Western region hot spring identification system and method based on remote sensing image

Abstract

The invention discloses a western region hot spring identification system and method based on a remote sensing image. The system comprises a data acquisition module, a model construction module, a mark extraction module and a fusion identification module. The data acquisition module is used for acquiring and preprocessing multi-source satellite remote sensing data of a hot spring monitoring area; the model building module is used for building a high-resolution remote sensing image classification model fusing dictionary learning and a visual converter; the mark extraction module is used for inputting the multi-source satellite remote sensing data into the high-resolution remote sensing image classification model and extracting each hot spring identification mark; and the fusion identification module is used for performing fusion analysis based on each hot spring identification mark to complete identification of the hot spring in the western region. According to the invention, more accurate and efficient exploration and development of hot spring resources can be realized.

Description

Technical Field

[0001] The invention belongs to the technical field of data processing, and in particular relates to a system and method for identifying hot springs in western regions based on remote sensing images. Background Art

[0002] Remote sensing technology has the advantages of fast information acquisition, wide coverage, and low cost, and plays an important role in geological surveys. Through remote sensing image interpretation, surface thermal anomalies and geothermal alteration caused by geothermal activities can be identified, providing important clues for the preliminary delineation of geothermal resources.

[0003] In geothermal hot spring exploration, the main purpose is to find fault fracture zones. Remote sensing images have unique advantages in interpreting geological structures. Their main role is to analyze and interpret the possible locations of fault fracture zones in the exploration area and to delineate key areas for geophysical exploration. The main role of geophysical exploration is to delineate the distribution areas of water-bearing fracture zones and thermal water storage and determine the appropriate locations for exploiting underground hot water.

[0004] There are very few existing technologies that explore the application of new technologies such as high-resolution remote sensing images, multi-source remote sensing data fusion, and intelligent interpretation algorithms in hot spring identification. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention proposes a hot spring identification system and method in the western region based on remote sensing images, which can realize more accurate and efficient exploration and development of hot spring resources.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A hot spring identification system in western China based on remote sensing images, comprising: a data acquisition module, a model building module, a sign extraction module and a fusion identification module;

[0008] The data acquisition module is used to acquire multi-source satellite remote sensing data of the hot spring monitoring area and perform preprocessing;

[0009] The model building module is used to establish a high-resolution remote sensing image classification model that integrates dictionary learning and visual converter;

[0010] The sign extraction module is used to input the multi-source satellite remote sensing data into the high-resolution remote sensing image classification model to extract the identification signs of each hot spring;

[0011] The fusion recognition module is used to perform fusion analysis based on the identification marks of each hot spring to complete the identification of hot springs in the western region.

[0012] Preferably, the multi-source satellite remote sensing data includes: visible light, infrared, thermal infrared multi-band images of the hot spring monitoring area, and auxiliary data of geology, topography and geomorphology;

[0013] The process of preprocessing the multi-source satellite remote sensing data includes: performing radiation correction, geometric correction, and atmospheric correction preprocessing on the remote sensing images.

[0014] Preferably, the high-resolution remote sensing image classification model includes five key sub-modules, namely, an embedding layer, an attention layer, a connection operation, a normalized connection structure, and a multi-layer perceptron, wherein the attention layer is an attention module based on sparse dictionary learning.

[0015] Preferably, the sign extraction module comprises: a feature information extraction unit, a feature representation formation unit and a recognition sign extraction unit;

[0016] The feature information extraction unit is used to reduce the dimension of multi-source satellite remote sensing data using a dictionary learning algorithm to obtain spatial and channel feature information of remote sensing images;

[0017] The feature representation forming unit is used to mine the long-distance dependency relationship within the scene based on the spatial and channel feature information of the remote sensing image using the visual transformer architecture to form a remote sensing image feature representation based on the Transformer structure;

[0018] The identification mark extraction unit is used to extract the identification marks of each hot spring through remote sensing image feature representation based on the Transformer structure;

[0019] Among them, the hot spring identification signs include: thermal anomaly characteristics, landform characteristics, vegetation and water body characteristics.

[0020] Preferably, the fusion identification module includes: a geological structure fusion analysis unit, a water temperature fusion analysis unit, a flow fusion analysis unit and a system identification analysis unit;

[0021] The geological structure fusion analysis unit is used to obtain the relationship between the fault zone and the hot spring distribution based on the identified major fault zones and geothermal activity areas in the western region, combined with historical hot spring outcrops and geological data;

[0022] The water temperature fusion analysis unit is used to record and analyze the variation range of the water temperature characteristics of the identified hot springs, and compare the water temperature data of different hot spring areas, and combine the geological structure analysis to obtain the relationship between the water temperature and the intensity of geothermal activity;

[0023] The flow fusion analysis unit is used to record and analyze the change law of the flow characteristics of the identified hot springs, and to obtain the causes and influencing factors of the hot spring flow in combination with the geological structure and hydrogeological conditions;

[0024] The system identification and analysis unit is used to systematically identify hot spring resources in the western region based on the relationship between fault zones and hot spring distribution, the relationship between water temperature and geothermal activity intensity, and the causes and influencing factors of hot spring flow.

[0025] The present invention also provides a method for identifying hot springs in the western region based on remote sensing images, comprising the following steps:

[0026] Acquire multi-source satellite remote sensing data of hot spring monitoring areas and perform preprocessing;

[0027] Establish a high-resolution remote sensing image classification model that integrates dictionary learning and visual converter;

[0028] Inputting the multi-source satellite remote sensing data into the high-resolution remote sensing image classification model to extract identification marks of each hot spring;

[0029] Based on the identification signs of each hot spring, a fusion analysis is carried out to complete the identification of hot springs in the western region.

[0030] Preferably, the multi-source satellite remote sensing data includes: visible light, infrared, thermal infrared multi-band images of the hot spring monitoring area, and auxiliary data of geology, topography and geomorphology;

[0031] The method for preprocessing the multi-source satellite remote sensing data includes: performing radiation correction, geometric correction, and atmospheric correction preprocessing on the remote sensing images.

[0032] Preferably, the high-resolution remote sensing image classification model includes five key sub-modules, namely, an embedding layer, an attention layer, a connection operation, a normalized connection structure, and a multi-layer perceptron, wherein the attention layer is an attention module based on sparse dictionary learning.

[0033] Preferably, the method of inputting the multi-source satellite remote sensing data into the high-resolution remote sensing image classification model to extract the identification marks of each hot spring includes:

[0034] Use dictionary learning algorithm to reduce the dimension of multi-source satellite remote sensing data and obtain the spatial and channel feature information of remote sensing images;

[0035] Based on the spatial and channel feature information of remote sensing images, the visual transformer architecture is used to mine the long-distance dependencies within the scene and form a remote sensing image feature representation based on the Transformer structure;

[0036] Through remote sensing image feature representation based on Transformer structure, the identification marks of each hot spring are extracted;

[0037] Among them, the hot spring identification signs include: thermal anomaly characteristics, landform characteristics, vegetation and water body characteristics.

[0038] Preferably, the method for completing the identification of hot springs in the western region by performing fusion analysis based on the identification marks of each hot spring includes:

[0039] Based on the identification of the main fault zones and geothermal activity areas in the western region, combined with historical hot spring outcrops and geological data, the relationship between fault zones and hot spring distribution is obtained;

[0040] Based on the identified hot spring water temperature characteristics, record and analyze its variation range, and compare the water temperature data of different hot spring areas. Combined with geological structure analysis, the relationship between water temperature and geothermal activity intensity is obtained;

[0041] Based on the identified flow characteristics of hot springs, record and analyze their changing patterns, and combine geological structures and hydrogeological conditions to obtain the causes and influencing factors of hot spring flow;

[0042] Hot spring resources in the western region are systematically identified based on the relationship between fault zones and hot spring distribution, the relationship between water temperature and the intensity of geothermal activity, and the causes and influencing factors of hot spring flow.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] Identification of hot springs in western China based on remote sensing images is an effective technical means. Through methods such as surface thermal anomaly identification, geothermal alteration information extraction and fault structure analysis, the distribution range and water storage conditions of hot springs can be preliminarily determined, and more accurate and efficient exploration and development of hot spring resources can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0046] Figure 1 The figure is a schematic diagram of a hot spring identification system in western China based on remote sensing images according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Embodiment 1

[0050] like Figure 1 As shown, the present invention provides a hot spring identification system in western China based on remote sensing images, including: a data acquisition module, a model building module, a sign extraction module and a fusion identification module;

[0051] The data acquisition module is used to acquire multi-source satellite remote sensing data of the hot spring monitoring area and perform preprocessing;

[0052] The model building module is used to build a high-resolution remote sensing image classification model that integrates dictionary learning and visual converter;

[0053] The sign extraction module is used to input the multi-source satellite remote sensing data into the high-resolution remote sensing image classification model to extract the identification signs of each hot spring;

[0054] The fusion recognition module is used to perform fusion analysis based on the identification marks of various hot springs to complete the identification of hot springs in the western region.

[0055] In this embodiment, the multi-source satellite remote sensing data includes: visible light, infrared, thermal infrared multi-band images of the hot spring monitoring area, and auxiliary data of geology, topography and geomorphology;

[0056] The process of preprocessing the multi-source satellite remote sensing data includes: performing radiation correction, geometric correction, and atmospheric correction preprocessing on the remote sensing images.

[0057] In this embodiment, the high-resolution remote sensing image classification model includes five key sub-modules, namely, an embedding layer, an attention layer, a connection operation, a normalized connection structure, and a multi-layer perceptron, wherein the attention layer is an attention module based on sparse dictionary learning.

[0058] Embedding layer Based on the Transformer concept adopted in the field of natural language processing and the suggestions of Dosovitskiy et al., an image Can be remapped into a series of flattened 2D image blocks The input matrix shape in Transformer is (N, D), where N represents the sequence length and D represents the dimension of each vector in the sequence. Therefore, after obtaining the two-dimensional image block, each (P 2 ×C) image block to compress its dimension to D by a linear transformation (fully connected layer). In addition, a learnable embedding vector is also required in the classification task as the category information and position encoding. Therefore, the appropriate expression is as follows:

[0059]

[0060] Where E represents the linear transformation layer, (P 2 ×C) is the input dimension, and D is the output dimension. Trainable variable E pos Used to represent the position information of the added sequence. When the positions are close, they tend to have similar encodings, and patches in the same row / column also have similar position encodings.

[0061] Position encoding generally includes sine and cosine position encoding, learning position vectors, and relative position expression. This architecture uses learnable position vectors. Because there is no concept of time step in Transformer, the bias vector is used as the position vector, and an independent vector is learned for the position of each image block. In the code, nn.Parameter is used to implement the learnable process.

[0062] The concatenation operation (Concat) is a unique operation of the multi-head attention layer, and its purpose is to enhance the ability of the network architecture to capture different structural feature information. The output result of each attention module is a "head", so the concatenation operation is required in the multi-head attention. If there are m heads, it can be shown by formula (2):

[0063] m(Attention)=Concat(h1,h2,...,h m ) (2)

[0064] Then the structure obtained after the calculation of the entire multi-head attention module is subjected to layer normalization. Different from batch normalization, layer normalization is to normalize all neurons in a certain layer and is not affected by the batch size.

[0065] Assume that a layer has M neurons, then the input of this layer is Its mean is: Its variance is: Its normalized formula can be expressed by formula (3):

[0066]

[0067] Among them: γ and β represent the parameter vectors of scaling and translation respectively, so that normalization does not affect the representation ability of the network.

[0068] The multilayer perceptron is mainly composed of an input layer, a hidden layer, and an output layer. This is a feedforward neural network structure with a relatively simple connection method. The main function of the multilayer perceptron is to simulate complex nonlinear functions. Different activation functions can be used in the hidden layer to enable the model to achieve nonlinear functions. The input layer does not bear the function processing function, only the hidden layer and the output layer will process the data.

[0069] In a multilayer perceptron, neurons between adjacent layers are usually connected in a fully connected manner, so it is also called a fully connected layer. The number of neurons in each hidden layer can be varied. More neurons will have better fitting capabilities, but it will also make the model more prone to overfitting. It is a versatile learning method that can be applied to almost all tasks, including classification, regression and even unsupervised learning.

[0070] The attention mechanism module based on dictionary learning proposed in the present invention adopts a dimensionality reduction method that combines manifold optimization and dictionary learning, so that when performing dimensionality reduction operations on high-dimensional image data, the structural characteristics of the data can be retained as much as possible.

[0071] First, the high-dimensional data to be processed Constrain the data on the Stiefel manifold, whose manifold space is denoted as M, and the data after dimensionality reduction is Then the complete dictionary and its corresponding sparse representation are obtained. The objective function of dictionary learning is as follows:

[0072]

[0073] in: represents the regularization term of the dictionary. In addition, That is to say, the dictionary set is the product manifold of s and the K-1-dimensional unit sphere, that is, S(n,k) constrains all All have unit norm. g(φ i ) usually uses the norm constraint to control the sparse coding φ i The sparsity of , L1-norm and L2-norm regularization are called ridge regression and Lasso regression respectively. In order to inherit the stability of ridge regression and the sparsity of Lasso regression, elastic network combines the two. So we get The closed-form solution of sparse coding then has the following form:

[0074]

[0075] By initializing different dictionaries D, different sparse coding matrices Φ1 and Φ2 are obtained. Then, layer normalization and average pooling operations are performed on Φ1 and Φ2 to obtain matrices Q and K, respectively. The calculation formulas are shown in Equations (6) and (7).

[0076] Q = layernorm(Φ1) (6)

[0077] K=AvgPool2d(Φ2) (7)

[0078] Then, matrix Q and matrix K are multiplied, and then a SoftMax operation is performed to obtain the required feature information.

[0079] DictAttn=SoftMax(QK T ) (8)

[0080] Dictionary learning is applied in the attention module to reconstruct and discover key feature areas. After introducing the dictionary and sparse coding conversion, the spatial attention and channel attention can be transferred to the model through the reconstruction matrix. Compared with the original self-attention mechanism, more redundant information can be removed and more feature information that is beneficial to classification can be obtained.

[0081] In this embodiment, the sign extraction module includes: a feature information extraction unit, a feature representation formation unit and a recognition sign extraction unit;

[0082] The feature information extraction unit is used to reduce the dimension of multi-source satellite remote sensing data using a dictionary learning algorithm to obtain spatial and channel feature information of remote sensing images;

[0083] The feature representation forming unit is used to mine the long-distance dependencies within the scene based on the spatial and channel feature information of the remote sensing image using the visual transformer architecture to form a remote sensing image feature representation based on the Transformer structure;

[0084] The identification mark extraction unit is used to extract the identification marks of each hot spring through the remote sensing image feature representation based on the Transformer structure;

[0085] Among them, the identification signs of hot springs include: thermal anomaly characteristics, geomorphological characteristics, vegetation and water characteristics.

[0086] Thermal anomaly identification: Use thermal infrared band images to identify thermal anomaly areas on the surface. Due to geothermal effects, the surface temperature of hot springs is usually higher than the surrounding environment, forming obvious thermal anomalies. By setting reasonable thresholds, thermal anomaly areas can be extracted, which may be potential locations of hot springs.

[0087] Analysis of geomorphic features: Combined with topographic and geomorphic data, analyze the topographic locations where hot springs may appear. Hot springs often appear in areas with obvious topographic and geomorphic features, such as river valleys, fault zones, and areas with complex geological structures. Use the texture, color tone and other information of remote sensing images to identify these geomorphic features and determine whether they are related to the appearance of hot springs.

[0088] Vegetation and water body analysis: Due to geothermal effects, vegetation growth in hot spring areas may be affected to a certain extent, forming unique vegetation communities. At the same time, hot spring areas may be accompanied by the existence of hot spring water bodies, which usually show unique color and texture characteristics in remote sensing images. By analyzing the distribution and characteristics of vegetation and water bodies, the potential location of hot springs can be further determined.

[0089] In this embodiment, the fusion identification module includes: a geological structure fusion analysis unit, a water temperature fusion analysis unit, a flow fusion analysis unit and a system identification analysis unit;

[0090] The geological structure fusion analysis unit is used to obtain the relationship between fault zones and hot spring distribution based on the identified major fault zones and geothermal activity areas in the western region, combined with historical hot spring outcrops and geological data;

[0091] The water temperature fusion analysis unit is used to record and analyze the range of changes of the water temperature characteristics of the identified hot springs, and compare the water temperature data of different hot spring areas, combined with geological structure analysis, to obtain the relationship between water temperature and geothermal activity intensity;

[0092] The flow fusion analysis unit is used to record and analyze the changing rules of the flow characteristics of the identified hot springs, and to obtain the causes and influencing factors of the hot spring flow in combination with the geological structure and hydrogeological conditions;

[0093] The system identification and analysis unit is used to systematically identify hot spring resources in the western region based on the relationship between fault zones and hot spring distribution, the relationship between water temperature and the intensity of geothermal activity, and the causes and influencing factors of hot spring flow.

[0094] Embodiment 2

[0095] The present invention also provides a method for identifying hot springs in the western region based on remote sensing images, comprising the following steps:

[0096] Acquire multi-source satellite remote sensing data of hot spring monitoring areas and perform preprocessing;

[0097] Establish a high-resolution remote sensing image classification model that integrates dictionary learning and visual converter;

[0098] Inputting the multi-source satellite remote sensing data into the high-resolution remote sensing image classification model to extract identification marks of each hot spring;

[0099] Based on the identification signs of each hot spring, a fusion analysis is carried out to complete the identification of hot springs in the western region.

[0100] In this embodiment, the multi-source satellite remote sensing data includes: visible light, infrared, thermal infrared multi-band images of the hot spring monitoring area, and auxiliary data of geology, topography and geomorphology;

[0101] The method for preprocessing the multi-source satellite remote sensing data includes: performing radiation correction, geometric correction, and atmospheric correction preprocessing on the remote sensing images.

[0102] In this embodiment, the high-resolution remote sensing image classification model includes five key sub-modules, namely, an embedding layer, an attention layer, a connection operation, a normalized connection structure, and a multi-layer perceptron, wherein the attention layer is an attention module based on sparse dictionary learning.

[0103] In this embodiment, the method of inputting the multi-source satellite remote sensing data into the high-resolution remote sensing image classification model and extracting the identification marks of each hot spring includes:

[0104] Use dictionary learning algorithm to reduce the dimension of multi-source satellite remote sensing data and obtain the spatial and channel feature information of remote sensing images;

[0105] Based on the spatial and channel feature information of remote sensing images, the visual transformer architecture is used to mine the long-distance dependencies within the scene and form a remote sensing image feature representation based on the Transformer structure;

[0106] Through remote sensing image feature representation based on Transformer structure, the identification marks of each hot spring are extracted;

[0107] Among them, the hot spring identification signs include: thermal anomaly characteristics, landform characteristics, vegetation and water body characteristics.

[0108] In this embodiment, the method for identifying hot springs in the western region by performing fusion analysis based on the identification marks of each hot spring includes:

[0109] Based on the identification of the main fault zones and geothermal activity areas in the western region, combined with historical hot spring outcrops and geological data, the relationship between fault zones and hot spring distribution is obtained;

[0110] Based on the identified hot spring water temperature characteristics, record and analyze its variation range, and compare the water temperature data of different hot spring areas. Combined with geological structure analysis, the relationship between water temperature and geothermal activity intensity is obtained;

[0111] Based on the identified flow characteristics of hot springs, record and analyze their changing patterns, and combine geological structures and hydrogeological conditions to obtain the causes and influencing factors of hot spring flow;

[0112] Hot spring resources in the western region are systematically identified based on the relationship between fault zones and hot spring distribution, the relationship between water temperature and the intensity of geothermal activity, and the causes and influencing factors of hot spring flow.

[0113] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A hot spring identification system in western China based on remote sensing images, characterized in that: include: Data acquisition module, model building module, landmark extraction module and fusion recognition module; The data acquisition module is used to acquire multi-source satellite remote sensing data of the hot spring monitoring area and perform preprocessing; The model building module is used to establish a high-resolution remote sensing image classification model that integrates dictionary learning and visual converter; The sign extraction module is used to input the multi-source satellite remote sensing data into the high-resolution remote sensing image classification model to extract the identification signs of each hot spring; The fusion recognition module is used to perform fusion analysis based on the identification marks of each hot spring to complete the identification of hot springs in the western region.

2. The hot spring identification system in western China based on remote sensing images according to claim 1 is characterized in that: The multi-source satellite remote sensing data include: visible light, infrared, thermal infrared multi-band images of the hot spring monitoring area, as well as geological, topographic and geomorphic auxiliary data; The process of preprocessing the multi-source satellite remote sensing data includes: performing radiation correction, geometric correction, and atmospheric correction preprocessing on the remote sensing images.

3. The hot spring identification system in western China based on remote sensing images according to claim 1 is characterized in that: The high-resolution remote sensing image classification model includes five key sub-modules, namely, an embedding layer, an attention layer, a connection operation, a normalized connection structure, and a multi-layer perceptron, wherein the attention layer is an attention module based on sparse dictionary learning.

4. The hot spring identification system in western China based on remote sensing images according to claim 1 is characterized in that: The sign extraction module includes: a feature information extraction unit, a feature representation formation unit and a recognition sign extraction unit; The feature information extraction unit is used to reduce the dimension of multi-source satellite remote sensing data using a dictionary learning algorithm to obtain spatial and channel feature information of remote sensing images; The feature representation forming unit is used to mine the long-distance dependency relationship within the scene based on the spatial and channel feature information of the remote sensing image using the visual transformer architecture to form a remote sensing image feature representation based on the Transformer structure; The identification mark extraction unit is used to extract the identification marks of each hot spring through remote sensing image feature representation based on the Transformer structure; Among them, the hot spring identification signs include: thermal anomaly characteristics, landform characteristics, vegetation and water body characteristics.

5. The hot spring identification system in western China based on remote sensing images according to claim 1 is characterized in that: The fusion identification module includes: a geological structure fusion analysis unit, a water temperature fusion analysis unit, a flow fusion analysis unit and a system identification analysis unit; The geological structure fusion analysis unit is used to obtain the relationship between the fault zone and the hot spring distribution based on the identified major fault zones and geothermal activity areas in the western region, combined with historical hot spring outcrops and geological data; The water temperature fusion analysis unit is used to record and analyze the variation range of the water temperature characteristics of the identified hot springs, and compare the water temperature data of different hot spring areas, and combine the geological structure analysis to obtain the relationship between the water temperature and the intensity of geothermal activity; The flow fusion analysis unit is used to record and analyze the change law of the flow characteristics of the identified hot springs, and to obtain the causes and influencing factors of the hot spring flow in combination with the geological structure and hydrogeological conditions; The system identification and analysis unit is used to systematically identify hot spring resources in the western region based on the relationship between fault zones and hot spring distribution, the relationship between water temperature and geothermal activity intensity, and the causes and influencing factors of hot spring flow.

6. A method for identifying hot springs in western China based on remote sensing images, characterized in that: The following steps are involved: Acquire multi-source satellite remote sensing data of hot spring monitoring areas and perform preprocessing; Establish a high-resolution remote sensing image classification model that integrates dictionary learning and visual converter; Inputting the multi-source satellite remote sensing data into the high-resolution remote sensing image classification model to extract identification marks of each hot spring; Based on the identification signs of each hot spring, a fusion analysis is carried out to complete the identification of hot springs in the western region.

7. The method for identifying hot springs in western China based on remote sensing images according to claim 6, characterized in that: The multi-source satellite remote sensing data include: visible light, infrared, thermal infrared multi-band images of the hot spring monitoring area, as well as geological, topographic and geomorphic auxiliary data; The method for preprocessing the multi-source satellite remote sensing data includes: performing radiation correction, geometric correction, and atmospheric correction preprocessing on the remote sensing images.

8. The method for identifying hot springs in western China based on remote sensing images according to claim 6, characterized in that: The high-resolution remote sensing image classification model includes five key sub-modules, namely, an embedding layer, an attention layer, a connection operation, a normalized connection structure, and a multi-layer perceptron, wherein the attention layer is an attention module based on sparse dictionary learning.

9. The method for identifying hot springs in western China based on remote sensing images according to claim 6, characterized in that: The method of inputting the multi-source satellite remote sensing data into the high-resolution remote sensing image classification model to extract the identification marks of each hot spring includes: Use dictionary learning algorithm to reduce the dimension of multi-source satellite remote sensing data and obtain the spatial and channel feature information of remote sensing images; Based on the spatial and channel feature information of remote sensing images, the visual transformer architecture is used to mine the long-distance dependencies within the scene to form a remote sensing image feature representation based on the Transformer structure; Through remote sensing image feature representation based on Transformer structure, the identification marks of each hot spring are extracted; Among them, the hot spring identification signs include: thermal anomaly characteristics, landform characteristics, vegetation and water body characteristics.

10. The method for identifying hot springs in western China based on remote sensing images according to claim 6, characterized in that: Based on the fusion analysis of the identification marks of various hot springs, the method of identifying hot springs in the western region includes: Based on the identification of the main fault zones and geothermal activity areas in the western region, combined with historical hot spring outcrops and geological data, the relationship between fault zones and hot spring distribution is obtained; Based on the identified hot spring water temperature characteristics, record and analyze its variation range, and compare the water temperature data of different hot spring areas. Combined with geological structure analysis, the relationship between water temperature and geothermal activity intensity is obtained; Based on the identified flow characteristics of hot springs, record and analyze their changing patterns, and combine geological structures and hydrogeological conditions to obtain the causes and influencing factors of hot spring flow; Hot spring resources in the western region are systematically identified based on the relationship between fault zones and hot spring distribution, the relationship between water temperature and the intensity of geothermal activity, and the causes and influencing factors of hot spring flow.

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