Method for extracting abnormal land use change range by using multi-time-sequence cultivated land monitoring image

Abstract

The invention provides a method for extracting an abnormal land use change range by using a multi-time-sequence cultivated land monitoring image. The method comprises the following steps: S1, acquiring remote sensing satellite image data at the same position on different dates; s2, performing background processing on the remote sensing satellite image data to obtain a remote sensing satellite image processing image; s3, a cultivated land planting area in the remote sensing satellite image processing image is obtained, whether buildings exist in the cultivated land planting area or not is judged, and if the buildings exist in the cultivated land planting area, land use of the cultivated land planting area is abnormal; if no building exists in the cultivated land planting area, the land use of the cultivated land planting area is normal. According to the invention, whether the building exists in the cultivated land planting area can be identified efficiently, and the workload and cost of manual field investigation are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cultivated land monitoring data processing, and in particular to a method for extracting abnormal land use change ranges by using multi-time series cultivated land monitoring images. Background Art

[0002] Traditional farmland monitoring methods mainly rely on manual field surveys. Although manual field survey methods can obtain relatively accurate information, they have problems such as large workload, low efficiency, high cost and poor timeliness, making it difficult to meet the needs of large-scale, real-time monitoring. 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 for extracting the range of abnormal land use changes by using multi-time series farmland monitoring images.

[0004] In order to achieve the above-mentioned object of the present invention, the present invention provides a method for extracting the range of abnormal land use changes using multi-time series cultivated land monitoring images, comprising the following steps:

[0005] S1, obtain remote sensing satellite image data of the same location on different dates;

[0006] S2, performing background blurring processing on the remote sensing satellite image data to obtain a remote sensing satellite image processing image;

[0007] S3, obtain the cultivated land area in the remote sensing satellite image processing image, and determine whether there are buildings in the cultivated land area:

[0008] If there are buildings in the cultivated land planting area, the land use of the cultivated land planting area is abnormal;

[0009] If there are no buildings in the cultivated land planting area, the land use of the cultivated land planting area is normal.

[0010] In a preferred embodiment of the present invention, the method for obtaining the cultivated land planting area in step S3 includes the following steps:

[0011] S31, acquiring historical image data containing rice crops, calculating the grayscale pixel value variance and the grayscale pixel value average of the historical image data containing rice crops, and obtaining a moving frame determination threshold based on the grayscale pixel value variance and the grayscale pixel value average;

[0012] S32, setting a moving frame, performing a moving scan on the remote sensing satellite image processing image, and calculating a moving determination value of each pixel in the remote sensing satellite image processing image according to the moving frame;

[0013] S33, determining the cultivated land and non-cultivated land areas in the remote sensing satellite image processing image according to the relationship between the moving frame judgment threshold and the moving determination value:

[0014] If f0 ≥ d0, f0 is the moving frame judgment threshold, d0 is the moving determination value; then the cultivated land planting area in the remote sensing satellite image processing image;

[0015] If f0 ≥ d0, the non-cultivated land in the remote sensing satellite image processing image is the planting area.

[0016] In a preferred embodiment of the present invention, step S2 includes the following steps:

[0017] S21, divide the remote sensing satellite image data into K0 pixel groups of uniform size. The number of groups is calculated as follows:

[0018]

[0019] K0 represents the number of pixel groups into which the remote sensing satellite image data is divided;

[0020] O K is the horizontal pixel number of remote sensing satellite image data;

[0021] O G is the number of vertical pixels of remote sensing satellite image data;

[0022] O K A factor of , and That is O K Can be divisibility;

[0023] O G A factor of , and That is O G Can be divisibility;

[0024] S22, performing pixel group processing on each pixel group using an image pixel group processing algorithm to obtain a remote sensing satellite processed image;

[0025] S23, determining the background judgment range of the remote sensing satellite processed image;

[0026] S24, obtaining the grayscale pixel value of each pixel in the remote sensing satellite processed image, and determining the pixel area where the pixels belong to the background judgment range are located as the background area of the remote sensing satellite processed image;

[0027] S25, performing fuzzy processing on the background area of the remote sensing satellite processed image to obtain a remote sensing satellite image processed image.

[0028] In a preferred embodiment of the present invention, the method for obtaining the cultivated land planting area in step S3 includes the following steps:

[0029] S31, acquiring historical image data containing rice crops, calculating the grayscale pixel value variance and the grayscale pixel value average of the historical image data containing rice crops, and obtaining a moving frame determination threshold based on the grayscale pixel value variance and the grayscale pixel value average;

[0030] S32, setting a moving frame, performing a moving scan on the remote sensing satellite image processing image, and calculating a moving determination value of each pixel in the remote sensing satellite image processing image according to the moving frame;

[0031] S33, determining the cultivated land and non-cultivated land areas in the remote sensing satellite image processing image according to the relationship between the moving frame judgment threshold and the moving determination value:

[0032] If f0 ≥ d0, f0 is the moving frame judgment threshold, d0 is the moving determination value; then the cultivated land planting area in the remote sensing satellite image processing image;

[0033] If f0 ≥ d0, the non-cultivated land in the remote sensing satellite image processing image is the planting area.

[0034] In a preferred embodiment of the present invention, the method for determining whether there is a building in the cultivated land planting area in step S3 is:

[0035] S3-1, collecting the grayscale pixel values of the same pixel point in the cultivated land planting area on each date to form a pixel value judgment set;

[0036] S3-2, arrange the pixel values in each pixel value judgment set in chronological order, as follows:

[0037] S1, S2, S3, ..., S h ,

[0038] S1 is the pixel value of the same pixel on the first day;

[0039] S2 is the pixel value at the same pixel point on the second day;

[0040] S3 is the pixel value at the same pixel point on the third day;

[0041] S h is the pixel value of the hth day at the same pixel point; h is the number of days;

[0042] S3-3, determine S α With S β The relationship between:

[0043] If |Sα -S β |≤S0, α=1, 2, 3,..., h; β=1, 2, 3,..., h; α-β=1; S α is the pixel value at the same pixel point on the αth day; S β is the pixel value of the same pixel point on the βth day; S0 is the judgment threshold between different dates; then there is a building at the pixel point in the cultivated land planting area;

[0044] Otherwise, there is no building at the pixel point in the cultivated land area;

[0045] S3-4, scan all pixel points in the cultivated land planting area. If there are buildings on one or more pixel points in the cultivated land planting area, then there are buildings in the cultivated land planting area; if there are no buildings on all pixel points in the cultivated land planting area, then there are no buildings in the cultivated land planting area.

[0046] In a preferred embodiment of the present invention, it also includes using a mobile phone or an office computer to enter the abnormal land use status detection platform.

[0047] The present invention also discloses a computer system, comprising:

[0048] processor;

[0049] a memory for storing processor-executable instructions;

[0050] Wherein, the processor is configured to implement the method of extracting the range of abnormal land use changes using multi-time series farmland monitoring images when executing the executable instructions.

[0051] The present invention also discloses a computer-readable storage medium, comprising:

[0052] a memory having a computer program stored thereon;

[0053] A processor is used to execute the program in the memory to implement the method of extracting the range of abnormal land use changes using multi-time series farmland monitoring images.

[0054] In summary, due to the adoption of the above technical solution, the present invention can efficiently identify whether there are buildings in the cultivated land planting area, reducing the workload and cost of manual field investigations.

[0055] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments with reference to the following drawings, in which:

[0057] Figure 1 It is a schematic block diagram of the process of the present invention. DETAILED DESCRIPTION

[0058] The following describes embodiments of the present invention in detail. Examples of the embodiments 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 intended only to explain the present invention and are not to be construed as limiting the present invention.

[0059] The present invention discloses a method for extracting abnormal land use change range by using multi-time series cultivated land monitoring images, such as Figure 1 As shown, the following steps are included:

[0060] S1, obtain remote sensing satellite image data of the same location on different dates;

[0061] S2, performing background blurring processing on the remote sensing satellite image data to obtain a remote sensing satellite image processing image;

[0062] S3, obtain the cultivated land area in the remote sensing satellite image processing image, and determine whether there are buildings in the cultivated land area:

[0063] If there are buildings in the cultivated land planting area, the land use of the cultivated land planting area is abnormal;

[0064] If there are no buildings in the cultivated land planting area, the land use of the cultivated land planting area is normal.

[0065] In a preferred embodiment of the present invention, the method for obtaining the cultivated land planting area in step S3 includes the following steps:

[0066] S31, obtaining historical image data containing rice crops, calculating the grayscale pixel value variance and the grayscale pixel value average of the historical image data containing rice crops, and obtaining a moving frame judgment threshold according to the grayscale pixel value variance and the grayscale pixel value average:

[0067]

[0068] Among them, f0 is the moving frame judgment threshold;

[0069] is the average grayscale pixel value of historical image data;

[0070] δ0 is the grayscale pixel value variance of historical image data;

[0071] S32, setting a moving frame, performing a moving scan on the remote sensing satellite image processing image, and calculating a moving determination value of each pixel in the remote sensing satellite image processing image according to the moving frame;

[0072]

[0073] Among them, d0 is the movement determination value;

[0074] l0 is the moving step of the moving frame;

[0075] K0 is the number of horizontal pixels of the set moving frame;

[0076] G0 is the number of vertical pixels of the set moving frame;

[0077] O K is the horizontal pixel number of remote sensing satellite image data;

[0078] O G is the number of vertical pixels of remote sensing satellite image data;

[0079] p τ is the pixel value of the τth pixel in the remote sensing satellite image processing image;

[0080] p(0,0) is the pixel value of the center point of the remote sensing satellite image processing image;

[0081] S33, determining the cultivated land and non-cultivated land areas in the remote sensing satellite image processing image according to the relationship between the moving frame judgment threshold and the moving determination value:

[0082] If f0 ≥ d0, f0 is the moving frame judgment threshold, d0 is the moving determination value; then the cultivated land planting area in the remote sensing satellite image processing image;

[0083] If f0 ≥ d0, the non-cultivated land in the remote sensing satellite image processing image is the planting area.

[0084] In a preferred embodiment of the present invention, step S2 includes the following steps:

[0085] S21, divide the remote sensing satellite image data into K0 pixel groups of uniform size. The number of groups is calculated as follows:

[0086]

[0087] K0 represents the number of pixel groups into which the remote sensing satellite image data is divided;

[0088] O K is the horizontal pixel number of remote sensing satellite image data;

[0089] OG is the number of vertical pixels of remote sensing satellite image data;

[0090] O K A factor of , and That is O K Can be divisibility;

[0091] O G A factor of , and That is O G Can be divisibility;

[0092] S22, performing pixel group processing on each pixel group using an image pixel group processing algorithm to obtain a remote sensing satellite processed image; the calculation formula of the image pixel group processing algorithm is:

[0093]

[0094] Among them, K υ Represents the grayscale pixel value of the υth pixel group in the remote sensing satellite processed image;

[0095] Represents the grayscale pixel value of the υth pixel group in the remote sensing satellite image;

[0096] m υ Represents the mean grayscale pixel value of the υth pixel group in the remote sensing satellite image; p υ,i is the grayscale pixel value of the i-th pixel in the υ-th pixel group, p υ,1 is the grayscale pixel value of the first pixel in the υth pixel group, p υ,2 is the grayscale pixel value of the second pixel in the υth pixel group, p υ,3 is the grayscale pixel value of the 3rd pixel in the υth pixel group, is the first pixel in the υth pixel group Grayscale pixel value of the pixel;

[0097] K ε Indicates the group adjustment threshold coefficient, which can be 2 here;

[0098] K0 represents the number of pixel groups into which the remote sensing satellite image data is divided;

[0099] S23, determine the background judgment range of the remote sensing satellite processed image, the background judgment range of the remote sensing satellite processed image (p min ,p max ) is determined as follows:

[0100]

[0101] Among them, p min Indicates the minimum value on the left side of the background judgment range;

[0102] p max Indicates the maximum value on the right side of the background judgment range;

[0103] O represents the number of pixels in the image processed by the remote sensing satellite; O=O K *O G , O K is the number of horizontal pixels of remote sensing satellite image data; O G is the number of vertical pixels of remote sensing satellite image data;

[0104] p j Represents the pixel value in the grayscale pixel value set of the jth row in the remote sensing satellite processed image; j = 1, 2, 3, ..., O G ;

[0105] Represents the average value of all grayscale pixel values in the remote sensing satellite processed image; p ji is the grayscale pixel value of the j-th row and i-th column in the image;

[0106] p0 is the pixel value adjustment threshold coefficient, which is 2 here;

[0107] min{} means taking the minimum value function;

[0108] max{} represents the minimum value function;

[0109] S24, obtaining the grayscale pixel value of each pixel in the remote sensing satellite processed image, and determining the pixel area where the pixels belong to the background judgment range are located as the background area of the remote sensing satellite processed image;

[0110] S25, performing fuzzy processing on the background area of the remote sensing satellite processed image to obtain a remote sensing satellite image processed image.

[0111] In a preferred embodiment of the present invention, the method for determining whether there is a building in the cultivated land planting area in step S3 is:

[0112] S3-1, collecting the grayscale pixel values of the same pixel point in the cultivated land planting area on each date to form a pixel value judgment set;

[0113] S3-2, arrange the pixel values in each pixel value judgment set in chronological order, as follows:

[0114] S1, S2, S3, ..., S h ,

[0115] S1 is the pixel value of the same pixel on the first day;

[0116] S2 is the pixel value at the same pixel point on the second day;

[0117] S3 is the pixel value at the same pixel point on the third day;

[0118] S h is the pixel value of the hth day at the same pixel point; h is the number of days;

[0119] S3-3, determine S α With S β The relationship between:

[0120] If |S α -S β |≤S0, α=1, 2, 3,..., h; β=1, 2, 3,..., h; α-β=1; S α is the pixel value at the same pixel point on the αth day; S β is the pixel value of the same pixel point on the βth day; S0 is the judgment threshold between different dates; then there is a building at the pixel point in the cultivated land planting area;

[0121] Otherwise, there is no building at the pixel point in the cultivated land area;

[0122] S3-4, scan all pixel points in the cultivated land planting area. If there are buildings on one or more pixel points in the cultivated land planting area, then there are buildings in the cultivated land planting area; if there are no buildings on all pixel points in the cultivated land planting area, then there are no buildings in the cultivated land planting area.

[0123] In a preferred embodiment of the present invention, step S0 is further included before step S1, using a mobile phone or an office computer to enter the abnormal land use status detection platform; after using a mobile phone or an office computer to enter the abnormal land use status detection platform, the abnormal land use in the cultivated land planting area is checked.

[0124] In a preferred embodiment of the present invention, in step S0, the method of using a mobile phone or an office computer to access the abnormal land use status detection platform includes the following steps:

[0125] S01: Enter your account number and password on the login interface of the Abnormal Land Use Status Detection Platform. Both the account number and the password must be at least 6 digits long.

[0126] S02, after entering the account number and password on the abnormal land use status detection platform login interface, the input account number and password are securely encrypted;

[0127] S03, after securely encrypting the input account number and password, the securely encrypted account number and password are sent to the abnormal land use status detection platform verification platform;

[0128] S04. After the abnormal land use status detection platform verification platform receives the securely encrypted account and password, it decrypts the received securely encrypted account and password; it can also use the public key held by the login end to securely encrypt the input account and password; correspondingly, the abnormal land use status detection platform verification platform uses the private key it holds to decrypt the received securely encrypted account and password, where the private key is held by the platform and the public key is held by the login end, and the private key and public key are a pair of asymmetric encryption and decryption keys.

[0129] S05, if the decrypted account number and password both exist in the abnormal land use status detection platform verification platform, then enter the abnormal land use status detection platform;

[0130] Otherwise, the account and password entered in the abnormal land use status detection platform login interface are incorrect, and you need to re-enter the correct account and password.

[0131] In a preferred embodiment of the present invention, the method for securely encrypting the input account number and password in step S02 includes the following steps:

[0132] S021, obtain the current time and convert it into a standard ten-digit format. The current time includes year, month, day, and hour. For example, if the obtained time is 17:00 on March 26, 2025, the standard ten-digit format is 2025032617. If the obtained time is 21:00 on April 16, 2024, the standard ten-digit format is 2024041621.

[0133] S022, determine the relationship between the number of digits of the input account number and password and 10:

[0134] S0221, if the number of digits in the account number is less than 10, remove the leading e1 digits from the tens-digit standard time, until the digits after removal equal the number of digits in the account number; then proceed to the next step; e1 = 10 - e0, where e0 is the number of digits in the account number and e1 is the number of digits removed from the time;

[0135] If the number of digits in the account number is greater than 10, then add digits e2 and e3 to the front of the tens-digit standard time, and the added digits are equal to the number of digits in the account number; then proceed to the next step; e2 = e0 - 10, where e0 is the number of digits in the account number, e2 is the number of digits in the added time, and e3 is the last digit in the tens-digit standard format;

[0136] If the number of digits in the account number is equal to 10, proceed to the next step;

[0137] S0222, if the number of digits in the password is less than 10, remove the f1 digits in front of the ten-digit standard time, and the number after removal is equal to the number of digits in the password; then proceed to the next step; f1 = 10 - f0, where f0 is the number of digits in the password and f1 is the number of digits in the time removed;

[0138] If the number of digits in the password is greater than 10, then add f2 and f3 to the front of the ten-digit standard format time, and the added value is equal to the number of digits in the password; then proceed to the next step; f2 = f0-10, where f0 is the number of digits in the password, f2 is the number of digits in the added time, and f3 is the last digit in the ten-digit standard format;

[0139] If the number of digits in the password is 10, proceed to the next step. For example, if the account number and password are Zhanghao123 and miMa3211, and the time in the standard format of tens digits is 2025050515, then the times after processing are 52025050515 and 25050515 respectively. If the account number and password are Zhanghao12332 and miMa3211222, and the time in the standard format of tens digits is 2025050515, then the times after processing are 5552025050515 and 552025050515 respectively.

[0140] The account and password remain unchanged, but the time corresponding to the account and password may change;

[0141] S023, shift the first digit of the account number and password backward by f4 digits, f4 = floor(f3 / 2), where f4 is the number of digits to be shifted backward. For example, if the account number and password are Zhang1hao3 and mi2Ma2, respectively, and the time in the tens-digit standard format is 2025060715, then the processed account number and password are haZng1hao3 and i2mMa2, respectively. If the account number and password are Zhang1hao3 and mi2Ma2, respectively, and the time in the tens-digit standard format is 2025060716, then the processed account number and password are hanZg1hao3 and i2Mma2, respectively.

[0142] S024, convert each character in the account number, password, and time into 7-bit binary ASCII form, calculate the account number in ASCII form with the time in ASCII form, and calculate the password in ASCII form with the time in ASCII form to obtain the encrypted account number and encrypted password respectively.

[0143]

[0144] g′2(φ) represents the value of the φth digit in the ASCII encrypted account number;

[0145] g2(φ) represents the value of the φth digit in the ASCII account number;

[0146] t2(φ) represents the value of the φth digit in the ASCII format time;

[0147] φ=1, 2, 3, ..., φ1; φ1 is the number of digits of the account number in ASCII format;

[0148] φ1=7φ2, φ2 is the number of digits in the account number;

[0149] ◇ represents the exchange value operator;

[0150]

[0151] v′2(θ) represents the value of the θth bit in the ASCII encrypted password;

[0152] v2(θ) represents the value of the θth bit in the ASCII password;

[0153] t′2(θ) represents the value of the θth digit in the ASCII form of time;

[0154] θ=1, 2, 3, ..., θ1; θ1 is the number of digits of the ASCII password;

[0155] θ1=7θ2, θ2 is the number of digits in the password;

[0156] Convert the ASCII encrypted account and ASCII encrypted password into character encrypted account and encrypted password.

[0157] For example, if the account and password entered are ZHangHao12234 and MIma912 respectively, the obtained time is 23:00 on January 22, 2025;

[0158] In step S021, the time in the tens-digit standard format is 2025012223;

[0159] In step S022, the account number is ZHangHao12234, and the time corresponding to the account number ZHangHao12234 is 3332025012223;

[0160] The password is MIma912, and the time corresponding to the password MIma912 is 5012223;

[0161] In step S023, the account number is HanZgHao12234, and the time corresponding to the account number HanZgHao12234 is 3332025012223;

[0162] The password is ImaM912, and the time corresponding to the password ImaM912 is 5012223;

[0163] In step S024, the ASCII format of the account number HanZgHao12234 is 1001000110000111011101011010110011110010001100001110111101100010110010011001100110110100, and the ASCII format of the time corresponding to the account number HanZgHao12234, 3332025012223, is 01100110110011011001 1011001001100000110010011010101100000110001011001001100100110010011; the encrypted account number in ASCII form is 111101110100101011101110100010101011111101010101010101011111000000000000000000000000000000010000111, and the corresponding encrypted account number is {R]hWzT_NUT NUT NUTSOH BEL, see Table 1 for details.

[0164] Table 1 Account and time and the ASCII code corresponding to each character in the encrypted account

[0165]

[0166]

[0167] The account number in ASCII form of the password ImaM912 is 1001001110110111000011001101011100101100010110010, and the time 5012223 corresponding to the password ImaM912 is 0110101011000001100010110010011001001100110011; the encrypted password in ASCII form is 111110010111011010000111111000101100000110000001, and the corresponding encrypted password is |]PDEL VT ETX SOH, see Table 2 for details.

[0168] Table 2 Password and time and ASCII code corresponding to each character in the encrypted password

[0169] character ASCII character ASCII character ASCII I 1001001 5 0110101 | 1111100 m 1101101 0 0110000 ] 1011101 a 1100001 1 0110001 P 1010000 M 1001101 2 0110010 DEL 1111111 9 0111001 2 0110010 VT 0001011 1 0110001 2 0110010 ETX 0000011 2 0110010 3 0110011 SOH 0000001

[0170] In a preferred embodiment of the present invention, the method for decrypting the received securely encrypted account and password in step S04 includes the following steps:

[0171] S041, obtain the current time and convert it into a standard decimal format;

[0172] S042, determine the relationship between the number of digits of the received encrypted account number and the encrypted password and 10:

[0173] S0221, if the number of digits in the encrypted account number is less than 10, remove the leading e1′ digits from the ten-digit standard time format until the digits after removal equal the number of digits in the encrypted account number; then proceed to the next step; e1′ = 10-e′0, where e′0 is the number of digits in the encrypted account number and e1′ is the number of digits removed from the time;

[0174] If the number of digits in the encrypted account number is greater than 10, then add e'2 digits e3' to the front of the time in the ten-digit standard format, and the added digits equal to the number of digits in the encrypted account number; then proceed to the next step; e'2 = e'0 - 10, where e'0 is the number of digits in the encrypted account number, e'2 is the number of digits in the added time, and e3' is the last digit in the ten-digit standard format;

[0175] If the number of digits in the encrypted account is equal to 10, proceed to the next step;

[0176] S0222, if the number of digits in the encrypted password is less than 10, remove the leading f1′ digits from the ten-digit standard format time until the number after removal equals the number of digits in the encrypted password; then proceed to the next step; f1′ = 10-f0′, where f0′ is the number of digits in the encrypted password and f1′ is the number of digits in the removed time;

[0177] If the number of digits in the encrypted password is greater than 10, then add f2′ and f3′ to the front of the time in the ten-digit standard format, and the added value is equal to the number of digits in the encrypted password; then proceed to the next step; f2′=f0′-10, where f0′ is the number of digits in the encrypted password, f2′ is the number of digits in the added time, and f3′ is the last digit in the ten-digit standard format;

[0178] If the number of bits in the encrypted password is equal to 10, proceed to the next step;

[0179] The encryption account and encryption password remain unchanged, but the time corresponding to the encryption account and encryption password may change;

[0180] S043, shift the first f4′ bits of the encrypted account number and the encrypted password backward by one bit, f4′=floor(f3′ / 2), where f4′ is the number of bits as a whole;

[0181] S044, converting each character in the encrypted account, encrypted password, and time into 7-bit binary ASCII form, calculating the encrypted account in ASCII form with the time in ASCII form, and calculating the encrypted password in ASCII form with the time in ASCII form, to obtain the decrypted account and decrypted password respectively.

[0182]

[0183] g′2″(φ′) represents the value of the φ′th digit in the decrypted account number in ASCII form;

[0184] g′2′(φ′) represents the value of the φ′th digit in the ASCII encrypted account number;

[0185] t2″(φ′) represents the value of the φ′th digit in the ASCII format time;

[0186] φ′=1, 2, 3, ..., φ1′; φ1′ is the number of digits of the encrypted account number in ASCII form;

[0187] φ1′=7φ2′, φ2′ is the number of digits of the encrypted account number;

[0188] ◇ represents the exchange value operator;

[0189]

[0190] v″′2(θ′) represents the value of the θ′th digit in the ASCII decrypted password;

[0191] v″2(θ′) represents the value of the θ′th digit in the ASCII encrypted password;

[0192] t″2(θ′) represents the value of the θ′th digit in the ASCII form of the time;

[0193] θ′=1, 2, 3, ..., θ′1; θ′1 is the number of bits of the ASCII encrypted password;

[0194] θ′1=7θ′2, θ′2 is the number of bits of the encryption code;

[0195] Convert the decrypted account number and decrypted password in ASCII format into the decrypted account number and decrypted password in character format.

[0196] In step S1, the source of remote sensing satellite image data is:

[0197] S11, acquiring remote sensing satellite image video data taken by a remote sensing satellite, and downloading the remote sensing satellite image video data taken by the remote sensing satellite to an abnormal land use status detection platform;

[0198] S12, after receiving the remote sensing satellite image video data, the abnormal land use status detection platform converts the remote sensing satellite image video data into remote sensing satellite image image data.

[0199] In a preferred embodiment of the present invention, step S11 includes the following steps:

[0200] S111, determining the size of the remote sensing satellite image video data to be downloaded to the abnormal land use status detection platform, and dividing the remote sensing satellite image video data to be downloaded into U0 copies, which are respectively the first remote sensing satellite image video data to be downloaded, the second remote sensing satellite image video data to be downloaded, the third remote sensing satellite image video data to be downloaded, ..., and the U0th remote sensing satellite image video data to be downloaded in a playback order from front to back; wherein the calculation formula of the number of remote sensing satellite image video data copies U0 is:

[0201]

[0202] U0 is the number of copies of remote sensing satellite image video data;

[0203] floor() represents the rounding function;

[0204] datasize() is the data size function;

[0205] spdata is the remote sensing satellite image video data to be downloaded;

[0206] Datasize0 is the size of each remote sensing satellite image video data;

[0207] {N *} is a set of integers;

[0208] S112, obtaining video parameters of each remote sensing satellite image video data to be downloaded, the video parameters including one or any combination of resolution Res, frame rate FPS, duration Tim′, video format Cod, and color depth Col (number of pixel bits);

[0209] The video format Cod includes a video encoding format and a video encapsulation format. The video encoding format includes one of H.264, H.265, VP9, and AV1. The video encapsulation format includes one of MP4, MKV, MOV, AVI, and FLV.

[0210] S113: Obtain the number of remote sensing satellite image data to be downloaded based on the acquired duration Tim′ video parameter of each remote sensing satellite image video data to be downloaded. The method for obtaining the number of remote sensing satellite image data to be downloaded based on the acquired duration Tim′ video parameter of each remote sensing satellite image video data to be downloaded is as follows:

[0211] Qua′=Tim′*FPS,

[0212] Wherein, Qua′ is the number of each remote sensing satellite image data to be downloaded;

[0213] Tim′ is the duration Tim′ of each remote sensing satellite image video data to be downloaded;

[0214] FPS is the frame rate FPS of remote sensing satellite image video data;

[0215] The resolution Res of each remote sensing satellite image data to be downloaded is consistent with the resolution Res video parameter of each remote sensing satellite image video data to be downloaded; through the above method, the duration Tim′ of each remote sensing satellite image video data to be downloaded can be converted into Qua′ remote sensing satellite images to be downloaded;

[0216] S114, calculating the image 0-1 digital string of the remote sensing satellite image video data to be downloaded in step S111, the calculation method of the image 0-1 digital string of the remote sensing satellite image video data to be downloaded is:

[0217] Imstr 0-1 =MD5Imstr 0-1 (spingdata),

[0218] Imstr 0-1 It is a 0-1 digital string of images, and its length is 128 bits;

[0219] MD5Imstr 0-1 () is the calculation function of the image 0-1 digital string, preferably using the MD5 digest function;

[0220] spingdata is the remote sensing satellite image video data to be downloaded;

[0221] S115, cyclically calculating the image 0-1 digital string and the pixel value of each channel in each remote sensing satellite image to be downloaded, to obtain a new remote sensing satellite image to be downloaded:

[0222]

[0223] Among them, R(μ,ν) New is the new value of the νth pixel corresponding to the μth pixel in the red channel from left to right and from top to bottom;

[0224] R(μ,ν) is the ν-th bit value in the pixel value corresponding to the μ-th pixel in the red color channel from left to right and from top to bottom. Here, the pixel value of each pixel is a binary pixel value, and the number of bits of the pixel value is equal to the color depth Col.

[0225] Imstr 0-1 New (γ) is the value of the γth bit in the new image 0-1 digital string;

[0226] Will The image 0-1 digital string is connected front and back to get a new image 0-1 digital string:

[0227]

[0228] Imstr 0-1 New New image 0-1 digital string;

[0229]

[0230] in, Indicates the number of 0-1 digital strings in the image;

[0231] is the color depth Col;

[0232] floor() represents the rounding function;

[0233] O K is the horizontal pixel number of remote sensing satellite image data;

[0234] O G is the number of vertical pixels of remote sensing satellite image data;

[0235]

[0236] Among them, G(μ,ν) New is the new value of the nth pixel corresponding to the μth pixel in the green channel from left to right and from top to bottom;

[0237] G(μ,ν) is the ν-th pixel value corresponding to the μ-th pixel in the green color channel from left to right and from top to bottom;

[0238] Imstr 0-1 New (γ) is the value of the γth bit in the new image 0-1 digital string;

[0239] The relationship between γ, μ and ν is:

[0240]

[0241] Among them, γ is the position of the new image 0-1 digital string from left to right;

[0242] μ is the pixel position from left to right and from top to bottom in the red / green / blue color channel; μ = 1, 2, 3, ..., O K *O G ;

[0243] is the color depth Col;

[0244] ν is the position of the pixel value from left to right,

[0245]

[0246] Among them, B(μ,ν) New is the new value of the νth pixel corresponding to the μth pixel in the blue color channel from left to right and from top to bottom;

[0247] B(μ,ν) is the ν-th pixel value corresponding to the μ-th pixel in the blue color channel from left to right and from top to bottom;

[0248] Imstr 0-1 New (γ) is the value of the γth bit in the new image 0-1 digital string;

[0249] Through the operation of step S115, a new remote sensing satellite image to be downloaded can be obtained at this time. All Qua' remote sensing satellite images to be downloaded are subjected to the operation of step S115 to obtain all new Qua' remote sensing satellite images to be downloaded;

[0250] S116: All new remote sensing satellite images to be downloaded are combined into new remote sensing satellite image video data to be downloaded in the order in which they were played. By performing step S116, a new set of remote sensing satellite image video data to be downloaded can be obtained. U0 new sets of remote sensing satellite image video data to be downloaded can be obtained by simply performing steps S115 and S116.

[0251] S117, transmit the image 0-1 digital string and U0 copies of new remote sensing satellite image video data to be downloaded to the abnormal land use status detection platform.

[0252] In a preferred embodiment of the present invention, after the abnormal land use status detection platform receives the image 0-1 digital string and U0 copies of downloaded remote sensing satellite image video data in step S12, the following steps are included:

[0253] S121, the abnormal land use status detection platform obtains the number of downloaded remote sensing satellite image data based on the received duration Tim′ video parameter of each downloaded remote sensing satellite image video data. The method for obtaining the number of downloaded remote sensing satellite image data based on the received duration Tim″ video parameter of each to-be-downloaded remote sensing satellite image video data is as follows:

[0254] Qua″=Tim″*FPS,

[0255] Wherein, Qua″ is the number of remote sensing satellite image data to be downloaded;

[0256] Tim″ is the duration Tim″ of each downloaded remote sensing satellite image video data;

[0257] FPS is the frame rate FPS of remote sensing satellite image video data;

[0258] The resolution Res of each remote sensing satellite image data is consistent with the resolution Res video parameter of each downloaded remote sensing satellite image video data; through the above method, the duration Tim′ of each downloaded remote sensing satellite image video data can be converted into Qua′ downloaded remote sensing satellite images;

[0259] S122, cyclically calculate the received image 0-1 digital string and the pixel value of each channel in each downloaded remote sensing satellite image to obtain the cultivated land platform remote sensing satellite image:

[0260]

[0261] Among them, R′(μ′,ν′) New is the new value of the ν′th platform of the pixel value corresponding to the μ′th pixel from left to right and from top to bottom in the red color channel;

[0262] R′(μ′,ν′) is the ν′th platform value in the pixel value corresponding to the μ′th pixel from left to right and from top to bottom in the red color channel. Here, the pixel value of each pixel is a binary pixel value, and the number of bits of the pixel value is equal to the color depth Col.

[0263] Im′str 0-1 New (γ′) is the value of the γ′th position in the new platform image 0-1 digital string;

[0264] Will The received image 0-1 digital strings are connected front to back to get the new platform image 0-1 digital string:

[0265]

[0266] Im′str 0-1 New New platform image 0-1 digital string;

[0267]

[0268] in, Indicates the number of connected 0-1 digital strings of the received image;

[0269] is the color depth Col;

[0270] floor() represents the rounding function;

[0271] O K is the horizontal pixel number of remote sensing satellite image data;

[0272] O G is the number of vertical pixels of remote sensing satellite image data;

[0273]

[0274] Among them, G′(μ′,ν′) New is the new value of the ν′th platform of the pixel value corresponding to the μ′th pixel from left to right and from top to bottom in the green color channel;

[0275] G′(μ′,ν′) is the ν′th platform value of the pixel value corresponding to the μ′th pixel from left to right and from top to bottom in the green color channel;

[0276] Im′str 0-1 New (γ′) is the value of the γ′th position in the new platform image 0-1 digital string;

[0277] The relationship between γ′, μ′ and ν′ is:

[0278]

[0279] Where γ′ is the position of the new platform image 0-1 digital string from left to right;

[0280] μ′ is the platform pixel position from left to right and from top to bottom in the red / green / blue color channel; μ′=1, 2, 3, ..., O K *O G ;

[0281] is the color depth Col;

[0282] ν′ is the position of the platform pixel value from left to right,

[0283]

[0284] Among them, B′(μ′,ν′) New is the new value of the ν′th platform of the pixel value corresponding to the μ′th pixel from left to right and from top to bottom in the blue color channel;

[0285] B′(μ′,ν′) is the ν′th platform value of the pixel value corresponding to the μ′th pixel from left to right and from top to bottom in the blue color channel;

[0286] Im′str 0-1 New (γ′) is the value of the γ′th position in the new platform image 0-1 digital string;

[0287] Through the operation of step S122, a new downloaded remote sensing satellite image can be obtained at this time. All Quad "pieces of downloaded remote sensing satellite image images are subjected to the operation of step S122 to obtain all new Qua "pieces of downloaded remote sensing satellite image images;

[0288] S123, all newly downloaded remote sensing satellite image images are combined into new remote sensing satellite image video data to be downloaded in the order in which they were played. By operating in step S123, a new set of downloaded remote sensing satellite image video data can be obtained; U0 new sets of downloaded remote sensing satellite image video data can be obtained by simply operating in steps S122 and S123;

[0289] S124: compose the new entire downloaded remote sensing satellite image video data by combining the U0 copies of the newly downloaded remote sensing satellite image video data in the order of playback.

[0290] In a preferred embodiment of the present invention, step S125 is included to calculate the new image 0-1 digital string of the entire downloaded remote sensing satellite image video data in step S124. The calculation method of the new image 0-1 digital string of the entire downloaded remote sensing satellite image video data is:

[0291] Im″str 0-1 =MD5Imstr 0-1 (spdata′),

[0292] Im″str 0-1 It is the image 0-1 digital string of the newly downloaded remote sensing satellite image video data, and its length is 128 bits;

[0293] MD5Imstr 0-1 () is the calculation function of the image 0-1 digital string, preferably using the MD5 digest function;

[0294] spdata′ is the newly downloaded remote sensing satellite image video data;

[0295] If the image 0-1 digital string of the new entire downloaded remote sensing satellite image video data is consistent with the image 0-1 digital string received in step S121, then the new entire downloaded remote sensing satellite image video data is the remote sensing satellite image video data obtained in step S11; otherwise, the new entire downloaded remote sensing satellite image video data is not the remote sensing satellite image video data obtained in step S11.

[0296] In a preferred embodiment of the present invention, in step S12, converting the remote sensing satellite image video data into remote sensing satellite image data includes the following steps:

[0297] S121, obtaining video parameters of remote sensing satellite image video data, where the video parameters include one or any combination of resolution Res, frame rate FPS, duration Tim, video format Cod, and color depth Col (number of pixel bits);

[0298] The video format Cod includes a video encoding format and a video encapsulation format. The video encoding format includes one of H.264, H.265, VP9, and AV1. The video encapsulation format includes one of MP4, MKV, MOV, AVI, and FLV.

[0299] S122: Obtain the amount of remote sensing satellite image data according to the acquired duration Tim video parameter of the remote sensing satellite image video data. The method for obtaining the amount of remote sensing satellite image data according to the acquired duration Tim video parameter of the remote sensing satellite image video data is as follows:

[0300] Qua=Tim*FPS,

[0301] Wherein, Qua is the number of remote sensing satellite image data;

[0302] Tim is the duration of remote sensing satellite image video data;

[0303] FPS is the frame rate FPS of remote sensing satellite image video data;

[0304] The resolution Res of each remote sensing satellite image data is consistent with the resolution Res video parameter of the remote sensing satellite image video data.

[0305] The present invention also discloses a computer system, comprising:

[0306] processor;

[0307] a memory for storing processor-executable instructions;

[0308] Wherein, the processor is configured to implement the method of extracting the range of abnormal land use changes using multi-time series farmland monitoring images when executing the executable instructions.

[0309] The present invention also discloses a computer-readable storage medium, comprising:

[0310] a memory having a computer program stored thereon;

[0311] A processor is used to execute the program in the memory to implement the method of extracting the range of abnormal land use changes using multi-time series farmland monitoring images.

[0312] While 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 invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A method for extracting abnormal land use change range using multi-time series cultivated land monitoring images, characterized in that: The following steps are involved: S1, obtain remote sensing satellite image data of the same location on different dates; S2, performing background processing on the remote sensing satellite image data to obtain a remote sensing satellite image processing image; S3, obtain the cultivated land area in the remote sensing satellite image processing image, and determine whether there are buildings in the cultivated land area: If there are buildings in the cultivated land planting area, the land use of the cultivated land planting area is abnormal; If there are no buildings in the cultivated land planting area, the land use of the cultivated land planting area is normal.

2. The method for extracting abnormal land use change range using multi-time series cultivated land monitoring images according to claim 1, characterized in that: The method for obtaining the cultivated land planting area in step S3 includes the following steps: S31, acquiring historical image data containing rice crops, calculating the grayscale pixel value variance and the grayscale pixel value average of the historical image data containing rice crops, and obtaining a moving frame determination threshold based on the grayscale pixel value variance and the grayscale pixel value average; S32, setting a moving frame, performing a moving scan on the remote sensing satellite image processing image, and calculating a moving determination value of each pixel in the remote sensing satellite image processing image according to the moving frame; S33, determining the cultivated land and non-cultivated land areas in the remote sensing satellite image processing image according to the relationship between the moving frame judgment threshold and the moving determination value: If f0 ≥ d0, f0 is the moving frame judgment threshold, d0 is the moving determination value; then the cultivated land planting area in the remote sensing satellite image processing image; If f0 ≥ d0, the non-cultivated land in the remote sensing satellite image processing image is the planting area.

3. The method for extracting abnormal land use change range using multi-time series cultivated land monitoring images according to claim 1, characterized in that: Step S2 includes the following steps: S21, divide the remote sensing satellite image data into K0 pixel groups of uniform size. The number of groups is calculated as follows: K0 represents the number of pixel groups into which the remote sensing satellite image data is divided; O K is the horizontal pixel number of remote sensing satellite image data; O G is the number of vertical pixels of remote sensing satellite image data; O K A factor of , and That is O K Can be divisibility; O G A factor of , and That is O G Can be divisibility; S22, performing pixel group processing on each pixel group using an image pixel group processing algorithm to obtain a remote sensing satellite processed image; S23, determining the background judgment range of the remote sensing satellite processed image; S24, obtaining the grayscale pixel value of each pixel in the remote sensing satellite processed image, and determining the pixel area where the pixels belong to the background judgment range are located as the background area of the remote sensing satellite processed image; S25, performing fuzzy processing on the background area of the remote sensing satellite processed image to obtain a remote sensing satellite image processed image.

4. The method for extracting abnormal land use change range using multi-time series cultivated land monitoring images according to claim 1, characterized in that: In step S3, the method for determining whether there is a building in the cultivated land planting area is: S3-1, collecting the grayscale pixel values of the same pixel point in the cultivated land planting area on each date to form a pixel value judgment set; S3-2, arrange the pixel values in each pixel value judgment set in chronological order according to date, as follows: <h2 style=";text-align:left;direction:ltr">S1, S2, S3,……, S<h2 style=";text-align:left;direction:ltr"> h <h2 style=";text-align:left;direction:ltr"> , S1 is the pixel value of the same pixel on the first day; S2 is the pixel value at the same pixel point on the second day; S3 is the pixel value at the same pixel point on the third day; S h is the pixel value of the hth day at the same pixel point; h is the number of days; S3-3, determine S α With S β The relationship between: If |S α -S β |≤S0, α=1, 2, 3,..., h; β=1, 2, 3,..., h; α-β=1; S α is the pixel value at the same pixel point on the αth day; S β is the pixel value of the same pixel point on the βth day; S0 is the judgment threshold between different dates; then there is a building at the pixel point in the cultivated land planting area; Otherwise, there is no building at the pixel point in the cultivated land area; S3-4, scanning all pixel points in the cultivated land planting area. If there is a building at one or more pixel points in the cultivated land planting area, then there is a building in the cultivated land planting area. If there are no buildings at all pixel points in the cultivated land planting area, then there are no buildings in the cultivated land planting area.

5. The method for extracting abnormal land use change range using multi-time series cultivated land monitoring images according to claim 1, characterized in that: It also includes using a mobile phone or office computer to access the abnormal land use status detection platform.

6. A computer system, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method for extracting the range of abnormal land use changes using multi-time series farmland monitoring images as described in one of claims 1 to 5 when executing the executable instructions.

7. 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 of extracting the range of abnormal land use changes using multi-time series farmland monitoring images as described in any one of claims 1 to 5.

Images