make up for the extraction loss of the built-up area, but also can improve the extraction accuracy of the built-up area through double constraint.
According to the technical scheme, compared with the prior art, the invention discloses and provides the urban built-up area extraction method fusing multi-source data in the transportation and delivery environment, firstly, on the basis of corrected NPP/VIIRS night light data, a night light correction Index PREANI (POI & Road Density & EVI adjusted NTL Index) taking into account natural elements of an EVI Index (Enhanced vector Index) and multi-source social elements such as POI and Road is constructed; then, based on the correction index and the reference built-up area, a dynamic threshold dichotomy is used to extract the built-up area. The boundary accuracy and urban spatial structure identification degree of the built-up area extracted by the method are high, and the more real urban spatial form is represented.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (7)

1. A city built-up area extraction method fusing multi-source data in a transportation and delivery environment is characterized by comprising the following steps:
s100: acquiring NPP/VIIRS night light data, EVI data, POI data and road network data of a city;
s200: denoising the NPP/VIIRS night light data to obtain a brightness value of the NPP/VIIRS light data;
s300: performing kernel density estimation on the POI data and the road network data to obtain road network density and POI kernel density;
s400: constructing a night light brightness index PRE-ANI based on the EVI data, the road network density and the POI kernel density;
s500: and correcting the brightness value of the NPP/VIIRS night light data based on the constructed night light brightness index PRE-ANI, and performing the established area extraction on the corrected night light data.
2. The method for extracting the urban built-up area fusing the multi-source data in the transportation delivery environment according to claim 1, wherein the step S200 comprises:
s210: eliminating the pixel brightness value with the negative DN value in the NPP/VIIRS night light data and obtaining the national maximum brightness value of light intensity;
s220: and smoothing the abnormal highest value of the NPP/VIIRS night light data by using the national light intensity highest value to obtain a brightness value corresponding to the NPP/VIIRS night light data.
3. The method for extracting the urban built-up area fusing the multi-source data in the transportation delivery environment according to claim 1, wherein the specific expression of the kernel density estimation in the step S300 is as follows:
Figure FDA0003013507770000011
in the formula, KjIs the weight of data point j; dijThe Euclidean distance between the space point i and the data point j; r is the bandwidth of the calculation rule area; n is the number of data points j within the calculation rule area.
4. The method for extracting the urban built-up area fusing the multi-source data in the transportation delivery environment according to claim 3, wherein the step S400 comprises:
s410: correcting the NPP/VIIRS night light data according to the EVI data;
s420: constructing a night light brightness index PRE-ANI, wherein the specific expression is as follows:
Figure FDA0003013507770000012
wherein EVI is an EVI index after standardization, P is POI kernel density after standardization, R is road network kernel density after standardization, and NTL is a brightness value of light at night after standardization.
5. The method for extracting the urban built-up area fusing multi-source data in the transportation delivery environment according to claim 1, wherein the step S500 includes:
s510: correcting the NPP/VIIRS night light data according to a night light brightness index PRE-ANI, and extracting the total area of the urban built-up area, wherein the specific expression is as follows:
Figure FDA0003013507770000021
wherein D isiDenotes the threshold value, S (D)i) Indicating greater than threshold D within the regioniDmax represents the maximum value of the area lamp brightness, f (D)j) Light brightness D in the display areajThe area of (d);
s520: and (3) referring to the built-up area data, and extracting the total area according to the built-up area to obtain a difference value corresponding to the built-up area and the total area, wherein the specific expression is as follows:
E(Di)＝S(Di)-S
wherein S represents a regional reference built-up region area, E (D)i) Representing the difference value between the total extracted area of the urban building area and the area of the reference built-up area;
s530: approximating the reference area by an iterative method, and determining whether the calculation result of the formula in S520 satisfies E (D)i-1)＞E(Di)＞E(Di+1) If the condition is met, outputting a result, wherein the output result is a threshold with the minimum error, namely an optimal threshold;
s540: and according to the optimal threshold value, performing built-up area extraction on the night light data after the night light brightness index PRE-ANI is corrected.
6. The method of claim 5A city built-up area extraction method fusing multi-source data in a transportation delivery environment is characterized in that if a formula calculation result in S520 does not meet E (D)i-1)＞E(Di-1)＞E(Di+1) If so, the process returns to step S510 to re-calculate until E (D) is satisfiedi-1)＞E(Di-1)＞E(Di+1) And (4) obtaining the optimal threshold value.
7. The method of claim 4, wherein before the revision index PRE-ANI, the method further comprises: standardizing the road network density and the POI kernel density, wherein the concrete expression is as follows:
Figure FDA0003013507770000022
wherein x isiIs the ith element data, min (x)i)Is the minimum value of index i, max (x)i) Is the maximum value of index i.
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