Geographic Information Acquisition System and Method Based on Remote Sensing Images

Abstract

The present invention relates to the technical field of geographic information systems, specifically a geographic information acquisition system and method based on remote sensing images, including a remote sensing image analysis module, a feature data extraction module, a classification and matching module, and a geographic data analysis module; by performing spectral reflectance analysis on all pixels, extracting color values and feature analysis within different band ranges, and extracting texture feature values containing geological information, the present invention can more accurately identify different surface cover types; by calculating the weighted Euclidean distance between the feature vectors of each image unit and each label classification vector, effective matching between the image unit and the geographic classification label is achieved; by extracting the statistical results of the geographic classification label matching to count various types of geographic information data, it helps to understand the geographic distribution characteristics and provides data support for subsequent geographic planning and resource utilization.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic information systems, and specifically to a geographic information acquisition system and method based on remote sensing images. Background Art

[0002] A geographic information system is a comprehensive technical field involving the collection, storage, management, analysis, and display of geospatial data. Remote sensing technology, on the other hand, uses sensors on remote sensing platforms such as satellites, airplanes, and drones to collect and record electromagnetic wave information (such as visible light, infrared, microwave, etc.) from target objects. These sensors can detect various features on the Earth's surface, such as terrain, landform, vegetation, water bodies, soil, etc., and convert them into digital signals or images. Remote sensing data has the characteristics of large area, rapid acquisition, multi-temporal, and multi-spectral, and can provide rich information about the Earth's surface.

[0003] However, there are still many problems in the existing geographic information system technology, especially in the recognition, segmentation, and classification of high-resolution remote sensing images.

[0004] The limitations of traditional single-pixel-based remote sensing image recognition methods are prone to classification errors. These methods rely on a single data source for feature extraction and do not integrate multiple features such as spectrum and texture for feature extraction, which limits the accuracy and effect of remote sensing image classification. The determination of the segmentation scale is easily affected by subjective judgment and complex surface morphology, making it difficult to construct a comprehensive and highly discriminatory feature space, reducing the accuracy of remote sensing image recognition and segmentation.

[0005] In view of the above problems, it is necessary to propose a geographic information acquisition system and method based on remote sensing images. Summary of the Invention

[0006] The purpose of the present invention is to solve the problems existing in the background art, and to propose a geographic information acquisition system and method based on remote sensing images.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] In a first aspect, the present invention provides a geographic information acquisition system based on remote sensing images, including a remote sensing image analysis module, a feature data extraction module, a classification and matching module, and a geographic data analysis module.

[0009] The remote sensing image analysis module obtains the remote sensing image of the information acquisition area, divides the remote sensing image with a grid of a preset size A×A pixels to obtain a number of image units. Number the image units, and the numbering symbol is i, where i = 1, 2, 3,..., n. Here, n is the total number of image units obtained after segmentation.

[0010] For each image unit, all the pixels therein are located, and the location coordinates are (w, h), where w is the pixel height and h is the pixel height; where 0 ≤ w ≤ A and 0 ≤ h ≤ A.

[0011] As a preferred embodiment of the present invention, spectral reflectance analysis is performed on all pixels to obtain the reflectance value Q at different wavelengths λ, and a reflectance-wavelength graph is plotted. The areas in different band ranges are extracted from the reflectance-wavelength graph and denoted as the color values of the pixel (w, h) in different bands, including:

[0012] Purple color value c1(w, h), with a band range of 390 nm - 455 nm;

[0013] Indigo color value c2(w, h), with a band range of 455 nm - 492 nm;

[0014] Green color value c3(w, h), with a band range of 492 nm - 577 nm;

[0015] Yellow color value c4(w, h), with a band range of 577 nm - 597 nm;

[0016] Orange color value c5(w, h), with a band range of 597 nm - 622 nm;

[0017] Red color value c6(w, h), with a band range of 622 nm - 770 nm;

[0018] Infrared color value C7(w, h), with a band range greater than 770 nm.

[0019] As a preferred embodiment of the present invention, the grayscale value G(w, h) of each pixel is obtained, where 0 ≤ G(w, h) ≤ 255. For each image unit i, an A×A grayscale co-occurrence matrix is generated

[0020] The feature data extraction module performs statistical feature analysis based on the color value and grayscale value data, specifically:

[0021] Through the formula The overall spectral feature parameter Ci of the image unit i is calculated, where Kp is a set of preset weight factors, and p = 1, 2, 3, 4, 5, 6, 7.

[0022] Through the formula The first-order grayscale moment e1i(w) of the w-th row and the first-order grayscale moment e1i(h) of the h-th column of each image unit i are calculated,

[0023] Through the formula Calculate the second-order gray moment e2i(w) of each image unit i in the w-th row and the second-order gray moment e2i(h) of each image unit i in the h-th column.

[0024] Through the formula Calculate the overall first-order moment E1i and the overall second-order moment E2i of each image unit i.

[0025] As a preferred embodiment of the present invention, further numerical analysis is performed according to the elements in the gray-level co-occurrence matrix to extract texture feature values containing geological information, specifically:

[0026] Through the formula Calculate the homogeneity feature value E3i of the image unit i.

[0027] Through the formula Calculate the contrast feature value E4i of the image unit i.

[0028] Through the formula Calculate the gray entropy E5i of the image unit i.

[0029] Through the formula Calculate the correlation feature value E6i of the image unit i.

[0030] For the same image unit i, obtain the indigo color value C2(w, h), green color value C3(w, h), red color value C6(w, h), and infrared color value C7(w, h) of each pixel (w, h).

[0031] Through the formula Calculate the vegetation index U1i, water index U2i, building index U3i, and soil index U4i of each image unit i.

[0032] The classification and matching module integrates and merges the spectral feature parameters Ci, overall first-order moment E1i, overall second-order moment E2i, homogeneity feature value E3i, contrast feature value E4i, gray entropy E5i, correlation feature value E6i, vegetation index U1i, water index U2i, building index U3i, and soil index U4i of each image unit i. Generate the feature vector (Ci, E1i, E2i, E3i, E4i, E5i, E6i, U1i, U2i, U3i, U4i) of each image unit i.

[0033] Retrieve the preset geographical classification labels, including water systems, forest land, cultivated land, orchards, sandy land, water systems, residential buildings, roads, and industrial and mining land. Each geographical classification label has a label classification vector (C, E1, E2, E3, E4, E5, E6, U1, U2, U3, U4).

[0034] Calculate the weighted Euclidean distance d between the feature vector of each image unit i and each label classification vector. When d is less than the preset threshold dMin, it is considered that the image unit i successfully matches the theoretical classification label corresponding to the label classification vector, and a mark is made.

[0035] The calculation formula of the weighted Euclidean distance is: where α1 is a preset weight factor; β t1 is a preset set of weight factors; γ t2 is a preset set of weight factors.

[0036] The geographical data analysis module obtains the geographical classification label matching results obtained by the classification matching module, statistically analyzes the geographical information data and performs identification and marking. Specifically:

[0037] The number of image units S1, S2, S3, S4, S5, S6, S7, S8, and S9 matched by water systems, forest land, cultivated land, garden land, sandy land, residential buildings, unused urban open spaces, roads, and industrial and mining land. Through the formula Calculate the water system evaluation index ε1 and the green space evaluation index ε2.

[0038] When the water system evaluation index ε1 is less than the preset threshold ε1Min, a water system degradation warning signal is generated;

[0039] When the greening evaluation index ε2 is less than the preset threshold ε2Min, a green space degradation warning signal is generated;

[0040] Obtain the slope angle θi and spectral feature parameter Ci of each image unit i, and calculate the illumination evaluation index ε3i of each image unit i through the formula ε3i = λ1×(θi - θ0) + λ2×Ci, where λ1 and λ2 are preset weight factors. Highlight the image unit i with an illumination evaluation index ε3i greater than the preset threshold ε3Max in yellow. Highlight the image unit i with an illumination evaluation index ε3i less than the preset threshold ε3Min in blue.

[0041] Obtain the weighted Euclidean distance d between each image unit i and other adjacent image units. When d is greater than the preset threshold dMax, mark the image unit i as a classification boundary unit. Traverse all the number symbols i of the image units for the above process to obtain all the classification boundary units, and highlight the classification boundary units in red.

[0042] In a second aspect, the present invention provides a geographical information acquisition system and method based on remote sensing images, specifically including the following steps:

[0043] Step 1: Remote sensing image analysis;

[0044] Obtain the remote sensing image of the information collection area, segment the remote sensing image with a grid of a preset size of A×A pixels to obtain a number of image units. Number the image units, and the numbering symbol is i, where i = 1, 2, 3,..., n. Here, n is the total number of image units obtained after segmentation.

[0045] For each image unit, locate all the pixels therein, and the location coordinates are (w, h), where w is the pixel height and h is the pixel height; where 0 ≤ w ≤ A and 0 ≤ h ≤ A.

[0046] Perform spectral reflectance analysis on all pixels to obtain their reflectance values Q at different wavelengths λ, and draw a reflectance-wavelength graph. Extract the areas in different band ranges in the reflectance-wavelength graph, and record them as the color values of the pixel (w, h) in different bands, including:

[0047] Purple color value c1(w, h), with a band range of 390nm - 455nm;

[0048] Indigo color value c2(w, h), with a band range of 455nm - 492nm;

[0049] Green color value c3(w, h), with a band range of 492nm - 577nm;

[0050] Yellow color value c4(w, h), with a band range of 577nm - 597nm;

[0051] Orange color value c5(w, h), with a band range of 597nm - 622nm;

[0052] Red color value c6(w, h), with a band range of 622nm - 770nm;

[0053] Infrared color value C7(w, h), with a band range greater than 770nm.

[0054] Through the formula Calculate the overall spectral feature parameter Ci of the image unit i, where Kp is a set of preset weight factors, and p = 1, 2, 3, 4, 5, 6, 7.

[0055] Obtain the gray value G(w, h) of each pixel, where 0 ≤ G(w, h) ≤ 255.

[0056] As a preferred embodiment of the present invention, for each image unit i, generate a gray co-occurrence matrix of A×A

[0057] Step 2: Remote sensing image feature extraction;

[0058] Through the formula Calculate the first-order gray moment e1i(w) of each image unit i in the w-th row and the first-order gray moment e1i(h) of the h-th column.

[0059] Through the formula Calculate the second-order gray moment e2i(w) of each image unit i in the w-th row and the second-order gray moment e2i(h) of the h-th column.

[0060] Through the formula Calculate the overall first-order moment E1i and the overall second-order moment E2i of each image unit i.

[0061] Conduct further numerical analysis based on the elements in the gray-level co-occurrence matrix, and extract the texture feature values containing geological information, specifically:

[0062] Through the formula Calculate the homogeneity feature value E3i of the image unit i.

[0063] Through the formula Calculate the contrast feature value E4i of the image unit i.

[0064] Through the formula Calculate the gray entropy E5i of the image unit i.

[0065] Through the formula Calculate the correlation feature value E6i of the image unit i.

[0066] Step 3: Geographical feature extraction;

[0067] For the same image unit i, obtain the indigo color value C2(w, h), green color value C3(w, h), red color value C6(w, h), and infrared color value C7(w, h) of each pixel (w, h).

[0068] Through the formula Calculate the vegetation index U1i, water index U2i, building index U3i, and soil index U4i of each image unit i.

[0069] Step 4: Image unit matching;

[0070] Integrate and merge the spectral feature parameters Ci, overall first-order moment E1i, overall second-order moment E2i, homogeneity feature value E3i, contrast feature value E4i, gray entropy E5i, correlation feature value E6i, vegetation index U1i, water index U2i, building index U3i, and soil index U4i of each image unit i. Generate the feature vector (Ci, E1i, E2i, E3i, E4i, E5i, E6i, U1i, U2i, U3i, U4i) of each image unit i.

[0071] Retrieve the preset geographical classification labels, including water systems, forest lands, cultivated lands, garden lands, sandy lands, water systems, residential buildings, roads, and industrial and mining lands. Each geographical classification label has a label classification vector (C, E1, E2, E3, E4, E5, E6, U1, U2, U3, U4).

[0072] Calculate the weighted Euclidean distance d between the feature vector of each image unit i and each label classification vector. When d is less than the preset threshold dMin, it is considered that the image unit i successfully matches the geographical classification label corresponding to the label classification vector, and a mark is made.

[0073] The calculation formula for the weighted Euclidean distance is: where α1 is a preset weight factor; β t1 is a preset set of weight factors; γ t2 is a preset set of weight factors.

[0074] Step Five: Geographical data analysis;

[0075] Extract the geographical classification label matching results obtained in Step Four, and count the geographical information data, including:

[0076] The number of image units S1, S2, S3, S4, S5, S6, S7, S8, and S9 of water systems, forest lands, cultivated lands, garden lands, sandy lands, residential buildings, unused urban open spaces, roads, and industrial and mining lands that are matched. Calculate the water system evaluation index ε1 and the green space evaluation index ε2 through the formula Calculate the water system evaluation index ε1 and the green space evaluation index ε2.

[0077] When the water system evaluation index ε1 is less than the preset threshold ε1Min, a water system degradation warning signal is generated;

[0078] When the greening evaluation index ε2 is less than the preset threshold ε2Min, a green space degradation warning signal is generated;

[0079] Obtain the slope angle θi and spectral feature parameter Ci of each image unit i, and calculate the illumination evaluation index ε3i of each image unit i through the formula ε3i = λ1×(θi - θ0) + λ2×Ci, where λ1 and λ2 are preset weight factors. Highlight the image unit i with an illumination evaluation index ε3i greater than the preset threshold ε3Max in yellow. Highlight the image unit i with an illumination evaluation index ε3i less than the preset threshold ε3Min in blue.

[0080] Obtain the weighted Euclidean distance d between each image unit i and other adjacent image units. When d is greater than the preset threshold dMax, mark the image unit i as a classification boundary unit. Traverse the serial numbers i of all image units in the above process to obtain all classification boundary units, and highlight the classification boundary units in red.

[0081] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0082] (1) By performing spectral reflectance analysis on all pixels and extracting color values within different wavelength ranges, the present invention can capture the spectral characteristics of surface objects more precisely; generate a gray-level co-occurrence matrix of A×A, and conduct statistical feature analyses such as first-order moment and second-order moment to extract texture feature values containing geological information, such as homogeneity feature values, contrast feature values, gray entropy, and correlation feature values; calculate parameters such as vegetation index, water body index, building index, and soil index by combining color values of multiple bands, which can more accurately identify different surface cover types; conduct quantitative analysis on the spectral reflection characteristics, microstructure, and roughness of surface objects, contributing to improving the classification accuracy.

[0083] (2) By retrieving preset geographical classification labels, the present invention provides clear classification criteria for various geographical features; by calculating the weighted Euclidean distance between the feature vectors of each image unit and the classification vectors of each label, effective matching between the image unit and the geographical classification label is achieved; both considering each dimension of the feature vector and emphasizing the importance of certain features through weight factors, thereby improving the accuracy and efficiency of the matching.

[0084] (3) By extracting the statistical results of the geographical classification label matching to count the number of image units of various geographical information data, such as water systems, forestlands, cultivated lands, etc.; this helps to understand the geographical distribution characteristics and provides data support for subsequent geographical planning and resource utilization; by calculating the water system evaluation index and the green space evaluation index, the health status of the water system and the green space can be evaluated, providing a scientific basis for environmental protection and ecological restoration; by evaluating the lighting conditions through the lighting evaluation index; this helps to identify areas with insufficient or excessive lighting, providing important references for urban planning, agricultural layout, etc.; by calculating the weighted Euclidean distance between each image unit and other intersecting image units, the classification boundary units are identified; this helps to distinguish the boundary areas of different geographical types and provides a reference basis for geographical classification and map production. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings:

[0086] Figure 1 is the system block diagram of the present invention;

[0087] Figure 2 is the reflectance - wavelength diagram of the present invention;

[0088] Figure 3 is the method flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0089] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0090] Please refer to Figure 1 As shown, a geographic information acquisition system based on remote sensing images includes a remote sensing image analysis module, a feature data extraction module, a classification and matching module, and a geographic data analysis module.

[0091] The remote sensing image analysis module acquires the remote sensing image of the information acquisition area, divides the remote sensing image with a grid having a preset size of A×A pixels to obtain a number of image units. The image units are numbered, and the numbering symbol is i, where i = 1, 2, 3,..., n. Here, n is the total number of image units obtained after segmentation.

[0092] For each image unit, all the pixels therein are located, and the location coordinates are (w, h), where w is the pixel height and h is the pixel height; where 0 ≤ w ≤ A and 0 ≤ h ≤ A.

[0093] Please refer to Figure 2 As shown, spectral reflectance analysis is performed on all pixels to obtain the reflectance value Q at different wavelengths λ, and a reflectance-wavelength graph is drawn. The areas in different band ranges are extracted from the reflectance-wavelength graph, which are recorded as the color values of the pixel (w, h) in different bands, including:

[0094] Purple color value c1(w, h), band range: 390nm - 455nm;

[0095] Indigo color value c2(w, h), band range: 455nm - 492nm;

[0096] Green color value c3(w, h), band range: 492nm - 577nm;

[0097] Yellow color value c4(w, h), band range: 577nm - 597nm;

[0098] Orange color value c5(w, h), band range: 597nm - 622nm;

[0099] Red color value c6(w, h), band range: 622nm - 770nm;

[0100] Infrared color value C7(w, h), band range: greater than 770nm.

[0101] Further, obtain the grayscale value G(w, h) of each pixel, where 0 ≤ G(w, h) ≤ 255. For each image unit i, generate a grayscale co-occurrence matrix of A × A

[0102]

[0103] The feature data extraction module performs further statistical feature analysis based on the color value and grayscale value data, specifically:

[0104] Through the formula Calculate the overall spectral feature parameter Ci of the image unit i, where Kp is a set of preset weight factors, p = 1, 2, 3, 4, 5, 6, 7.

[0105] Through the formula Calculate the first-order grayscale moment e1i(w) of the w-th row and the first-order grayscale moment e1i(h) of the h-th column of each image unit i

[0106] Through the formula Calculate the second-order grayscale moment e2i(w) of the w-th row and the second-order grayscale moment e2i(h) of the h-th column of each image unit i.

[0107] It should be noted that the first-order grayscale moment is a measure of the average value of the grayscale value, and the second-order grayscale moment is a measure of the uniformity of the grayscale value. The larger the first-order grayscale moment, the higher the average grayscale value of that row or column. The larger the second-order grayscale moment, the more uneven the distribution of the grayscale value of that row or column.

[0108] Through the formula Calculate the overall first-order moment E1i and the overall second-order moment E2i of each image unit i.

[0109] Further, perform further numerical analysis based on the elements in the grayscale co-occurrence matrix, and extract the texture feature values containing geological information, specifically:

[0110] Through the formula Calculate the homogeneity feature value E3i of the image unit i.

[0111] It should be noted that the homogeneity feature value is a measure of the uniformity of the texture. The larger the homogeneity feature value, the more uniform the texture distribution of the image unit i.

[0112] Through the formula Calculate the contrast feature value E4i of the image unit i.

[0113] It should be noted that the contrast feature value is a measure of the total amount of texture change. The larger the contrast feature value, the more obvious the texture difference of the image unit i.

[0114] Through the formula Calculate the gray entropy E5i of the image unit i.

[0115] It should be noted that the spectral value entropy is a measure of the degree of texture disorder and complexity. The larger the spectral value entropy, the more disordered the texture distribution of the image unit i.

[0116] Through the formula Calculate the correlation eigenvalue E6i of the image unit i.

[0117] It should be noted that the correlation eigenvalue is a measure of the correlation degree of spectral values in different rows and different columns. The larger the correlation eigenvalue, the greater the correlation degree of spectral values of the image unit i in the row and column dimensions.

[0118] For the same image unit i, obtain the indigo color value C2(w, h), green color value C3(w, h), red color value C6(w, h), and infrared color value C7(w, h) of each pixel (w, h).

[0119] Through the formula Calculate the vegetation index U1i, water body index U2i, building index U3i, and soil index U4i of each image unit i.

[0120] The classification matching module integrates and merges the spectral feature parameters Ci, overall first moment E1i, overall second moment E2i, homogeneous eigenvalue E3i, contrast eigenvalue E4i, gray entropy E5i, correlation eigenvalue E6i, vegetation index U1i, water body index U2i, building index U3i, and soil index U4i of each image unit i. Generate the feature vector (Ci, E1i, E2i, E3i, E4i, E5i, E6i, U1i, U2i, U3i, U4i) of each image unit i.

[0121] Retrieve the preset geographical classification labels, including water systems, forest land, cultivated land, garden land, sandy land, water systems, residential buildings, roads, and industrial and mining land. Each geographical classification label has a label classification vector (C, E1, E2, E3, E4, E5, E6, U1, U2, U3, U4).

[0122] Calculate the weighted Euclidean distance d between the feature vector of each image unit i and each label classification vector. When d is less than the preset threshold dMin, it is considered that the image unit i matches successfully with the geographical classification label corresponding to the label classification vector, and a mark is made.

[0123] The calculation formula of the weighted Euclidean distance is: Where α1 is the preset weight factor; β t1 Is a preset set of weight factors; γ t2 Is a preset set of weight factors.

[0124] The geographical data analysis module obtains the geographical classification label matching results obtained by the classification matching module, counts the geographical information data, and performs identification and marking. Specifically:

[0125] The number of image units S1, S2, S3, S4, S5, S6, S7, S8, and S9 matched by water systems, forest lands, cultivated lands, garden lands, sandy lands, residential buildings, unused urban open spaces, roads, and industrial and mining lands. Through the formula Calculate the water system evaluation index ε1 and the green space evaluation index ε2.

[0126] When the water system evaluation index ε1 is less than the preset threshold ε1Min, a water system degradation warning signal is generated;

[0127] When the greening evaluation index ε2 is less than the preset threshold ε2Min, a green space degradation warning signal is generated;

[0128] Obtain the slope angle θi and spectral feature parameter Ci of each image unit i. Calculate the illumination evaluation index ε3i of each image unit i through the formula ε3i = λ1×(θi - θ0) + λ2×Ci, where λ1 and λ2 are preset weight factors. Highlight the image unit i with the illumination evaluation index ε3i greater than the preset threshold ε3Max in yellow. Highlight the image unit i with the illumination evaluation index ε3i less than the preset threshold ε3Min in blue.

[0129] Obtain the weighted Euclidean distance d between each image unit i and other adjacent image units. When d is greater than the preset threshold dMax, mark the image unit i as a classification boundary unit. Traverse the serial number i of all image units in the above process to obtain all classification boundary units, and highlight the classification boundary units in red.

[0130] Please refer to Figure 3 As shown, the geographical information acquisition system and method based on remote sensing images specifically include the following steps:

[0131] Step 1: Remote sensing image analysis;

[0132] Obtain the remote sensing image of the information acquisition area, divide the remote sensing image with a grid of a preset size A×A pixels to obtain a number of image units. Number the image units, and the serial number is i, i = 1, 2, 3,..., n. Where n is the total number of image units obtained after segmentation.

[0133] For each image unit, locate all the pixels in it, and the location coordinates are (w, h), where w is the pixel height and h is the pixel height; where 0 ≤ w ≤ A, 0 ≤ h ≤ A.

[0134] Perform spectral reflectance analysis on all pixels to obtain their reflectance values Q at different wavelengths λ, and plot a reflectance-wavelength graph. Extract the areas in different band ranges in the said reflectance-wavelength graph, and record them as the color values of the pixel (w, h) in different bands, including:

[0135] The purple color value c1(w, h), with a band range of 390nm - 455nm;

[0136] The indigo color value c2(w, h), with a band range of 455nm - 492nm;

[0137] The green color value c3(w, h), with a band range of 492nm - 577nm;

[0138] The yellow color value c4(w, h), with a band range of 577nm - 597nm;

[0139] The orange color value c5(w, h), with a band range of 597nm - 622nm;

[0140] The red color value c6(w, h), with a band range of 622nm - 770nm;

[0141] The infrared color value C7(w, h), with a band range greater than 770nm.

[0142] Through the formula Calculate the overall spectral feature parameter Ci of the image unit i, where Kp is a set of preset weight factors, and p = 1, 2, 3, 4, 5, 6, 7.

[0143] Obtain the grayscale value G(w, h) of each pixel, where 0 ≤ G(w, h) ≤ 255.

[0144] Furthermore, for each image unit i, generate a grayscale co-occurrence matrix of A×A

[0145] Step 2: Remote sensing image feature extraction;

[0146] Through the formula Calculate the first-order grayscale moment e1i(w) of the i-th image unit in the w-th row and the first-order grayscale moment e1i(h) of the h-th column,

[0147] Through the formula Calculate the second-order grayscale moment e2i(w) of the i-th image unit in the w-th row and the second-order grayscale moment e2i(h) of the h-th column.

[0148] Through the formula Calculate the overall first-order moment E1i and the overall second-order moment E2i of each image unit i.

[0149] Further numerical analysis is carried out according to the elements in the gray-level co-occurrence matrix to extract texture feature values containing geological information, specifically as follows:

[0150] Through the formula Calculate the homogeneity feature value E3i of the image unit i.

[0151] Through the formula Calculate the contrast feature value E4i of the image unit i.

[0152] Through the formula Calculate the gray entropy E5i of the image unit i.

[0153] Through the formula Calculate the correlation feature value E6i of the image unit i.

[0154] Step 3: Geographical feature extraction;

[0155] For the same image unit i, obtain the indigo color value C2(w, h), green color value C3(w, h), red color value C6(w, h), and infrared color value C7(w, h) of each pixel (w, h).

[0156] Through the formula Calculate the vegetation index U1i, water body index U2i, building index U3i, and soil index U4i of each image unit i.

[0157] Step 4: Image unit matching;

[0158] Integrate and merge the spectral feature parameters Ci, overall first moment E1i, overall second moment E2i, homogeneity feature value E3i, contrast feature value E4i, gray entropy E5i, correlation feature value E6i, vegetation index U1i, water body index U2i, building index U3i, and soil index U4i of each image unit i. Generate the feature vector (Ci, E1i, E2i, E3i, E4i, E5i, E6i, U1i, U2i, U3i, U4i) of each image unit i.

[0159] Retrieve the preset geographical classification labels, including water system, forest land, cultivated land, garden land, sandy land, water system, residential buildings, roads, and industrial and mining land. Each geographical classification label has a label classification vector (C, E1, E2, E3, E4, E5, E6, U1, U2, U3, U4).

[0160] Calculate the weighted Euclidean distance d between the feature vector of each image unit i and each label classification vector. When d is less than the preset threshold dMin, it is considered that the image unit i matches successfully with the geographical classification label corresponding to the label classification vector, and mark it.

[0161] The calculation formula of the weighted Euclidean distance is: where α1 is a preset weight factor; β t1 is a preset set of weight factors; γ t2 is a preset set of weight factors.

[0162] Step Five: Geographical data analysis;

[0163] Extract the geographical classification label matching results obtained in Step Four, and count the geographical information data, including:

[0164] The number of image units S1, S2, S3, S4, S5, S6, S7, S8, and S9 matched by water systems, forest lands, cultivated lands, garden lands, sandy lands, residential buildings, unused urban open spaces, roads, and industrial and mining lands. Calculate the water system evaluation index ε1 and the green space evaluation index ε2 through the formula Calculate the water system evaluation index ε1 and the green space evaluation index ε2.

[0165] When the water system evaluation index ε1 is less than the preset threshold ε1Min, a water system degradation warning signal is generated;

[0166] When the greening evaluation index ε2 is less than the preset threshold ε2Min, a green space degradation warning signal is generated;

[0167] Obtain the slope angle θi and spectral feature parameter Ci of each image unit i, and calculate the illumination evaluation index ε3i of each image unit i through the formula ε3i = λ1×(θi - θ0)+λ2×Ci, where λ1 and λ2 are preset weight factors. Highlight the image unit i with the illumination evaluation index ε3i greater than the preset threshold ε3Max in yellow. Highlight the image unit i with the illumination evaluation index ε3i less than the preset threshold ε3Min in blue.

[0168] Obtain the weighted Euclidean distance d between each image unit i and other adjacent image units. When d is greater than the preset threshold dMax, mark the image unit i as a classification boundary unit. Traverse the serial numbers i of all image units in the above process to obtain all classification boundary units, and highlight the classification boundary units in red.

[0169] It should be understood that the terms "including" and "comprising" used in the specification and claims of this disclosure indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0170] It should also be understood that the terminology used herein in this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the disclosure. As used in this disclosure specification and the claims, unless the context clearly dictates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms. It should further be understood that the term "and / or" as used in this disclosure specification and the claims refers to any combination and all possible combinations of one or more of the associated listed items and includes these combinations;

[0171] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only the specific embodiments. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A geographic information acquisition system based on remote sensing images, including a feature remote sensing image analysis module, a data extraction module, a classification matching module and a geographic data analysis module, characterized in that: The remote sensing image analysis module obtains the remote sensing image of the information collection area, and divides the remote sensing image into squares with a preset size of A×A pixels to obtain a number of image units; the image units are numbered, and the number symbol is i, i=1, 2, 3, ..., n; wherein n is the total number of image units obtained after segmentation; For each image unit, all pixels in it are located, and the positioning coordinates are (w, h), where w is the pixel height, h is the pixel height; where 0≤w≤A, 0≤h≤A; Perform spectral reflectance analysis on all pixels, obtain their reflectance values ​​Q at different wavelengths λ, and draw a reflectance-wavelength graph; extract the areas in different characteristic bands from the reflectance-wavelength graph and record them as the color values ​​of pixels (w, h) in different characteristic bands, including: Get the gray value G(w, h) of each pixel, where 0≤G(w, h)≤255; for each image unit i, generate an A×A grayscale companion matrix ; The feature data extraction module performs statistical feature analysis based on color value and gray value data. Specifically, the formula Calculate the overall spectral characteristic parameter Ci of image unit i, where Kp is a set of preset weight factors, p=1, 2, 3, 4, 5, 6, 7; Where cp is the color value of the pixel (w, h) in different bands, including: Purple color value c1 (w, h), band range is 390nm-455nm; The color value of indigo is c2 (w, h), and the wavelength range is 455nm-492nm; Green color value c3 (w, h), band range is 492nm-577nm; Yellow color value c4 (w, h), band range is 577nm-597nm; Orange color value c5 (w, h), band range 597nm-622nm; Red color value c6 (w, h), band range is 622nm-770nm; Infrared color value C7 (w, h), the band range is greater than 770nm; By formula Calculate the first-order moment of grayscale e1i(w) in the wth row and the first-order moment of grayscale e1i(h) in the hth column of each image unit i, using the formula Calculate the gray second moment e2i(w) of each image unit i in the wth row and the gray second moment e2i(h) of the hth column; by formula Calculate the overall first-order moment E1i and the overall second-order moment E2i of each image unit i; Further numerical analysis is performed based on the elements in the grayscale associated matrix to calculate the texture feature values ​​containing geological information: the spectral analysis results are retrieved to calculate the vegetation index, water index, building index and soil index of each image unit; The classification matching module integrates and merges the spectral characteristic parameters Ci, the overall first-order moment E1i, the overall second-order moment E2i, the homogeneous eigenvalue E3i, the contrast eigenvalue E4i, the grayscale entropy E5i, the correlation eigenvalue E6i, the vegetation index U1i, the water index U2i, the building index U3i and the soil index U4i of each image unit i; generates the characteristic vector of each image unit i (Ci, E1i, E2i, E3i, E4i, E5i, E6i, U1i, U2i, U3i, U5i); calls the preset geographic classification label, each geographic classification label has a label classification vector (C, E1, E2, E3, E4, E5, E6, U1, U2, U3, U5); Calculate the weighted Euclidean distance d between the feature vector of each image unit i and each label classification vector. When d is less than the preset threshold dMin, it is considered that the geographic classification label corresponding to the image unit i and the label classification vector is successfully matched and marked; The geographic data analysis module obtains the geographic classification label matching results obtained by the classification matching module, counts the geographic information data, and performs identification and marking; The number of image units matched to each geographic classification label is obtained and the water system evaluation index ε1 and the green space evaluation index ε2 are obtained through numerical calculation; when the water system evaluation index ε1 is less than the preset threshold ε1Min, a water system degradation warning signal is generated; when the green space evaluation index ε2 is less than the preset threshold ε2Min, a green space degradation warning signal is generated; Obtain the slope angle θi and spectral characteristic parameter Ci of each image unit i, and calculate the illumination evaluation index ε3i of each image unit i by the formula ε3i=λ1×(θi-θ0)+λ2×Ci, where λ1 and λ2 are preset weight factors; highlight the image unit i whose illumination evaluation index ε3i is greater than the preset threshold ε3Max in yellow; highlight the image unit i whose illumination evaluation index ε3i is less than the preset threshold ε3Min in blue; Obtain the weighted Euclidean distance d between each image unit i and other intersecting image units. When d is greater than the preset threshold dMax, mark the image unit i as a classification boundary unit. Traverse the classification boundary unit marking process through the number symbols i of all image units to obtain all classification boundary units, and highlight the classification boundary units in red.

2. The geographic information acquisition system based on remote sensing images according to claim 1 is characterized in that: The specific process of numerically analyzing the elements in the grayscale associated matrix and calculating the texture feature values ​​containing geological information is as follows: By formula Calculate the homogeneous eigenvalue E3i of image unit i; By formula Calculate the contrast eigenvalue E4i of image unit i; By formula Calculate the grayscale entropy E5i of image unit i; By formula Calculate the relevant eigenvalue E6i of image unit i.

3. The geographic information acquisition system based on remote sensing images according to claim 1 is characterized in that: The specific process of retrieving the spectral analysis results and calculating the vegetation index, water index, building index and soil index of each image unit is as follows: For the same image unit i, obtain the indigo color value C2 (w, h), the green color value C3 (w, h), the red color value C6 (w, h) and the infrared color value C7 (w, h) of each pixel (w, h); By formula Calculate the vegetation index U1i, water index U2i, building index U3i and soil index U4i of each image unit i.

4. The geographic information acquisition system based on remote sensing images according to claim 1 is characterized in that: The characteristic bands include: Purple color value c1 (w, h), band range is 390nm-455nm; The color value of indigo is c2 (w, h), and the wavelength range is 455nm-492nm; Green color value c3 (w, h), band range is 492nm-577nm; Yellow color value c4 (w, h), band range is 577nm-597nm; Orange color value c5 (w, h), band range 597nm-622nm; Red color value c6 (w, h), band range is 622nm-770nm; Infrared color value C7 (w, h), the band range is greater than 770nm.

5. The geographic information acquisition system based on remote sensing images according to claim 1 is characterized in that: The geographical classification labels include: water system, forest land, cultivated land, garden land, sandy land, water system, residential buildings, roads and industrial and mining land.

6. The geographic information acquisition system based on remote sensing images according to claim 1 is characterized in that: The calculation formula of the weighted Euclidean distance is specifically: ,in is the preset weight factor; is a set of preset weight factors; is a set of preset weight factors; Wherein, t1=1, 2, 3, 4, 5, 6; Et1i includes the overall first-order moment E1i, the overall second-order moment E2i, the homogeneous eigenvalue E3i, the contrast eigenvalue E4i, the gray entropy E5i and the correlation eigenvalue E6i; Among them, Ut2i is; t2 = 1, 2, 3, 4; Ut2i includes vegetation index U1i, water index U2i, building index U3i and soil index U4i; Among them, Etl and Ut2 are the preset values ​​corresponding to each element in the label classification vector (C, E1, E2, E3, E4, E5, E6, U1, U2, U3, U4) corresponding to each geographic classification label, including the overall first-order moment preset value E1, the overall second-order moment preset value E2, the homogeneous eigenvalue preset value E3, the contrast eigenvalue preset value E4, the grayscale entropy preset value E5, the correlation eigenvalue preset value E6, the vegetation index preset value U1, the water body index preset value U2, the building index preset value U3 and the soil index preset value U4.

7. The geographic information acquisition system based on remote sensing images according to claim 1 is characterized in that: The specific process of obtaining the water system evaluation index and green space evaluation index through numerical calculation is as follows: Get the preset geographic classification labels, that is, the number of image units S1, S2, S3, S4, S5, S6, S7, S8 and S9 matched to water system, forest land, cultivated land, garden land, sand land, residential buildings, unused urban open space, roads and industrial and mining land; through the formula Calculate the water system evaluation index ε1 and green space evaluation index ε2.

8. A method for collecting geographic information based on remote sensing images, characterized in that The geographic information acquisition system based on remote sensing images applied to any one of claims 1 to 7 comprises the following steps: Step 1: Remote sensing image analysis; Acquire a remote sensing image of the information collection area, segment the remote sensing image into squares of a preset size of A×A pixels to obtain a plurality of image units; number the image units, where the number symbol is i, i=1, 2, 3, ..., n; wherein n is the total number of image units obtained after segmentation; For each image unit, all pixels in it are located, and the positioning coordinates are (w, h), where w is the pixel height and h is the pixel height; Perform spectral reflectance analysis on all pixels, obtain their reflectance values ​​Q at different wavelengths λ, and draw a reflectance-wavelength graph; extract the areas in different bands from the reflectance-wavelength graph and record them as the color values ​​of pixels (w, h) in different bands; By formula Calculate the overall spectral characteristic parameter Ci of image unit i, where Kp is a set of preset weight factors, p=1, 2, 3, 4, 5, 6, 7; Get the gray value G(w,h) of each pixel, where 0≤G(w,h)≤255; For each image unit i, generate an A×A grayscale companion matrix ; Step 2: Remote sensing image feature extraction; By formula Calculate the first-order moment of grayscale e1i(w) in the wth row and the first-order moment of grayscale e1i(h) in the hth column of each image unit i, By formula Calculate the gray second-order moment e2i(w) of each image unit i in the wth row and the gray second-order moment e2i(h) in the hth column; By formula Calculate the overall first-order moment E1i and the overall second-order moment E2i of each image unit i; Further numerical analysis is performed based on the elements in the grayscale associated matrix to extract the texture feature values ​​containing geological information, specifically: By formula Calculate the homogeneous eigenvalue E3i of image unit i; By formula Calculate the contrast eigenvalue E4i of image unit i; By formula Calculate the grayscale entropy E5i of image unit i; By formula Calculate the relevant eigenvalue E6i of image unit i; Step 3: Geographic feature extraction; For the same image unit i, obtain the indigo color value C2 (w, h), the green color value C3 (w, h), the red color value C6 (w, h) and the infrared color value C7 (w, h) of each pixel (w, h); By formula Calculate the vegetation index U1i, water index U2i, building index U3i and soil index U4i of each image unit i; Step 4: Image unit matching; Integrate and merge the spectral characteristic parameters Ci, overall first-order moment E1i, overall second-order moment E2i, homogeneous eigenvalue E3i, contrast eigenvalue E4i, grayscale entropy E5i, correlation eigenvalue E6i, vegetation index U1i, water index U2i, building index U3i and soil index U4i of each image unit i; generate the characteristic vector (Ci, E1i, E2i, E3i, E4i, E5i, E6i, U1i, U2i, U3i, U4i) of each image unit i; Retrieve preset geographic classification labels, including water system, forest land, cultivated land, garden land, sandy land, water system, residential buildings, roads and industrial and mining land; each geographic classification label has a label classification vector (C, E1, E2, E3, E4, E5, E6, U1, U2, U3, U4); Calculate the weighted Euclidean distance d between the feature vector of each image unit i and each label classification vector. When d is less than the preset threshold dMin, it is considered that the geographic classification label corresponding to the image unit i and the label classification vector is successfully matched and marked; The calculation formula of weighted Euclidean distance is: ,in is the preset weight factor; is a set of preset weight factors; is a set of preset weight factors; Wherein, t1=1, 2, 3, 4, 5, 6; Et1i includes the overall first-order moment E1i, the overall second-order moment E2i, the homogeneous eigenvalue E3i, the contrast eigenvalue E4i, the gray entropy E5i and the correlation eigenvalue E6i; Among them, Ut2i is; t2 = 1, 2, 3, 4; Ut2i includes vegetation index U1i, water index U2i, building index U3i and soil index U4i; Among them, Etl and Ut2 are the preset values ​​corresponding to each element in the label classification vector (C, E1, E2, E3, E4, E5, E6, U1, U2, U3, U4) corresponding to each geographic classification label, including the overall first-order moment preset value E1, the overall second-order moment preset value E2, the homogeneous eigenvalue preset value E3, the contrast eigenvalue preset value E4, the gray entropy preset value E5, the correlation eigenvalue preset value E6, the vegetation index preset value U1, the water body index preset value U2, the building index preset value U3 and the soil index preset value U4; Step 5: Geographic data analysis; Extract the geographic classification label matching results obtained in step 4 and count the geographic information data, including: The number of image units matched to water system, forest land, cultivated land, garden land, sand land, residential buildings, unused urban open space, roads and industrial and mining land is S1, S2, S3, S4, S5, S6, S7, S8 and S9; through the formula Calculate the water system evaluation index ε1 and the green space evaluation index ε2; When the water system evaluation index ε1 is less than the preset threshold ε1Min, a water system degradation warning signal is generated; When the greening evaluation index ε2 is less than the preset threshold ε2Min, a green space degradation warning signal is generated; Obtain the slope angle θi and spectral characteristic parameter Ci of each image unit i, and calculate the illumination evaluation index ε3i of each image unit i by the formula ε3i=λ1×(θi-θ0)+λ2×Ci, where λ1 and λ2 are preset weight factors; highlight the image unit i whose illumination evaluation index ε3i is greater than the preset threshold ε3Max in yellow; highlight the image unit i whose illumination evaluation index ε3i is less than the preset threshold ε3Min in blue; Obtain the weighted Euclidean distance d between each image unit i and other intersecting image units. When d is greater than a preset threshold dMax, mark the image unit i as a classification boundary unit. Perform the process on the number symbols i of all image units to obtain all classification boundary units, and highlight the classification boundary units in red.

Images