A geological and topographical analysis and exploration method and system based on remote sensing technology

Abstract

The application discloses a geological and geomorphologic analysis and exploration method and system based on remote sensing technology, and belongs to the field of remote sensing application. The method comprises the following steps: collecting images of a target area through an exploration terminal to obtain a remote sensing image set, performing image preprocessing, extracting data information in the remote sensing preprocessed image set to obtain a geological and geomorphologic data set, obtaining elevation data of the target area through a digital earth platform, correcting the geological and geomorphologic data set to obtain a corrected data set, performing feature extraction according to the corrected data set and the remote sensing preprocessed image set to obtain a feature set, inputting a regional normalization model for analysis, splicing images in the remote sensing preprocessed image set according to relative position labels to obtain a target area remote sensing image. The technical problems of low geological and geomorphologic exploration precision and inaccurate image processing result are solved. The technical effect of improving the geological and geomorphologic exploration precision and image processing accuracy through remote sensing technology is achieved.

Description

Technical Field

[0001] This application relates to the field of remote sensing applications, and in particular to a method and system for geological and geomorphological analysis and exploration based on remote sensing technology. Background Technology

[0002] By studying the morphological characteristics, formation, distribution and evolution of the Earth's surface, geomorphological information can be obtained. At the same time, by conducting geological surveys and explorations, suitable bearing layers can be identified. Based on the bearing capacity of the bearing layers, the foundation type can be determined, foundation parameters can be calculated, and geological information can be obtained. This is of great significance for engineering construction, agricultural production and mineral exploration.

[0003] Currently, with the continuous advancement of science and technology, many new technologies have emerged in engineering geological exploration, which have continuously improved the level of geological exploration and geomorphological analysis. Using planar remote sensing, various maps are collected by scanning to obtain basic data. The data is then interpreted in planar mode to obtain geological information, thereby providing a reference for actual production.

[0004] However, due to the diverse nature of the images, including landforms, regional geology, hydrogeology, meteorology, and ecological environment, and their large spatial and temporal span, the data processing workload is enormous. This makes it difficult to effectively utilize data resources to obtain reliable exploration results, ultimately leading to inaccurate analysis and exploration results, and consequently, engineering accidents in actual production. Technical problems exist regarding low accuracy in geological and geomorphological exploration and inaccurate image processing results. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for geological and geomorphological analysis and exploration based on remote sensing technology, in order to solve the technical problems of low accuracy in geological and geomorphological exploration and inaccurate image processing results in the existing technology.

[0006] In view of the above problems, this application provides a method and system for geological and geomorphological analysis and exploration based on remote sensing technology.

[0007] In a first aspect, this application provides a method for geological and geomorphological analysis and exploration based on remote sensing technology. The method is applied to a geological and geomorphological analysis and exploration system, which is communicatively connected to an exploration terminal. The method includes: acquiring images of a target area through the exploration terminal to obtain a set of remote sensing images; performing image preprocessing on the set of remote sensing images to obtain a set of preprocessed remote sensing images; extracting data information from the preprocessed remote sensing images to obtain a geological and geomorphological dataset; obtaining elevation data of the target area through a digital earth platform and correcting the geological and geomorphological dataset to obtain a corrected dataset; extracting features based on the corrected dataset and the preprocessed remote sensing images to obtain a feature set; inputting the feature set into a region normalization model for analysis and outputting relative position labels; and stitching the images in the preprocessed remote sensing images based on the relative position labels to obtain a remote sensing image of the target area.

[0008] On the other hand, this application also provides a geological and geomorphological analysis and exploration system based on remote sensing technology, wherein the system includes: an image acquisition module, which is used to acquire images of a target area through an exploration terminal to obtain a remote sensing image set; a preprocessing module, which is used to preprocess the remote sensing image set to obtain a remote sensing preprocessed image set; a data extraction module, which is used to extract data information from the remote sensing preprocessed image set to obtain a geological and geomorphological dataset; a correction module, which is used to obtain elevation data of the target area through a digital earth platform and correct the geological and geomorphological dataset to obtain a corrected dataset; a feature extraction module, which is used to extract features based on the corrected dataset and the remote sensing preprocessed image set to obtain a feature set; a label output module, which is used to input the feature set into a region normalization model for analysis and output relative position labels; and a stitching module, which is used to stitch the images in the remote sensing preprocessed image set according to the relative position labels to obtain a remote sensing image of the target area.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application acquires images of a target area using an exploration terminal, obtaining a remote sensing image set. The image set undergoes noise reduction and enhancement processing to obtain a preprocessed remote sensing image set. Data information is then extracted from this preprocessed image set to obtain a geological and geomorphological dataset. Elevation data of the target area is obtained using a digital earth platform, and the geological and geomorphological dataset is corrected in the height direction to obtain a corrected dataset. Feature extraction is then performed based on the corrected dataset and the preprocessed remote sensing image set to obtain a feature set. This feature set is then input into a regional integration model for analysis, outputting relative position labels. Images in the preprocessed remote sensing image set are stitched together based on these relative position labels to obtain a remote sensing image of the target area. This achieves the goal of improving the accuracy of geological and geomorphological analysis and exploration using remote sensing technology, thus enhancing the accuracy of remote sensing image processing. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a geological and geomorphological analysis and exploration method based on remote sensing technology, provided for an embodiment of this application;

[0013] Figure 2 This is a schematic diagram of the image preprocessing process for the remote sensing image set in a geological and geomorphological analysis and exploration method based on remote sensing technology, provided in an embodiment of this application.

[0014] Figure 3 A schematic diagram illustrating the process of obtaining the remote sensing denoising image set in a geological and geomorphological analysis and exploration method based on remote sensing technology, provided in an embodiment of this application;

[0015] Figure 4 This is a schematic diagram of the structure of a geological and geomorphological analysis and exploration system based on remote sensing technology according to this application;

[0016] Figure labeling: Image acquisition module 11, preprocessing module 12, data extraction module 13, correction module 14, feature extraction module 15, label output module 16, stitching module 17. Detailed Implementation

[0017] This application provides a method and system for geological and geomorphological analysis and exploration based on remote sensing technology, which solves the technical problems of low accuracy in geological and geomorphological exploration and inaccurate image processing results in existing technologies. It achieves the technical effect of improving the accuracy of geological and geomorphological exploration using remote sensing technology and enhancing the accuracy of image processing.

[0018] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0019] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0020] Example 1

[0021] like Figure 1 As shown, this application provides a geological and geomorphological analysis and exploration method based on remote sensing technology. The method is applied to a geological and geomorphological analysis and exploration system, which is communicatively connected to an exploration terminal. The method includes:

[0022] Step S100: Collect images of the target area through the exploration terminal to obtain a set of remote sensing images;

[0023] Specifically, the exploration terminal is a terminal that acquires images of the target area from the air using an airborne remote sensing 3D imager. The target area is the region to be subjected to any geological and geomorphological analysis and exploration. The remote sensing images contain the 3D location and remote sensing spectral information of the target area, enabling simultaneous acquisition of location and qualitative data, ensuring the accuracy of the data. The remote sensing image set is a collection of images characterizing the topographic and geomorphological information of the target area.

[0024] Step S200: Perform image preprocessing on the remote sensing image set to obtain a remote sensing preprocessed image set;

[0025] Furthermore, such as Figure 2 As shown, the image preprocessing of the remote sensing image set, in this embodiment of the application, step S200 further includes:

[0026] Step S210: Build an image extraction model to extract images from the remote sensing image set step by step;

[0027] Step S220: The primary index of the image extraction model is imaging angle similarity, the secondary index is imaging feature similarity, and the tertiary index is imaging quality value.

[0028] Step S230: Input the remote sensing image set into the image extraction model and output the extracted image set.

[0029] Specifically, the obtained remote sensing image set may have unclear image information and significant overlap due to the influence of the acquisition instrument and the surrounding environment. Direct use of such images can lead to inaccurate analysis. Therefore, it is necessary to preprocess the remote sensing image set to filter out qualified images and to eliminate noise and improve image quality.

[0030] Specifically, the image extraction model is used to progressively filter and extract images from the remote sensing image set according to indicators. The first-level indicator is imaging angle similarity. Based on this first-level indicator, images with high imaging angle similarity in the remote sensing image set are classified. Then, based on the second-level indicator, imaging feature similarity, features are further extracted from the classified images. Images with similar features are further classified. Finally, based on the third-level indicator, imaging quality value, the images classified in the second level are filtered according to their image quality, and high-quality images with similar imaging features are selected to form the extracted image set. The extracted image set is a preliminary screening of the remote sensing image set, classifying highly repetitive photos based on imaging angle and imaging features, and further filtering based on image quality. This achieves the goal of improving the quality of analyzed images, reducing the workload of subsequent image processing, and achieving the technical effect of improving image processing efficiency and accuracy.

[0031] Furthermore, such as Figure 3 As shown, after the output image set is extracted, step S200 of this embodiment further includes:

[0032] Step S240: Traverse each image in the extracted image set and perform Fourier transform to obtain the amplitude value and phase of each pixel in each image, thereby obtaining multiple sets of image amplitude values ​​and multiple sets of image phases, wherein the multiple sets of image amplitude values ​​and the multiple sets of image phases correspond one-to-one;

[0033] Step S250: By filtering the multiple sets of image amplitude values ​​respectively, multiple sets of filtered image amplitude values ​​are obtained;

[0034] Step S260: Perform an inverse Fourier transform based on the multiple image phase sets and the multiple filtered image amplitude value sets to obtain the remote sensing denoised image set.

[0035] Furthermore, after obtaining the remote sensing denoised image set, step S200 of this embodiment further includes:

[0036] Step S270: Evaluate the image quality of the remote sensing denoised image set and obtain the quality evaluation result;

[0037] Step S280: Determine whether the quality assessment result exceeds a predetermined quality threshold. If it does not exceed the threshold, obtain an image enhancement preprocessing instruction.

[0038] Step S290: Perform image enhancement preprocessing on the remote sensing denoised image set according to the image enhancement preprocessing instruction to obtain a remote sensing preprocessed image set.

[0039] Specifically, noise in the image is removed by performing a Fourier transform. By performing a Fourier transform on each image in the extracted image set, the image can be transformed into the frequency domain, obtaining the phase and amplitude values ​​of each pixel in each image. The phase represents the positional and shape information in the image, allowing pixels in the noise-removed image to be repositioned back to their original locations; the phase is not processed during the noise removal process. The amplitude value represents the energy level of a pixel; noise can be removed by filtering based on the amplitude values.

[0040] Specifically, the plurality of image amplitude value sets are the sets of amplitude values ​​corresponding to all pixels in each image of the extracted image set. The plurality of image phase sets are the sets of phase values ​​corresponding to all pixels in each image of the extracted image set. By establishing a one-to-one correspondence between the plurality of image amplitude value sets and the plurality of image phase sets, it can be ensured that the pixels filtered after Fourier transform can still correspond to their original positions.

[0041] Specifically, the filtering of multiple image amplitude value sets involves selecting amplitude values ​​below a preset amplitude threshold, resulting in a filtered set of image amplitude values ​​after removing high-frequency amplitude values. Then, by performing an inverse Fourier transform on the corresponding sets of image phases and the filtered set of image amplitude values, the noise-removed image, i.e., the remote sensing denoising image set, can be obtained. Since only low-frequency amplitude values ​​are retained after filtering, and pixel points can be obtained from the resulting set of image amplitude values, the goal of removing noise from the image is achieved, resulting in improved image quality and enhanced analytical accuracy.

[0042] Step S300: Extract data information from the remote sensing preprocessed image set to obtain a geological and geomorphological dataset;

[0043] Step S400: Obtain elevation data of the target area through the digital earth platform, and correct the geological and geomorphological dataset to obtain a corrected dataset;

[0044] Furthermore, the geological and geomorphological dataset is corrected. In this embodiment, step S400 further includes:

[0045] Step S410: Extract control points from the geological and geomorphological dataset to obtain a control point set;

[0046] Step S420: Obtain the geometric correction module;

[0047] Step S430: Input the elevation data and the set of control points into the geometric correction module, and output the correction dataset.

[0048] Specifically, the geological and geomorphological dataset is a collection of data reflecting the geological and geomorphological conditions of the target area, including: elevation of the landform surface, relative relief of the surface, spectral waveforms, etc. The digital earth platform provides rich global digital terrain and digital impact data. Optionally, the digital earth platform can be Google Earth. Elevation data of the target area is automatically obtained from the digital earth platform, where the elevation data refers to the set of distances from each point within the target area along the vertical direction to the absolute datum. Control points in the geological and geomorphological dataset are discrete points reflecting the topographic and geomorphic edges of the target area. Optionally, the control points can be the starting points of slopes, ramps, geological cross-sections, etc. The geometric correction module is used to correct the position of the control points in the height direction. By inputting the elevation data and the control point set into the geometric correction module, the points in the geological and geomorphological dataset can be corrected in the height direction, thereby achieving the technical effect of quantitative analysis and improving the accuracy of image analysis during image interpretation.

[0049] Step S500: Perform feature extraction based on the calibration dataset and the remote sensing preprocessed image set to obtain a feature set;

[0050] Step S600: Analyze the feature set input region normalization model and output relative position labels;

[0051] Furthermore, in the step S600 of this embodiment, which involves analyzing the feature set input region normalization model, the method further includes:

[0052] Step S610: Obtain the historical feature set of the target region;

[0053] Step S620: Divide the historical feature set into a training set and a validation set, and label the validation set to obtain the labeled validation set;

[0054] Step S630: Train the deep learning network using the training set to obtain the initial normalization model for the region;

[0055] Step S640: Validate the initial region reshaping model based on the identifier validation set until a preset accuracy is achieved, and obtain the region reshaping model.

[0056] Specifically, the feature extraction involves extracting features that reflect the edges of terrain and landforms. The feature set is a collection reflecting the division of edges, such as the boundaries of mountains and valleys, or the division between plains and river valleys. The region integration model is a functional model used to extract and analyze the feature set, marking the relative positions of each feature within the target region to obtain the relative position labels.

[0057] Specifically, the historical feature set of the target region is a collection of geological and geomorphological features within the region over a certain period of time. The training set is used to train a deep learning network, practicing converting features into relative positions to obtain an initial regional reshaping model. Then, the initial regional reshaping model is validated using the identifier validation set to verify the accuracy of the relative positions relative to the actual positions, thus obtaining the accuracy of the initial regional reshaping model. This process continues until the accuracy reaches a pre-set model accuracy, at which point the regional reshaping model is obtained.

[0058] Step S700: The images in the remote sensing preprocessed image set are stitched together according to the relative position labels to obtain the remote sensing image of the target area.

[0059] Furthermore, step S700 in this embodiment of the application also includes:

[0060] Based on natural disaster information for the target area within a predetermined period, wherein the natural disaster information includes: natural disaster type information and natural disaster frequency information;

[0061] Based on the analysis of the types of natural disasters and their correlation with the geological and geomorphological formation of the target area, a correlation coefficient is obtained.

[0062] Based on the correlation coefficient and the natural disaster frequency information, the image acquisition frequency of the target area is calculated;

[0063] The remote sensing image of the target area is optimized and updated based on the image acquisition frequency.

[0064] Specifically, the feature set corresponds one-to-one with the images in the remote sensing preprocessed image set. By stitching the images in the remote sensing preprocessed image set according to the relative position labels, an accurate remote sensing image of the target area can be obtained, that is, an image of the target area obtained through remote sensing technology.

[0065] The natural disaster information refers to abnormal natural phenomena occurring within the target area that alter the geological and geomorphological structure. The types of natural disasters are determined based on the geographical location of the target area and may include drought, high temperatures, floods, typhoons, earthquakes, landslides, and debris flows. Different natural disasters cause varying degrees of change in the geological and geomorphological structure. Therefore, analyzing the correlation between natural disasters and the formation of the geological and geomorphological structure of the target area yields the correlation coefficient of the impact of natural disasters on the formation of the geological and geomorphological structure of the target area. The natural disaster frequency information refers to the frequency of different natural disasters occurring in the target area. The image acquisition frequency of the target area can be obtained through the correlation coefficient and the natural disaster frequency information. This enables timely updates of remote sensing images of the target area, achieving the technical effect of improving image accuracy.

[0066] In summary, the geological and geomorphological analysis and exploration method based on remote sensing technology provided in this application has the following technical effects:

[0067] 1. This application acquires images of a target area using an exploration terminal, preprocesses the images to obtain a set of preprocessed remote sensing images, extracts data from these images to obtain a geological and geomorphological dataset, obtains elevation data of the target area using a digital earth platform, corrects the geological and geomorphological dataset in the height direction to obtain a corrected dataset, and then extracts features from the corrected dataset and the preprocessed remote sensing images to obtain a feature set. This feature set is then input into a regional integration model for analysis, outputting relative position labels. The images in the preprocessed remote sensing image set are then stitched together based on these relative position labels to obtain a remote sensing image of the target area. This achieves the technical effect of improving the accuracy of geological and geomorphological exploration and image processing using remote sensing technology.

[0068] 2. This application extracts the amplitude and phase values ​​of each pixel in an image set by performing a Fourier transform on each image. This yields multiple sets of image amplitude values ​​and multiple sets of image phase values. The amplitude value sets are then filtered to select those with lower amplitude values, resulting in a set of filtered image amplitude values. Finally, an inverse Fourier transform is performed on the image phase sets and the filtered amplitude value sets to obtain a set of denoised remote sensing images. This achieves the goal of removing noise from the images, thereby improving image quality and enhancing the accuracy of the analysis.

[0069] Example 2

[0070] Based on the same inventive concept as the remote sensing-based geological and geomorphological analysis and exploration method described in the foregoing embodiments, such as Figure 4 As shown, this application also provides a geological and geomorphological analysis and exploration system based on remote sensing technology, wherein the system includes:

[0071] Image acquisition module 11, which is used to acquire images of the target area through the exploration terminal to obtain a set of remote sensing images;

[0072] Preprocessing module 12, the preprocessing module 12 is used to perform image preprocessing on the remote sensing image set to obtain a remote sensing preprocessed image set;

[0073] Data extraction module 13 is used to extract data information from the remote sensing preprocessed image set to obtain a geological and geomorphological dataset;

[0074] Correction module 14 is used to obtain elevation data of the target area through the digital earth platform, correct the geological and geomorphological dataset, and obtain a corrected dataset.

[0075] Feature extraction module 15, which is used to extract features based on the calibration dataset and the remote sensing preprocessed image set to obtain a feature set;

[0076] The label output module 16 is used to analyze the feature set input region normalization model and output relative position labels;

[0077] The stitching module 17 is used to stitch together the images in the remote sensing preprocessed image set according to the relative position labels to obtain the remote sensing image of the target area.

[0078] Furthermore, the system also includes:

[0079] A step-by-step extraction unit is used to build an image extraction model to extract images from the remote sensing image set step by step.

[0080] The setting unit is used for the image extraction model. The first-level index is imaging angle similarity, the second-level index is imaging feature similarity, and the third-level index is imaging quality value.

[0081] An output image unit is used to input the remote sensing image set into the image extraction model and output an extracted image set.

[0082] Furthermore, the system also includes:

[0083] The transformation unit is used to traverse each image in the extracted image set and perform Fourier transform to obtain the amplitude value and phase of each pixel in each image, thereby obtaining multiple sets of image amplitude values ​​and multiple sets of image phases, wherein the multiple sets of image amplitude values ​​and the multiple sets of image phases correspond one-to-one.

[0084] A filtering unit is configured to filter multiple sets of image amplitude values ​​to obtain multiple sets of filtered image amplitude values.

[0085] An inverse transform unit is used to perform an inverse Fourier transform based on the plurality of image phase sets and the plurality of filtered image amplitude value sets to obtain the remote sensing noise-reduced image set.

[0086] Furthermore, the system also includes:

[0087] A quality assessment unit is used to assess the image quality of the remote sensing denoised image set and obtain a quality assessment result.

[0088] A judgment unit is used to determine whether the quality assessment result exceeds a predetermined quality threshold. If it does not exceed the threshold, an image enhancement preprocessing instruction is obtained.

[0089] An enhancement preprocessing unit is configured to perform image enhancement preprocessing on the remote sensing denoised image set according to the image enhancement preprocessing instruction, thereby obtaining a remote sensing preprocessed image set.

[0090] Furthermore, the system also includes:

[0091] A control point extraction unit is used to extract control points from the geological and geomorphological dataset to obtain a control point set.

[0092] A calibration module acquisition unit, wherein the calibration module acquisition unit is used to acquire a geometric calibration module;

[0093] A correction data output unit is used to input the elevation data and the set of control points into the geometric correction module and output the correction dataset.

[0094] Furthermore, the system also includes:

[0095] A historical feature acquisition unit is used to acquire the historical feature set of the target region.

[0096] An identification unit is used to divide the historical feature set into a training set and a verification set, and to identify the verification set to obtain an identified verification set.

[0097] A training unit is used to train a deep learning network using the training set to obtain an initial normalization model for the region.

[0098] A verification unit is used to verify the initial regional reshaping model based on the identifier verification set until a preset accuracy is achieved, thereby obtaining the regional reshaping model.

[0099] Furthermore, the system also includes:

[0100] The natural disaster information acquisition unit is used to acquire natural disaster information of the target area within a predetermined period, wherein the natural disaster information includes: natural disaster type information and natural disaster frequency information;

[0101] A correlation coefficient acquisition unit is used to analyze the correlation between the natural disaster type information and the geological and geomorphological formation of the target area, and obtain the correlation coefficient.

[0102] The calculation unit is used to calculate the image acquisition frequency of the target area based on the correlation coefficient and the natural disaster frequency information;

[0103] An optimization unit is used to optimize and update the remote sensing image of the target area according to the image acquisition frequency.

[0104] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The method and specific examples of geological and geomorphological analysis and exploration based on remote sensing technology in Example 1 are also applicable to the geological and geomorphological analysis and exploration system based on remote sensing technology in this embodiment. Through the foregoing detailed description of the method of geological and geomorphological analysis and exploration based on remote sensing technology, those skilled in the art can clearly understand the geological and geomorphological analysis and exploration system based on remote sensing technology in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section.

[0105] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for geological and geomorphological analysis and exploration based on remote sensing technology, characterized in that, The method is applied to a geological and geomorphological analysis and exploration system, which is communicatively connected to an exploration terminal. The method includes: The exploration terminal acquires images of the target area to obtain a set of remote sensing images; The remote sensing image set is preprocessed to obtain a remote sensing preprocessed image set; The method for preprocessing the remote sensing image set further includes: An image extraction model is built to extract images from the remote sensing image set step by step. The primary metric of the image extraction model is imaging angle similarity, the secondary metric is imaging feature similarity, and the tertiary metric is imaging quality value. By inputting the set of remotely sensed images into the image extraction model, the model outputs an extracted set of images. Each image in the extracted image set is traversed and a Fourier transform is performed to obtain the amplitude value and phase of each pixel in each image, resulting in multiple sets of image amplitude values ​​and multiple sets of image phases, wherein the multiple sets of image amplitude values ​​and the multiple sets of image phases correspond one-to-one; By filtering multiple sets of image amplitude values ​​separately, multiple sets of filtered image amplitude values ​​are obtained; An inverse Fourier transform is performed on the multiple image phase sets and the multiple filtered image amplitude value sets to obtain a remote sensing denoised image set; The image quality of the remote sensing denoised image set is evaluated to obtain quality evaluation results; Determine whether the quality assessment result exceeds a predetermined quality threshold; if not, obtain an image enhancement preprocessing instruction. According to the image enhancement preprocessing instructions, the remote sensing denoised image set is subjected to image enhancement preprocessing to obtain a remote sensing preprocessed image set; Extract data information from the remote sensing preprocessed image set to obtain a geological and geomorphological dataset; The elevation data of the target area is obtained through the Digital Earth platform, and the geological and geomorphological dataset is corrected to obtain a corrected dataset. Feature extraction is performed based on the calibration dataset and the remote sensing preprocessed image set to obtain a feature set; The feature set is input into a model for analysis, and relative position labels are output. The images in the remote sensing preprocessed image set are stitched together according to the relative position labels to obtain the remote sensing image of the target area.

2. The method as described in claim 1, characterized in that, The method further includes correcting the geological and geomorphological dataset, and further includes: Extract control points from the geological and geomorphological dataset to obtain a control point set; Obtain the geometric correction module; The elevation data and the set of control points are input into the geometric correction module, and the correction dataset is output.

3. The method as described in claim 1, characterized in that, The method of analyzing the feature set input region normalization model further includes: Obtain the historical feature set of the target region; The historical feature set is divided into a training set and a validation set, and the validation set is labeled to obtain the labeled validation set; The deep learning network is trained using the training set to obtain an initial region normalization model; The initial regional reshaping model is validated based on the identifier validation set until a preset accuracy is achieved, thereby obtaining the regional reshaping model.

4. The method as described in claim 1, characterized in that, The method further includes: Based on natural disaster information for the target area within a predetermined period, wherein the natural disaster information includes: natural disaster type information and natural disaster frequency information; Based on the analysis of the types of natural disasters and their correlation with the geological and geomorphological formation of the target area, a correlation coefficient is obtained. Based on the correlation coefficient and the natural disaster frequency information, the image acquisition frequency of the target area is calculated; The remote sensing image of the target area is optimized and updated based on the image acquisition frequency.

5. A geological and geomorphological analysis and exploration system based on remote sensing technology, characterized in that, The system includes: An image acquisition module is used to acquire images of the target area through an exploration terminal to obtain a set of remote sensing images. The preprocessing module is used to perform image preprocessing on the remote sensing image set to obtain a remote sensing preprocessed image set. The preprocessing module includes: A step-by-step extraction unit is used to build an image extraction model to extract images from the remote sensing image set step by step. The setting unit is used for the image extraction model. The first-level index is imaging angle similarity, the second-level index is imaging feature similarity, and the third-level index is imaging quality value. An output image unit is used to input the remote sensing image set into the image extraction model and output an extracted image set. The data extraction module is used to extract data information from the remote sensing preprocessed image set to obtain a geological and geomorphological dataset. The transformation unit is used to traverse each image in the extracted image set and perform Fourier transform to obtain the amplitude value and phase of each pixel in each image, thereby obtaining multiple sets of image amplitude values ​​and multiple sets of image phases, wherein the multiple sets of image amplitude values ​​and the multiple sets of image phases correspond one-to-one. A filtering unit is used to obtain multiple sets of image amplitude values ​​by filtering multiple sets of image amplitude values ​​respectively; An inverse transform unit is used to perform an inverse Fourier transform based on the plurality of image phase sets and the filtered plurality of image amplitude value sets to obtain a remote sensing denoised image set. A quality assessment unit is used to assess the image quality of the remote sensing denoised image set and obtain a quality assessment result. A judgment unit is used to determine whether the quality assessment result exceeds a predetermined quality threshold. If it does not exceed the threshold, an image enhancement preprocessing instruction is obtained. An enhanced preprocessing unit is configured to perform image enhancement preprocessing on the remote sensing denoised image set according to the image enhancement preprocessing instruction, so as to obtain a remote sensing preprocessed image set. A correction module is used to obtain elevation data of the target area through a digital earth platform, and to correct the geological and geomorphological dataset to obtain a corrected dataset. The feature extraction module is used to extract features based on the calibration dataset and the remote sensing preprocessed image set to obtain a feature set; The label output module is used to analyze the feature set input region model and output relative position labels. A stitching module is used to stitch together images in the remote sensing preprocessed image set according to the relative position labels to obtain the remote sensing image of the target area.

Images