Seawall feature information extraction method and system based on multi-source remote sensing data

Abstract

The invention relates to the technical field of data processing, and discloses a seawall feature information extraction method and system based on multi-source remote sensing data. The method comprises the following steps: carrying out wave band combination enhancement, geometric correction and radiation correction on a multi-source remote sensing image to obtain standardized data; extracting a seawall target by using multi-scale segmentation; recognizing structural features through profile analysis and spectrum matching; calculating vegetation indexes and texture features to obtain ecological information; establishing a precision evaluation system; and finally constructing a feature information database. Through the technical means of multi-source data fusion processing, multi-scale feature extraction, ecological feature evaluation and the like, the precision and efficiency of seawall feature information extraction are improved, and the problems existing in the aspects of data acquisition, processing and evaluation in a traditional method are solved.

Description

Technical Field

[0001] This application relates to the field of data processing, and particularly to a method and system for extracting seawall feature information based on multi-source remote sensing data. Background Art

[0002] As an important engineering facility for tide and wave prevention, the construction and management of seawalls have received extensive attention. Currently, the extraction of seawall feature information mainly relies on manual on-site surveys and single remote sensing data analysis. Among them, manual on-site surveys obtain basic information such as the location and elevation of the seawall through measuring instruments, while remote sensing data analysis mainly uses satellite remote sensing images for visual interpretation and feature extraction of the seawall. These methods have achieved certain results in obtaining seawall information and provided basic data support for seawall management and maintenance. Traditional remote sensing data processing methods mainly include steps such as image preprocessing, feature enhancement, and target extraction, and complete the identification of seawall features and the extraction of attribute information through a human-computer interaction method.

[0003] However, the traditional methods for extracting seawall feature information have obvious limitations. First, the information extraction method based on a single data source is difficult to comprehensively reflect the structural and ecological features of the seawall. Especially in complex terrains and diverse ecological environments, the expression ability of single-source data is limited. Second, the manual survey method is time-consuming and laborious, and is greatly affected by factors such as weather and terrain, making it difficult to achieve large-scale and high-efficiency information acquisition. Third, the existing remote sensing data processing methods lack standardized and automated processing procedures, with low data processing efficiency and strong subjectivity, making it difficult to ensure the consistency and reliability of the extraction results. In addition, there are still technical gaps in the extraction and evaluation of the ecological features of seawalls in the existing methods, which cannot meet the needs of seawall ecological construction and management. Summary of the Invention

[0004] This application provides a method and system for extracting seawall feature information based on multi-source remote sensing data, which improves the accuracy and efficiency of seawall feature information extraction through technical means such as multi-source data fusion processing, multi-scale feature extraction, and ecological feature evaluation, and solves the problems existing in data acquisition, processing, and evaluation of traditional methods.

[0005] In a first aspect, this application provides a method for extracting seawall feature information based on multi-source remote sensing data, and the method for extracting seawall feature information based on multi-source remote sensing data includes: For the collected multi-temporal satellite remote sensing images, aerial remote sensing images, and unmanned aerial vehicle oblique image data, perform multi-spectral information enhancement through band combination, perform geometric correction and orthorectification based on ground control points, and perform multi-source image radiometric consistency processing in combination with the histogram matching method to obtain standardized multi-temporal remote sensing data; Based on the standardized multi-temporal remote sensing data, the multi-scale segmentation algorithm is used to extract the target area of the seawall. Combining with the oblique photogrammetry data, the three-dimensional elevation information is calculated. Through the comparison and analysis of the temporal characteristics, the boundary range and changes of the seawall are determined, and the spatio-temporal characteristic information of the seawall is obtained; According to the spatio-temporal characteristic information of the seawall, the slope information of the seawall is extracted through cross-section profile analysis. Based on the material spectral feature library, the slope protection structure is identified. Combining with the calculation of the terrain undulation degree, the sectional structure characteristics of the seawall are determined, and the seawall structure feature dataset is generated; For the seawall structure feature dataset, the normalized difference vegetation index is calculated using multi-spectral data. The texture analysis method is used to extract the vegetation distribution density and type. Through spectral curve matching, the degree of slope protection ecologicalization is determined, and the seawall ecologicalization characteristic information is generated; Based on the seawall ecologicalization characteristic information, an accuracy evaluation sample set is established according to the field measurement data. The position accuracy and attribute accuracy are calculated through multi-level cross-validation. Combining with the temporal images, the dynamic changes are tested, and a feature extraction quality evaluation report is generated; The seawall structure feature dataset, the seawall ecologicalization characteristic information and the feature extraction quality evaluation report are used to construct a seawall feature information database through a hierarchical data organization method.

[0006] In a second aspect, the present application provides a seawall feature information extraction system based on multi-source remote sensing data. The seawall feature information extraction system based on multi-source remote sensing data includes: An enhancement module for enhancing the multi-spectral information of the collected multi-temporal satellite remote sensing images, aerial remote sensing images and UAV oblique image data through band combination, performing geometric correction and orthorectification based on ground control points, and combining the histogram matching method to perform radiation consistency processing on multi-source images to obtain standardized multi-temporal remote sensing data; An extraction module for extracting the target area of the seawall according to the standardized multi-temporal remote sensing data, calculating the three-dimensional elevation information in combination with the oblique photogrammetry data, and determining the boundary range and changes of the seawall through the comparison and analysis of the temporal characteristics to obtain the spatio-temporal characteristic information of the seawall; An identification module for extracting the slope information of the seawall through cross-section profile analysis according to the spatio-temporal characteristic information of the seawall, identifying the slope protection structure based on the material spectral feature library, and determining the sectional structure characteristics of the seawall in combination with the calculation of the terrain undulation degree to generate a seawall structure feature dataset; A generation module for calculating the normalized difference vegetation index using multi-spectral data for the seawall structure feature dataset, extracting the vegetation distribution density and type using the texture analysis method, and determining the degree of slope protection ecologicalization through spectral curve matching to generate the seawall ecologicalization characteristic information; A building module, configured to establish an accuracy evaluation sample set based on the ecological characteristic information of the seawall according to the field measurement data, calculate the position accuracy and the attribute accuracy through multi-level cross-validation, perform dynamic change inspection in combination with time-series images, and generate a feature extraction quality evaluation report; A construction module, configured to construct a seawall feature information database by means of a hierarchical data organization method with the seawall structure feature data set, the ecological characteristic information of the seawall, and the feature extraction quality evaluation report.

[0007] In the technical solution provided by the present application, by performing band combination and multi-spectral information enhancement on multi-temporal satellite remote sensing images, aerial remote sensing images, and UAV oblique image data, the recognition accuracy of seawall features is improved, making the seawall structure and ecological features more clearly distinguishable. Geometric correction and orthorectification based on ground control points, combined with histogram matching method for multi-source image radiation consistency processing, ensure the spatial position accuracy and radiation feature consistency between different data sources, laying a foundation for subsequent feature extraction. At the same time, using a multi-scale segmentation algorithm to extract the seawall target area, and calculating three-dimensional elevation information in combination with oblique photogrammetry data not only improves the accuracy of target extraction but also obtains the three-dimensional structure information of the seawall. By analyzing the comparison of time-series features, the boundary range and change situation of the seawall are determined, realizing the dynamic monitoring of seawall features. Based on the spatio-temporal feature information of the seawall, cross-section profile analysis and material spectral feature recognition can accurately extract the slope information of the seawall and the type of slope protection structure, and further determine the sectional structure features of the seawall in combination with the calculation of terrain undulation, making the extraction of seawall structure features more comprehensive and accurate. In addition, by calculating the normalized difference vegetation index, using texture analysis methods to extract the vegetation distribution density and type, and then determining the degree of slope protection ecologicalization through spectral curve matching, the quantitative evaluation of seawall ecological features is realized. Finally, an accuracy evaluation sample set is established based on field measurement data, and through multi-level cross-validation and dynamic change inspection of time-series images, not only the reliability of the feature extraction results is ensured, but also a quality evaluation system is established. The entire technical solution constructs a seawall feature information database through a hierarchical data organization method, realizing the standardized management and efficient utilization of seawall feature information. Description of the Drawings

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0009] Figure 1 It is a schematic diagram of an embodiment of the method for extracting seawall feature information based on multi-source remote sensing data in the embodiments of the present application; Figure 2 This is a schematic diagram of an embodiment of the seawall feature information extraction system based on multi-source remote sensing data in the embodiments of the present application. Detailed implementation manners

[0010] The embodiments of the present application provide a method and a system for extracting seawall feature information based on multi-source remote sensing data. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, so that the embodiments described here can be implemented in an order different from that illustrated or described here. In addition, the term "including" or "having" and any deformation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0011] For ease of understanding, the specific process of the embodiments of the present application is described below. Please refer to Figure 1 An embodiment of the method for extracting seawall feature information based on multi-source remote sensing data in the embodiments of the present application includes: Step S101: For the collected multi-temporal satellite remote sensing images, aerial remote sensing images and UAV oblique image data, perform multi-spectral information enhancement through band combination, perform geometric correction and ortho-rectification based on ground control points, and perform radiation consistency processing of multi-source images in combination with the histogram matching method to obtain standardized multi-temporal remote sensing data; Step S102: According to the standardized multi-temporal remote sensing data, use the multi-scale segmentation algorithm to extract the seawall target area, calculate the three-dimensional elevation information in combination with the oblique photogrammetry data, determine the boundary range and change situation of the seawall through temporal and spatial feature comparison and analysis, and obtain the spatio-temporal feature information of the seawall; Step S103: Based on the spatio-temporal feature information of the seawall, extract the slope information of the seawall through cross-section analysis, identify the slope protection structure based on the material spectral feature library, and determine the segmented structure feature of the seawall in combination with the calculation of terrain undulation degree to generate a seawall structure feature data set; Step S104: For the seawall structure feature data set, calculate the normalized difference vegetation index using multi-spectral data, extract the vegetation distribution density and type using the texture analysis method, and determine the degree of slope protection ecologicalization through spectral curve matching to generate seawall ecologicalization feature information; Step S105: Based on the ecological characteristic information of the seawall, establish an accuracy evaluation sample set according to the field measurement data, calculate the position accuracy and attribute accuracy through multi-level cross-validation, and conduct dynamic change inspection in combination with time-series images to generate a feature extraction quality evaluation report; Step S106: Construct a seawall feature information database by means of hierarchical data organization with the seawall structure feature data set, the ecological characteristic information of the seawall, and the feature extraction quality evaluation report.

[0012] It can be understood that the execution entity of this application can be a seawall feature information extraction system based on multi-source remote sensing data, or a terminal or a server. Specifically, it is not limited here. This application embodiment is described by taking the server as the execution entity as an example.

[0013] Specifically, data preprocessing is carried out. This process starts from multi-temporal satellite remote sensing images, aerial remote sensing images, and unmanned aerial vehicle oblique image data, and performs multi-spectral information enhancement processing through band combination. Multi-spectral information enhancement refers to the process of combining remote sensing images of different bands to highlight the features of ground objects. Specifically, for the extraction of seawall features, a combination method of the near-infrared band, the red band, and the green band is mainly adopted. In this combination method, the near-infrared band is particularly sensitive to vegetation information and can clearly reflect the vegetation coverage on the seawall; the red band can better reflect the characteristics of surface buildings and bare ground; the green band has a good effect on the recognition of water body boundaries. During the band combination process, by adjusting the band weight coefficients, the seawall structure can be made more clearly distinguishable in the image. After obtaining the image data enhanced by band combination, geometric correction and ortho-rectification need to be carried out based on ground control points. Ground control points refer to the feature points with accurate coordinate information obtained from field measurements. Through these points, the corresponding relationship between the image space and the geographic space is established. Geometric correction uses a polynomial correction model, and the transformation parameters are calculated by the least squares method to eliminate the geometric deformation in the image caused by factors such as terrain undulation and lens distortion. Ortho-rectification is the process of converting the central projection image into an ortho-projection image using digital elevation model (DEM) data, and this process eliminates the position deviation caused by terrain undulation.

[0014] After completing geometric correction and orthorectification, radiometric consistency processing of multi-source images is carried out. Histogram matching method is used for radiometric consistency processing. This method adjusts the gray-scale distributions of images acquired at different times and by different sensors to make them have similar statistical characteristics. In specific operations, first, a reference image is selected, and then the histograms of other images are adjusted through non-linear transformation to be similar to that of the reference image, so as to achieve the standardization of multi-source remote sensing data. Next, using the standardized multi-temporal remote sensing data, a multi-scale segmentation algorithm is adopted to extract the target area of the seawall. The multi-scale segmentation algorithm is a segmentation method based on graph theory. By calculating the heterogeneity and homogeneity between image objects, the image is segmented into regions with similar characteristics. During the extraction process of the seawall target area, first, appropriate segmentation scale parameters are set, including shape factor, compactness factor, etc., and then through iterative calculation, the image is segmented into objects of appropriate size. After segmentation, the target is extracted using the shape characteristics (linear, strip-shaped), spectral characteristics (reflection characteristics of building materials such as concrete and stone), and texture characteristics (regular artificial building characteristics) of the seawall.

[0015] After obtaining the target area of the seawall, three-dimensional elevation information is calculated in combination with oblique photogrammetry data. Oblique photogrammetry obtains multi-view images of the target by taking pictures from multiple angles, and then uses photogrammetry principles to reconstruct the three-dimensional structure of the target. In actual operations, first, an unmanned aerial vehicle is used to obtain multi-angle images of the seawall, and then through steps such as homologous point matching and aerial triangulation, a three-dimensional point cloud model of the seawall is constructed. Based on the point cloud model, three-dimensional characteristics such as elevation information and slope information of the seawall can be extracted. Temporal feature comparison and analysis determine the boundary range and change situation of the seawall by comparing the feature changes of the seawall in images at different times. Images at different times are registered, and then by calculating normalized difference indices (such as NDVI, NDWI, etc.), the change characteristics of the seawall and its surrounding environment are analyzed. Temporal analysis can effectively identify the actual range of the seawall and monitor the change situation of the seawall.

[0016] After obtaining the spatio-temporal feature information of the seawall, slope information of the seawall is extracted through cross-section profile analysis. Cross-section profile analysis extracts the elevation change curve along the cross-section direction of the seawall, and by analyzing the characteristics of the elevation change, structural characteristics such as the slope and steps of the seawall are identified. In actual operations, first, a sampling line is set along the cross-section direction of the seawall to extract the elevation values on the sampling line, and then by calculating the elevation difference and horizontal distance between adjacent points, the slope information of the seawall is obtained.

[0017] The material spectral feature library is a dataset containing the spectral reflection characteristics of various slope protection materials (such as concrete, stone, vegetation, etc.). By matching the spectral features in the seawall image with the standard spectra in the feature library, the structural type of the seawall slope protection can be identified. In practical applications, first, the spectral curve in the seawall image is extracted, then the similarity with the spectra of various materials in the feature library is calculated, and the material type with the highest similarity is selected as the identification result of the slope protection structure. The calculation of terrain undulation is to identify the segmented structure of the seawall by analyzing the elevation change characteristics of the seawall surface. First, the elevation standard deviation of the local area is calculated based on the three-dimensional point cloud data, and then by setting a threshold, the areas with significant elevation changes are identified, and these areas usually correspond to the structural segmentation points of the seawall.

[0018] After obtaining the seawall structure feature dataset, the normalized difference vegetation index (NDVI) is calculated using multi-spectral data. NDVI is a vegetation index calculated using the difference in the reflection characteristics of vegetation in the red and near-infrared bands. The larger its value, the higher the vegetation coverage. Through the statistical analysis of NDVI values, the vegetation distribution density information of the seawall slope protection can be obtained. Texture analysis methods are used to extract the vegetation distribution density and type. Texture analysis includes methods such as gray-level co-occurrence matrix (GLCM) feature calculation and edge density analysis, which can identify the spatial distribution patterns of different types of vegetation. In practical applications, first, the texture feature parameters of the image are calculated, and then the vegetation type classification and identification are carried out based on these parameters.

[0019] Spectral curve matching is used to determine the degree of slope protection ecologicalization. By comparing the similarity between the spectral features of the slope protection area and the standard vegetation spectral features, the ecologicalization level of the slope protection can be evaluated. In specific operations, first, the spectral curve of the slope protection area is extracted, and then the correlation coefficient with the standard vegetation spectrum is calculated. The higher the correlation coefficient, the higher the degree of ecologicalization. After the feature extraction is completed, it is necessary to establish an accuracy evaluation sample set for multi-level cross-validation. The accuracy evaluation sample set includes true value data such as field measurement location data and attribute data. The cross-validation adopts the K-fold cross-validation method, randomly dividing the samples into K parts, taking one part as the validation set each time, and the rest as the training set, and calculating the average accuracy through multiple validations.

[0020] Finally, all the extracted feature information and evaluation results are organized in a hierarchical manner to construct a seawall feature information database. The database adopts an object-oriented organization method to associate and store the spatial information, attribute information, and temporal information of the seawall. In specific applications, for example, during the feature extraction process of a certain section of the seawall, the spatial range of the seawall is identified through multi-temporal remote sensing images, and the calculated NDVI values are distributed between 0.2 and 0.6, indicating a medium degree of vegetation coverage. Through cross-section analysis, the average slope is 25 degrees, and the slope protection structure is a concrete structure. These information are organized and stored in the database for subsequent query and analysis.

[0021] In the embodiments of the present application, by performing band combination and multi-spectral information enhancement on multi-temporal satellite remote sensing images, aerial remote sensing images, and UAV oblique image data, the recognition accuracy of seawall features is improved, making the seawall structure and ecological features more clearly distinguishable. Geometric correction and ortho-rectification are carried out based on ground control points, and the radiation consistency of multi-source images is processed by combining the histogram matching method, which ensures the spatial position accuracy and radiation feature consistency between different data sources, laying a foundation for subsequent feature extraction. At the same time, the multi-scale segmentation algorithm is used to extract the seawall target area, and the three-dimensional elevation information is calculated by combining the oblique photogrammetry data, which not only improves the accuracy of target extraction but also obtains the three-dimensional structure information of the seawall. The boundary range and changes of the seawall are determined through the comparison and analysis of temporal features, realizing the dynamic monitoring of seawall features. Based on the spatio-temporal feature information of the seawall, cross-section profile analysis and material spectral feature recognition can accurately extract the slope information and the type of slope protection structure of the seawall, and further determine the segmented structure features of the seawall by combining the calculation of terrain undulation, making the extraction of seawall structure features more comprehensive and accurate. In addition, by calculating the normalized difference vegetation index, using the texture analysis method to extract the vegetation distribution density and type, and then determining the degree of slope protection ecologicalization through spectral curve matching, the quantitative evaluation of seawall ecological features is realized. Finally, an accuracy evaluation sample set is established based on field measurement data. Through multi-level cross-validation and dynamic change inspection of temporal images, not only the reliability of the feature extraction results is ensured, but also a quality evaluation system is established. The entire technical solution constructs a seawall feature information database through a hierarchical data organization method, realizing the standardized management and efficient utilization of seawall feature information.

[0022] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (1) Based on multi-temporal satellite remote sensing images, aerial remote sensing images, and UAV oblique image data, perform band separation processing on different data sources, extract near-infrared band, red band, and green band information from them, and perform multi-spectral information enhancement through band combination to obtain multi-spectral enhanced data; (2) For the multi-spectral enhanced data, construct a geometric transformation model through the coordinate information of ground control points, perform correction and resampling calculation on the image spatial position to obtain geometric correction data; (3) Input the geometric correction data into the ortho-rectification processing algorithm, establish a terrain correction model to eliminate projection deformation, and obtain ortho-rectified data; (4) For the ortho-rectified data, extract the gray histogram features of each band image, and perform multi-source image radiation consistency processing through the histogram matching method to obtain radiation consistency data; (5) Organize the radiation consistency data in time series, establish a temporal correlation relationship, and obtain multi-temporal correlation data; (6) Standardize the multi-temporal associated data to obtain standardized multi-temporal remote sensing data.

[0023] Specifically, perform band separation processing on the acquired multi-temporal satellite remote sensing images, aerial remote sensing images, and UAV oblique image data. Band separation processing refers to separating different bands in the multi-spectral remote sensing image to obtain image data of a single band. In remote sensing data, the near-infrared band is particularly sensitive to vegetation information, the red band is sensitive to surface buildings and bare surface features, and the green band is sensitive to water body features. By extracting and combining these three bands, the characteristics of the seawall and its surrounding environment can be better highlighted. Multi-spectrum information enhancement through band combination is a process of assigning different weights to different bands and combining them into a new image. In the extraction of seawall features, a combination of the near-infrared band, red band, and green band is used, and by adjusting the weight coefficients of each band, the display effect of the seawall structure is enhanced. The selection of weight coefficients needs to consider factors such as the material characteristics of the seawall, vegetation coverage, and water environment. For example, for a seawall with a concrete structure, the weight of the red band will be appropriately increased to highlight the building features; for an ecological seawall, the weight of the near-infrared band will be increased to highlight the vegetation information.

[0024] After obtaining the multi-spectrum enhanced data, geometric correction needs to be performed through ground control points. Ground control points refer to feature points with accurate coordinate information obtained through field measurement, and these points can be accurately identified both on the image and at the actual ground position. The geometric transformation model is a mathematical model that establishes the corresponding relationship between the image space coordinates and the actual ground coordinates, usually using a polynomial model or a rational function model. In practical applications, first select obvious ground feature points on the image as control points, then use the coordinate data measured in the field to calculate the transformation parameters, and finally perform resampling calculations on the entire image to obtain the geometrically corrected image data. For the orthorectification processing of geometric correction data, a terrain correction model needs to be established to eliminate the projection deformation caused by terrain undulation. The mathematical model of orthorectification can be expressed as: ; Where: represents the coordinate value after orthorectification; is the terrain correction coefficient; is the weight coefficient of the i-th control point; is the coordinate function of the i-th control point; is the projection deformation correction coefficient; is the weight coefficient of the j-th terrain feature point; is the function of the elevation, slope, and azimuth of the j-th terrain feature point; n is the number of control points; m is the number of terrain feature points.

[0025] After obtaining the orthorectified data, radiometric consistency processing is performed. Radiometric consistency processing is to eliminate the radiometric differences between images acquired at different times and by different sensors. The gray histogram features of each band image are extracted, and then the image is processed by the histogram matching method. Histogram matching is a process of adjusting the gray distribution of the image to be processed to be similar to that of the reference image, so that images at different times have similar statistical features, facilitating subsequent temporal analysis.

[0026] Organizing the radiometric consistency data in a time series is to establish the temporal variation relationship of the seawall features. When establishing the temporal correlation relationship, factors such as the time interval between image acquisitions and seasonal changes need to be considered. By establishing a time series index, image data at different times are associated. Finally, the multi-temporal associated data are standardized, including unifying data formats, coordinate systems, resolutions, etc., to obtain standardized multi-temporal remote sensing data.

[0027] For example: The satellite images obtained are band-separated to extract data in the near-infrared band (760 - 900 nm), red band (630 - 690 nm), and green band (520 - 600 nm). By setting the weight combination of the near-infrared band to 0.4, the red band to 0.35, and the green band to 0.25, the structural features of the seawall and the vegetation distribution are highlighted. Then, 5 obvious inflection points within the seawall area are selected as ground control points, and the precise coordinates of these points are obtained by RTK measurement to establish a geometric transformation model for correction. During orthorectification, a terrain correction model is established using 1-meter resolution DEM data to eliminate the position deviation caused by terrain undulation. In radiometric consistency processing, the summer image with the best lighting conditions is selected as the reference, and histogram matching is performed on images of other seasons to make the gray distributions of images at different times tend to be consistent. The image data of the four seasons are organized in chronological order to establish a temporal analysis data set. Through standardization processing, it is ensured that the spatial resolution of all data is 2 meters, and the projection coordinate system is unified as the UTM projection.

[0028] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) According to the standardized multi-temporal remote sensing data, perform gray normalization conversion on the image data, construct a four-level pyramid image according to the scale ratio of 1:2:4:8, calculate the homogeneity index for each level of image through the multi-scale segmentation algorithm, determine the optimal segmentation threshold, extract the initial seawall target area, and generate seawall segmentation data; (2) Perform spatial registration on the seawall segmentation data and the oblique photogrammetry data, establish the mapping relationship between geographic coordinates and pixel coordinates, perform dense matching on the corner points using the bilinear interpolation method, calculate the three-dimensional coordinate values of the feature points, and generate three-dimensional point cloud data; (3) Organize the three-dimensional point cloud data using a K-D tree spatial index structure. Calculate the elevation value within each grid through least squares adjustment, calculate the local dip value by combining the coordinate differences of adjacent points, and extract the slope value according to the eight-neighborhood relationship to obtain three-dimensional elevation information; (4) For the three-dimensional elevation information, establish a cross-section curve based on the elevation mutation points, extract the slope change trend along the cross-section direction, and determine the inner and outer contour lines of the seawall boundary range through the region growing method to obtain the seawall boundary data; (5) Project the seawall boundary data onto the standardized multi-temporal remote sensing data of different time phases, calculate the gray difference, texture change amount, and regional area change rate of adjacent time-phase images, and obtain the temporal change characteristic values through weighted fusion to generate the change situation data; (6) Conduct spatio-temporal clustering analysis on the change situation data, extract the dynamic evolution characteristics of the seawall boundary, and combine with the spatial distribution characteristics of the seawall boundary data to obtain the spatio-temporal characteristic information of the seawall.

[0029] Specifically, perform gray normalization conversion on the standardized multi-temporal remote sensing data. Gray normalization conversion refers to the process of mapping the gray values of image data into the interval [0, 1], aiming to eliminate the gray value differences under different sensors and different imaging conditions. After the conversion, construct a four-level pyramid image according to the ratio of 1:2:4:8. The pyramid image is a multi-resolution image representation method, and the resolution of each level of the image is 1 / 2 of the previous level, obtained through downsampling. This structure is beneficial to improving the efficiency of image processing. The multi-scale segmentation algorithm is an image segmentation method based on region growing, which determines the optimal segmentation threshold by calculating the homogeneity index. The homogeneity index includes parameters such as spectral feature similarity and shape feature similarity. For each pixel point, calculate the feature difference between it and the adjacent region. When the difference is less than the threshold, merge the pixel point into the corresponding region. In this way, the image is segmented into objects with similar features to form the initial seawall target area.

[0030] The seawall segmentation data needs to be spatially registered with the oblique photogrammetry data, and this process requires establishing a mapping relationship between geographic coordinates and pixel coordinates. The oblique photogrammetry data is image data obtained by taking photos from multiple angles and contains the three-dimensional information of the target. Spatial registration uses bilinear interpolation to perform dense matching on the corner points. Bilinear interpolation is a method of interpolation in a two-dimensional plane, and the interpolation result is obtained by calculating the weighted average of the four nearest points around the point to be calculated. For each feature point, calculate its corresponding position in the images from different perspectives, and then use photogrammetry principles to calculate its three-dimensional coordinate values to generate three-dimensional point cloud data. For the generated three-dimensional point cloud data, a K-D tree spatial index structure is used for organization. A K-D tree is a data structure for indexing multi-dimensional space data. By continuously dividing the space to organize data points, it can improve the efficiency of spatial queries and nearest neighbor searches. After constructing the spatial index, calculate the elevation value within each grid through least squares adjustment, and then calculate the local dip value based on the coordinate differences of adjacent points.

[0031] The calculation formulas for the local dip value and the slope value are as follows: ; Where: is the slope value at point (i, j); is the weight coefficient of the p-th neighborhood point; is the three-dimensional coordinate of the p-th neighborhood point; is the distance between adjacent points; is the terrain undulation correction coefficient; p is the neighborhood point serial number, ranging from 1 to 8.

[0032] Based on the obtained three-dimensional elevation information, establish a cross-section curve with the elevation mutation points as the benchmark. Elevation mutation points refer to the positions where the elevation values change significantly, usually corresponding to the edges or structural changes of the seawall. Extract the slope change trend along the cross-section direction, and determine the inner and outer contour lines of the seawall boundary range through the region growing method. The region growing method is a method that starts from seed points and gradually merges adjacent pixels with similar features into the growing region. For the extraction of the seawall boundary, select the elevation mutation points as seed points and perform region growing according to the similarity of elevation values and slope values. Project the extracted seawall boundary data into the standardized multi-temporal remote sensing data of different time phases, and calculate the gray difference, texture change amount, and regional area change rate of adjacent time-phase images. The gray difference reflects the change in image brightness, the texture change amount represents the change degree of image texture features, and the regional area change rate reflects the dynamic change of the seawall range. By performing weighted fusion on these characteristic parameters, obtain the time-series change characteristic value to characterize the change situation of the seawall.

[0033] Performing spatio-temporal clustering analysis on the data of the changing situation is to identify the law of the change of the seawall boundary over time. Spatio-temporal clustering analysis comprehensively considers the spatial location and time attributes of the data, clusters the regions with similar change characteristics together, so as to extract the dynamic evolution characteristics of the seawall boundary. Combining with the spatial distribution characteristics of the seawall boundary data, the spatio-temporal characteristic information of the seawall is obtained.

[0034] For example: perform gray normalization on the obtained multi-temporal remote sensing images, convert the DN value of the original image to the interval [0, 1], and then construct a four-level pyramid image. Segment the top-level image (with the lowest resolution), set the spectral heterogeneity threshold to 0.3 and the shape heterogeneity threshold to 0.5, and obtain the initial segmentation result through iterative calculation. Then, use the multi-angle images obtained by oblique photography, select feature points for matching, and calculate the three-dimensional coordinates of the feature points through aerial triangulation. Organize these three-dimensional point cloud data into a K-D tree structure, perform least squares adjustment calculation on each 5-meter × 5-meter grid to obtain the grid elevation value. Based on the elevation data, use the eight-neighborhood method to calculate the slope value, and identify the area with a slope greater than 15 degrees as the candidate area of the seawall. Extract the seawall boundary through the region growing method, and select the seed point at the position of elevation mutation (the position where the elevation difference is greater than 2 meters). Finally, project the boundary onto three images of different time phases, calculate the average gray value, texture entropy value and area change rate of the boundary region, and obtain the time series change characteristic value through weighted average. Through spatio-temporal clustering analysis, determine the spatial range and change characteristics of the seawall.

[0035] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) According to the spatio-temporal characteristic information of the seawall, set cross-section sampling points at 10-meter intervals in the main area of the seawall, construct a cross-section elevation curve through a triangular network, extract the slope change points of each cross-section from it, and generate slope sampling data; (2) Conduct statistical analysis on the slope sampling data, calculate the ratio of the elevation difference and the horizontal distance between adjacent sampling points, screen the slope mutation points with 0.5 degrees as the threshold, connect all the mutation points to generate a slope gradient line, and obtain slope information; (3) Compare the slope information with the characteristic curves in the material spectral feature library, calculate the spectral reflectance of each band, extract the material feature parameters, and establish a slope protection structure feature set; (4) For the slope protection structure feature set, extract the three-dimensional coordinate values of each discrete point, calculate the elevation difference coefficient between adjacent points, establish a terrain change curve, and obtain terrain undulation data; (5) Based on the terrain undulation data, count the undulation change amplitude of each slope section, and set the points with a slope change exceeding 30% as section nodes to obtain section structure characteristics; (6) Perform a spatial correlation analysis on the slope information, the slope protection structure feature set, and the segmented structure features, calculate the correlation coefficients between each feature, and generate the sea dike structure feature data set.

[0036] Specifically, based on the obtained spatio-temporal feature information of the sea dike, cross-section sampling is carried out at a fixed interval of 10 meters in the main area of the sea dike. Cross-section sampling refers to setting sampling points on the vertical cross-section of the main body of the sea dike, and the positions of these sampling points are determined by the spatial contour of the sea dike. Constructing a cross-section elevation curve through a triangulation network means connecting the sampling points using an irregular triangulation network (TIN) to form a continuous surface model. The triangulation network consists of a series of non-overlapping triangles, and the vertices of each triangle correspond to a sampling point with three-dimensional coordinates. Based on the constructed cross-section elevation curve, the slope change points of each cross-section are extracted. The slope change points refer to the positions on the elevation curve where the slope changes significantly, and these points usually correspond to the important feature positions of the sea dike structure. When performing statistical analysis on the obtained slope sampling data, it is necessary to calculate the ratio of the elevation difference and the horizontal distance between adjacent sampling points. This ratio is actually the tangent value of the slope, and through this calculation, the slope change situation of each part of the sea dike can be obtained. Using 0.5 degrees as the threshold for screening slope mutation points means that when the slope difference between two adjacent calculation points exceeds 0.5 degrees, this point is marked as a slope mutation point. By connecting all the mutation points, a slope gradient line is generated, and this gradient line reflects the change trend of the sea dike slope.

[0037] Comparing the obtained slope information with the characteristic curves in the material spectral feature library is an important step. The material spectral feature library is a data set containing the spectral reflection characteristics of various common sea dike slope protection materials (such as concrete, stone, vegetation, etc.). When calculating the spectral reflectance of each band, the intensity ratio of the incident light and the reflected light needs to be considered. Through this calculation and comparison, material characteristic parameters can be extracted, including the reflectance characteristics, absorption characteristics, etc. of the material, and these parameters together constitute the slope protection structure feature set.

[0038] For the data in the slope protection structure feature set, extract the three-dimensional coordinate values of each discrete point and calculate the elevation difference coefficient between adjacent points. The calculation formula for the elevation difference coefficient is: ; Where: represents the elevation difference coefficient at point (r, q); is the weight coefficient of the m-th sampling point; are the elevation values of adjacent sampling points; is the terrain undulation correction factor; is the distance attenuation coefficient; is the terrain gradient factor; N is the total number of sampling points.

[0039] By calculating the elevation difference coefficient, a topographic change curve can be established to reflect the undulation characteristics of the seawall surface. The topographic undulation data is an important index characterizing the intensity of topographic changes, which directly reflects the structural characteristics of the seawall slope protection. Based on the topographic undulation data, statistically analyze the undulation change amplitude of each slope section. When the slope change exceeds 30% of the point position, set these positions as sectional nodes. The determination of sectional nodes is of great significance for understanding the structural distribution of the seawall because these nodes often correspond to the conversion positions of the seawall structure types or important functional partition positions.

[0040] Finally, conduct a spatial correlation analysis on the slope information, slope protection structure feature set, and sectional structure features. The spatial correlation analysis reveals the degree of association between different features by calculating the correlation coefficients between various features. The calculation of the correlation coefficients needs to consider the spatial distribution law and numerical change trend of the features to generate a seawall structure feature data set.

[0041] For example: First, within the main body area of a 500-meter-long seawall, 50 cross-section sampling points are set at 10-meter intervals. Obtain the three-dimensional coordinates of these points through RTK measurement, and use triangular network interpolation to generate a continuous cross-section elevation curve. When analyzing the slope changes of each cross-section, it is found that there are obvious slope changes at 20 meters and 40 meters from the top of the dike. The ratios of the elevation differences to the horizontal distances at these positions are 0.6 and 0.8 respectively, exceeding the threshold of 0.5 degrees, so they are marked as slope mutation points. By analyzing the distribution law of these mutation points, determine the main slope change characteristics of this section of the seawall. Then, use hyperspectral remote sensing images to extract the spectral reflection characteristics of the slope protection of this section of the seawall, and identify the structural characteristics of the upper part being concrete slope protection and the lower part being riprap slope protection by comparing with the material feature library. When calculating the topographic undulation, it is found that the elevation difference coefficient at the slope protection structure conversion position reaches 1.5, which is much higher than the average value of 0.8 at other positions, and this position is subsequently determined as the sectional node. In this way, the structural feature information of this section of the seawall is obtained, including slope distribution, material change, and sectional features, etc.

[0042] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Extract the near-infrared band and red band of the multispectral data from the seawall structure feature data set, calculate the normalized difference vegetation index for each pixel point, and obtain the vegetation index distribution data; (2) Conduct a sliding window statistics on the vegetation index distribution data, calculate the mean and standard deviation of the vegetation index within each grid, establish a density classification standard, and obtain the vegetation distribution density data; (3) Resample the vegetation distribution density data according to a 20×20-meter grid, extract the gray-level co-occurrence matrix features of each grid, and statistically calculate the angular second moment and entropy value parameters to obtain the texture feature data; (4) Identify the vegetation types based on the texture feature data, and statistically analyze the spatial distribution patterns of various vegetation types to obtain the vegetation type data; (5) Use the vegetation type data to extract the typical spectral curves of each type of vegetation, and calculate the similarity with the spectral curves of the slope protection area to obtain the slope protection ecological degree data; (6) Perform feature fusion on the vegetation distribution density data, vegetation type data, and slope protection ecological degree data to generate the seawall ecological feature information.

[0043] Specifically, extracting multispectral data from the seawall structure feature dataset is the primary step. Multispectral data contains spectral information in different bands. The near-infrared band and the red band are particularly important for vegetation identification because vegetation has significant differences in reflection characteristics in these two bands. The near-infrared band mainly reflects the biomass information of vegetation, while the red band mainly reflects the absorption characteristics of chlorophyll. The calculation formula for the normalized difference vegetation index (NDVI) of each pixel point is: ; Where: represents the normalized difference vegetation index value at coordinates (x, y); is the weight coefficient of the kth spectral band; is the reflection value of the near-infrared band; is the reflection value of the red band; is the vegetation reflection feature correction coefficient; is the atmospheric influence correction coefficient; is the terrain influence correction coefficient; M is the number of bands involved in the calculation.

[0044] After obtaining the vegetation index distribution data, it is necessary to perform sliding window statistical analysis on it. The sliding window is a local feature extraction method that calculates the statistical features of the data within a window of a fixed size by moving the window over the data. Calculate the mean and standard deviation of the vegetation index within each grid, and these statistical values reflect the density and uniformity of vegetation cover in the local area. Based on these statistical values, establish a density classification standard, divide the vegetation cover degree into different levels, and form vegetation distribution density data. Resampling the vegetation distribution density data is to standardize the spatial resolution. Resampling according to a grid size of 20×20 meters can make the data have a unified spatial scale. On the resampled data, extract the gray-level co-occurrence matrix features of each grid. The gray-level co-occurrence matrix is an important tool for describing the texture features of an image, and it statistically analyzes the spatial correlation of the gray values in the image. The angular second moment reflects the smoothness of the image, while the entropy value parameter reflects the complexity of the image. These parameters together constitute the texture feature data of the vegetation distribution.

[0045] When identifying vegetation types based on the texture feature data, it is necessary to combine the typical texture features of different vegetation types. Different types of vegetation, such as herbaceous plants, shrubs, trees, etc., show different characteristics in texture features. By statistically analyzing the spatial distribution laws of various types of vegetation, establish the spatial distribution pattern of vegetation types and obtain detailed vegetation type data. For each identified vegetation type, it is necessary to extract its typical spectral curve. The typical spectral curve refers to the standard curve that can represent the spectral characteristics of this type of vegetation, and it contains the reflection characteristics of the vegetation in different bands. Calculate the similarity between these typical spectral curves and the actual spectral curves in the slope protection area, and evaluate the ecologicalization degree of the slope protection by comparing the shape features of the spectral curves and the reflection values of the key bands.

[0046] Integrate the vegetation distribution density data, vegetation type data, and slope protection ecologicalization degree data together through feature fusion. The feature fusion process needs to consider the correlation and weight distribution between different data to ensure that the fused result can comprehensively reflect the ecologicalization characteristics of the seawall.

[0047] For example: First, extract the data of the near-infrared band (wavelength range: 760 - 900 nm) and the red band (wavelength range: 630 - 690 nm) from the multispectral image. By calculating the normalized difference vegetation index, a vegetation index distribution map is obtained, and it is found that the distribution of vegetation index values in the seawall slope protection area shows obvious stratification characteristics. A 5×5 pixel sliding window is used to statistically analyze the vegetation index, and the mean and standard deviation within each window are calculated. Based on these statistical values, the vegetation density is divided into three levels: high, medium, and low. After resampling the data to a 20×20 meter grid, the characteristic parameters of the gray-level co-occurrence matrix are calculated, and it is found that in the area with good vegetation coverage, the angular second moment value is lower and the entropy value is higher, indicating that the vegetation distribution is relatively uniform and has a higher complexity. According to these texture characteristics, two main vegetation types, herbaceous plants and shrubs, are identified on this section of the seawall. Among them, herbaceous plants are mainly distributed in the lower slope protection, while shrubs are mainly distributed in the upper slope protection. The typical spectral curves of these two types of vegetation are extracted and compared with the actual spectral curves in the slope protection area, and the calculated similarity values reflect that the ecologicalization degree of this section of the seawall is relatively good. By integrating all feature information, a seawall ecologicalization feature dataset is generated.

[0048] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) According to the seawall ecologicalization feature information, group the on-site measurement data according to the spatial position and attribute type, construct a feature type comparison table, and establish an accuracy evaluation sample set; (2) Divide the accuracy evaluation sample set into a training set and a validation set according to a 7:3 ratio, and select test samples through the stratified sampling method to obtain multi-level verification data; (3) Use the multi-level verification data to calculate the coordinate deviation and attribute consistency of each inspection point, and statistically analyze the position accuracy index to obtain position accuracy data; (4) Establish a confusion matrix based on the position accuracy data, calculate the recognition accuracy rate of each type of feature, and obtain attribute accuracy data; (5) Spatially and temporally match the attribute accuracy data with the time-series images, calculate the change amount between each time phase, and obtain dynamic change test data; (6) Conduct a comprehensive evaluation of the position accuracy data, attribute accuracy data, and dynamic change test data to generate a feature extraction quality evaluation report.

[0049] Specifically, the accuracy assessment work is carried out based on the ecological characteristic information of the seawall to reasonably organize and classify the field measurement data. The field measurement data are high-precision geographic information data obtained at the seawall site through measurement equipment such as RTK and total station, including spatial position coordinates and attribute characteristic information. Grouping these data according to spatial position means classifying and sorting the data according to different areas of the seawall (such as the top of the embankment, the slope of the embankment, the toe of the embankment, etc.); while grouping according to attribute types is to classify according to the characteristic attributes of the measurement points (such as elevation, slope, material, etc.). Through this grouping method, a characteristic type comparison table is constructed, which contains information such as the spatial coordinates, type attributes, and measurement accuracy of each measurement point, thereby establishing a benchmark data set for accuracy assessment. The processing of the accuracy assessment sample set adopts the method of separating the training set and the validation set. Dividing the training set and the validation set in a ratio of 7:3 is to ensure that there are enough samples for model training, and at the same time leave a certain proportion of independent samples for verification. Stratified sampling is a sampling method that takes into account the data distribution characteristics, which ensures that samples of different types and different regions can be reasonably selected. In specific operations, first stratify the samples according to different characteristic types, and then randomly select samples in each layer according to a certain proportion, so as to obtain representative multi-level verification data.

[0050] When using multi-level verification data for accuracy assessment, the coordinate deviation of each inspection point needs to be calculated first. The coordinate deviation refers to the difference between the coordinates of the extracted feature points and the field measurement coordinates, including the plane position deviation and the elevation deviation. The attribute consistency refers to the degree of conformity between the extracted feature types and the field survey results. By statistically analyzing these deviation values and consistency indicators, a data set reflecting the position accuracy is obtained.

[0051] A confusion matrix is established based on the position accuracy data, and the recognition accuracy rate of each type of feature is calculated. The confusion matrix is an important tool for evaluating the accuracy of classification results, and its calculation formula is: ; Where: is the correct rate of the i-th type of feature being recognized as the j-th type; is the e-th weight factor; is the value of the confusion matrix element; is the feature weight coefficient; is the normalization coefficient; is the accuracy correction coefficient; K is the total number of feature categories; N is the total number of samples.

[0052] Performing spatio-temporal matching of attribute accuracy data with time-series imagery is to verify the temporal consistency of feature extraction results. Time-series imagery refers to remote sensing imagery data acquired at different time points. By calculating the feature change amounts between different phases, the temporal stability and reliability of feature extraction results can be evaluated. When performing spatio-temporal matching, the impacts of factors such as imagery acquisition time, spatial resolution, and imaging conditions need to be considered. Comprehensive evaluation is conducted on position accuracy data, attribute accuracy data, and dynamic change inspection data. Comprehensive evaluation requires considering multiple aspects of indicators, including spatial position accuracy, attribute recognition accuracy, temporal consistency, etc. By comprehensively analyzing these indicators, a feature extraction quality assessment report is generated.

[0053] For example, during the feature extraction quality assessment process of a section of seawall, first, data of 100 field measurement points were collected, and these measurement points covered various feature areas of the seawall. According to spatial positions, these points were divided into four groups: the top of the seawall area, the uphill area, the middle slope area, and the toe of the seawall area. At the same time, according to attribute types, they were divided into three categories: elevation points, structural points, and vegetation points. According to the ratio of 7:3, 70 points were selected as training samples, and the remaining 30 points were used as verification samples. This ratio was maintained in each group to ensure the representativeness of the samples. The three-dimensional coordinates of these points were obtained through RTK measurement, and the measured values were compared with the feature extraction results to calculate the planar position deviation and elevation deviation. According to the calculation results, a confusion matrix was established to analyze the recognition situation of each type of feature. At the same time, remote sensing imagery of four periods of this section of the seawall was collected. By comparing and analyzing the changes of feature points in different periods, the temporal consistency of feature extraction was verified. Finally, by integrating all evaluation indicators, a quality assessment report was formed, which detailed the accuracy situation and existing problems of feature extraction.

[0054] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Establish an index for the seawall structure feature dataset according to spatial position and attribute type, construct a structure feature data table, and obtain structure feature storage data; (2) Classify and code the ecological feature information of the seawall, establish an ecological feature hierarchical relationship, and obtain ecological feature storage data; (3) Conduct quantitative analysis on the accuracy indicators in the feature extraction quality assessment report, establish a quality assessment index system, and obtain quality assessment storage data; (4) Based on the structure feature storage data, establish a spatial reference framework, construct the core architecture of a geographic database, and obtain the basic database structure; (5) Import the ecological feature storage data and quality assessment storage data into the basic database structure, establish data association relationships, and obtain an associated database structure; (6) Optimize the hierarchical organization of the associated database structure to construct a seawall feature information database.

[0055] Specifically, an index is established for the seawall structure feature dataset. Index establishment refers to creating a directory structure for rapid data retrieval. Among them, the spatial location index is a spatial retrieval structure established based on the geographic coordinate system, including information such as the starting coordinates, ending coordinates, and spatial scope of the seawall, while the attribute type index is an attribute retrieval structure established for the structural features of the seawall (such as the type of dike, slope protection type, slope, etc.). The structure feature data table needs to contain basic information such as field definitions, data types, and field lengths. At the same time, constraints such as primary keys and foreign keys need to be set to ensure the integrity and consistency of the data. The classification and coding of the ecological feature information of the seawall is a process of systematically organizing data. The classification and coding adopt a hierarchical structure, organizing ecological features at different levels. For example, the first level is the vegetation type (herbs, shrubs, trees, etc.), the second level is the vegetation distribution density (sparse, medium, dense, etc.), and the third level is the degree of ecologicalization (low, medium, high, etc.). During the coding process, a unique coding rule needs to be set for each level to ensure the uniqueness and scalability of the coding. The establishment of the hierarchical relationship of ecological features needs to consider the subordinate relationship and association relationship between features, and express these relationships by establishing a hierarchical tree structure.

[0056] The quantitative analysis of the accuracy indicators in the feature extraction quality assessment report is a process of converting qualitative assessment results into measurable numerical indicators. The quality assessment index system includes location accuracy indicators (such as planar accuracy, elevation accuracy, etc.), attribute accuracy indicators (such as classification accuracy rate, integrity, etc.), and time consistency indicators (such as change detection accuracy rate, etc.). Each indicator needs to clearly define the calculation method, evaluation criteria, and weight coefficient to form an evaluation system. When establishing a spatial reference framework based on the structure feature storage data, it is necessary to first determine a unified coordinate system and projection method. The spatial reference framework is the basis of the geographic database, which defines the spatial positioning rules and measurement standards of the data. The construction of the core architecture of the geographic database needs to consider multiple aspects such as data organization methods, storage structures, and indexing mechanisms. The core architecture includes basic functional modules such as a spatial data engine, attribute data management, and spatial indexing.

[0057] The process of importing ecological feature storage data and quality assessment storage data into the basic database structure requires establishing the association relationships between the data. The data association relationships are mainly achieved through primary key - foreign key constraints. For example, using the seawall section number as the primary key to associate the structural features, ecological features, and quality assessment data. During the data import process, operations such as data format conversion, coordinate conversion, and attribute mapping are carried out to ensure the consistency and integrity of the data. The hierarchical organization optimization of the associated database structure is the last important step. The purpose of hierarchical organization optimization is to improve data access efficiency and management convenience. The optimization process includes table structure optimization (such as field design, index optimization, etc.), relationship optimization (such as reducing redundancy, improving normalization, etc.), and storage optimization (such as partitioning strategy, caching mechanism, etc.).

[0058] Take a specific example to illustrate the entire database construction process: For a 2 - kilometer - long seawall, establish an R - tree index for its structural feature data according to the spatial location for quickly retrieving seawall information in a specific area; at the same time, establish a B - tree index according to the attribute type (such as the slope protection type) for facilitating querying data by specific attributes. The structural feature data table contains fields such as "section number" (primary key), "starting coordinate", "ending coordinate", "slope protection type", "slope", etc.

[0059] In the ecological feature classification coding, a four - digit coding scheme is adopted. The first digit represents the vegetation type (1 - herbaceous, 2 - shrub, 3 - tree), the second digit represents the distribution density (1 - sparse, 2 - medium, 3 - dense), and the third and fourth digits represent the specific subclass features. For example, the code "2301" represents "densely distributed mangroves". These codes are associated with the structural feature table through foreign keys.

[0060] In the quality assessment index system, the position accuracy is represented by the root - mean - square error, the attribute accuracy is represented by the discrimination accuracy rate of the confusion matrix, and the time consistency is represented by the correct rate of change detection. These indexes are associated with other data through the seawall section number. When establishing the spatial reference framework, the 2000 National Geodetic Coordinate System and the Gauss - Krüger projection are adopted, and a two - layer storage structure containing spatial data and attribute data is established. During the data import process, data conversion and loading are carried out through ETL tools to ensure the unity of the coordinate system and the correct mapping of attribute fields. Finally, by establishing spatial indexes and attribute indexes, the query performance is optimized, and the seawall feature information database is constructed.

[0061] The above describes the method for extracting seawall feature information based on multi - source remote sensing data in the embodiments of the present application. Next, the system for extracting seawall feature information based on multi - source remote sensing data in the embodiments of the present application will be described. Please refer to Figure 2 One embodiment of the system for extracting seawall feature information based on multi - source remote sensing data in the embodiments of the present application includes: Enhancement module 201 is used to perform multi-spectral information enhancement on the collected multi-temporal satellite remote sensing images, aerial remote sensing images, and UAV oblique image data through band combination, perform geometric correction and ortho-rectification based on ground control points, and perform radiometric consistency processing of multi-source images by combining the histogram matching method to obtain standardized multi-temporal remote sensing data; Extraction module 202 is used to extract the seawall target area according to the standardized multi-temporal remote sensing data, calculate the three-dimensional elevation information by combining the oblique photogrammetry data, determine the boundary range and changes of the seawall through temporal feature comparison and analysis, and obtain the spatio-temporal feature information of the seawall; Recognition module 203 is used to extract the slope information of the seawall through cross-section profile analysis based on the spatio-temporal feature information of the seawall, identify the slope protection structure based on the material spectral feature library, and determine the sectional structure characteristics of the seawall by combining the calculation of terrain undulation degree to generate a seawall structure feature data set; Generation module 204 is used to calculate the normalized difference vegetation index for the seawall structure feature data set by using multi-spectral data, extract the vegetation distribution density and type by using the texture analysis method, and determine the degree of slope protection ecologicalization by spectral curve matching to generate the seawall ecologicalization feature information; Establishment module 205 is used to establish an accuracy evaluation sample set based on the seawall ecologicalization feature information according to the field measurement data, calculate the position accuracy and attribute accuracy through multi-level cross-validation, and perform dynamic change inspection by combining the temporal images to generate a feature extraction quality evaluation report; Construction module 206 is used to construct a seawall feature information database by organizing the seawall structure feature data set, the seawall ecologicalization feature information, and the feature extraction quality evaluation report in a hierarchical data organization manner.

[0062] Through the collaborative cooperation of the above-mentioned various components, by performing band combination and multi-spectral information enhancement on multi-temporal satellite remote sensing images, aerial remote sensing images, and UAV oblique image data, the recognition accuracy of seawall features is improved, making the seawall structure and ecological features more clearly distinguishable. And based on ground control points for geometric correction and orthorectification, combined with the histogram matching method for radiometric consistency processing of multi-source images, it ensures the spatial position accuracy and radiometric feature consistency between different data sources, laying a foundation for subsequent feature extraction. At the same time, using the multi-scale segmentation algorithm to extract the seawall target area and calculating the three-dimensional elevation information in combination with the oblique photogrammetry data not only improves the accuracy of target extraction but also obtains the three-dimensional structure information of the seawall. By comparing and analyzing the temporal features, the boundary range and changes of the seawall are determined, realizing the dynamic monitoring of seawall features. And based on the spatio-temporal feature information of the seawall for cross-section profile analysis and material spectral feature recognition, the slope information and the type of slope protection structure of the seawall can be accurately extracted. Combining with the calculation of terrain undulation degree to further determine the sectional structure features of the seawall makes the extraction of seawall structure features more comprehensive and accurate. In addition, by calculating the normalized difference vegetation index, using the texture analysis method to extract the vegetation distribution density and type, and then determining the degree of slope protection ecologicalization through spectral curve matching, the quantitative assessment of seawall ecological features is realized. Finally, based on the field measurement data, an accuracy assessment sample set is established. Through multi-level cross-validation and dynamic change inspection of temporal images, it not only ensures the reliability of the feature extraction results but also establishes a quality assessment system. The entire technical solution constructs a seawall feature information database through a hierarchical data organization method, realizing the standardized management and efficient utilization of seawall feature information.

[0063] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A method for extracting seawall feature information based on multi-source remote sensing data, characterized in that: The method for extracting seawall feature information based on multi-source remote sensing data includes: For the collected multi-temporal satellite remote sensing images, aerial remote sensing images and UAV oblique image data, multi-spectral information enhancement is performed through band combination, geometric correction and orthorectification are performed according to ground control points, and multi-source image radiation consistency processing is performed in combination with the histogram matching method to obtain standardized multi-temporal remote sensing data; Based on standardized multi-time series remote sensing data, a multi-scale segmentation algorithm is used to extract the seawall target area, and the three-dimensional elevation information is calculated by combining the oblique photogrammetry data. The boundary range and changes of the seawall are determined through time series feature comparison and analysis, and the spatiotemporal feature information of the seawall is obtained. According to the spatiotemporal characteristic information of the seawall, the slope information of the seawall is extracted through cross-section profile analysis, the slope protection structure is identified based on the material spectral feature library, and the segmented structural characteristics of the seawall are determined by combining the terrain undulation calculation to generate a seawall structural characteristic data set; For the seawall structure characteristic data set, the normalized vegetation index is calculated using multispectral data, the vegetation distribution density and type are extracted using texture analysis methods, the ecological degree of the slope protection is determined by spectral curve matching, and the ecological characteristic information of the seawall is generated; Based on the ecological characteristics of the seawall, an accuracy assessment sample set is established according to field measurement data, the position accuracy and attribute accuracy are calculated through multi-level cross-validation, and dynamic change tests are performed in combination with time series images to generate a feature extraction quality assessment report; The seawall structural feature dataset, seawall ecological feature information and feature extraction quality assessment report are organized in a hierarchical manner to construct a seawall feature information database.

2. The method for extracting seawall feature information based on multi-source remote sensing data according to claim 1, characterized in that: The multi-temporal satellite remote sensing images, aerial remote sensing images and drone oblique image data collected are enhanced by band combination, geometric correction and orthorectification are performed according to ground control points, and multi-source image radiation consistency processing is performed in combination with the histogram matching method to obtain standardized multi-temporal remote sensing data, including: Based on multi-temporal satellite remote sensing images, aerial remote sensing images and drone oblique image data, band separation processing is performed on different data sources to extract near-infrared band, red light band and green light band information, and multi-spectral information is enhanced through band combination to obtain multi-spectral enhanced data; For the multi-spectral enhanced data, a geometric transformation model is constructed through the coordinate information of the ground control points, and the image space position is corrected and resampled to obtain geometric correction data; Inputting the geometric correction data into an orthorectification processing algorithm, establishing a terrain correction model to eliminate projection deformation, and obtaining orthorectified data; Extracting the grayscale histogram features of each band image from the orthorectified data, performing multi-source image radiation consistency processing by a histogram matching method, and obtaining radiation consistency data; Organizing the radiation consistency data according to time series, establishing time series correlation relationships, and obtaining multi-time series correlation data; The multi-time series correlation data are standardized to obtain standardized multi-time series remote sensing data.

3. The method for extracting seawall feature information based on multi-source remote sensing data according to claim 1, characterized in that: The method uses standardized multi-time series remote sensing data, uses a multi-scale segmentation algorithm to extract the seawall target area, combines the oblique photogrammetry data to calculate the three-dimensional elevation information, determines the boundary range and changes of the seawall through time series feature comparison and analysis, and obtains the spatiotemporal feature information of the seawall, including: According to the standardized multi-time series remote sensing data, the image data is normalized and converted to grayscale, and a four-level pyramid image is constructed according to the scale ratio of 1:2:4:

8. For each level of image, the homogeneity index is calculated by the multi-scale segmentation algorithm, the optimal segmentation threshold is determined, the initial seawall target area is extracted, and the seawall segmentation data is generated; The seawall segmentation data is spatially registered with the oblique photogrammetry data, a mapping relationship between geographic coordinates and pixel coordinates is established, bilinear interpolation is used to densely match corner points, three-dimensional coordinate values ​​of feature points are calculated, and three-dimensional point cloud data is generated; The three-dimensional point cloud data is organized using a KD tree spatial index structure, the elevation value in each grid is calculated by least squares adjustment, the local inclination value is calculated by combining the coordinate difference of adjacent points, the slope value is extracted according to the eight-neighborhood relationship, and the three-dimensional elevation information is obtained; For the three-dimensional elevation information, a cross-section curve is established based on the elevation mutation point, the slope change trend is extracted along the cross-section direction, and the inner and outer contours of the seawall boundary range are determined by the regional growing method to obtain the seawall boundary data; Projecting the seawall boundary data onto standardized multi-time series remote sensing data of different time phases, calculating the grayscale difference, texture change and regional area change rate of adjacent time phase images, obtaining the time series change characteristic value through weighted fusion, and generating change data; A spatiotemporal clustering analysis is performed on the change data to extract the dynamic evolution characteristics of the seawall boundary, and the spatiotemporal characteristic information of the seawall is obtained by combining the spatial distribution characteristics of the seawall boundary data.

4. The method for extracting seawall feature information based on multi-source remote sensing data according to claim 1, characterized in that: The method extracts the slope information of the seawall through cross-section analysis based on the spatiotemporal characteristic information of the seawall, identifies the slope protection structure based on the material spectral feature library, and determines the segmented structural characteristics of the seawall by combining the terrain undulation calculation to generate a seawall structural characteristic data set, including: According to the spatiotemporal characteristic information of the seawall, section sampling points are set at intervals of 10 meters in the main area of ​​the seawall, and the section elevation curve is constructed through the triangulation network. The slope change points of each section are extracted from it to generate slope sampling data; Statistical analysis is performed on the slope sampling data to calculate the elevation difference and horizontal distance ratio between adjacent sampling points, and slope mutation points are screened with a threshold of 0.5 degrees. All mutation points are connected to generate a slope gradient line to obtain slope information; Compare the slope information with the characteristic curve in the material spectral feature library, calculate the spectral reflectance of each band, extract the material characteristic parameters, and establish the slope protection structure feature set; For the slope protection structure feature set, extract the three-dimensional coordinate value of each discrete point, calculate the elevation difference coefficient between adjacent points, establish a terrain change curve, and obtain terrain undulation data; Based on the terrain undulation data, the undulation variation of each slope section is counted, and the points where the slope variation exceeds 30% are set as segment nodes to obtain segment structure characteristics; The slope information, slope protection structure feature set and segmented structure feature are subjected to spatial correlation analysis, and the correlation coefficient between each feature is counted to generate a seawall structure feature data set.

5. The method for extracting seawall feature information based on multi-source remote sensing data according to claim 1, characterized in that: The said seawall structural characteristic data set uses multispectral data to calculate the normalized vegetation index, uses texture analysis method to extract vegetation distribution density and type, determines the ecological degree of slope protection through spectral curve matching, and generates seawall ecological characteristic information, including: Extract the near infrared band and red light band of multispectral data from the seawall structure feature data set, calculate the normalized vegetation index of each pixel point, and obtain the vegetation index distribution data; Perform sliding window statistics on the vegetation index distribution data, calculate the mean and standard deviation of the vegetation index in each grid, establish a density classification standard, and obtain vegetation distribution density data; The vegetation distribution density data is resampled according to a 20×20 meter grid, the gray level co-occurrence matrix features of each grid are extracted, and the angular second-order moment and entropy value parameters are statistically analyzed to obtain texture feature data; Based on the texture feature data, vegetation type identification is performed, and spatial distribution patterns of various types of vegetation are counted to obtain vegetation type data; The typical spectral curve of each vegetation is extracted by using the vegetation type data, and similarity is calculated with the spectral curve of the slope protection area to obtain the ecological degree data of the slope protection; The vegetation distribution density data, vegetation type data and slope protection ecological degree data are feature fused to generate seawall ecological feature information.

6. The method for extracting seawall feature information based on multi-source remote sensing data according to claim 1, characterized in that: Based on the ecological feature information of the seawall, an accuracy assessment sample set is established according to field measurement data, the position accuracy and attribute accuracy are calculated through multi-level cross-validation, dynamic change inspection is performed in combination with time series images, and a feature extraction quality assessment report is generated, including: According to the ecological characteristics of the seawall, the field measurement data are grouped according to spatial location and attribute type, a feature type comparison table is constructed, and an accuracy assessment sample set is established; The accuracy evaluation sample set is divided into a training set and a validation set according to a ratio of 7:3, and a test sample is selected by a stratified sampling method to obtain multi-level validation data; Calculate the coordinate deviation and attribute consistency of each checkpoint using the multi-level verification data, count the position accuracy index, and obtain the position accuracy data; A confusion matrix is ​​established based on the position accuracy data, and the recognition accuracy of each type of feature is calculated to obtain attribute accuracy data; Performing spatiotemporal matching of the attribute accuracy data and the time series images, calculating the amount of change between each time phase, and obtaining dynamic change test data; A comprehensive evaluation is performed on the position accuracy data, attribute accuracy data and dynamic change inspection data to generate a feature extraction quality evaluation report.

7. The method for extracting seawall feature information based on multi-source remote sensing data according to claim 1, characterized in that: The seawall structural feature data set, seawall ecological feature information and feature extraction quality assessment report are organized in a hierarchical manner to construct a seawall feature information database, including: The seawall structure feature data set is indexed according to the spatial position and attribute type, a structure feature data table is constructed, and the structure feature storage data is obtained; Classify and encode the ecological characteristic information of the seawall, establish the hierarchical relationship of ecological characteristics, and obtain ecological characteristic storage data; Quantitatively analyze the accuracy indicators in the feature extraction quality assessment report, establish a quality assessment indicator system, and obtain quality assessment storage data; Based on the data stored in the structural features, a spatial reference frame is established, a core architecture of a geographic database is constructed, and a basic database structure is obtained; Importing the ecological characteristic storage data and the quality assessment storage data into a basic database structure, establishing a data association relationship, and obtaining an associated database structure; The hierarchical organization of the associated database structure is optimized to construct a seawall feature information database.

8. A seawall feature information extraction system based on multi-source remote sensing data, used to implement the seawall feature information extraction method based on multi-source remote sensing data as described in any one of claims 1 to 7, characterized in that: The seawall feature information extraction system based on multi-source remote sensing data includes: The enhancement module is used to enhance the multi-spectral information of the collected multi-temporal satellite remote sensing images, aerial remote sensing images and UAV oblique image data through band combination, perform geometric correction and orthorectification according to ground control points, and combine the histogram matching method to perform multi-source image radiation consistency processing to obtain standardized multi-temporal remote sensing data; The extraction module is used to extract the seawall target area based on the standardized multi-time series remote sensing data using a multi-scale segmentation algorithm, calculate the three-dimensional elevation information in combination with the oblique photogrammetry data, determine the boundary range and changes of the seawall through time series feature comparison and analysis, and obtain the spatiotemporal feature information of the seawall; An identification module is used to extract the slope information of the seawall through cross-section profile analysis based on the spatiotemporal characteristic information of the seawall, identify the slope protection structure based on the material spectral feature library, determine the segmented structural characteristics of the seawall in combination with the terrain undulation calculation, and generate a seawall structural feature data set; A generation module is used to calculate the normalized vegetation index using multispectral data for the seawall structure feature data set, extract the vegetation distribution density and type using a texture analysis method, determine the ecological degree of the slope protection through spectral curve matching, and generate seawall ecological feature information; Establish a module for establishing an accuracy assessment sample set based on the ecological feature information of the seawall and field measurement data, calculating the position accuracy and attribute accuracy through multi-level cross-validation, combining time series images for dynamic change inspection, and generating a feature extraction quality assessment report; The construction module is used to construct a seawall feature information database by organizing the seawall structure feature data set, seawall ecological feature information and feature extraction quality assessment report in a hierarchical data organization manner.

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