Mountain city construction land expansion characteristic influence factor analysis method and system and medium

Abstract

The invention provides a mountain city construction land expansion characteristic influence factor analysis method. The method comprises the following steps: S1, acquiring remote sensing image data of a mountain city; s2, acquiring a shooting moment of the mountain city remote sensing image data in the step S1 according to the mountain city remote sensing image data acquired in the step S1; s3, generating an image sequence of the mountain city remote sensing image data according to the mountain city remote sensing image data shooting time obtained in the step S2; and S4, performing image processing on the image sequence, and displaying the processed image sequence on a mountain city construction land display platform. The mountain city construction land expansion display device can display mountain city construction land expansion, and is vivid and visual.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method, system and medium for analyzing factors affecting the expansion characteristics of construction land in mountainous cities. Background Art

[0002] Measuring the expansion trend of urban construction land, identifying the expansion mode of urban construction land, and analyzing the characteristics of the spatial expansion pattern of urban construction land are important bases for measuring whether the urbanization process is healthy, efficient, and intensive, and are necessary conditions for optimizing the urban spatial development pattern. It is difficult to obtain and process data when analyzing the factors affecting the expansion characteristics of urban construction land in mountainous areas. It is urgent to simplify the difficulty of data acquisition and processing through innovative remote sensing image data technology, scientifically and rationally analyze the expansion characteristics of urban construction land in mountainous areas, and provide support for scientifically regulating the spatial layout of mountainous urban construction. This technology selects the central urban area of ​​Chongqing as the research object, which is representative. The expansion of urban construction land in the central urban area of ​​Chongqing is different from the expansion mode of plain cities and the expansion characteristics of cities in developed areas. The complexity of its topography and landforms poses a challenge to remote sensing image data processing technology. It is hoped that through the analysis of the factors affecting the expansion characteristics of urban construction land in the central urban area of ​​Chongqing, a method can be provided for the analysis of the impact of the expansion characteristics of growth construction land in similar regions. Summary of the invention

[0003] The present invention aims to at least solve the technical problems existing in the prior art, and in particular innovatively proposes a method, system and medium for analyzing factors affecting the expansion characteristics of construction land in mountainous cities.

[0004] In order to achieve the above-mentioned object of the present invention, the present invention provides a method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities, comprising the following steps:

[0005] S1, obtain remote sensing image data of mountainous cities;

[0006] S2, obtaining the shooting time of the mountainous city remote sensing image data in step S1 according to the mountainous city remote sensing image data obtained in step S1;

[0007] S3, generating an image sequence of the mountainous city remote sensing image data according to the shooting time of the mountainous city remote sensing image data obtained in step S2;

[0008] S4, image processing is performed on the image sequence, and the processed image sequence is displayed on the mountain city construction land display platform.

[0009] In a preferred embodiment of the present invention, the method for generating an image sequence of mountainous city remote sensing image data according to the shooting time of the mountainous city remote sensing image data obtained in step S2 in step S3 is:

[0010] S31. Statistically obtain the number of mountain city remote sensing image data, denoted as K, where K represents the number of mountain city remote sensing image data; let the serial number k = 1;

[0011] S32. Extract the k-th mountain city remote sensing image S k The shooting time, remove the day, minute, and second in the time, and retain the year and month in the time;

[0012] S33. If the shooting time of the k-th mountain city remote sensing image S k is y year and m month, then put the k-th mountain city remote sensing image S k into the mountain city image dataset s y,m ;

[0013] S34. Judge the relationship between k and K:

[0014] If k ≥ K, then execute the next step;

[0015] If k < K, then execute step S32;

[0016] S35. Generate an image sequence in chronological order of year and month; that is:

[0017]

[0018] Among them, S represents the image sequence of mountain city remote sensing image data;

[0019] s y,m represents the mountain city image dataset taken by the remote sensing satellite in y year and m month; y = y1, y2, y3,..., y n , m = 1, 2, 3,..., 12; ζ = 2, 3,..., n, that is y1 < y2 < y3 <... < y n ≤ 2024;

[0020]

[0021] Among them, s y,m represents the mountain city image dataset taken by the remote sensing satellite in y year and m month;

[0022] s y,m,1 represents the mountain city image dataset s y,m the first mountain city image in;

[0023] s y,m,2 represents the mountain city image dataset s y,m the second mountain city image in;

[0024] s y,m,3 represents the mountain city image dataset sy,m The 3rd mountain city image in;

[0025] Represents the mountain city image dataset s y,m The ψth y,m Mountain city image;ψ y,m Represents the mountain city image dataset s y,m The number of images of cities in the middle mountains;

[0026] Or / and also includes determining the expansion of urban construction land in mountainous areas based on the acquired topography or / and slope or / and neighborhood or / and transportation.

[0027] In a preferred embodiment of the present invention, the method for obtaining remote sensing image data of a mountain city in step S1 comprises the following steps:

[0028] S11, generating an image matrix of remote sensing image data of a mountain city from the stored image;

[0029] S12, converting the pixel value of each pixel point into a binary pixel value. After converting the pixel value of each pixel point into a binary pixel value, if the number of bits of the binary pixel value is not equal to the number of bits of the image, add 0 to the front of the binary pixel value so that the number of bits after adding 0 is equal to the number of bits of the image;

[0030] S13, using the check string and the pixel value at each pixel point to calculate and obtain the actual pixel value; using the check string and the pixel value at each pixel point to calculate and obtain the actual pixel value is:

[0031] R y ″ ,x =R y,x,1 R y,x,2 R y,x,3 …R y,x,h ⊥c1c2c3…c h ,

[0032] Among them, R y ″ ,x represents the actual binary red pixel value;

[0033] R y,x,1 Represents the first bit value in the binary red pixel value;

[0034] R y,x,2 Represents the second bit value in the binary red pixel value;

[0035] R y,x,3 Represents the third bit value in the binary red pixel value;

[0036] R y,x,h Represents the hth bit value in the binary red pixel value;

[0037] h represents the number of bits of the image;

[0038] c1 means to check the first value in the string;

[0039] c2 means checking the second digit value in the string;

[0040] c3 means checking the third digit value in the string;

[0041] c h Indicates checking the hth bit value in the string;

[0042] ⊥ represents the pixel value operator. If R y,x,τ =c τ =1, or R y,x,τ =c τ =0, then equal to 1; if R y,x,τ =1 and c τ =0, or R y,x,τ =0, c τ ,1, then it is equal to 0;τ=1,2,3,……,h;

[0043] R y ' ,x =R y,x,1 R y,x,2 R y,x,3 …R y,x,h ,

[0044] R y ' ,x represents the binary red pixel value;

[0045] c=c1c2c3…c h ,

[0046] c indicates the check string.

[0047] In a preferred embodiment of the present invention, the method for performing image processing on the image sequence in step S4 comprises the following steps:

[0048] S41, performing image conversion on the image sequence;

[0049] S42, determining the construction land area according to the conversion value;

[0050] S43, generating an annual or monthly construction land area curve chart based on the construction land area.

[0051] In a preferred embodiment of the present invention, the method for determining the construction land area according to the conversion value in step S42 includes the following steps:

[0052] S421, if the conversion value is between the first preset conversion value and the second preset conversion value, and the second preset conversion value is greater than the first preset conversion value, then the conversion value is a construction land pixel point;

[0053] If the conversion value is not between the first preset conversion value and the second preset conversion value, and the second preset conversion value is greater than the first preset conversion value, then the conversion value is not a construction land pixel;

[0054] S422, counting the number of construction land pixel points, and calculating the construction land area according to the number of construction land pixel points.

[0055] In a preferred embodiment of the present invention, the calculation method of the construction land area in step S422 is:

[0056] S a =Ds,

[0057] Among them, S a Indicates the construction land area;

[0058] D represents the number of pixels of construction land;

[0059] s represents the area occupied by a pixel.

[0060] In a preferred embodiment of the present invention, step S4 also includes logging into a mountainous city construction land display platform.

[0061] In a preferred embodiment of the present invention, the display information further includes one or any combination of the urban construction land expansion speed, urban construction land expansion intensity, urban construction land fractal dimension, and urban construction land fragmentation;

[0062] The calculation method of urban construction land expansion rate is:

[0063]

[0064] Among them, S i is the growth rate of urban construction land in region i

[0065] ΔU ij The increase in urban construction land area in region i during period j;

[0066] ΔT is the time interval in years;

[0067] The calculation method of urban construction land expansion intensity is:

[0068]

[0069] Among them, I i The expansion intensity of urban construction land;

[0070] ΔU ij The increase in urban construction land area in region i during period j;

[0071] ΔT is the time interval in years;

[0072] U i is the total area of ​​urban construction land in region i at the initial stage of the study;

[0073] The calculation method of the fractal dimension of urban construction land is:

[0074] D T =2ln(P T / 4) / lnA T ,

[0075] Among them, D T is the fractal dimension of urban construction land in a certain study area during period T;

[0076] P T is the perimeter of the urban construction land patch in the study area during period T;

[0077] A T is the area of ​​urban construction land patches in the study area during period T;

[0078] The calculation method of urban construction land fragmentation is:

[0079] C T =N T / A T ,

[0080] Among them, C T is the landscape fragmentation index of the study area at time point T;

[0081] N T is the number of urban construction map spots at time T;

[0082] A T is the urban construction land area at time T;

[0083] C T The larger the value, the more fragmented the urban construction land plots in the region.

[0084] The present invention also discloses a computer system, comprising:

[0085] processor;

[0086] a memory for storing processor-executable instructions;

[0087] The processor is configured to implement the mountain city construction when executing the executable instructions. The present invention also discloses a computer-readable storage medium, including:

[0088] a memory having a computer program stored thereon;

[0089] A processor is used to execute the program in the memory to implement the method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities.

[0090] In summary, due to the adoption of the above technical solution, the present invention can realize the display of the expansion of construction land in mountainous cities in a vivid and intuitive manner.

[0091] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0093] Figure 1 It is a schematic block diagram of the process of the present invention.

[0094] Figure 2 It is a schematic display of the topography and landforms of the study area of ​​the present invention.

[0095] Figure 3 It is a schematic diagram showing the sector division of the study area of ​​the present invention.

[0096] Figure 4 It is a schematic diagram showing the division of circles in the study area of ​​the present invention.

[0097] Figure 5 It is a schematic diagram showing the growth trend of urban construction land in the central urban area of ​​the present invention.

[0098] Figure 6 It is a schematic diagram showing the growth trend of urban construction land in each administrative district of the central urban area of ​​the present invention.

[0099] Figure 7 It is a schematic display diagram of the radar of the growth rate of urban construction land in the central urban area of ​​the present invention.

[0100] Figure 8 It is a schematic display diagram of the radar of the growth intensity of urban construction land in the central urban area of ​​the present invention.

[0101] Fig. 9 It is a schematic diagram showing the trend of the growth rate of urban construction land in the central urban area of ​​the present invention.

[0102] Fig.10 It is a schematic diagram showing the trend of the growth intensity circles of urban construction land in the central urban area of ​​the present invention. DETAILED DESCRIPTION

[0103] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0104] The present invention discloses a method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities. Figure 1 As shown, the following steps are included:

[0105] S1, obtain remote sensing image data of mountainous cities;

[0106] S2, obtaining the shooting time of the mountainous city remote sensing image data in step S1 according to the mountainous city remote sensing image data obtained in step S1;

[0107] S3, generating an image sequence of the mountainous city remote sensing image data according to the shooting time of the mountainous city remote sensing image data obtained in step S2;

[0108] S4, image processing is performed on the image sequence, and the processed image sequence is displayed on the mountain city construction land display platform.

[0109] In a preferred embodiment of the present invention, the method for generating an image sequence of mountainous city remote sensing image data according to the shooting time of the mountainous city remote sensing image data obtained in step S2 in step S3 is:

[0110] S31, counting the number of acquired mountain city remote sensing image data, recorded as K, K represents the number of mountain city remote sensing image data; let the sequence number k=1;

[0111] S32, extracting the kth mountain city remote sensing image S k The time of shooting, remove the day, minute and second in the time, and keep the year and month;

[0112] S33, if the k-th mountain city remote sensing image S k The shooting time is y year m month, then the k-th mountain city remote sensing image S k Put in the mountain city image dataset s y,m ;

[0113] That is, if the k-th mountain city remote sensing image S k The shooting time is January of year y1, then the k-th mountain city remote sensing image S k Insert mountain city image dataset

[0114] If the kth mountain city remote sensing image S kThe shooting time is February of year y1, then the k-th mountain city remote sensing image S k Insert mountain city image dataset

[0115] If the kth mountain city remote sensing image S k The shooting time is March of year y1, then the k-th mountain city remote sensing image S k Insert mountain city image dataset

[0116] ……;

[0117] If the kth mountain city remote sensing image S k The shooting time is December of year y1, then the k-th mountain city remote sensing image S k Insert mountain city image dataset

[0118] If the kth mountain city remote sensing image S k The shooting time is January of year y2, then the k-th mountain city remote sensing image S k Insert mountain city image dataset

[0119] If the kth mountain city remote sensing image S k The shooting time is February of year y2, then the k-th mountain city remote sensing image S k Insert mountain city image dataset

[0120] If the kth mountain city remote sensing image S k The shooting time is March of year y2, then the k-th mountain city remote sensing image S k Insert mountain city image dataset

[0121] ……;

[0122] If the kth mountain city remote sensing image S k The shooting time is December of year y2, then the k-th mountain city remote sensing image S k Insert mountain city image dataset

[0123] If the kth mountain city remote sensing image S k The shooting time is January of year y3, then the k-th mountain city remote sensing image S k Insert mountain city image dataset

[0124] If the kth mountain city remote sensing image S kThe shooting time is February of year y3, then the k-th mountain city remote sensing image S k Insert mountain city image dataset

[0125] If the kth mountain city remote sensing image S k The shooting time is March of year y3, then the k-th mountain city remote sensing image S k Insert mountain city image dataset

[0126] ……;

[0127] If the kth mountain city remote sensing image S k The shooting time is December of year y3, then the k-th mountain city remote sensing image S k Insert mountain city image dataset

[0128] ……;

[0129] If the kth mountain city remote sensing image S k The shooting time is y n In January of the year, the kth mountain city remote sensing image S k Insert mountain city image dataset

[0130] If the kth mountain city remote sensing image S k The shooting time is y n In February of the year, the kth mountain city remote sensing image S k Insert mountain city image dataset

[0131] If the kth mountain city remote sensing image S k The shooting time is y n In March of the year, the kth mountain city remote sensing image S k Insert mountain city image dataset

[0132] ……;

[0133] If the kth mountain city remote sensing image S k The shooting time is y n In December of the year, the kth mountain city remote sensing image S k Insert mountain city image dataset

[0134] S34, determine the relationship between k and K:

[0135] If k>K, execute the next step;

[0136] If k≤K, execute step S32;

[0137] S35, generating an image sequence in chronological order; that is:

[0138]

[0139] Where S represents the image sequence of mountain city remote sensing image data;

[0140] s y,m represents a mountain city image dataset taken by remote sensing satellites in year y and month m; y = y1, y2, y3, ..., y n , m=1, 2, 3, ...,

[0141] 12; ζ=2、3、……、n, That is, y1<y2<y3<……<y n ≤2024;

[0142] represents the mountain city image dataset taken by remote sensing satellite in January of year y1;

[0143] It represents the mountain city image dataset taken by remote sensing satellite in February y1;

[0144] represents the mountain city image dataset taken by remote sensing satellite in March of year y1;

[0145] It represents the mountain city image dataset taken by remote sensing satellite in December of year y1;

[0146] represents a mountain city image dataset taken by remote sensing satellites in January y2;

[0147] It represents the mountain city image dataset taken by remote sensing satellite in February of year y2;

[0148] It represents the mountain city image dataset taken by remote sensing satellite in March of y2;

[0149] It represents the mountain city image dataset taken by remote sensing satellite in December y2;

[0150] It represents the mountain city image dataset taken by remote sensing satellite in January y3;

[0151] It represents the mountain city image dataset taken by remote sensing satellite in February y3;

[0152] It represents the mountain city image dataset taken by remote sensing satellite in March y3;

[0153] It represents the mountain city image dataset taken by remote sensing satellite in December y3;

[0154] Represents y n A dataset of mountainous cities images taken by remote sensing satellites in January 2017;

[0155] Represents y n A dataset of mountainous cities images taken by remote sensing satellites in February 2017;

[0156] Represents y n A dataset of mountainous cities images taken by remote sensing satellites in March 2017;

[0157] Represents y n A dataset of mountainous cities images taken by remote sensing satellites in December 2017;

[0158]

[0159] Among them, s y,m It represents the mountain city image dataset taken by remote sensing satellites in month m of year y;

[0160] s y,m,1 Represents the mountain city image dataset s y,m The first mountain city image in;

[0161] s y,m,2 Represents the mountain city image dataset s y,m The 2nd mountain city image in;

[0162] s y,m,3 Represents the mountain city image dataset s y,m The 3rd mountain city image in;

[0163] Represents the mountain city image dataset s y,m The ψth y,m Mountain city image;ψ y,m Represents the mountain city image dataset s y,m The number of images of cities in the middle mountains;

[0164] That is

[0165] in, represents the mountain city image dataset taken by remote sensing satellite in January of year y1;

[0166] Represents a mountain city image dataset The first mountain city image in;

[0167] Represents a mountain city image dataset The 2nd mountain city image in;

[0168] Represents a mountain city image dataset The 3rd mountain city image in;

[0169] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0170]

[0171] in, It represents the mountain city image dataset taken by remote sensing satellite in February y1;

[0172] Represents a mountain city image dataset The first mountain city image in;

[0173] Represents a mountain city image dataset The 2nd mountain city image in;

[0174] Represents a mountain city image dataset The 3rd mountain city image in;

[0175] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0176]

[0177] in, represents the mountain city image dataset taken by remote sensing satellite in March of year y1;

[0178] Represents a mountain city image dataset The first mountain city image in;

[0179] Represents a mountain city image dataset The 2nd mountain city image in;

[0180] Represents a mountain city image dataset The 3rd mountain city image in;

[0181] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0182]

[0183] in, It represents the mountain city image dataset taken by remote sensing satellite in December of year y1;

[0184] Represents a mountain city image dataset The first mountain city image in;

[0185] Represents a mountain city image dataset The 2nd mountain city image in;

[0186] Represents a mountain city image dataset The 3rd mountain city image in;

[0187] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0188]

[0189] in, represents a mountain city image dataset taken by remote sensing satellites in January y2;

[0190] Represents a mountain city image dataset The first mountain city image in;

[0191] Represents a mountain city image dataset The 2nd mountain city image in;

[0192] Represents a mountain city image dataset The 3rd mountain city image in;

[0193] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0194]

[0195] in, It represents the mountain city image dataset taken by remote sensing satellite in February of year y2;

[0196] Represents a mountain city image dataset The first mountain city image in;

[0197] Represents a mountain city image dataset The 2nd mountain city image in;

[0198] Represents a mountain city image dataset The 3rd mountain city image in;

[0199] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0200]

[0201] in, It represents the mountain city image dataset taken by remote sensing satellite in March of y2;

[0202] Represents a mountain city image dataset The first mountain city image in;

[0203] Represents a mountain city image dataset The 2nd mountain city image in;

[0204] Represents a mountain city image dataset The 3rd mountain city image in;

[0205] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0206]

[0207] in, It represents the mountain city image dataset taken by remote sensing satellite in December y2;

[0208] Represents a mountain city image dataset The first mountain city image in;

[0209] Represents a mountain city image dataset The 2nd mountain city image in;

[0210] Represents a mountain city image dataset The 3rd mountain city image in;

[0211] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0212]

[0213] in, It represents the mountain city image dataset taken by remote sensing satellite in January y3;

[0214] Represents a mountain city image dataset The first mountain city image in;

[0215] Represents a mountain city image dataset The 2nd mountain city image in;

[0216] Represents a mountain city image dataset The 3rd mountain city image in;

[0217] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0218]

[0219] in, It represents the mountain city image dataset taken by remote sensing satellite in February y3;

[0220] Represents a mountain city image dataset The first mountain city image in;

[0221] Represents a mountain city image dataset The 2nd mountain city image in;

[0222] Represents a mountain city image dataset The 3rd mountain city image in;

[0223] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0224]

[0225] in, It represents the mountain city image dataset taken by remote sensing satellite in March y3;

[0226] Represents a mountain city image dataset The first mountain city image in;

[0227] Represents a mountain city image dataset The 2nd mountain city image in;

[0228] Represents a mountain city image dataset The 3rd mountain city image in;

[0229] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0230]

[0231] in, It represents the mountain city image dataset taken by remote sensing satellite in December y3;

[0232] Represents a mountain city image dataset The first mountain city image in;

[0233] Represents a mountain city image dataset The 2nd mountain city image in;

[0234] Represents a mountain city image dataset The 3rd mountain city image in;

[0235] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0236]

[0237] in, Represents y n A dataset of mountainous cities images taken by remote sensing satellites in January 2017;

[0238] Represents a mountain city image dataset The first mountain city image in;

[0239] Represents a mountain city image dataset The 2nd mountain city image in;

[0240] Represents a mountain city image dataset The 3rd mountain city image in;

[0241] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0242]

[0243] in, Represents yn A dataset of mountainous cities images taken by remote sensing satellites in February 2017;

[0244] Represents a mountain city image dataset The first mountain city image in;

[0245] Represents a mountain city image dataset The 2nd mountain city image in;

[0246] Represents a mountain city image dataset The 3rd mountain city image in;

[0247] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0248]

[0249] in, Represents y n A dataset of mountainous city images taken by remote sensing satellites in March 2017;

[0250] Represents a mountain city image dataset The first mountain city image in;

[0251] Represents a mountain city image dataset The 2nd mountain city image in;

[0252] Represents a mountain city image dataset The 3rd mountain city image in;

[0253] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0254]

[0255] in, Represents y n A dataset of mountainous cities images taken by remote sensing satellites in December 2017;

[0256] Represents a mountain city image dataset The first mountain city image in;

[0257] Represents a mountain city image dataset The 2nd mountain city image in;

[0258] Represents a mountain city image dataset The 3rd mountain city image in;

[0259] Represents a mountain city image dataset The Mountain city images; Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0260]

[0261] Where K represents the number of mountain city remote sensing image data;

[0262] Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0263] Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0264] Represents a mountain city image dataset The number of images of cities in the middle mountains;

[0265] Represents a mountain city image dataset The number of images of cities in the mountains.

[0266] Or / and also include determining the expansion of mountainous urban construction land based on the acquired topography and / or slope and / or neighborhood and / or transportation. Figures 2 to 10 As shown in the example map, different colors are used to represent mountains, lines are used to represent the scope of each district and county, and broken line graphs are used to represent the changes in each year or region, which is vivid and intuitive.

[0267] Among them, the areas below 200m above sea level in the central urban area of ​​Chongqing are mostly river valleys, which are affected by flood levels and are not suitable for development and construction. Areas above 500m above sea level are mostly located in the "Four Mountains Control" area, where urban development and construction are prohibited. In the built-up areas in 2010, 48.14% of urban construction land was distributed in areas with an altitude of 200-300m, 41.19% of urban construction land was distributed in areas with an altitude of 300-400m, and the built-up area in areas above 400m accounted for only 10.67%. From 2010 to 2018, the newly added urban construction land in the central urban area of ​​Chongqing was still concentrated between 200 and 400m above sea level, but the proportion of newly added urban construction land in areas above 400m above sea level increased to 27.98%, and the proportion of newly added urban construction land in areas below 300m above sea level increased to 30.58% of the total newly added urban construction land. By 2018, the total amount of urban construction land distributed at an altitude of more than 400m in the central urban area of ​​Chongqing has reached 15.89%, an increase of 5.22 percentage points from 2010. The changes in the altitude distribution of newly added urban construction land in different periods show that the proportion of newly added urban construction land in the 200-300m altitude area has a decreasing trend, and the proportion of newly added urban construction land in the 300-400m altitude area has an increasing trend. Among them, in the two periods of 2010-2012 and 2013-2015, the proportion of newly added urban construction land in the 300-400m altitude area accounted for 40.75% and 36.96% of the newly added urban construction land in the period respectively; and in 2016-2018, the newly added construction land in the 300-400m altitude area accounted for 46.84% of the total newly added construction land in the period. The trend of increasing proportion of newly added urban construction land in higher altitude areas indicates that due to the exhaustion of suitable construction resources in areas with flat terrain and lower altitudes, urban construction has begun to expand to areas with more complex terrain conditions, resulting in more fragmented urban space. Therefore, topography has an important impact on the expansion pattern of mountainous cities.

[0268] Compared with the slope distribution of built-up areas in 2010, by 2018, the proportion of urban construction land with a slope of less than 6° in the central urban area of ​​Chongqing had decreased significantly, while the proportion of urban construction land with a slope of more than 6° had increased significantly. From 2010 to 2018, the newly added urban construction land in the central urban area of ​​Chongqing was concentrated between 2 and 6° and 6 and 15°, accounting for 24.06% and 31.15% of the new additions, respectively. However, by comparing the slope distribution of newly added urban construction land in the three periods of 2010 to 2012, 2013 to 2015, and 2016 to 2018, it was found that the incremental proportion of slopes of 6 to 15° and 15 to 25° was gradually increasing. The development and construction of towns in areas with higher slope levels, on the one hand, means that urban development and construction have gradually broken through the terrain restrictions, and on the other hand, it also means that mountainous urban construction will lead to large-scale excavation of mountains, which will intrude on urban green space. Therefore, slope is also an important terrain factor affecting the expansion form of urban construction land.

[0269] Although the expansion of urban construction land in the central urban area of ​​Chongqing presents a decentralized development, the circle analysis shows that under the overall decentralized pattern, the expansion of urban construction land still revolves around the core area and presents a relatively concentrated pattern. Since 2010, 31.7% of the increase in urban construction land in the central urban area of ​​Chongqing has been concentrated in the circle range of 10-20km from the city center, and 55.21% of the increase in urban construction land has been concentrated in the circle range of 20-30km2 from the city center. Within the circle range of 10km from the city center, due to the large number of built-up areas and the increasingly mature urban development, there is less space for new urban development, and the increase in urban construction land accounts for only 4.8% of the increase in urban construction land during the study period. The increase in urban construction land in the circle range of more than 30km from the city center has decreased, among which the increase in urban construction land in the circle range of 30-40km from the city center accounts for only 7.83%, and the increase in urban construction land in the area of ​​more than 40km from the city center accounts for only 0.77%. Under the overall slowing trend of the increase of urban construction land in the study area, the expansion intensity and speed of urban construction land in each circle have decreased, but the circle 20-30km away from the city center is still the core circle of urban construction land growth. From 2010 to 2012, the growth rate of the circle 20-30km away from the city center was 30.33km2 / a, and the growth intensity reached 28.78%, which was the circle with the highest growth intensity and the fastest speed. From 2016 to 2018, the growth rate of urban construction land in this circle decreased to 10.04km2 / a, and the intensity decreased to 9.52%, but this circle was still the area with the highest growth intensity during the same period. This shows that although the central urban area of ​​Chongqing reflects a dispersed cluster expansion model, the central place theory can still explain the driving mechanism of mountain city growth. Various urban elements have formed a continuous high-intensity urban area within 10km from the city center; under the guidance of the spillover of urban elements and spatial planning, the peripheral areas close to the core circle show a secondary peak of urban growth; in areas relatively far away from the urban core circle, the agglomeration capacity of urban expansion is still relatively weak.

[0270] Some studies have quantitatively demonstrated the driving effect of the distance from highways, railways, and urban trunk roads on the expansion of urban construction land. However, for mountainous cities, the impact of roads on the expansion of mountainous cities is not a simple distance variable. Tunnels and bridges can effectively improve regional traffic accessibility and have point-shaped, linear, and ring-shaped radiation effects on urban expansion. The experience of cross-river cities shows that cross-river channels can strengthen the urban road network pattern and optimize the urban layout. In the early days, the urban expansion of the main urban area of ​​Chongqing was concentrated in the narrow area between Tongluo Mountain and Zhongliang Mountain, expanding in the north-south direction. In recent years, the construction of tunnels and bridges has promoted urban development across mountains and barriers, showing a leapfrog development, and forming urban spillover areas in the east-west direction. For example, the Shuangbei Bridge and Shuangbei Tunnel built in 2014 connected Jiangbei District and the University Town area, greatly promoting the city's westward expansion. Therefore, transportation factors are important neighborhood elements for cities to promote urban expansion.

[0271] In a preferred embodiment of the present invention, the method for obtaining remote sensing image data of a mountainous city in step S1 comprises the following steps:

[0272] S11, generates an image matrix of remote sensing image data of mountainous cities from the stored images:

[0273]

[0274] Among them, (R y,x ,G y,x ,B y,x ) represents the red, green and blue pixel values ​​at the pixel point (x, y) of the remote sensing image of the mountain city; x = 1, 2, 3, ..., X, y = 1, 2, 3, ..., Y;

[0275] R y,x Represents the red value of the remote sensing image of a mountain city at the pixel point (x, y);

[0276] G y,x Represents the green value of the remote sensing image of a mountain city at the pixel point (x, y);

[0277] B y,x Represents the blue value of the remote sensing image of a mountain city at the pixel point (x, y);

[0278] That is (R 1,1 ,G 1,1 ,B 1,1 ) represents the red, green and blue pixel values ​​at the pixel point (1,1) of the remote sensing image of the mountain city;

[0279] (R 1,2 ,G 1,2 ,B 1,2 ) represents the red, green and blue pixel values ​​at the pixel point (1,2) of the remote sensing image of the mountain city;

[0280] (R 1,3 ,G 1,3 ,B 1,3 ) represents the red, green and blue pixel values ​​at the pixel point (1,3) of the remote sensing image of the mountain city;

[0281] (R 1,X ,G 1,X ,B 1,X ) represents the red, green and blue pixel values ​​at the pixel point (1,X) of the remote sensing image of the mountain city;

[0282] (R 2,1 ,G 2,1 ,B 2,1 ) represents the red, green and blue pixel values ​​at the pixel point (2,1) of the remote sensing image of the mountain city;

[0283] (R 2,2 ,G 2,2 ,B 2,2 ) represents the red, green and blue pixel values ​​at the pixel point (2,2) of the remote sensing image of the mountain city;

[0284] (R 2,3 ,G 2,3 ,B 2,3 ) represents the red, green and blue pixel values ​​at the pixel point (2,3) of the remote sensing image of the mountain city;

[0285] (R 2,X ,G 2,X ,B 2,X ) represents the red, green and blue pixel values ​​at the pixel point (2,X) of the remote sensing image of the mountain city;

[0286] (R 3,1 ,G 3,1 ,B 3,1 ) represents the red, green and blue pixel values ​​at the pixel point (3,1) of the remote sensing image of the mountain city;

[0287] (R 3,2 ,G 3,2 ,B 3,2 ) represents the red, green and blue pixel values ​​at the pixel point (3,2) of the remote sensing image of the mountain city;

[0288] (R 3,3 ,G 3,3 ,B 3,3 ) represents the red, green and blue pixel values ​​at the pixel point (3,3) of the remote sensing image of the mountain city;

[0289] (R 3,X ,G 3,X ,B 3,X ) represents the red, green and blue pixel values ​​at the pixel point (3,X) of the remote sensing image of the mountain city;

[0290] (R Y,1 ,G Y,1 ,B Y,1 ) represents the red, green and blue pixel values ​​at the pixel point (Y,1) of the remote sensing image of the mountain city;

[0291] (R Y,2 ,G Y,2 ,B Y,2 ) represents the red, green and blue pixel values ​​at the pixel point (Y,2) of the remote sensing image of the mountain city;

[0292] (R Y,3 ,G Y,3 ,B Y,3 ) represents the red, green and blue pixel values ​​at the pixel point (Y,3) of the remote sensing image of the mountain city;

[0293] (R Y,X ,G Y,X ,B Y,X ) represents the red, green and blue pixel values ​​at the pixel point (Y, X) of the remote sensing image of the mountain city;

[0294] S12, converting the pixel value of each pixel point into a binary pixel value. After converting the pixel value of each pixel point into a binary pixel value, if the number of bits of the binary pixel value is not equal to the number of bits of the image, add 0 to the front of the binary pixel value so that the number of bits after adding 0 is equal to the number of bits of the image;

[0295] S13, using the check string and the pixel value at each pixel point to calculate and obtain the actual pixel value; using the check string and the pixel value at each pixel point to calculate and obtain the actual pixel value is:

[0296] R y ″ ,x =R y,x,1 R y,x,2 R y,x,3 …R y,x,h ⊥c1c2c3…c h ,

[0297] Among them, R y ″ ,x represents the actual binary red pixel value;

[0298] R y,x,1 Represents the first bit value in the binary red pixel value;

[0299] R y,x,2 Represents the second bit value in the binary red pixel value;

[0300] R y,x,3 Represents the third bit value in the binary red pixel value;

[0301] R y,x,h Represents the hth bit value in the binary red pixel value;

[0302] h represents the number of bits of the image;

[0303] c1 means to check the first value in the string;

[0304] c2 means checking the second digit value in the string;

[0305] c3 means checking the third digit value in the string;

[0306] c h Indicates checking the hth bit value in the string;

[0307] ⊥ represents the pixel value operator. If R y,x,τ =c τ =1, or R y,x,τ =c τ =0, then equal to 1; if R y,x,τ =1 and c τ =0, or R y,x,τ =0, c τ ,1, then it is equal to 0;τ=1,2,3,……,h;

[0308] R′ y,x =R y,x,1 R y,x,2 R y,x,3 …R y,x,h ,

[0309] R′ y,x represents the binary red pixel value;

[0310] c=c1c2c3…c h ,

[0311] c represents the check string; preferably all are 1;

[0312] G″ y,x =G y,x,1 G y,x,2 G y,x,3 …G y,x,h ⊥c1c2c3…c h ,

[0313] Among them, G y,x represents the actual binary green pixel value;

[0314] G y,x,1 Indicates the first bit value in the binary green pixel value;

[0315] G y,x,2 Indicates the second bit value in the binary green pixel value;

[0316] G y,x,3 Indicates the value of the third bit in the binary green pixel value;

[0317] G y,x,h Represents the hth bit value in the binary green pixel value;

[0318] h represents the number of bits of the image;

[0319] c1 means checking the first digit value in the string;

[0320] c2 means checking the second digit value in the string;

[0321] c3 means checking the third digit value in the string;

[0322] c h Indicates checking the hth bit value in the string;

[0323] ⊥ represents the pixel value operator. If G y,x,τ =c τ =1, or G y,x,τ =c τ =0, then it is equal to 1; if G y,x,τ =1 and c τ =0, or G y,x,τ =0, c τ ,1, then it is equal to 0;τ=1,2,3,……,h;

[0324] G y ' ,x =G y,x,1 G y,x,2 G y,x,3 …G y,x,h ,

[0325] G y ' ,x represents the binary green pixel value;

[0326] c=c1c2c3…c h ,

[0327] c represents the check string; preferably all are 1;

[0328] B y ″ ,x =B y,x,1 B y,x,2 B y,x,3 …B y,x,h ⊥c1c2c3…c h ,

[0329] Among them, B y ″ ,x represents the actual binary blue pixel value;

[0330] B y,x,1 Indicates the first bit value in the binary blue pixel value;

[0331] B y,x,2 Indicates the second bit value in the binary blue pixel value;

[0332] B y,x,3 Indicates the third bit value in the binary blue pixel value;

[0333] B y,x,h Represents the hth bit value in the binary blue pixel value;

[0334] h represents the number of bits of the image;

[0335] c1 means to check the first value in the string;

[0336] c2 means checking the second digit value in the string;

[0337] c3 means checking the third digit value in the string;

[0338] c h Indicates checking the hth bit value in the string;

[0339] ⊥ represents the pixel value operator. If B y,x,τ =c τ =1, or B y,x,τ =c τ =0, then equal to 1; if B y,x,τ =1 and c τ =0, or B y,x,τ =0, c τ ,1, then it is equal to 0;τ=1,2,3,……,h;

[0340] B y ' ,x =B y,x,1 B y,x,2 B y,x,3 …B y,x,h ,

[0341] G y ' ,x represents the binary blue pixel value;

[0342] c=c1c2c3…c h ,

[0343] c represents the check string; preferably all are 1.

[0344] The images corresponding to all actual pixel values ​​are the acquired remote sensing image data of the mountain city. Similarly, the actual pixel values ​​can be converted into stored pixel values ​​through steps S11 to S13.

[0345] In a preferred embodiment of the present invention, the method for performing image processing on the image sequence in step S4 comprises the following steps:

[0346] S41, performing image conversion on the image sequence;

[0347] S42, determining the construction land area according to the conversion value;

[0348] S43, generating an annual or monthly construction land area curve chart based on the construction land area.

[0349] In a preferred embodiment of the present invention, the method for performing image conversion on the image sequence in step S41 is:

[0350] f(x v ,y v )=λ r RED(x v ,y v )+λ g GREEN(x v ,y v )+λ b BLUE(x v ,y v ),

[0351] Among them, f(x v ,y v ) represents the vth sequence image at pixel point (x v ,y v ) at the conversion value; v∈S;

[0352] RED(x v ,y v ) represents the vth sequence image at pixel point (x v ,y v ) is the pixel value of the red channel at ;

[0353] GREEN(x v ,y v ) represents the vth sequence image at pixel point (x v ,y v ) is the pixel value of the green channel at ;

[0354] BLUE(x v ,y v ) represents the vth sequence image at pixel point (x v ,y v ) is the pixel value of the blue channel at ;

[0355] Constraints:

[0356] λ r , g, b ∈[0,1];

[0357] λ r +λ g +λ b =1;

[0358] x v =1, 2, 3, ..., X v ;X v Indicates the number of pixels in the x-axis direction of the v-th sequence image;

[0359] y v =1, 2, 3, ..., Y v ; Y v Indicates the number of pixels in the vth sequence image in the Y-axis direction.

[0360] In a preferred embodiment of the present invention, the method for determining the construction land area according to the conversion value in step S42 includes the following steps:

[0361] S421, if the conversion value is between the first preset conversion value and the second preset conversion value, and the second preset conversion value is greater than the first preset conversion value, then the conversion value is a construction land pixel point;

[0362] If the conversion value is not between the first preset conversion value and the second preset conversion value, and the second preset conversion value is greater than the first preset conversion value, then the conversion value is not a construction land pixel;

[0363] S422, counting the number of construction land pixel points, and calculating the construction land area according to the number of construction land pixel points. The calculation method of the construction land area is:

[0364] S a =Ds,

[0365] Among them, S a Indicates the construction land area;

[0366] D represents the number of pixels of construction land;

[0367] s represents the area occupied by a pixel.

[0368] In a preferred embodiment of the present invention, step S4 further includes logging into the mountainous city construction land display platform, and the method for logging into the mountainous city construction land display platform includes the following steps:

[0369] S4-1, determine whether the login virtual button is clicked:

[0370] If the login virtual button is clicked, proceed to the next step;

[0371] If the login virtual button is not clicked, execute step S4-1;

[0372] S4-2, obtain the 11-digit mobile phone number entered in the mobile phone number input box and the 6-digit verification code entered in the verification code input box, wherein the 11-digit mobile phone number is one of 0 to 9 or any combination of 11 digits, and the first digit cannot be 0, and the 6-digit verification code is one of 0 to 9, A to Z, a to z, or any combination of 6 digits;

[0373] S4-3, after obtaining the 11-digit mobile phone number entered in the mobile phone number input box and the 6-digit verification code entered in the verification code input box, the 11-digit mobile phone number and the 6-digit verification code are shifted respectively to obtain a mobile phone shift number and a verification shift code;

[0374] S4-4, shifting the 11-digit mobile phone number and the 6-digit verification code respectively to obtain the mobile phone shift number and the verification shift code, combining the mobile phone shift number and the verification shift code to obtain the mobile phone verification number; sending the mobile phone verification number to the mountainous city construction land display platform;

[0375] S4-5, after receiving the mobile phone verification number, the mountainous city construction land display platform separates the mobile phone verification number to obtain a mobile phone separation number and a verification separation number respectively;

[0376] S4-6, determine whether the verification separation number exists in the mountainous city construction land display platform:

[0377] If the verification separation number exists in the mountainous urban construction land display platform, extract the mobile phone storage number corresponding to the verification separation number and execute the next step;

[0378] If the verification separation number does not exist in the Mountain City Construction Land Display Platform, the login fails; the verification code entered is incorrect;

[0379] S4-7, process the mobile phone separation number to obtain the mobile phone processing number, and determine whether the mobile phone processing number is the same as the mobile phone storage number corresponding to the extracted verification separation number:

[0380] If the mobile phone processing number is the same as the mobile phone storage number corresponding to the extracted verification separation number, the login is successful;

[0381] If the mobile phone processing number is not the same as the mobile phone storage number corresponding to the extracted verification separation number, the login fails.

[0382] In a preferred embodiment of the present invention, in step S4-3, the 11-digit mobile phone number is shifted to obtain the mobile phone shift number as follows:

[0383] Determine whether the last digit of the mobile phone number is equal to 0:

[0384] If the last digit of the mobile phone number is equal to 0, then shift the mobile phone number by 10 digits from the back to the front; the mobile phone shift number can be obtained;

[0385] If the last digit of the mobile phone number is not equal to 0, the mobile phone number is shifted Q places from back to front to get the mobile phone shift number; Q is the last digit of the mobile phone number;

[0386] In step S4-3, the 6-bit verification code is shifted to obtain the verification shift code as follows:

[0387] Determine whether the first digit of the verification number is equal to 0, A to Z, or a to z:

[0388] If the first digit of the verification number is equal to one of 0, A-Z, a-z, then the verification number is shifted 10 digits from the back to the front; the verification shift number can be obtained;

[0389] If the first digit of the verification number is not equal to one of 0, A-Z, a-z, then the verification number is shifted by W digits from back to front to obtain the verification shift number; W is the first digit value of the verification number;

[0390] In a preferred embodiment of the present invention, in step S4-4, the method of combining the mobile phone shift number and the verification shift code to obtain the mobile phone verification number is:

[0391] Phonecom=Phonecode&Yzhengcode,

[0392] Among them, Phonecom represents the mobile phone verification number;

[0393] Phonecode indicates the mobile phone shift number;

[0394] & represents a combining character;

[0395] Yzhengcode means verification shift code;

[0396] In a preferred embodiment of the present invention, the method of separating the mobile phone verification number in step S4-5 to obtain the mobile phone separation number and the verification separation number is as follows:

[0397] Phonesepcode=Phonecom 11 ,

[0398] Among them, Phonesepcode represents the mobile phone separation number;

[0399] Phonecom 11 Indicates the first 11 digits of the mobile phone verification number;

[0400] Ysepcode=Phonecom6

[0401] Ysepcode indicates the verification separation number

[0402] Phonecom6 represents the last 6 digits of the mobile phone verification number.

[0403] In a preferred embodiment of the present invention, the method for processing the mobile phone separation number in step S4-7 to obtain the mobile phone processing number is:

[0404] Pcode=Phoneprocessway(Phonesepcode),

[0405] Among them, Pcode represents the mobile phone processing number;

[0406] Phoneprocessway() represents the method of generating the mobile phone processing number, preferably using the MD algorithm;

[0407] Phonesepcode indicates the phone sepcode number.

[0408] In a preferred embodiment of the present invention, the method for obtaining the 6-digit verification code in step S4-3 comprises the following steps:

[0409] S4-3-1, determine whether the verification code is clicked to obtain the virtual button:

[0410] If you click the verification code to get the virtual button, proceed to the next step;

[0411] If the verification code is not clicked to obtain the virtual key, step S4-3-1 is executed;

[0412] S4-3-2, obtain the 11-digit mobile phone number entered in the mobile phone number input box, where the 11-digit mobile phone number is one of 0 to 9 or any combination of 11 digits, and the first digit cannot be 0; send the mobile phone shift number to the mountain city construction land display platform;

[0413] S4-3-3, after receiving the mobile phone number, the mountainous urban construction land display platform performs shift processing on the mobile phone number to obtain the mobile phone shift number; processes the mobile phone shift number to obtain the mobile phone storage number; the method for processing the mobile phone shift number to obtain the mobile phone storage number is:

[0414] Pstocode=Phonestoway(Phonemovcode),

[0415] Among them, Pstocode represents the mobile phone storage number;

[0416] Phonestoway() represents the method for generating the mobile phone storage number, preferably using the MD algorithm;

[0417] Phonemovcode represents the mobile phone shift code.

[0418] S4-3-4, determine whether the mobile phone number exists in the mountainous city construction land display platform:

[0419] If the mobile phone processing number exists in the mountainous urban construction land display platform, a 6-digit verification code is randomly generated and sent to the received mobile phone number, and the next step is executed;

[0420] If the mobile phone number does not exist in the Mountain City Construction Land Display Platform, it will prompt that it is not registered. Please register first;

[0421] S4-3-5, determine whether the first digit of the verification number is equal to one of 0, A to Z, and a to z:

[0422] If the first digit of the verification number is equal to one of 0, A-Z, a-z, then the verification number is shifted 10 digits from the back to the front; the verification shift number can be obtained;

[0423] If the first digit of the verification number is not equal to one of 0, A-Z, a-z, then the verification number is shifted by W digits from back to front to obtain the verification shift number; W is the first digit value of the verification number;

[0424] The verification shift number and the mobile phone processing number form a storage pair and are stored in the mountainous urban construction land display platform; at this time, the mobile phone processing number in the storage pair is the mobile phone storage number.

[0425] In a preferred embodiment of the present invention, the display information further includes one or any combination of the urban construction land expansion speed, urban construction land expansion intensity, urban construction land fractal dimension, and urban construction land fragmentation;

[0426] The calculation method of urban construction land expansion rate is:

[0427]

[0428] Among them, S i is the growth rate of urban construction land in region i, ΔU ij The increase in urban construction land area in region i during period j;

[0429] ΔT is the time interval in years;

[0430] The calculation method of urban construction land expansion intensity is:

[0431]

[0432] Among them, I i The expansion intensity of urban construction land;

[0433] ΔUij The increase in urban construction land area in region i during period j;

[0434] ΔT is the time interval in years;

[0435] U i is the total area of ​​urban construction land in region i at the initial stage of the study;

[0436] The calculation method of the fractal dimension of urban construction land is:

[0437] D T =2ln(P T / 4) / lnA T ,

[0438] Among them, D T is the fractal dimension of urban construction land in a certain study area during period T; P T is the perimeter of the urban construction land patch in the study area during period T;

[0439] A T is the area of ​​urban construction land patches in the study area during period T;

[0440] The calculation method of urban construction land fragmentation is:

[0441] C T =N T / A T ,

[0442] Among them, C T is the landscape fragmentation index of the study area at time point T;

[0443] N T is the number of urban construction map spots at time T;

[0444] A T is the urban construction land area at time T;

[0445] C T The larger the value, the more fragmented the urban construction land plots in the region.

[0446] The present invention also discloses a computer system, comprising:

[0447] processor;

[0448] a memory for storing processor-executable instructions;

[0449] Wherein, the processor is configured to implement the method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities when executing the executable instructions.

[0450] The present invention also discloses a computer-readable storage medium, comprising:

[0451] a memory having a computer program stored thereon;

[0452] A processor is used to execute the program in the memory to implement the method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities.

[0453] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities, characterized in that: It includes the following steps: S1. Obtain the remote sensing image data of the mountain city; S2. Obtain the shooting time of the remote sensing image data of the mountain city in step S1 based on the remote sensing image data of the mountain city obtained in step S1; S3. Generate an image sequence of the remote sensing image data of the mountain city according to the shooting time of the remote sensing image data of the mountain city obtained in step S2; S4. Perform image processing on the image sequence, and display it on the construction land display platform of the mountain city after processing.

2. The method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities according to claim 1 is characterized in that: The method for generating an image sequence of the remote sensing image data of the mountain city according to the shooting time of the remote sensing image data of the mountain city obtained in step S2 in step S3 is as follows: S31. Count the number of the obtained remote sensing image data of the mountain city, denoted as K, where K represents the number of the remote sensing image data of the mountain city; let the serial number k = 1; S32, extracting the kth mountain city remote sensing image S k The time of shooting, remove the day, minute and second in the time, and keep the year and month; S33, if the k-th mountain city remote sensing image S k The shooting time is y year m month, then the k-th mountain city remote sensing image S k Put in the mountain city image dataset s y,m ; S34. Judge the relationship between k and K: If k ≥ K, then perform the next step; If k < K, then execute step S32; S35. Generate an image sequence in chronological order of year and month; that is: Among them, S represents the image sequence of the remote sensing image data of the mountain city; s y,m represents a mountain city image dataset taken by remote sensing satellites in year y and month m; y = y1, y2, y3, ..., y n , m=1, 2, 3,..., 12; That is, y1<y2<y3<……<y n ≤2024; Among them, s y,m It represents the mountain city image dataset taken by remote sensing satellites in month m of year y; s y,m,1 Represents the mountain city image dataset s y,m The first mountain city image in; s y,m,2 Represents the mountain city image dataset s y,m The 2nd mountain city image in; s y,m,3 Represents the mountain city image dataset s y,m The 3rd mountain city image in; Represents the mountain city image dataset s y,m The ψth y,m Mountain city image;ψ y,m Represents the mountain city image dataset s y,m The number of images of cities in the middle mountains; Or / and it also includes determining the expansion of the construction land of the mountain city according to the obtained topography or / and slope or / and neighborhood or / and traffic.

3. The method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities according to claim 1 is characterized in that: The method for performing image processing on the image sequence in step S4 includes the following steps: S41. Perform image conversion on the image sequence; S42. Judge the construction land area according to the conversion value; S43. Generate a curve graph of the construction land area per year or per month according to the construction land area.

4. The method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities according to claim 1 is characterized in that: The method for judging the construction land area according to the conversion value in step S42 includes the following steps: S421. If the conversion value is between the first preset conversion value and the second preset conversion value, and the second preset conversion value is greater than the first preset conversion value, then the conversion value is the construction land pixel point; If the conversion value is not between the first preset conversion value and the second preset conversion value, and the second preset conversion value is greater than the first preset conversion value, then the conversion value is not the construction land pixel point; S422. Count the number of construction land pixel points, and calculate the construction land area according to the number of construction land pixel points.

5. The method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities according to claim 1 is characterized in that: The calculation method of the construction land area in step S422 is: S a =Ds, Among them, S a Indicates the construction land area; D represents the number of construction land pixel points; s represents the area occupied by one pixel point.

6. The method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities according to claim 1 is characterized in that: The method for obtaining the remote sensing image data of the mountain city in step S1 includes the following steps: S11. Generate an image matrix of the remote sensing image data of the mountain city from the stored image; S12. Convert the pixel value of each pixel point into a binary pixel value. After converting the pixel value of each pixel point into a binary pixel value, if the number of digits of the binary pixel value is not equal to the number of digits of the image, then add 0 in front of the binary pixel value to make the number of digits after adding 0 equal to the number of digits of the image; S13. Perform an operation on the pixel value on each pixel point with the viewing string to obtain the actual pixel value; the method for performing an operation on the pixel value on each pixel point with the viewing string to obtain the actual pixel value is: R y ″ ,x =R y,x,1 R y,x,2 R y,x,3 …R y,x,h ⊥c1c2c3…c h , Among them, R y ″ ,x represents the actual binary red pixel value; R y,x,1 Represents the first bit value in the binary red pixel value; R y,x,2 Represents the second bit value in the binary red pixel value; R y,x,3 Indicates the third bit value in the binary red pixel value; R y,x,h Represents the hth bit value in the binary red pixel value; h represents the number of digits of the image; c1 represents the first digit value in the viewing string; c2 represents the second digit value in the viewing string; c3 represents the third digit value in the viewing string; c h Indicates checking the hth bit value in the string; ⊥ represents the pixel value operator. If R y,x,τ =c τ =1, or R y,x,τ =c τ =0, then equal to 1; if R y,x,τ =1 and c τ =0, or R y,x,τ =0, c τ ,1, then it is equal to 0;τ=1,2,3,……,h; R y ′ ,x =R y,x,1 R y,x,2 R y,x,3 …R y,x,h , R y ' ,x represents the binary red pixel value; c=c1c2c3…c h , c represents the viewing string.

7. The method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities according to claim 1 is characterized in that: Step S4 also includes logging into the mountainous city construction land display platform.

8. The method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities according to claim 1 is characterized in that: The displayed information also includes one or any combination of the expansion speed of urban construction land, the expansion intensity of urban construction land, the fractal dimension of urban construction land, and the fragmentation of urban construction land; The calculation method of urban construction land expansion rate is: Among them, S i is the growth rate of urban construction land in region i ΔU ij The increase in urban construction land area in region i during period j; ΔT is the time interval in years; The calculation method of urban construction land expansion intensity is: Among them, I i The expansion intensity of urban construction land; ΔU ij The increase in urban construction land area in region i during period j; ΔT is the time interval in years; U i is the total area of ​​urban construction land in region i at the initial stage of the study; The calculation method of the fractal dimension of urban construction land is: D T =2ln(P T / 4) / lnA T , Among them, D T is the fractal dimension of urban construction land in a certain study area during period T; P T is the perimeter of the urban construction land patch in the study area during period T; A T is the area of ​​urban construction land patches in the study area during period T; The calculation method of urban construction land fragmentation is: C T =N T / A T , Among them, C T is the landscape fragmentation index of the study area at time point T; N T is the number of urban construction map spots at time T; A T is the urban construction land area at time T; C T The larger the value, the more fragmented the urban construction land plots in the region.

9. A computer system, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities as described in one of claims 1 to 8 when executing the executable instructions.

10. A computer-readable storage medium, characterized in that: include: a memory having a computer program stored thereon; A processor is used to execute the program in the memory to implement the method for analyzing factors affecting the expansion characteristics of construction land in mountainous cities as described in any one of claims 1 to 8.

Images