Granite identification method and device based on SDGSAT-1 satellite thermal infrared remote sensing

Abstract

The invention discloses a granite identification method and device based on SDGSAT-1 satellite thermal infrared remote sensing, and the method comprises the steps: carrying out the preprocessing of obtained SDG SAT-1 satellite thermal infrared remote sensing data, and obtaining a remote sensing image of a research region; establishing a corresponding relation between mineral spectral curve characteristics of the research area and a satellite sensor spectral domain; according to the remote sensing image and the mineral spectrum curve in the corresponding relation, a GTI granite index graph is extracted through a GTI granite index formula, and according to the GTI granite index graph, a granite distribution result graph meeting the preset precision is extracted by combining an image segmentation and edge detection method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing image processing analysis and modern sedimentology, and particularly relates to a granite identification method and device based on SDGSAT-1 satellite thermal infrared remote sensing. BACKGROUND

[0002] Granite is an intrusive igneous rock formed by cooling and solidification of magma in the deep crust, which contains rich mineral elements. When it intrudes into surrounding rocks, it usually causes hydrothermal activity, metasomatism and tectonic movement, etc., providing a heat source for the formation of ore deposits. Traditional granite identification relies on field exploration, which consumes a large amount of human and material resources. Remote sensing technology has the characteristics of wide coverage and dynamic monitoring, which can greatly reduce the field work. Therefore, the granite identification method is of great significance for mineral resource exploration, geological evolution research and basic geological survey.

[0003] The research results show that in the existing research, the remote sensing extraction of granite is mostly based on ASTER and Landsat multispectral data. Zhang et al. used Landsat 8OLI color synthesis to assist in interpreting granite bodies and regional tectonic features and tectonic evolution. Zhou et al. used ASTER VNIR-SWIR spectral domain data, extracted and combined characteristic bands, and used RF classification method to distinguish different subtypes of granite bodies. In 2013, Zheng et al. based on ASTER VNIR-SWIR and TIR data, established a band ratio method to extract granite in Karamay area. However, the quartz-feldspar mineral of granite has no obvious diagnostic spectrum in VNIR-SWIR spectral domain, and the extraction result is not satisfactory. In the TIR band where silicate minerals have response characteristics, the spatial resolution of traditional multispectral data is low (ASTER is 90m, Landsat-8OLI is 100m), which is difficult to meet the accuracy requirement. Therefore, it is urgent to seek image data with higher spatial resolution for remote sensing extraction of granite.

[0004] The Sustainable Development Science Satellite-1(SDGSAT-1) was launched on November 5, 2021, and is China's first satellite dedicated to serving the United Nations 2030 Sustainable Development Agenda. It carries three payloads of multispectral, low-light and thermal infrared, with high resolution and large swath data acquisition capability, and has shown good application value soon after launch.

[0005] So far, the research related to SDGSAT-1 is mostly about urban expansion using micro-light data, and there are few studies using thermal infrared data, especially no research has been found on extracting lithology using SDGSAT-1 data. However, the thermal infrared imager of SDGSAT-1 has the ability to obtain 300km wide, 30m spatial resolution data, which is the most advanced spaceborne thermal infrared sensor so far, and has obvious advantages in granite extraction.

[0006] Therefore, how to realize a granite identification method based on SDGSAT-1 satellite thermal infrared remote sensing to accurately identify the surface granite is an urgent problem to be solved. SUMMARY

[0007] The present application provides a method and device for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing. The method is based on SDGSAT-1 satellite thermal infrared remote sensing data, through spectral analysis of granite in the study area, and extraction of GTI granite index map, finally realizing accurate identification of surface granite.

[0008] In a first aspect, the present application provides a method for identifying granite based on satellite thermal infrared remote sensing, which comprises:

[0009] Pretreating the obtained SDGSAT-1 satellite thermal infrared remote sensing data to obtain a remote sensing image of the study area;

[0010] Establishing a corresponding relationship between the mineral spectral curve characteristics of the study area and the spectral domain of the satellite sensor;

[0011] According to the processed remote sensing image, the mineral spectral curve in the corresponding relationship, and using the GTI granite index formula to extract the GTI granite index map;

[0012] According to the GTI granite index map, combining image segmentation and edge detection method to extract the granite distribution result map meeting the preset accuracy.

[0013] Optionally, the pretreatment of the obtained SDGSAT-1 satellite thermal infrared remote sensing data comprises:

[0014] Radiometric calibration, atmospheric correction, emissivity extraction and mosaic cropping.

[0015] Optionally, the establishment of the corresponding relationship between the mineral spectral curve characteristics of the study area and the spectral domain of the satellite sensor comprises:

[0016] Determining the granite mineral spectral curve characteristics of the study area;

[0017] Obtaining the spectral domain of SDGSAT-1 sensor;

[0018] Establish the correspondence between the spectral characteristics of granite minerals and the spectral domain of the SDGSAT-1 sensor.

[0019] Optionally, determining the granite mineral spectral curve characteristics of the study area includes:

[0020] The mineral composition of the granite in the study area was determined; the granite components include quartz and feldspar; the spectral curves of the granite minerals were obtained by experimental methods.

[0021] Determine the absorption characteristics of the mineral's spectral curve.

[0022] Optionally, establishing the correspondence between the spectral characteristics of granite minerals and the spectral domain of the SDG SAT-1 sensor includes:

[0023] Determine the location of absorption peaks in the absorption characteristics of mineral spectral curves;

[0024] The corresponding characteristic bands are determined based on the location of the absorption peak; wherein, the characteristic bands are the first, second and third bands of SDGSAT-1.

[0025] Optionally, the formula for the GTI granite index is:

[0026] GTI = (DNb2 * DNb3) / (DNb1 * DNb1)

[0027] Wherein, GTI represents the granite index, DN represents the gray value of each pixel in the processed remote sensing images of different bands, b1 is the first band of SDGSAT-1, b2 is the second band of SDGSAT-1, and b3 is the third band of SDGSAT-1.

[0028] Optionally, the step of extracting a granite distribution map that meets a preset accuracy from the image processed according to the ratio, combined with image segmentation and edge detection methods, includes:

[0029] Based on the GTI granite index map processed by ratio, image segmentation is performed to obtain the first predicted granite distribution result map;

[0030] Edge detection processing is performed on the first predicted granite distribution map to obtain the second predicted granite distribution map;

[0031] The accuracy of the second predicted granite distribution map is evaluated to obtain a granite distribution map that meets the preset accuracy.

[0032] Optionally, the step of performing image segmentation on the GTI granite index map processed according to the ratio to obtain a first predicted granite distribution result map includes:

[0033] Based on the GTI granite index map, the maximum inter-class variance between the two land cover types was determined using the OTSU method; this maximum variance was then used as the global segmentation threshold.

[0034] Image segmentation is performed based on the global segmentation threshold to obtain the first predicted granite distribution result map.

[0035] Optionally, the formula for the inter-class variance between the two types of land cover in the OTSU method is:

[0036] σ=P A (M A -M) 2 +P B (M B -M) 2

[0037] Where σ represents the inter-class variance between the two types of land features, A and B represent the granite and non-granite pixels, and the granite pixel values ​​are [v1, v2, v3, ..., v k The pixel value for non-granite is [v]. k+1 v k+2 v k+3 , ..., v m ], v k P represents the threshold value for segmentation. A P B These represent the proportions of the two types of pixels in the entire image, respectively, and M represents the average pixel value of the image. A M B These represent the average values ​​of pixels in classes A and B, respectively.

[0038] Optionally, the accuracy of the second predicted granite distribution map is evaluated to obtain a granite distribution map that meets the preset accuracy, including:

[0039] The extraction accuracy of granite was determined by combining precision, recall, and F1 score.

[0040] If all three accuracy evaluation parameters meet the predetermined values, a granite distribution result map that meets the preset accuracy will be obtained.

[0041] Precision rate refers to the proportion of samples whose predicted value is 1 and whose actual value is also 1 among all samples whose predicted value is 1.

[0042] Recall rate is the proportion of samples whose predicted value is 1 and whose true value is also 1 out of all samples whose true value is 1.

[0043] The F1 score is the harmonic mean of precision and recall.

[0044] The predicted value is the value from the granite distribution map, while the actual value is the value obtained in practice.

[0045] Secondly, embodiments of the present invention also provide an apparatus for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing. The apparatus includes a memory and a processor. The memory is used to store a program for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing, and the processor is used to read and execute the program for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing, and execute the method described in any of the above embodiments.

[0046] Thirdly, embodiments of the present invention also provide a computer-readable storage medium storing a data processing program, wherein the data processing program is executed by a processor using the method for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing as described in any of the above embodiments.

[0047] Compared with related technologies, this application provides a method and apparatus for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing. The method includes: preprocessing the acquired SDGSAT-1 satellite thermal infrared remote sensing data to obtain a remote sensing image of the study area; establishing a correspondence between the mineral spectral curve features of the study area and the spectral domain of the satellite sensor; extracting a GTI granite index map using the GTI granite index formula based on the mineral spectral curves in the remote sensing image and the correspondence; and extracting a granite distribution result map that meets a preset accuracy based on the GTI granite index map, combined with image segmentation and edge detection methods. This application, based on SDGSAT-1 satellite thermal infrared remote sensing data, performs spectral analysis on the granite in the study area and extracts the GTI granite index map, ultimately achieving accurate identification of surface granite.

[0048] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. Other advantages of this application can be realized and obtained by means of the solutions described in the description and the accompanying drawings. Attached Figure Description

[0049] The accompanying drawings are used to provide an understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0050] Figure 1 This is a flowchart illustrating the method for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing, according to an embodiment of this application.

[0051] Figure 2 This is a schematic diagram of a device for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing, according to an embodiment of this application.

[0052] Figure 3 Here is a flowchart illustrating the lithological classification method for SDGSAT-1 satellite data in some exemplary embodiments;

[0053] Figure 4 This is a schematic diagram of the location of the study area in some exemplary embodiments;

[0054] Figure 5 Here are the emission spectrum curves of quartz and feldspar group minerals and the sensor spectrum diagrams in some exemplary embodiments;

[0055] Figure 6 This is a schematic diagram of the band calculation results of the GTI granite index in some exemplary embodiments;

[0056] Figure 7 This is a schematic diagram of the OTSU results in some exemplary embodiments;

[0057] Figure 8 This is a schematic diagram of granite extraction results in some exemplary embodiments. Detailed Implementation

[0058] This application describes several embodiments, but these descriptions are exemplary and not restrictive, and it will be apparent to those skilled in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment may be used in combination with, or may replace, any feature or element of any other embodiment.

[0059] This application includes and contemplates combinations of features and elements known to those skilled in the art. The embodiments, features, and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive scheme as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive schemes to form another unique inventive scheme as defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application may be implemented individually or in any suitable combination. Therefore, the embodiments are not limited except by the limitations imposed by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.

[0060] Furthermore, in describing representative embodiments, the specification may have presented methods and / or processes as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that it does not depend on such a specific order. As will be understood by those skilled in the art, other sequences of steps are also possible. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation of the claims. Moreover, the claims concerning the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of this application.

[0061] This invention provides a method for granite identification based on SDGSAT-1 satellite thermal infrared remote sensing, such as... Figure 1 As shown, the method includes steps S100-S130:

[0062] S 100: The acquired SDGSAT-1 satellite thermal infrared remote sensing data is preprocessed to obtain remote sensing images of the study area;

[0063] S 110: Establish the correspondence between the mineral spectral curve characteristics of the study area and the spectral domain of satellite sensors;

[0064] S120: Based on the mineral spectral curves in the remote sensing images and corresponding relationships, extract the GTI granite index map using the GTI granite index formula;

[0065] S 130: Based on the GTI granite index map, extract a granite distribution result map that meets the preset accuracy by combining image segmentation and edge detection methods.

[0066] In one exemplary embodiment, the preprocessing operation of the acquired SDGSAT-1TIS remote sensing data includes:

[0067] Radiation calibration, atmospheric correction, emissivity extraction, mosaicking and clipping.

[0068] In one exemplary embodiment, establishing the correspondence between the mineral spectral curve characteristics of the granite study area and the spectral domain of satellite sensors includes:

[0069] Step 1: Determine the spectral characteristics of granite minerals in the granite study area;

[0070] Step 2: Obtain the spectral domain of the SDGSAT-1 sensor, as shown in Table 1, SDGSAT-1TIS satellite sensor parameter table;

[0071] Step 3: Establish the relationship between the spectral curve characteristics and the spectral domain of the SDGSAT-1TIS sensor.

[0072] In one exemplary embodiment, determining the spectral characteristics of granite minerals in a granite study area includes:

[0073] Step 1: Determine the mineral composition of the granite in the study area. The granite is composed of quartz and feldspar.

[0074] Step 2: Obtain the spectral curve of granite minerals using experimental methods;

[0075] In this step, the spectral curves of granite minerals can be obtained in two ways: one is to directly obtain standard spectral curves from a spectral library, and the other is to directly measure the mineral spectral curves on-site using a ground-based spectrometer.

[0076] Step 3: Determine the absorption characteristics of the mineral's spectral curve.

[0077] In one exemplary embodiment, the relationship between the spectral curve characteristics and the spectral domain of the SDGSAT-1TIS sensor is analyzed:

[0078] Step 1: Determine the values ​​of the absorption peaks in the absorption characteristics of the mineral's spectral curve;

[0079] Step 2: Determine the corresponding characteristic bands based on the absorption peak values; wherein, the characteristic bands are the first, second, and third bands of SDGSAT-1.

[0080] In one exemplary embodiment, the process of extracting a GTI granite index map based on the processed remote sensing image, the mineral spectral curves in the corresponding relationship, and the GTI granite index formula includes:

[0081] Based on the mineral spectral curves in the correspondence, the GTI granite extraction index is proposed. Based on the processed remote sensing image, the GTI granite index map is extracted using the GTI granite index formula.

[0082] The formula for the GTI granite index is:

[0083] GTI = (DNb2 * DNb3) / (DNb1 * DNb1)

[0084] Wherein, GTI represents the granite index, DN represents the gray value of each pixel in the processed remote sensing images of different bands, and b1, b2, and b3 represent the 1st, 2nd, and 3rd bands of SDGSAT-1TIS, respectively.

[0085] In one exemplary embodiment, based on the GTI granite index map, a granite distribution result map meeting a preset accuracy is extracted using image segmentation and edge detection methods, including:

[0086] The GTI granite index map is extracted according to the GTI granite index extraction formula, and image segmentation is performed to obtain the first predicted granite distribution result map.

[0087] Edge detection processing is performed on the first predicted granite distribution map to obtain the second predicted granite distribution map;

[0088] The accuracy of the second predicted granite distribution map is evaluated to obtain a granite distribution map that meets the preset accuracy.

[0089] In one exemplary embodiment, the step of extracting the GTI granite index map according to the GTI granite index extraction formula, performing image segmentation, and obtaining a first predicted granite distribution result map includes:

[0090] Step 1: Extract the GTI granite index map according to the GTI granite index extraction formula, and use the OTSU method to maximize the inter-class variance between the two types of land features;

[0091] The second step is to use the maximum variance as the global segmentation threshold.

[0092] The third step is to segment the image based on the global segmentation threshold to obtain the first predicted granite distribution result map.

[0093] In one exemplary embodiment, the calculation process of the inter-class variance between two types of land features using the OTSU method is as follows:

[0094] Assume the pixel values ​​of the granite in the study area are [v1, v2, v3, ..., v k The pixel value of the non-granite area is [v]. k+1， v k+2 v k+3 , ..., v m The variance between classes is:

[0095] σ=P A (M A -M) 2 +P B (M B -M) 2

[0096] Among them, v k The threshold for segmentation is represented by σ, the calculated inter-class variance is represented by A and B, and the pixels represent the granite and non-granite classes, respectively. A P B M represents the proportion of the two types of pixels in the entire image, respectively. A M B represents the average value of pixels in classes A and B, respectively, and M represents the average value of image pixels.

[0097] In one exemplary embodiment, the accuracy of the second predicted granite distribution result map is evaluated to obtain a granite distribution result map that meets a preset accuracy, including:

[0098] Precision, recall, and F1 score are calculated based on the predicted and actual values ​​of the second predicted granite distribution map.

[0099] The extraction accuracy of granite is determined by comprehensively considering precision, recall, and F1 score.

[0100] Precision refers to the proportion of samples whose predicted value is 1 and whose actual value is also 1 out of all samples whose predicted value is 1.

[0101]

[0102] In this formula, TP is a sample whose predicted value is 1 and whose actual value is also 1, and FP is a sample whose predicted value is 1 and whose actual value is not 1.

[0103] Recall is the proportion of samples whose predicted value is 1 and whose true value is also 1 out of all samples whose true value is 1.

[0104]

[0105] TP is a sample whose predicted value is 1 and whose actual value is also 1, and FN is a sample whose predicted value is not 1 and whose actual value is 1.

[0106] The F1 score is defined as the harmonic mean of precision and recall.

[0107]

[0108] The predicted value is derived from the GTI granite index extraction formula, while the actual value is obtained by interpreting Google Earth Pro high-resolution images combined with measured 1:200,000 geological maps by experts.

[0109] The method for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing implemented in this embodiment has the following technical effects:

[0110] First, this invention makes full use of SDGSAT-1 satellite thermal infrared spectroscopy (TIS) data to propose an innovative method for the accurate identification of surface granite, detect the spatial distribution of surface granite, analyze its distribution patterns, and provide a basis for the study of surface lithology, geological structure and environmental changes.

[0111] Secondly, this method has achieved remarkable results in practical applications. Compared with traditional field exploration methods, it not only improves the comprehensiveness and accuracy of the analysis, but also reduces the time and investment in field exploration, providing beneficial technical application effects for the distribution of surface lithology.

[0112] Secondly, embodiments of the present invention also provide a device for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing, such as... Figure 2 As shown, the device includes a memory 200 and a processor 210; the memory is used to store a program for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing, and the processor is used to read and execute the program for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing, and to execute the method described in any of the above embodiments.

[0113] Thirdly, embodiments of the present invention also provide a computer-readable storage medium storing a data processing program, wherein the data processing program is executed by a processor using the method for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing as described in any of the above embodiments.

[0114] Example 1

[0115] This example demonstrates the specific implementation process of a method for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing, such as... Figure 3 As shown, the specific implementation process is as follows:

[0116] Step 1: Acquire remote sensing data of the study area

[0117] Figure 4 This is a geographical map of the study area. The study area is located in the Karamay Back Mountain area of ​​West Junggar, in the southwestern part of the Central Asian Orogenic Belt, with geographical coordinates of 84°06′17″~85°34′12″E and 45°36′23″~46°13′53″N. Most of the area consists of thick Carboniferous volcanic clastic sedimentary rock strata, from which large-scale Late Carboniferous-Early Permian intermediate-acidic granite bodies are produced. The main rock types are alkali feldspar granite, syenite, and monzogranite, including the Karamay, Xiarpu, and Hongshan rock bodies. The region has an arid and semi-arid climate, which is very suitable for remote sensing application research.

[0118] The remote sensing data for the study area were acquired on February 4, 2022, using domestically produced SDGSAT-1 thermal infrared (TIS) images. The specific parameters are shown in Table 1, which contains the SDGSAT-1TIS satellite sensor parameters. As can be seen from Table 1, the SDGSAT-1TIS thermal infrared imager detects three bands with a swath width of 300 km and a spatial resolution of 30 m.

[0119] Table 1

[0120]

[0121] Step 2: Preprocess the remote sensing data;

[0122] The remote sensing data is classified as L4A. Since the data has undergone precise geometric and topographic corrections, the preprocessing steps include radiometric calibration, atmospheric correction, emissivity extraction, and mosaic clipping.

[0123] The specific preprocessing steps are as follows:

[0124] Radiometric calibration converts the digital quantization values ​​(DN values) received by the optical sensor into apparent reflectance; atmospheric correction converts the apparent reflectance into the actual reflectance of the Earth's surface.

[0125] Emissivity extraction separates emissivity and surface temperature from surface reflectivity;

[0126] Mosaic clipping performs geometric registration and integration of multiple images of the study area, and then clips them to the required range of the study area;

[0127] Geometric registration aligns images from different sources at different times in space so that corresponding cells represent the same object;

[0128] Image mosaicking seamlessly stitches together remote sensing images from different sensors or at different resolutions into a new image; image cropping is performed according to the study area to generate a base map of the study area.

[0129] Step 3: Determine the spectral characteristics of granite minerals in the study area.

[0130] In this step, the mineral composition of the granite is first analyzed as a diagnostic mineral for spectral analysis;

[0131] Then, the spectral characteristics of these minerals were analyzed, mainly the thermal infrared emission spectrum, corresponding to the spectral domain of the TIS sensor.

[0132] The spectral characteristics of granite-forming minerals are analyzed, primarily focusing on the absorption characteristics of their spectral curves. The specific steps are as follows:

[0133] Step 1: Analyze the mineral composition of the granite.

[0134] First, we analyze the mineral composition of granite. The main diagenetic minerals of granite are quartz and feldspar, with orthoclase being the most abundant feldspar.

[0135] The second step is to determine the mineral spectral characteristics of the granite.

[0136] The main focus is on the thermal infrared emission spectrum curve, corresponding to the spectral domain of the TIS sensor.

[0137] The third step is to determine the relationship between the spectral curve characteristics and the spectral domain of the SDGSAT-1TIS sensor, and to identify the characteristic bands involved in the calculation.

[0138] The spectral characteristics of granite diagenetic minerals are mainly analyzed by examining the absorption characteristics of their spectral curves.

[0139] Step 4: Establish the correspondence between the spectral characteristics of granite minerals and the spectral domain of the SDGSAT-1 sensor.

[0140] In this step, the relationship between the absorption characteristics of the spectral curve and the spectral domain of the SDGSAT-1TIS sensor is analyzed to determine the characteristic bands involved in the calculation. For example... Figure 5 The emission spectrum curves of quartz and feldspar group minerals shown are compared with the sensor spectrum. Figure 5 It can be seen that the emission spectra of the two types of minerals in the spectral library have absorption peaks at 9.2 μm and 9.7 μm, respectively.

[0141] Step 5: Extract the GTI granite index map

[0142] In this step, specifically: the emission spectra of the two types of minerals in the spectral library show absorption peaks at 9.2 μm and 9.7 μm, respectively, corresponding to the first band of the TIS sensor. The emissivity gradually increases after 10 μm, corresponding to the second and third bands of the TIS sensor. Figure 5 As shown. Therefore, the following formula is established using bands 1, 2, and 3 of the SDGSAT-1TIS to extract the Granite Index (GTI):

[0143] GTI = (DNb2 * DNb3) / (DNb1 * DNb1)

[0144] Wherein, GTI represents the granite index, DN represents the gray value of each pixel in the processed remote sensing images of different bands, and b1, b2, and b3 represent the 1st, 2nd, and 3rd bands of SDGSAT-1TIS, respectively.

[0145] like Figure 6 The diagram shows the results of GTI granite index calculation for the shown band. As can be seen from the diagram, granite-bearing regions should have concentrated peaks in the grayscale histogram.

[0146] Step 4: Perform image segmentation using the OTSU method.

[0147] Image segmentation methods refer to dividing an image into regions with distinct characteristics and extracting the target of interest. Here, we choose to use the OTSU method, which can generate the optimal threshold. This algorithm finds the global segmentation threshold when the inter-class variance between two types of land features is maximized.

[0148] Specifically:

[0149] Assume the pixel values ​​of the granite in the study area are [v1, v2, v3, ..., v k The pixel value of the non-granite area is [v]. k+1， v k+2 v k+3 , ..., v m The variance between classes is:

[0150] σ=P A (M A -M) 2 +P B (M B -M) 2

[0151] Among them, v k The threshold for segmentation is represented by σ, the calculated inter-class variance is represented by A and B, and the pixels represent the granite and non-granite classes, respectively. A P B M represents the proportion of the two types of pixels in the entire image. A M B represents the average value of pixels in classes A and B, respectively, and M represents the average value of image pixels.

[0152] like Figure 7 The image shown is a schematic diagram of the OTSU results; via v k After threshold segmentation, the granite mineral areas were highlighted, distributed in the northeast and southwest regions of the study area, accounting for about 1 / 3 of the study area.

[0153] Step 5: After performing image segmentation using the OTSU method, perform edge processing on the segmentation results.

[0154] The OTSU method segmented the region mainly into orthogonal granite, and the segmented image exhibited blurred boundaries and scattered pixels. Therefore, a self-defined threshold (≥143.438) was used to extract diorite. Simultaneously, the Canny operator was employed for edge extraction. The Canny operator detected edges through five steps: Gaussian filtering, pixel gradient calculation, non-maximum suppression, hysteresis thresholding, and isolated weak edge suppression. A few discontinuous edges were detected; these were connected to obtain the final edge image. Combined with the image segmentation results, the final granite region was delineated. Figure 8 The image shows a schematic diagram of the granite extraction results.

[0155] Step 6: Evaluate the extraction accuracy of the granite.

[0156] In this step, the accuracy evaluation is based on the confusion matrix calculated from the predicted and true values. The accuracy of granite extraction is judged by indicators such as precision, recall, and F1 score, as shown in Table 2.

[0157] Precision refers to the proportion of samples whose predicted value is 1 and whose actual value is also 1 out of all samples whose predicted value is 1.

[0158]

[0159] Recall is the proportion of samples whose predicted value is 1 and whose true value is also 1 out of all samples whose true value is 1.

[0160]

[0161] The F1 score is defined as the harmonic mean of precision and recall.

[0162]

[0163] The predicted value is the result of this method, which is the extracted granite distribution result, while the true value is obtained by expert interpretation of Google Earth Pro high-resolution imagery combined with measured 1:200,000 geological maps.

[0164] Table 2

[0165] Precision / % Recall / % F1 Score / % 96.55 98.43 97.48

[0166] This invention fully utilizes SDGSAT-1 satellite thermal infrared spectroscopy (TIS) data to propose an innovative method for the precise identification of surface granite. It detects the spatial distribution of surface granite, analyzes its distribution patterns, and provides a basis for research on surface lithology, geological structure, and environmental changes. This method has achieved significant results in practical applications. Compared with traditional field exploration methods, it not only improves the comprehensiveness and accuracy of the analysis but also reduces the time and effort required for field exploration, providing beneficial technical applications for understanding surface lithology distribution.

[0167] It will be understood by those skilled in the art that all or some of the steps, systems, or apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

Claims

1. A method for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing, characterized in that, The method comprises: preprocessing the acquired SDGSAT-1 satellite thermal infrared remote sensing data to obtain a remote sensing image of the study area; establishing a correspondence between mineral spectral curve characteristics of the study area and a spectral domain of a satellite sensor; extracting a GTI granite index map using a GTI granite index formula according to the remote sensing image and the mineral spectral curve in the correspondence; extracting a granite distribution result map satisfying a preset accuracy according to the GTI granite index map in combination with an image segmentation and edge detection method.

2. The granite identification method based on SDGSAT-1 satellite thermal infrared remote sensing according to claim 1, characterized in that: the preprocessing operation of the acquired SDGSAT-1 satellite thermal infrared remote sensing data comprises: radiometric calibration, atmospheric correction, emissivity extraction and mosaic cropping.

3. The granite identification method based on SDGSAT-1 satellite thermal infrared remote sensing according to claim 1, characterized in that: the establishing of the correspondence between the mineral spectral curve characteristics of the study area and the spectral domain of the satellite sensor comprises: determining granite mineral spectral curve characteristics of the study area; acquiring a spectral domain of the SDGSAT-1 sensor; establishing a correspondence between the granite mineral spectral curve characteristics and the spectral domain of the SDGSAT-1 sensor. 4.The method for identifying granite based on the thermal infrared remote sensing of SDGSAT-1 according to claim 3, characterized in that, The determination of the granite mineral spectral curve characteristics of the study area comprises: determining granite mineral components of the study area; wherein the granite components include quartz and feldspar; measuring the granite mineral spectral curve using an experimental method; determining absorption characteristics of the mineral spectral curve according to the granite mineral spectral curve.

5. The method for identifying granite based on the thermal infrared remote sensing of the SDGSAT-1 satellite according to claim 4, characterized in that, The establishing of the correspondence between the granite mineral spectral curve characteristics and the spectral domain of the SDGSAT-1 sensor comprises: determining positions of absorption peak values in the absorption characteristics of the mineral spectral curve; determining corresponding characteristic wavebands according to the positions of the absorption peak values; wherein the characteristic wavebands are a first waveband, a second waveband and a third waveband of the SDGSAT-1.

6. The granite identification method based on SDGSAT-1 satellite thermal infrared remote sensing according to claim 1, characterized in that: the GTI granite index formula is: GTI=(DNb2*DNb3) / (DNb1*DNb1) wherein GTI represents the granite index, DN represents a gray value of each pixel in a processed remote sensing image of different wavebands, b1 is the first waveband of the SDGSAT-1, b2 is the second waveband of the SDGSAT-1, and b3 is the third waveband of the SDGSAT-1.

7. The method according to claim 3, wherein the method is based on the thermal infrared remote sensing of the SDGSAT-1 satellite. The extracting of the granite distribution result map satisfying the preset accuracy according to the GTI granite index map in combination with the image segmentation and edge detection method comprises: performing image segmentation on the GTI granite index map processed by ratio to obtain a first predicted granite distribution result map; performing edge detection processing on the first predicted granite distribution result map to obtain a second predicted granite distribution result map; performing accuracy evaluation on the second predicted granite distribution result map to obtain the granite distribution result map satisfying the preset accuracy.

8. The method according to claim 7, wherein the GTI granite index map obtained by the ratio processing is subjected to image segmentation to obtain a first predicted granite distribution result map, and the first predicted granite distribution result map comprises: a GTI granite index map obtained by the ratio processing; an OTSU method for obtaining a maximum variance between two types of ground objects; the maximum variance being used as a global threshold for segmentation; and image segmentation based on the global threshold for segmentation to obtain the first predicted granite distribution result map. The formula for the variance between two types of ground objects by the OTSU method is: wherein, is the variance between two types of ground objects by the OTSU method, is the mean of the first type of ground object, and is the mean of the second type of ground object. The second predicted granite distribution result map is subjected to precision evaluation to obtain a granite distribution result map satisfying a preset precision, and the precision evaluation comprises: a precision, a recall, and an F1 score are used to comprehensively determine the extraction precision of the granite; if the three precision evaluation parameters satisfy predetermined values, the granite distribution result map satisfying the preset precision is obtained; wherein, the precision refers to the proportion of samples with a predicted value of 1 and a true value of 1 in all samples with a predicted value of 1; the recall refers to the proportion of samples with a predicted value of 1 and a true value of 1 in all samples with a true value of 1; and the F1 score is a harmonic mean of the precision and the recall. The device comprises a memory and a processor, the memory is used to save a program for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing, and the processor is used to read and execute the program for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing, and execute the method according to any one of claims 1-10.

12. A computer readable storage medium, wherein a data processing program is stored on the computer readable storage medium, and the data processing program is executed by a processor to execute the method for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing according to any one of claims 1-10. 9.The method for identifying granite based on SDGSAT-1 satellite thermal infrared remote sensing according to claim 8, characterized in that, ​ σ = P A (M A -M) 2 + P B (M B -M) 2 Wherein, σ represents the inter-class variance between the two types of ground objects, A and B represent two types of pixels of granite and non-granite, the pixel value of granite is [v1, v2, v3, …, v k ], the pixel value of non-granite is [v k+1 , v k+2 , v k+3 , …, v m ], v k represents the threshold value divided out; P A , P B respectively represent the proportion of two types of pixels in the whole image, M represents the average value of image pixels, M A , M B respectively represent the average value of pixels in A and B types. 10.The method for identifying granite based on the thermal infrared remote sensing of SDGSAT-1 according to claim 7, characterized in that, ​ ​ ​ ​ ​ ​ ​ 11. A device for identifying granite based on the thermal infrared remote sensing of the SDGSAT-1 satellite, characterized by, ​ ​

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