A geoscientific knowledge-guided SAR image deep learning flood mapping method and system

Abstract

The present invention discloses a method and system for flood mapping using deep learning of SAR images guided by geological knowledge. The method comprises: obtaining and preprocessing SAR images of an application area before and after a flood; extracting multi-scale ground feature features from the SAR images before and after the flood; converting the extracted initial multi-scale features from the spatial domain to the frequency domain, performing global correlation modeling and filtering in the frequency domain, and restoring them to the spatial domain to quantify the differences between the SAR images before and after the flood and generate an initial flood probability map; learning and extracting high-quality multi-scale difference features from the multi-scale ground feature features before and after the flood, concatenating the generated multi-scale difference features to obtain a comprehensive difference feature set; and generating a final binary flood map using the difference feature set as input. The present invention not only has high computational efficiency and strong operability, but also achieves high accuracy and automation in flood mapping, is easy to implement, and has strong scalability.

Description

Technical Field

[0001] The present invention belongs to the field of remote sensing image processing, and in particular relates to a SAR image deep learning flood mapping method and system guided by geological knowledge. Background Art

[0002] Timely and accurate flood mapping is crucial for flood prevention and disaster relief efforts. Due to the numerous advantages of remote sensing technology, such as timeliness, efficiency, wide monitoring coverage, and low cost, remote sensing imagery-based mapping has become the mainstream approach for flood mapping. SAR imagery, in particular, is a key data source for remote sensing flood mapping, as it provides all-day, all-weather coverage and is unaffected by climatic factors such as cloud cover and rainfall.

[0003] However, due to the limited band information and high noise content of SAR images, traditional flood mapping methods based on SAR imagery often suffer from suboptimal mapping accuracy, with significant omissions and false detections, and a high degree of manual intervention in the mapping process. With the continuous breakthroughs of deep learning technology in various related fields, its application in remote sensing image processing is becoming increasingly widespread. In particular, convolutional neural network models such as ResNet possess powerful feature extraction capabilities, capable of extracting deep semantic information about objects in remote sensing imagery, thereby significantly improving the accuracy and reliability of remote sensing image interpretation. Therefore, in this context, it is crucial to explore how to effectively leverage existing geological knowledge, combined with the advantages of deep learning models in image deep feature modeling and extraction, to develop more effective flood mapping methods using SAR images to improve their accuracy and reliability. Summary of the Invention

[0004] The purpose of the present invention is to address the above-mentioned deficiencies in the prior art and to provide a method and system for flood mapping using deep learning of SAR images guided by geological knowledge.

[0005] In order to solve the above problems, the present invention adopts the following technical solutions:

[0006] A method for flood mapping using deep learning of SAR images guided by geological knowledge includes the following steps:

[0007] Step 1: Obtain SAR images of the application area before and after the flood, and preprocess them;

[0008] Step 2: Use a deep convolutional neural network model to extract multi-scale ground feature features from SAR images before and after the flood;

[0009] Step 3: The multi-scale features of the SAR images before and after the flood extracted in Step 2 are converted from the spatial domain to the frequency domain. A self-attention mechanism is used in the frequency domain to model the global correlation of low-frequency signals, and high-frequency signals are filtered. After global correlation modeling and filtering, the features are restored from the frequency domain to the spatial domain to obtain enhanced multi-scale features of the features before and after the flood.

[0010] Step 4: Combining existing geological knowledge, a dual-temporal image difference measurement model is used to quantify the differences between SAR images before and after the flood, and to generate an initial flood probability map P0;

[0011] Step 5: Construct a flood probability map-guided difference feature extraction module. Using the enhanced multi-scale features before and after the flood obtained in step 3 as input, the constructed flood probability map-guided difference feature extraction module extracts high-quality multi-scale difference features.

[0012] Step 6: Integrate the multi-scale high-quality difference features generated in step 5 in a serial manner to obtain the difference feature set for the final flood mapping. And the difference feature set As input, a multi-layer perceptron (MLP) is used as a classifier to generate the final binary flood map.

[0013] Furthermore, the fast Fourier transform used in step 3 converts the multi-scale ground feature features of the SAR images before and after the flood from the spatial domain to the frequency domain to obtain processed features, and the inverse fast Fourier transform is used to restore the processed features from the frequency domain to the spatial domain.

[0014] Furthermore, the process of using the self-attention mechanism to model the global correlation of low-frequency signals in the frequency domain in step 3 is as follows:

[0015] Use three different linear transformations to transform the low-frequency signal into three different matrices X Q , X K , X V ;

[0016] Calculate the global attention weight Attention(X Q ,X K ,X V )

[0017] The original low-frequency signal is combined with the global attention weight Attention(X Q ,X K ,X V ) are multiplied to obtain a new low-frequency signal containing global correlation information, thus completing the modeling of global correlation.

[0018] Furthermore, the global attention weight Attention(X Q ,X K ,X V ) is calculated as follows:

[0019]

[0020] Where d represents X Q , X K , X V dimension, and softmax(·) is a normalized exponential function.

[0021] Furthermore, in step 4, a neighborhood ratio operator is used to measure the pixel-by-pixel difference between the SAR images before and after the flood, thereby generating a difference map M; and an initial flood probability map P0 is generated from the difference map M:

[0022]

[0023] Among them, M max and M min They represent the maximum and minimum values ​​of all pixels in the difference map M respectively.

[0024] Furthermore, the workflow of constructing the difference feature extraction module in step 5 is as follows:

[0025] The initial difference feature ΔF is generated by direct matrix subtraction of the features before and after the flood at scale s extracted in step 3;

[0026] Use the flood probability map P generated at the previous scale s-1 s-1 As a guide map to guide the extraction of difference features, that is, the flood probability map P s-1 Resample to the same spatial resolution as the initial difference feature ΔF, and integrate it with ΔF in series to form a new feature set ΔF′;

[0027] Based on the new feature set ΔF′, the self-attention mechanism from the visual transformer is used to extract the global context-dependent features of the flood, and the extracted dependent features are integrated with the feature set ΔF′ in a serial manner to obtain the feature set ΔF″;

[0028] The hybrid attention model is used to refine the feature set ΔF″ to generate the final difference features at scale s

[0029] Furthermore, the difference features at each scale are generated under the guidance of the flood probability map of the previous scale. For the first scale, the difference features are extracted under the guidance of the initial flood probability map P0;

[0030] Flood probability map P at the previous scale s-1 s-1 It can be generated by the following formula:

[0031]

[0032] in, represents the final difference feature set generated by the difference feature extraction module at scale s-1; N represents The number of differential features contained in ; express The nth difference feature in , n = 1, 2, ..., N.

[0033] In another aspect, the present invention provides a SAR image deep learning flood mapping system guided by geological knowledge, comprising:

[0034] SAR image acquisition module: It is used to obtain SAR images of the application area before and after the flood, and perform pre-processing on them;

[0035] Multi-scale ground feature extraction module: It is used to extract multi-scale ground feature features from SAR images before and after floods using a deep convolutional neural network model;

[0036] Multi-scale surface feature enhancement module: This module is used to convert the multi-scale surface feature extracted from SAR images before and after the flood from the spatial domain to the frequency domain. In the frequency domain, a self-attention mechanism is used to model the global correlation of low-frequency signals and filter the high-frequency signals. After global correlation modeling and filtering, the features are restored from the frequency domain to the spatial domain to obtain enhanced multi-scale surface feature features before and after the flood.

[0037] Initial flood probability map generation module: It is used to combine existing geological knowledge, use a dual-temporal image difference measurement model to quantify the difference between SAR images before and after the flood, and generate the initial flood probability map P0;

[0038] Difference feature extraction module: This module is used to construct a flood probability map-guided difference feature extraction module. The module uses the enhanced multi-scale features before and after the flood as input and extracts high-quality multi-scale difference features based on the constructed flood probability map-guided difference feature extraction module.

[0039] Flood map generation module: It is used to integrate the multi-scale high-quality difference features generated in series to obtain the difference feature set for the final flood mapping And the difference feature set As input, a multi-layer perceptron (MLP) is used as a classifier to generate the final binary flood map.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] (1) The method proposed in this paper effectively utilizes the advantages of the convolutional neural network model in extracting deep features of images, and uses existing geological knowledge to guide the learning and extraction of multi-scale difference features. Therefore, high-quality multi-scale difference features between SAR images before and after floods can be obtained, which can effectively improve the accuracy and reliability of flood mapping.

[0042] (2) The deep learning-based SAR image flood mapping method proposed in this invention is not only computationally efficient and highly operable, but also has high accuracy and automation, is easy to implement, and has strong scalability. Therefore, this invention has high practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 Flowchart of an embodiment of the present invention.

[0045] Figure 2 Schematic diagram of some initial features extracted from post-flood images according to an embodiment of the present invention.

[0046] Figure 3 This is a schematic diagram of the enhanced features obtained in step 3 of an embodiment of the present invention.

[0047] Figure 4 Schematic diagram of flood probability generated at different scales according to an embodiment of the present invention.

[0048] Figure 5 1 is a result diagram of an embodiment of the present invention and a comparison diagram with an existing commonly used method. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0050] It should be noted that, in the description of the embodiments of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "include a ..." do not exclude the presence of other identical elements in the process, method, article or device comprising the elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0051] The terms "first," "second," and the like in this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that such terms are interchangeable where appropriate, so that embodiments of this application can be implemented in an order other than that illustrated or described herein. Furthermore, the terms "first," "second," and the like generally distinguish objects of a class and do not limit the number of objects; for example, the first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the connected objects.

[0052] Example 1

[0053] like Figure 1 、 2 As shown, the present invention takes flood mapping of a certain area of ​​Poyang Lake in China as an example to implement the method proposed by the present invention. The process of the embodiment of the present invention includes the following steps:

[0054] Step 1: Taking a region of Poyang Lake in China as an example, obtain SAR images of the region before and after the flood. Use the image preprocessing tools in ENVI software to perform necessary preprocessing such as denoising, calibration, and registration.

[0055] Step 2: Use the currently mature convolutional neural network model ResNet to extract initial multi-scale ground object details and semantic features from the SAR images before and after the flood. These features include feature maps generated by the ResNet network at different stages. Figure 2 Shows some initial features extracted from post-flood imagery (dark colors indicate water areas, and grayish-white indicates non-water areas).

[0056] Step 3: First, use the Fast Fourier Transform algorithm to convert the multi-scale ground feature details and semantic features of the SAR images before and after the flood in Step 2 from the spatial domain to the frequency domain. Then, use the self-attention mechanism in the frequency domain to model the global correlation of low-frequency signals. The specific process is as follows:

[0057] First, use three different linear transformations to transform the low-frequency signal into three different matrices X Q , X K , X V Then, the global attention weight Attention(X Q ,X K ,X V ), where d represents X Q , X K , X V dimension, and softmax(·) is a normalized exponential function.

[0058]

[0059] Finally, by combining the original low-frequency signal with the global attention weight Attention(X Q ,X K ,X V ) are multiplied to obtain a new low-frequency signal containing global correlation information, thus completing the modeling of global correlation.

[0060] At the same time, an adaptive Gaussian filter is used in the frequency domain to filter the high-frequency signals. After global correlation modeling and filtering, the inverse fast Fourier transform algorithm is used to transform the features from the frequency domain back to the spatial domain, obtaining enhanced multi-scale ground feature features before and after the flood. Figure 3 Some of the enhanced features obtained in this step are shown ( Figure 3 The five features in Figure 2 Enhanced features of the 5 initial features in ). Compare Figure 3 and Figure 2 It can be seen that compared with the initial features, the difference between water bodies and non-water bodies in the enhanced feature map is more significant, and the noise phenomenon is also weakened, which will help improve the accuracy of flood detection and mapping.

[0061] Step 4: In combination with existing geological knowledge, the existing dual-temporal image difference measurement model is used to quantify the differences between SAR images before and after the flood, and to generate the initial flood probability map P0. In the flood probability map, the larger the pixel value, the greater the possibility that the pixel is a flood pixel.

[0062] Specifically, the Neighborhood-Based Ratio operator (NRoperator) is used here to measure the pixel-by-pixel difference between the SAR images before and after the flood, and then generate the difference map M. The initial flood probability map P0 is generated from the difference map M using the following formula (2).

[0063]

[0064] In formula (2), M max and M min They represent the maximum and minimum values ​​of all pixels in the difference map M respectively.

[0065] Step 5: Construct a flood probability map-guided difference feature extraction module, and use the enhanced multi-scale features before and after the flood obtained in step 3 as input to extract high-quality multi-scale difference features using the constructed module.

[0066] Specifically, the workflow of the difference feature extraction module constructed here is as follows: Taking scale s as an example, in the difference feature extraction module, the initial difference feature ΔF is first generated by direct matrix subtraction of the features before and after the flood at scale s extracted in step 3. Then, the flood probability map P generated at the previous scale s-1 is used s-1 As a guide map to guide the extraction of difference features, that is, the flood probability map P s-1 Resample to the same spatial resolution as the initial difference feature ΔF, and integrate it with ΔF in series to form a new feature set ΔF′. Subsequently, based on the new feature set ΔF′, the self-attention mechanism from the visual transformer is used to extract the global context-dependent features of the flood, and the extracted dependent features are also integrated with the feature set ΔF′ in series to obtain the feature set ΔF″. Finally, the hybrid attention model CBAM (Convolutional Block Attention Module) is used to refine the feature set ΔF″ to generate the final difference feature at scale s.

[0067] The features before and after the flood at different scales extracted in step 3 are processed according to the above process to obtain high-quality difference features at different scales. It should be noted that the difference features at each scale are generated under the guidance of the flood probability map of the previous scale, and for the first scale, the difference features are extracted under the guidance of the initial flood probability map P0 (generated in step 4). Taking scale s as an example, the flood probability map P at the previous scale s-1 is generated. s-1 It can be generated by formula (3).

[0068]

[0069] In the above formula, represents the final difference feature set generated by the difference feature extraction module at scale s-1; N represents The number of differential features contained in ; express The nth difference feature in , n = 1, 2, ..., N. Figure 4 The flood probability map generated by step-by-step iteration and the initial flood probability map are shown when the scale parameter s is 1, 2, 3, and 4 respectively. Figure 4 As can be seen from the figure, as the number of iterations increases (s increases), the generated flood probability map tends to be smoother, the noise is significantly reduced, and the white area (indicating a higher probability of flood occurrence) is closer to the white area. Figure 5 The spatial distribution of flooded areas in the ground reference data is more consistent. Therefore, guided by the flood probability map, the proposed module can more easily focus on areas where flooding actually occurs, learning high-quality differential features that can more effectively distinguish flooded areas from non-flooded areas, thereby enhancing the accuracy of flood detection and mapping.

[0070] Step 6: Integrate the multi-scale high-quality difference features generated in step 5 in a serial manner to obtain a difference feature set that can be used for final flood mapping. And the difference feature set As input, a multi-layer perceptron (MLP) is used as a classifier to generate the final binary flood map.

[0071] Figure 5 The results of the embodiment of the present invention (in a certain area of ​​Poyang Lake in China) are shown, including a comparison of flood mapping results of the proposed new method and existing commonly used methods (PCAKmeans, MRF_FCM, GarborPCANet, NR_ELM). Figure 5 It can be seen from the figure that compared with the existing commonly used methods, the method of the present invention can more accurately identify flood areas, especially can effectively reduce the noise phenomenon in the generated flood map (such as Figure 5 (The red oval marks the area in the figure). From the perspective of the quantitative accuracy index OA, the overall mapping accuracy OA of the method of the present invention is significantly higher than that of other methods (1.13% to 11.18% higher than other existing methods). The results of the above examples demonstrate the superiority of the method of the present invention.

[0072] Example 2

[0073] This embodiment provides a SAR image deep learning flood mapping system guided by geological knowledge, including:

[0074] SAR image acquisition module: It is used to obtain SAR images of the application area before and after the flood, and perform pre-processing on them;

[0075] Multi-scale ground feature extraction module: It is used to extract multi-scale ground feature features from SAR images before and after floods using a deep convolutional neural network model;

[0076] Multi-scale surface feature enhancement module: This module is used to convert the multi-scale surface feature extracted from SAR images before and after the flood from the spatial domain to the frequency domain. In the frequency domain, a self-attention mechanism is used to model the global correlation of low-frequency signals and filter the high-frequency signals. After global correlation modeling and filtering, the features are restored from the frequency domain to the spatial domain to obtain enhanced multi-scale surface feature features before and after the flood.

[0077] Initial flood probability map generation module: It is used to combine existing geological knowledge, use a dual-temporal image difference measurement model to quantify the difference between SAR images before and after the flood, and generate the initial flood probability map P0;

[0078] Difference feature extraction module: This module is used to construct a flood probability map-guided difference feature extraction module. The module uses the enhanced multi-scale features before and after the flood as input and extracts high-quality multi-scale difference features based on the constructed flood probability map-guided difference feature extraction module.

[0079] Flood map generation module: It is used to integrate the generated multi-scale high-quality difference features in a serial manner to obtain the difference feature set for the final flood mapping And the difference feature set As input, a multi-layer perceptron (MLP) is used as a classifier to generate the final binary flood map.

[0080] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A SAR image deep learning flood mapping method guided by geological knowledge, characterized by: The following steps are involved: Step 1: Obtain SAR images of the application area before and after the flood, and preprocess them; Step 2: Use a deep convolutional neural network model to extract multi-scale ground feature features from SAR images before and after the flood; Step 3: The multi-scale features of the SAR images before and after the flood extracted in Step 2 are converted from the spatial domain to the frequency domain. A self-attention mechanism is used in the frequency domain to model the global correlation of low-frequency signals, and high-frequency signals are filtered. After global correlation modeling and filtering, the features are restored from the frequency domain to the spatial domain to obtain enhanced multi-scale features of the features before and after the flood. Step 4: Combining existing geological knowledge, a dual-temporal image difference measurement model is used to quantify the differences between SAR images before and after the flood, and to generate an initial flood probability map P0; Step 5: Construct a flood probability map-guided difference feature extraction module. Using the enhanced multi-scale features before and after the flood obtained in step 3 as input, the constructed flood probability map-guided difference feature extraction module extracts high-quality multi-scale difference features. Step 6: Integrate the multi-scale high-quality difference features generated in step 5 in a serial manner to obtain the difference feature set for the final flood mapping. And the difference feature set As input, a multi-layer perceptron (MLP) is used as a classifier to generate the final binary flood map.

2. The method for flood mapping using deep learning of SAR images guided by geological knowledge according to claim 1, characterized in that: The fast Fourier transform used in step 3 converts the multi-scale ground feature features of the SAR images before and after the flood from the spatial domain to the frequency domain to obtain processed features, and uses the inverse fast Fourier transform to restore the processed features from the frequency domain to the spatial domain.

3. The method for flood mapping using SAR images guided by geological knowledge according to claim 1, wherein: The process of using the self-attention mechanism to model the global correlation of low-frequency signals in the frequency domain in step 3 is as follows: Use three different linear transformations to transform the low-frequency signal into three different matrices X Q , X K , X V ; Calculate the global attention weight Attention(X Q ,X K ,X V ) The original low-frequency signal is combined with the global attention weight Attention(X Q ,X K ,X V ) are multiplied to obtain a new low-frequency signal containing global correlation information, thus completing the modeling of global correlation.

4. The method for flood mapping using SAR images guided by geological knowledge according to claim 3, wherein: Global attention weight Attention(X Q ,X K ,X V ) is calculated as follows: Where d represents X Q , X K , X V dimension, and softmax(·) is a normalized exponential function.

5. The method for flood mapping using SAR images guided by geological knowledge according to claim 1, wherein: In step 4, a neighborhood ratio operator is used to measure the pixel-by-pixel difference between the SAR images before and after the flood, thereby generating a difference map M. The initial flood probability map P0 is generated from the difference map M: Among them, M max and M min They represent the maximum and minimum values ​​of all pixels in the difference map M respectively.

6. The method for flood mapping using SAR images guided by geological knowledge according to claim 1, wherein: The workflow of constructing the difference feature extraction module in step 5 is as follows: The initial difference feature ΔF is generated by direct matrix subtraction of the features before and after the flood at scale s extracted in step 3; Use the flood probability map P generated at the previous scale s-1 s-1 As a guide map to guide the extraction of difference features, that is, the flood probability map P s-1 Resample to the same spatial resolution as the initial difference feature ΔF, and integrate it with ΔF in series to form a new feature set ΔF′; Based on the new feature set ΔF′, the self-attention mechanism from the visual transformer is used to extract the global context-dependent features of the flood, and the extracted dependent features are integrated with the feature set ΔF′ in a serial manner to obtain the feature set ΔF″; The hybrid attention model is used to refine the feature set ΔF″ to generate the final difference features at scale s 7. The method for flood mapping using SAR images guided by geological knowledge according to claim 6, wherein: The difference features at each scale are generated under the guidance of the flood probability map of the previous scale. For the first scale, the difference features are extracted under the guidance of the initial flood probability map P0; Flood probability map P at the previous scale s-1 s-1 It can be generated by the following formula: in, represents the final difference feature set generated by the difference feature extraction module at scale s-1; N represents The number of differential features contained in ; express The nth difference feature in , n = 1, 2, ..., N.

8. A SAR image deep learning flood mapping system guided by geological knowledge, characterized by: include: SAR image acquisition module: It is used to obtain SAR images of the application area before and after the flood, and perform pre-processing on them; Multi-scale ground feature extraction module: It is used to extract multi-scale ground feature features from SAR images before and after floods using a deep convolutional neural network model; Multi-scale surface feature enhancement module: This module is used to convert the multi-scale surface feature extracted from SAR images before and after the flood from the spatial domain to the frequency domain. In the frequency domain, a self-attention mechanism is used to model the global correlation of low-frequency signals and filter the high-frequency signals. After global correlation modeling and filtering, the features are restored from the frequency domain to the spatial domain to obtain enhanced multi-scale surface feature features before and after the flood. Initial flood probability map generation module: It is used to combine existing geological knowledge, use a dual-temporal image difference measurement model to quantify the difference between SAR images before and after the flood, and generate the initial flood probability map P0; Difference feature extraction module: This module is used to construct a flood probability map-guided difference feature extraction module. The module uses the enhanced multi-scale features before and after the flood as input and extracts high-quality multi-scale difference features based on the constructed flood probability map-guided difference feature extraction module. Flood map generation module: It is used to integrate the generated multi-scale high-quality difference features in a serial manner to obtain the difference feature set for the final flood mapping And the difference feature set As input, a multi-layer perceptron (MLP) is used as a classifier to generate the final binary flood map; The geological knowledge-guided SAR image deep learning flood mapping system is used to execute the steps in the geological knowledge-guided SAR image deep learning flood mapping method described in any one of claims 1-7.

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