A monitoring method and system for illegal land use of natural resources

Abstract

The present invention discloses a method and system for monitoring illegal land use of natural resources, which relates to the technical field of image processing. The remote sensing image information of the target plot is collected to obtain the texture image of the target plot. The sparsity index and clarity index of the texture in the texture image are obtained, and the texture evaluation index of the texture image is constructed. According to the chronological order, the remote sensing image information of several target plots is collected, and the texture evaluation index of the texture graph is calculated respectively. Through the time series model, the law of the change of the texture evaluation index over time is obtained. The detection of abnormal land use of a certain target plot is taken as the first target detection, the time interval between the first target detection and the second target detection is obtained, the texture evaluation index of the image at the second target detection is predicted, and when the texture evaluation index in the image corresponding to the second target detection is different from the predicted value, an alarm prompt is sent to the relevant management personnel.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and specifically to a method and system for monitoring illegal use of natural resources land. Background Art

[0002] Land resources are one of the important resources. In the traditional process of identifying abnormal land use, relevant management personnel need to patrol the land parcels. This method not only requires a lot of physical labor from the relevant management personnel, but also has low timeliness. With the development of information transmission and image analysis technologies, the image information of relevant land parcels is gradually collected and analyzed to obtain whether the relevant land parcels are abnormally occupied.

[0003] In the relevant methods of image comparison, it is necessary to compare the differences between the currently collected image and the images stored in history to obtain whether there are abnormalities in the currently collected image. In terms of land resource monitoring, due to the wide monitoring scope and long monitoring time, a large amount of image data will be generated during the monitoring process of a certain land parcel. If the currently collected image is compared with the images in the historical data one by one, first, a large amount of storage space is required to store the historical data, and second, a large amount of computing resources are required for image comparison, and thus the abnormal land use conditions cannot be effectively feedback. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for monitoring illegal use of natural resources land to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] Step S100: Take a land parcel under abnormal land use monitoring as a target land parcel, collect the remote sensing image information of the target land parcel, obtain the image texture area of the image information, and obtain the texture image of the target land parcel;

[0007] Step S200: Obtain the sparsity index and clarity index of the texture in the texture image, and construct the texture evaluation index of the texture image;

[0008] Step S300: When there is no abnormality in the target land parcel, according to the chronological order, collect the remote sensing image information of several target land parcels, calculate the texture evaluation index of the texture graph respectively, and obtain the law of change of the texture evaluation index over time through a time series model;

[0009] Step S400: Take the abnormal land use detection of a certain target plot as the first target detection, take the corresponding image during the first target detection as the first target image, obtain the time interval between the first target detection and the second target detection, predict the texture evaluation index of the image during the second target detection, and when the texture evaluation index in the image corresponding to the second target detection is different from the predicted value, give an alarm prompt to the relevant management personnel.

[0010] Further, step S100 includes:

[0011] Step S101: Collect the remote sensing image information of any target plot, convert the remote sensing image into a grayscale image, and the quantization interval of the grayscale is [0, G], where G represents the maximum grayscale value in the grayscale image quantization interval;

[0012] Step S102: Perform rasterization processing on the grayscale image, divide the grayscale image into several unit grayscale images with the same shape and size, obtain the grayscale values of all unit grayscale images, and calculate the average value of the grayscale values of all unit grayscale images, denoted as Gra;

[0013] Step S103: Obtain the i-th unit grayscale image ui in all unit grayscale images, obtain the corresponding grayscale value G ui of ui, and calculate the grayscale judgment value D ui , Du i = G ui + α × Gra, where α represents the proportionality coefficient, and α ∈ (0, 2);

[0014] Screen the dark parts in the image, filter out the relatively bright parts from the image, and the remaining are the darker parts in the image, including the boundary parts in the image, and the boundary parts also represent the transition area information between each figure in the image;

[0015] Step S104: When D ui ≥ G, set the i-th unit grayscale image as the selected unit image, and gather all the selected unit images in the grayscale image to form the image set Re;

[0016] Step S105: Obtain the image set A composed of all unit grayscale images in the grayscale image, calculate the texture image set B, B = A - Re, gather all the unit grayscale images in the texture image set B, and form the texture image TU of the grayscale image;

[0017] Screen out some areas in the image to reduce the computing resources occupied during image processing.

[0018] Further, in step S200, the calculation steps of the sparsity index include:

[0019] Step S201: Calculate the ratio β of the texture image set B to the image set A, β = numB / numA, where numB represents the number of unit gray-scale images in the texture image set B, and numA represents the number of unit gray-scale images in the image set A;

[0020] Step S202: In the texture image set B, set the image composed of consecutive unit gray-scale images as an image block, and label all the image blocks in the texture image set B;

[0021] Step S203: Obtain the geometric centers of each image block in the texture image TU, obtain the distances between the geometric centers of any two image blocks in the texture image TU, calculate the variance var of all the distances between the geometric centers, and calculate the sparsity index K1, K1 = β × var;

[0022] The variance represents the degree of dispersion of the data. By calculating the degree of dispersion of the geometric center distances, the distribution of the image blocks in the texture image TU is obtained.

[0023] Further, in step S200, the calculation steps of the clarity index include:

[0024] Step S204: Obtain the j-th image block in the texture image TU, obtain the maximum gradient vector grd of the gray-scale value change in the j-th image block j , calculate the modulus m of the maximum gradient vector grd j ; j ;

[0025] The modulus of the gradient vector represents the rate of change and the maximum value of the directional derivative of the function at a certain point. The sharpness of the pixel change is represented by the modulus of the gradient vector. However, when the modulus is larger, the gray-scale value changes faster and the image appears sharper;

[0026] Step S205: In the j-th image block, obtain the unit gray-scale image with the largest gray-scale value and the unit gray-scale image with the smallest gray-scale value in the direction of the maximum gradient vector grd j , where the gray-scale value of the unit gray-scale image with the largest gray-scale value is denoted as G j max , and the gray-scale value of the unit gray-scale image with the smallest gray-scale value is denoted as G j min ;

[0027] Step S206: Calculate the gray-scale value change coefficient γ j , γ j = (G j max - G j min ) / G j max, calculate the sharpness index k of the j-th image block j , k j =(1 - γ j )×m j , denote the average value of the sharpness indices of all image blocks as the sharpness index K2;

[0028] Under the condition of the same change rate, further compare the change range of the gray values. When the change rate of the gray values is constant, the smaller the change range, the higher the sharpness of the image.

[0029] Furthermore, step S200 further includes:

[0030] Step S207: Construct a gray-scale image texture evaluation index H, H = K1×ln(1 + K2), where ln represents the logarithmic function;

[0031] To balance the values of K1 and K2, compress K2 through the logarithmic function so that its magnitude is closer to that of K1, and the change of H can better take into account the changes of both K1 and K2 parameters.

[0032] Generally speaking, the natural changes in the land have a periodic pattern and the changes are not drastic. The artificial use of the land will cause relatively drastic changes in the land's topography and landform. Therefore, by collecting the changes of the gray-scale image texture evaluation index over time and comparing them after numerical prediction, the result of whether there are traces of artificial use on the land can be obtained.

[0033] Furthermore, step S400 includes:

[0034] Step S401: Obtain an abnormal land use detection plan for the target plot. In two adjacent detections of the abnormal land use of the target plot, set the previous detection as the first target detection and the subsequent detection as the second target detection;

[0035] Step S402: Denote the image of the target plot obtained during the first target detection as the first target image. When there is no abnormal land use situation in the first target image, obtain the texture evaluation index of the first target image, denoted as Q1;

[0036] Step S403: Obtain the time interval between the first target detection and the second target detection. According to the law of the change of the texture evaluation index over time, calculate the texture evaluation prediction value Q pre 2;

[0037] Step S404: Denote the image of the target plot obtained during the second target detection as the second target image, and obtain the texture evaluation index of the second target image, denoted as Q2;

[0038] Step S405: Obtain the difference resolution coefficient r, and calculate Qpre2 and the difference degree ω between Q and Q2, ω = |Q pre2 - Q2|. When ω > r, an alarm prompt is sent to relevant management personnel.

[0039] To better implement the above method, a monitoring system for illegal land use of natural resources is also proposed. The system includes: a texture acquisition module, a sparsity index management module, a clarity index management module, a grayscale image texture evaluation module, a time series model management module, and a judgment module. Among them, the texture acquisition module is used to acquire the texture image of the target plot, the sparsity index management module is used to calculate the sparsity index of the texture image, the clarity index management module is used to calculate the clarity index of the texture image, the grayscale image texture evaluation module is used to calculate the grayscale image texture evaluation index, the time series model management module is used to obtain the law of change of the texture evaluation index over time through a time series model, and the judgment module is used to compare the texture evaluation index in the image with the predicted value.

[0040] Furthermore, the texture acquisition module includes: a grayscale image acquisition unit, a grayscale value screening unit, and a texture image management unit. Among them, the grayscale image acquisition unit is used to acquire the grayscale image of the remote sensing image information of the target plot, the grayscale value screening unit is used to screen the unit grayscale images in the grayscale image according to the grayscale value, and the texture image management unit is used to collect the screened grayscale images to form the texture image of the grayscale image.

[0041] Furthermore, the sparsity index management module includes: an image ratio calculation unit, a geometric center management unit, and a sparsity index calculation unit. Among them, the image ratio calculation unit is used to calculate the ratio of the texture image to the grayscale image, the geometric center management unit is used to obtain the geometric center of the image block, and the sparsity index calculation unit is used to calculate the sparsity index.

[0042] Furthermore, the clarity index management module includes: a gradient vector acquisition unit, a grayscale value change coefficient calculation unit, and a clarity index calculation unit. Among them, the gradient vector acquisition unit is used to acquire the maximum gradient vector of the change in grayscale value in the image block, the grayscale value change coefficient calculation unit is used to calculate the grayscale value change coefficient, and the clarity index calculation unit is used to calculate the clarity index.

[0043] Furthermore, the judgment module includes: a plan management unit, a texture evaluation index prediction unit, a texture evaluation index comparison unit, and an information prompt unit. Among them, the plan management unit is used to obtain the detection plan for abnormal land use, the texture evaluation index prediction unit is used to predict the value of the texture evaluation index of the second target image, the texture evaluation index comparison unit is used to compare the difference between the actual value and the predicted value of the texture evaluation index of the second target image, and the information prompt unit is used to send an alarm prompt to relevant management personnel when the difference degree meets the conditions.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention collects the image information corresponding to the land, preprocesses the image information, and reduces the storage volume of the images in the unit quantity. The texture information of the images is collected, and the texture is evaluated according to the density and clarity of the texture to obtain the characteristics of the texture in the images. The law of the change of the texture characteristics in the images over time is obtained, and a reference value for the next detection is generated. By the above method, the calculation amount of the image similarity calculation in the process of image feature comparison is reduced, and at the same time, the historical image features are extracted, reducing the storage space required for the historical image information. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic structural diagram of a natural resources illegal land use monitoring system of the present invention;

[0046] Figure 2 It is a schematic flow diagram of a natural resources illegal land use monitoring method of the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] Embodiment: As Figure 1 and Figure 2 shown, the present invention provides a technical solution:

[0049] Step S100: A plot of land under abnormal land use monitoring is used as the target plot, the remote sensing image information of the target plot is collected, and the image texture area of the image information is obtained to obtain the texture image of the target plot;

[0050] In the embodiment, after the remote sensing image information of the target plot is collected, the remote sensing image is denoised;

[0051] For example, the noise processing of the remote sensing image is divided into additive noise and multiplicative noise for separate processing;

[0052] Among them, the additive noise in the remote sensing image is processed by a mean filter, the remote sensing image is subjected to Fourier transform, the time-domain image is changed into a frequency-domain image, and the frequency components where the noise is located are filtered out;

[0053] Among them, step S100 includes:

[0054] Step S101: Collect the remote sensing image information of any target plot, convert the remote sensing image into a grayscale image, and the quantization interval of the grayscale is [0, G], where G represents the maximum grayscale value in the grayscale image quantization interval;

[0055] Usually, the interval [0, 255] is adopted in the grayscale value calculation process, where 0 represents black and 255 represents white;

[0056] Step S102: Perform rasterization processing on the grayscale image, divide the grayscale image into several unit grayscale images with the same shape and size, obtain the grayscale values of all unit grayscale images, and calculate the average value of the grayscale values of all unit grayscale images, denoted as Gra;

[0057] Step S103: Obtain the i-th unit grayscale image ui in all unit grayscale images, obtain the corresponding grayscale value G of ui ui and calculate the grayscale judgment value D ui , Du i = G ui + α × Gra, where α represents the proportionality coefficient and α ∈ (0, 2);

[0058] Step S104: When D ui ≥ G, set the i-th unit grayscale image as the selected unit image, and gather all the selected unit images in the grayscale image to form an image set Re;

[0059] Step S105: Obtain the image set A composed of all unit grayscale images in the grayscale image, calculate the texture image set B, B = A - Re, gather all the unit grayscale images in the texture image set B, and form the texture image TU of the grayscale image.

[0060] Step S200: Obtain the sparsity index and clarity index of the texture in the texture image, and construct the texture evaluation index of the texture image;

[0061] Among them, Step S200 includes:

[0062] Step S201: Calculate the proportion β of the texture image set B in the image set A, β = numB / numA, where numB represents the number of unit grayscale images in the texture image set B, and numA represents the number of unit grayscale images in the image set A;

[0063] Step S202: In the texture image set B, set the image composed of continuous unit grayscale images as an image block, and label all the image blocks in the texture image set B;

[0064] Step S203: Obtain the geometric centers of each image patch in the texture image TU, obtain the distances between the geometric centers of any two image patches in the texture image TU, calculate the variance var of all the distances between the geometric centers, and calculate the sparsity index K1, where K1 = β × var;

[0065] Step S204: Obtain the j-th image patch in the texture image TU, and obtain the maximum gradient vector grd of the gray value change in the j-th image patch j , and calculate the modulus m of the maximum gradient vector grd j ; j ;

[0066] Step S205: In the j-th image patch, obtain the unit gray image with the maximum gray value and the unit gray image with the minimum gray value in the direction of the maximum gradient vector grd j , where the gray value of the unit gray image with the maximum gray value is denoted as G j max , and the gray value of the unit gray image with the minimum gray value is denoted as G j min ;

[0067] Step S206: Calculate the gray value change coefficient γ j , where γ j = (G j max - G j min ) / G j max , calculate the clarity index k of the j-th image patch j , where k j = (1 - γ j ) × m j , and denote the average value of the clarity indices of all the image patches as the clarity index K2;

[0068] Step S207: Construct the gray map texture evaluation index H, where H = K1 × ln(1 + K2), and ln represents the logarithmic function.

[0069] Step S300: When there is no abnormality in the target plot, collect the remote sensing image information of several target plots according to the time sequence, calculate the texture evaluation indices of the texture patterns respectively, and obtain the variation law of the texture evaluation index with time through a time series model;

[0070] The time series model is used to obtain the variation law of the texture evaluation index with respect to time. Commonly used time series models include, for example, the ARMA model, the ARIMA model, and the long short-term memory network (LSTM).

[0071] Step S400: Take the abnormal land use detection of a certain target plot as the first target detection, take the corresponding image during the first target detection as the first target image, obtain the time interval between the first target detection and the second target detection, predict the texture evaluation index of the image during the second target detection, and when the texture evaluation index in the image corresponding to the second target detection is different from the predicted value, give an alarm prompt to the relevant management personnel;

[0072] Among them, step S400 includes:

[0073] Step S401: Obtain the abnormal land use detection plan for the target plot. In two adjacent abnormal land use detections of the target plot, set the previous detection as the first target detection and the subsequent detection as the second target detection;

[0074] Step S402: Record the image of the target plot obtained during the first target detection as the first target image. When there is no abnormal land use situation in the first target image, obtain the texture evaluation index of the first target image, denoted as Q1;

[0075] Step S403: Obtain the time interval between the first target detection and the second target detection, and calculate the texture evaluation prediction value Q pre 2 of the second target detection according to the law of the change of the texture evaluation index with time;

[0076] Step S404: Record the image of the target plot obtained during the second target detection as the second target image, and obtain the texture evaluation index of the second target image, denoted as Q2;

[0077] Step S405: Obtain the difference resolution coefficient r, calculate the difference degree ω between Q pre2 and Q2, ω = |Q pre2 - Q2|. When ω > r, give an alarm prompt to the relevant management personnel;

[0078] R is the discrimination coefficient, which is used to represent the threshold of the obvious difference degree when exceeding a certain numerical range. The relevant personnel can adjust the difference degree judgment threshold through experience. When there is no threshold setting in the system, select r = 0.1×|Q pre2 - Q2|.

[0079] The system includes:

[0080] A texture acquisition module, a sparsity index management module, a clarity index management module, a grayscale image texture evaluation module, a time series model management module, and a judgment module;

[0081] Among them, the texture acquisition module is used to acquire the texture image of the target plot. The texture acquisition module includes: a grayscale image acquisition unit, a grayscale value screening unit, and a texture image management unit. The grayscale image acquisition unit is used to acquire the grayscale image of the remote sensing image information of the target plot. The grayscale value screening unit is used to screen the unit grayscale images in the grayscale image according to the grayscale value. The texture image management unit is used to collect the screened grayscale images to form the texture image of the grayscale image;

[0082] Among them, the sparsity index management module is used to calculate the sparsity index of the texture image. The sparsity index management module includes: an image ratio calculation unit, a geometric center management unit, and a sparsity index calculation unit. The image ratio calculation unit is used to calculate the ratio of the texture image to the grayscale image. The geometric center management unit is used to obtain the geometric center of the image block. The sparsity index calculation unit is used to calculate the sparsity index;

[0083] Among them, the clarity index management module is used to calculate the clarity index of the texture image. The clarity index management module includes: a gradient vector acquisition unit, a grayscale value change coefficient calculation unit, and a clarity index calculation unit. The gradient vector acquisition unit is used to acquire the maximum gradient vector of the grayscale value change in the image block. The grayscale value change coefficient calculation unit is used to calculate the grayscale value change coefficient. The clarity index calculation unit is used to calculate the clarity index;

[0084] Among them, the grayscale image texture evaluation module is used to calculate the grayscale image texture evaluation index;

[0085] Among them, the time series model management module is used to obtain the law of change of the texture evaluation index over time through the time series model;

[0086] Among them, the judgment module is used to compare the texture evaluation index in the image with the predicted value. The judgment module includes: a plan management unit, a texture evaluation index prediction unit, a texture evaluation index comparison unit, and an information prompt unit. The plan management unit is used to obtain the detection plan for abnormal land use. The texture evaluation index prediction unit is used to predict the value of the texture evaluation index of the second target image. The texture evaluation index comparison unit is used to compare the difference between the actual value and the predicted value of the texture evaluation index of the second target image. The information prompt unit is used to give an alarm prompt to the relevant management personnel when the difference degree meets the conditions.

[0087] It is obvious to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A method for monitoring illegal use of land for natural resources, characterized in that: Step S100: Take a plot of land under abnormal land use monitoring as the target plot, collect remote sensing image information of the target plot, obtain the image texture area of the image information, and obtain the texture image of the target plot; Step S200: Obtain the sparsity index and clarity index of the texture in the texture image, and construct a texture evaluation index for the texture image; Step S200 includes: Step S201: Calculate the ratio β of the texture image set B to the image set A, β = numB / numA, where numB represents the number of unit gray images in the texture image set B, and numA represents the number of unit gray images in the image set A; Step S202: In the texture image set B, set the image composed of consecutive unit gray images as an image block, and label all the image blocks in the texture image set B; Step S203: Obtain the geometric centers of each image block in the texture image TU, obtain the distance between the geometric centers of any two image blocks in the texture image TU, calculate the variance var of the distances between all geometric centers, and calculate the sparsity index K1, K1 = β×var; Step S200 also includes: Construction method of texture evaluation index: Construct the gray-scale image texture evaluation index H, H = K1×ln(1 + K2), where ln represents the logarithmic function, and K2 represents the clarity index; Step S300: When there is no abnormality in the target plot, collect remote sensing image information of several target plots in chronological order, calculate the texture evaluation index of the texture image respectively, and obtain the law of change of the texture evaluation index with time through a time series model; Step S400: Take a certain detection of illegal land use of the target plot as the first target detection, take the image of the target plot corresponding to the first target detection process as the first target image, obtain the time interval between the first target detection and the second target detection, predict the texture evaluation index of the image at the second target detection, and when the texture evaluation index in the image corresponding to the second target detection is different from the predicted value, give an alarm prompt to the relevant management personnel.

2. The method for monitoring illegal land use of natural resources according to claim 1, characterized in that: Step S100 includes: Step S101: Collect remote sensing image information of any target plot, convert the remote sensing image into a gray-scale image, and the quantization interval of the gray scale is [0, G], where G represents the maximum gray value in the gray-scale image quantization interval; Step S102: Perform rasterization processing on the gray-scale image, divide the gray-scale image into several unit gray images with the same shape and size, obtain the gray values of all unit gray images, and calculate the average value of the gray values of all unit gray images, denoted as Gra; Step S103: Obtain the i-th unit grayscale image ui among all unit grayscale images, and obtain the grayscale value G corresponding to ui ui , calculate the grayscale judgment value D of ui ui , D ui = G ui + α × Gra, where α represents the proportionality coefficient, and α ∈ (0, 2); Step S104: When D ui ≥G, set the i-th unit gray image as the selected unit image, and collect all the selected unit images in the gray-scale image to form an image set Re; Step S105: Obtain the image set A composed of all unit gray images in the gray-scale image, calculate the texture image set B, B = A - Re, collect all the unit gray images in the texture image set B, and form the texture image TU of the gray-scale image.

3. The monitoring method for illegal use of land in natural resources according to claim 2, wherein: In step S200, the calculation steps of the clarity index include: Step S204: Obtain the j-th image block in the texture image TU, and obtain the maximum gradient vector grd of the change in gray value in the j-th image block j , and calculate the modulus m of the maximum gradient vector grd j ; j ; Step S205: In the j-th image block, obtain the unit gray-scale image with the maximum gray-scale value and the unit gray-scale image with the minimum gray-scale value in the direction of the maximum gradient vector grd j where the gray-scale value of the unit gray-scale image with the maximum gray-scale value is denoted as G j max and the gray-scale value of the unit gray-scale image with the minimum gray-scale value is denoted as G j min ; Step S206: Calculate the grayscale value change coefficient γ j , γ j =(G j max -G j min ) / G j max , calculate the clarity index k of the j-th image block j , k j =(1 - γ j )×m j , and denote the average value of the clarity indices of all image blocks as the clarity index K2.

4. The monitoring method for illegal use of land of natural resources according to claim 1, characterized in that: Step S400 includes: Step S401: Obtain the abnormal land use detection plan for the target plot. In two adjacent detections of the abnormal land use of the target plot, set the previous detection as the first target detection and the subsequent detection as the second target detection; Step S402: Record the image of the target plot obtained during the first target detection as the first target image. When there is no abnormal land use situation in the first target image, obtain the texture evaluation index of the first target image, denoted as Q1; Step S403: Obtain the time interval between the first target detection and the second target detection, and calculate the predicted texture evaluation value Q of the second target detection according to the law of change of the texture evaluation index over time pre 2; Step S404: Record the image of the target plot obtained during the second target detection as the second target image, and obtain the texture evaluation index of the second target image, denoted as Q2; Step S405: Obtain the difference resolution coefficient r and calculate the difference degree ω between Q pre 2 and Q2, ω = |Q pre 2 - Q2|. When ω > r, give an alarm prompt to relevant management personnel.

5. A monitoring system for illegal land use of natural resources, which is used to execute the monitoring method for illegal land use of natural resources according to any one of claims 1-4, characterized in that: The system includes: A texture acquisition module, a sparsity index management module, a clarity index management module, a grayscale image texture evaluation module, a time series model management module, and a judgment module. Among them, the texture acquisition module is used to acquire the texture image of the target plot, the sparsity index management module is used to calculate the sparsity index of the texture image, the clarity index management module is used to calculate the clarity index of the texture image, the grayscale image texture evaluation module is used to calculate the grayscale image texture evaluation index, the time series model management module is used to obtain the law of change of the texture evaluation index over time through a time series model, and the judgment module is used to compare the texture evaluation index in the image with the predicted value.

6. The monitoring system for illegal use of land in natural resources according to claim 5, wherein: The texture acquisition module includes: a grayscale image acquisition unit, a grayscale value screening unit, and a texture image management unit. Among them, the grayscale image acquisition unit is used to acquire the grayscale image of the remote sensing image information of the target plot, the grayscale value screening unit is used to screen the unit grayscale images in the grayscale image according to the grayscale value, and the texture image management unit is used to collect the screened grayscale images to form the texture image of the grayscale image.

7. The monitoring system for illegal use of land in natural resources according to claim 5, characterized in that: The sparsity index management module includes: an image ratio calculation unit, a geometric center management unit, and a sparsity index calculation unit. Among them, the image ratio calculation unit is used to calculate the ratio of the texture image to the grayscale image, the geometric center management unit is used to obtain the geometric center of the image block, and the sparsity index calculation unit is used to calculate the sparsity index; The clarity index management module includes: a gradient vector acquisition unit, a grayscale value change coefficient calculation unit, and a clarity index calculation unit. Among them, the gradient vector acquisition unit is used to obtain the maximum gradient vector of the change in grayscale value in the image block, the grayscale value change coefficient calculation unit is used to calculate the grayscale value change coefficient, and the clarity index calculation unit is used to calculate the clarity index.

8. The monitoring system for illegal use of land in natural resources according to claim 5, characterized in that: The judgment module includes: a plan management unit, a texture evaluation index prediction unit, a texture evaluation index comparison unit, and an information prompt unit. Among them, the plan management unit is used to obtain the detection plan for abnormal land use, the texture evaluation index prediction unit is used to predict the value of the texture evaluation index of the second target image, the texture evaluation index comparison unit is used to compare the difference between the actual value and the predicted value of the texture evaluation index of the second target image, and the information prompt unit is used to give an alarm prompt to relevant management personnel when the difference degree meets the conditions.

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