Method, device and storage medium for evaluating morphological characteristics of Martian rampart impact craters

Abstract

The present invention discloses a method, device and storage medium for evaluating the morphological characteristics of a Martian barrier crater, which relates to the field of planetary remote sensing technology, and is mainly capable of improving the evaluation efficiency and accuracy of the morphological characteristics of a Martian barrier crater. The method comprises: in response to a morphological characteristic evaluation signal of a target barrier crater, obtaining the position information of the target barrier crater; based on the position information, determining the remote sensing image to which the target barrier crater belongs, and matching a digital elevation model homologous to the remote sensing image; based on the remote sensing image and the digital elevation model, segmenting the contour area of ​​the target barrier crater and its corresponding sputtering material in the remote sensing image; determining the geometric characteristic parameters of the contour area, and based on the geometric characteristic parameters, evaluating the morphological characteristics of the target barrier crater. The present invention is applicable to scenarios for evaluating the morphological characteristics of Martian barrier craters.

Description

Technical Field

[0001] The present invention relates to the field of planetary remote sensing technology, and in particular to a method, device and storage medium for evaluating the morphological characteristics of a Martian rampart impact crater. Background Art

[0002] Rampart craters are widely distributed on the Martian surface, often accompanied by fluid-related landforms. To reveal the nature of impact events and understand Martian geology, it is necessary to assess the morphological characteristics of Rampart craters on Mars.

[0003] Currently, the morphological characteristics of Martian rampart craters are typically identified visually. However, this manual assessment method is subject to significant subjective influences, resulting in large errors in the assessment of the morphological characteristics of Martian rampart craters. Furthermore, manual assessment is time-consuming and labor-intensive. Summary of the Invention

[0004] The present invention provides a method, device and storage medium for evaluating the morphological characteristics of Martian barrier craters, which are mainly capable of improving the evaluation efficiency and evaluation accuracy of the morphological characteristics of Martian barrier craters.

[0005] According to a first aspect of the present invention, a method for evaluating the morphological characteristics of a Martian rampart impact crater is provided, comprising:

[0006] In response to a morphological characteristic evaluation signal of a target barrier impact crater, obtaining position information of the target barrier impact crater;

[0007] Based on the location information, determining the remote sensing image to which the target barrier crater belongs, and matching a digital elevation model homologous to the remote sensing image;

[0008] Based on the remote sensing image and the digital elevation model, segmenting the target barrier impact crater and the corresponding sputtering area in the remote sensing image;

[0009] The geometric characteristic parameters of the contour area are determined, and the morphological characteristics of the target barrier impact crater are evaluated based on the geometric characteristic parameters.

[0010] Optionally, the location information is the latitude and longitude coordinates of the target barrier crater;

[0011] Determining the remote sensing image to which the target barrier crater belongs based on the position information includes:

[0012] Converting the latitude and longitude coordinates of the target rampart impact crater to the coordinates of the Martian sphere where the target rampart impact crater is located to obtain the converted coordinates of the target rampart impact crater;

[0013] Based on the converted coordinates, the map range of the target barrier crater on the Martian sphere is determined, and a remote sensing image matching the map range is searched in a preset remote sensing image database, wherein the preset remote sensing image database stores remote sensing images corresponding to different map ranges.

[0014] Optionally, matching a digital elevation model homologous to the remote sensing image includes:

[0015] Determining an initial digital elevation model corresponding to the target barrier crater based on the position information of the target barrier crater;

[0016] Projecting the remote sensing image and the initial digital elevation model onto the same coordinate system, and processing the projected remote sensing image and the projected initial digital elevation model to the same resolution to obtain the processed remote sensing image and the processed initial digital elevation model;

[0017] Feature point matching of the target barrier crater is performed in the processed remote sensing image and the processed initial digital elevation model, and based on the matching result, a digital elevation model homologous to the remote sensing image is identified in the initial digital elevation model.

[0018] Optionally, segmenting the target barrier impact crater and its corresponding sputtering area in the remote sensing image based on the remote sensing image and the digital elevation model includes:

[0019] Obtaining prompt information for segmenting the remote sensing image;

[0020] Obtaining a large remote sensing model, wherein the large remote sensing model includes a multi-head self-attention mechanism for extracting impact crater features, a feedforward neural network for transforming impact crater features, an encoder for encoding prompt information, and an image segmentation network for segmenting remote sensing images;

[0021] The remote sensing image, the digital elevation model, and the prompt information are inputted into the remote sensing large model, the remote sensing image and the digital elevation model are subjected to impact crater feature extraction by the multi-head self-attention mechanism to obtain impact crater features, the impact crater features are subjected to nonlinear transformation by the feedforward neural network to obtain nonlinear impact crater features, the prompt information is encoded by the encoder to obtain encoding features, the nonlinear impact crater features and the encoding features are subjected to image segmentation by the image segmentation network to obtain the contour area of ​​the target barrier impact crater and its corresponding spatter;

[0022] After segmenting the target barrier impact crater and its corresponding sputtering area in the remote sensing image based on the remote sensing image and the digital elevation model, the method further includes:

[0023] If the contour area is a label mask file, the label mask file is converted into a vector file containing geometric feature parameters of the contour area using a preset conversion method, wherein the method of converting the label mask file into a vector file containing geometric feature parameters of the contour area using the preset conversion method includes:

[0024] Converting the label mask file into a binary image, and processing the binary image into a raster image;

[0025] Determining a geographic transformation matrix of the raster image, and extracting an affine matrix for converting from pixel coordinates to geographic coordinates from the geographic transformation matrix;

[0026] The inverse matrix of the affine matrix is ​​calculated, and based on the inverse matrix, the Martian geographic coordinates of the contour area are converted into pixel coordinates. Based on the contour area after the pixel coordinates are converted, a vector file containing the geometric feature parameters of the contour area is determined.

[0027] Optionally, the geometric characteristic parameters include the pit area and pit radius of the target barrier impact crater in the contour area, and the sputtering perimeter and sputtering area of ​​the sputtering seat formed by each of the sputtering objects;

[0028] The step of evaluating the morphological characteristics of the target barrier crater based on the geometric characteristic parameters includes:

[0029] determining a flow coefficient of the sputtered material corresponding to the target barrier impact crater based on the crater area, the sputtering area, and the crater radius;

[0030] determining a lobation coefficient of the target barrier impact crater based on the sputtering perimeter and the sputtering area;

[0031] Based on the flow coefficient and the lobation coefficient, the morphological characteristics of the target barrier impact crater are determined.

[0032] Optionally, before matching the digital elevation model homologous to the remote sensing image, the method further includes:

[0033] Filtering the remote sensing image using a preset filter, and projecting the filtered remote sensing image to a Martian geographic coordinate system where the target rampart impact crater is located, to obtain the projected remote sensing image;

[0034] Determining the center position and extension range of the target barrier crater, and based on the center position and the extension range, cropping a target remote sensing image corresponding to the target barrier crater from the projected remote sensing image;

[0035] The matching of the digital elevation model having the same source as the remote sensing image includes:

[0036] Matching a digital elevation model that is homologous to the target remote sensing image.

[0037] Optionally, after segmenting the target barrier impact crater and its corresponding sputtering area in the remote sensing image based on the remote sensing image and the digital elevation model, the method further includes:

[0038] Taking the contour area as the contour area to be verified, obtaining a reference contour area of ​​the target barrier impact crater and its corresponding sputtering material segmented in the remote sensing image, and determining a reference contour area and a reference contour perimeter of the reference contour area, and a contour area to be verified and a contour perimeter to be verified of the contour area to be verified;

[0039] Calculating the degree of area overlap between the reference contour area and the contour area to be verified, and determining a perimeter detection parameter based on the reference contour perimeter and the contour perimeter to be verified, and determining an area detection parameter based on the reference contour area and the contour area to be verified;

[0040] Based on the area overlap, the perimeter detection parameters, and the area detection parameters, determine whether the contour area to be verified meets the segmentation requirements. If so, determine the morphological characteristics of the target barrier impact crater based on the contour area to be verified. Otherwise, re-segment the contour area of ​​the target barrier impact crater and its corresponding spatter in the remote sensing image.

[0041] According to a second aspect of the present invention, there is provided a device for evaluating the morphological characteristics of a Martian rampart impact crater, comprising:

[0042] an acquisition unit, configured to acquire position information of the target barrier impact crater in response to a morphological characteristic evaluation signal of the target barrier impact crater;

[0043] a matching unit, configured to determine, based on the position information, the remote sensing image to which the target barrier crater belongs, and to match a digital elevation model homologous to the remote sensing image;

[0044] a segmentation unit, configured to segment the target barrier impact crater and its corresponding sputtering material contour area in the remote sensing image based on the remote sensing image and the digital elevation model;

[0045] An evaluation unit is used to determine geometric characteristic parameters of the contour area and evaluate the morphological characteristics of the target barrier impact crater based on the geometric characteristic parameters.

[0046] According to a third aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for evaluating the morphological characteristics of Martian rampart impact craters.

[0047] According to a fourth aspect of the present invention, there is provided a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for evaluating the morphological characteristics of Martian rampart impact craters is implemented.

[0048] According to the present invention, a method, device, and storage medium for evaluating the morphological characteristics of a Martian rampart crater are provided. Compared to the current method of identifying the morphological characteristics of Martian rampart craters by human eyes, the present invention obtains the position information of the target rampart crater in response to the morphological characteristic evaluation signal of the target rampart crater; and based on the position information, determines the remote sensing image to which the target rampart crater belongs and matches it with a digital elevation model homologous to the remote sensing image; then, based on the remote sensing image and the digital elevation model, segments the contour area of ​​the target rampart crater and its corresponding ejecta in the remote sensing image; finally, determines the geometric characteristic parameters of the contour area, and finally, evaluates the morphological characteristics of the target rampart crater based on the geometric characteristic parameters. Thus, by segmenting the contour area of ​​the target rampart crater and its corresponding ejecta in the remote sensing image using the remote sensing image and the digital elevation model to which the target rampart crater belongs, and finally, evaluating the morphological characteristics of the target rampart crater based on the geometric characteristic parameters of the contour area, the present invention can realize automated evaluation of the morphological characteristics of the rampart crater, avoid manual intervention, and thus improve the efficiency and accuracy of evaluation of the morphological characteristics of Martian rampart craters. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0050] Figure 1 A flow chart of a method for evaluating the morphological characteristics of a Martian rampart impact crater provided by an embodiment of the present invention is shown;

[0051] Figure 2 A flow chart of another method for evaluating the morphological characteristics of a Martian rampart impact crater provided by an embodiment of the present invention is shown;

[0052] Figure 3A schematic structural diagram of a device for evaluating the morphological characteristics of a Martian rampart impact crater provided by an embodiment of the present invention is shown;

[0053] Figure 4 A schematic structural diagram of another device for evaluating the morphological characteristics of a Martian rampart impact crater provided by an embodiment of the present invention is shown;

[0054] Figure 5 A schematic diagram of the physical structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0055] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0056] At present, the method of identifying the morphological characteristics of Martian rampart craters with the naked eye is greatly affected by human subjective factors, which will result in large errors in the assessment of the morphological characteristics of Martian rampart craters. At the same time, manual assessment is time-consuming and labor-intensive.

[0057] In order to solve the above problems, the embodiment of the present invention provides a method for evaluating the morphological characteristics of the Martian rampart impact crater, such as Figure 1 As shown, the method includes:

[0058] 101. In response to a morphological characteristic evaluation signal of a target barrier impact crater, obtain position information of the target barrier impact crater.

[0059] The target barrier crater can be any Martian barrier crater distributed on the surface of Mars; the location information is the latitude and longitude coordinates of the target barrier crater. The number of target barrier craters can be one or more, and the embodiment of the present invention does not specifically limit the number of target barrier craters.

[0060] In the embodiment of the present invention, when a morphological characteristic evaluation signal of a target barrier crater is received, the latitude and longitude coordinates of the target barrier crater are determined using geological survey data, geographic information system and other software.

[0061] 102. Based on the location information, determine the remote sensing image to which the target barrier crater belongs, and match the digital elevation model with the remote sensing image.

[0062] For the embodiment of the present invention, in order to evaluate the morphological characteristics of the target barrier crater, it is first necessary to determine the remote sensing image to which the target barrier crater belongs. Based on this, step 102 specifically includes: converting the latitude and longitude coordinates of the target barrier crater to the coordinates of the Martian sphere where the target barrier crater is located, to obtain the converted coordinates of the target barrier crater; based on the converted coordinates, determining the map range of the target barrier crater on the Martian sphere, and searching for remote sensing images that match the map range in a preset remote sensing image database, wherein the preset remote sensing image database stores remote sensing images corresponding to different map ranges.

[0063] The Martian spherical coordinates are the coordinates in the Martian coordinate system. The Martian coordinate system is a coordinate system based on the shape, size, and rotation characteristics of Mars. Specifically, the longitude coordinate conversion is performed according to the following formula:

[0064]

[0065] in, is the converted coordinate corresponding to the longitude of the target rampart crater, is the longitude coordinate of the target Rampart crater, is the radius of the Martian sphere where the target rampart crater is located. Further, the latitude coordinate conversion is performed according to the following formula:

[0066]

[0067] in, is the converted coordinate corresponding to the latitude of the target rampart crater, is the latitude coordinate of the target Rampart crater, is the radius of the Martian sphere where the target Rampart crater is located. The two formulas above can be used to obtain the converted coordinates of the target Rampart crater. The map range of the target Rampart crater on the Martian sphere is then determined based on the converted coordinates. The search range is then determined based on the map range, and finally, the remote sensing image belonging to the target Rampart crater is searched for in the preset remote sensing image database according to the search range. For example, if the converted longitude coordinate of the target Rampart crater is 153.4° and the converted latitude coordinate is 20.5°, then the longitude range searched is 152° to 154°, and the latitude range searched is -20° to -22°. An automated search script is written, and the script is executed to ultimately output the remote sensing image within the above longitude and latitude ranges, and this remote sensing image is identified as the remote sensing image belonging to the target Rampart crater. The searched remote sensing image is then verified to verify the accuracy of the remote sensing image search.

[0068] Furthermore, after determining the remote sensing image to which the target barrier crater belongs, it is also necessary to match a digital elevation model homologous to the remote sensing image. Based on this, the method includes: determining the initial digital elevation model corresponding to the target barrier crater based on the position information of the target barrier crater; projecting the remote sensing image and the initial digital elevation model onto the same coordinate system, and processing the projected remote sensing image and the projected initial digital elevation model to the same resolution to obtain the processed remote sensing image and the processed initial digital elevation model; matching the feature points of the target barrier crater in the processed remote sensing image and the processed initial digital elevation model, and identifying the digital elevation model homologous to the remote sensing image in the initial digital elevation model based on the matching results.

[0069] The characteristic points of the target barrier crater may be edge points, corner points, or other points with significant characteristics of the target barrier crater.

[0070] Specifically, the coordinate system of the target rampart crater is first ensured to be consistent with the coordinate system of the digital elevation model data source. Then, based on the latitude and longitude coordinates of the target rampart crater, an initial digital elevation model (DEM) that covers the target rampart crater is determined in the DEM data source (wherein the DEM data source stores multiple DEM data). The initial DEM and the remote sensing image are then converted to the same projection coordinate system, and the projected remote sensing image and the projected initial DEM are processed to the same resolution, for example, the resolution of the remote sensing image is processed to the same resolution as the initial DEM. A feature extraction algorithm is then used to extract feature points of the target rampart crater from the processed remote sensing image and the processed initial DEM, respectively. The feature points extracted from the remote sensing image are then matched with the feature points extracted from the initial DEM to find matching feature points in the initial DEM that match the feature points in the remote sensing image. Finally, the area corresponding to each matching feature point is determined in the initial DEM, and the DEM within this area is determined to be the DEM of the same source as the remote sensing image. Finally, the morphological characteristics of the barrier crater are estimated based on remote sensing images and digital elevation models, which can realize the automation of the estimation of the morphological characteristics of the barrier crater, avoid the time consumed by manual participation and estimation errors, thereby improving the estimation accuracy and efficiency of the morphological characteristics of the barrier crater.

[0071] 103. Based on remote sensing images and digital elevation models, the target barrier impact crater and its corresponding splash area are segmented in the remote sensing image.

[0072] The spatter of a barrier crater refers to the material excavated and ejected from the periphery of the barrier crater due to the impact of a high-speed impactor during the barrier crater's formation. The contour area in this embodiment of the present invention is the area corresponding to the target barrier crater containing the spatter.

[0073] For the embodiment of the present invention, the remote sensing image is first subjected to geometric correction and radiometric correction to ensure the accuracy and readability of the image, and to ensure that the resolution of the digital elevation model matches the remote sensing image, or appropriate resampling is performed to match the resolution. The remote sensing image and the digital elevation model are then used as multi-source inputs, and image fusion or data assimilation technology is used to integrate the information of the two. An image segmentation algorithm is applied to more accurately identify the contour areas of the impact crater and the ejecta. The segmented contour areas are then smoothed to reduce the influence of noise and irregular edges. Morphological operations (such as dilation and erosion) can be used to further adjust the shape and size of the contour area to ensure the extraction accuracy of the contour area and further improve the estimation accuracy of the morphological characteristics of the barrier crater.

[0074] 104. Determine the geometric characteristic parameters of the contour area, and based on the geometric characteristic parameters, evaluate the morphological characteristics of the target barrier impact crater.

[0075] For an embodiment of the present invention, after extracting the contour area of ​​the target barrier crater and its corresponding ejecta, if the contour area is a label mask file, the label mask file also needs to be converted into a file format. Based on this, the method includes: if the contour area is a label mask file, the label mask file is converted into a vector file containing the geometric feature parameters of the contour area using a preset conversion method, wherein the method of converting the label mask file into a vector file containing the geometric feature parameters of the contour area using a preset conversion method includes: converting the label mask file into a binary image, and processing the binary image into a raster image; determining the geographic transformation matrix of the raster image, and extracting an affine matrix for converting from pixel coordinates to geographic coordinates from the geographic transformation matrix; calculating the inverse matrix of the affine matrix, and based on the inverse matrix, converting the Martian geographic coordinates of the contour area into pixel coordinates, and determining the vector file containing the geometric feature parameters of the contour area based on the contour area after the pixel coordinates are converted.

[0076] The tags in a labeled mask file uniquely identify contour areas. A mask file is an image file containing mask information. A mask is a "mask" overlaid on a layer. It allows you to create complex image effects by controlling layer visibility without directly modifying the layer's content. A vector file is a file that describes contour areas in digital form. Preset conversion methods are selected based on actual needs, such as the geographic transformation matrix conversion method.

[0077] In the embodiment of the present invention, the contour area image under the label mask file is binarized to obtain a binary image, which is then processed into a raster image, and then the geographic transformation matrix of the raster image is determined. The geographic transformation matrix is ​​determined by: according to the geographic coordinates of the upper left corner pixel in the raster image , the horizontal resolution of the upper left pixel , the horizontal rotation component of the raster image , the geographic coordinates of the upper right pixel of the raster image , the vertical resolution of the upper right corner pixel , the vertical rotation component of the raster image , determine the geographic transformation matrix of the raster image As shown below:

[0078]

[0079] Furthermore, the affine matrix A for transforming from pixel coordinates to geographic coordinates is extracted from the geographic transformation matrix as follows:

[0080]

[0081] On this basis, determine the inverse matrix of the affine matrix A As shown below:

[0082]

[0083] in, Furthermore, according to the above inverse matrix, the Martian geographic coordinates of the contour area are converted to pixel coordinates. The conversion formula is as follows:

[0084]

[0085] in, is the pixel coordinate of the contour area, are the Martian geographic coordinates of the outline area, are the geographic coordinates of the upper left pixel in the raster image, is the geographic coordinate of the pixel in the upper right corner of the raster image. In this way, the contour area after the coordinate conversion can be obtained, and the geometric feature parameters of the contour area can be determined in the remote sensing image and the digital elevation model. Finally, the contour area containing the geometric feature parameters is constructed as a vector file. If the morphological characteristics of the barrier crater are to be estimated later, the geometric feature parameters of the contour area corresponding to the barrier crater can be directly obtained in the vector file, thereby improving the estimation efficiency of the morphological characteristics of the barrier crater. Therefore, through the remote sensing image and digital elevation model to which the target barrier crater belongs, the contour area of ​​the target barrier crater and its corresponding splash material are segmented in the remote sensing image. Finally, according to the geometric feature parameters of the contour area, the morphological characteristics of the target barrier crater are evaluated. This can realize the automated evaluation of the morphological characteristics of the barrier crater, avoid manual participation, and thus improve the evaluation efficiency and accuracy of the morphological characteristics of the Martian barrier crater.

[0086] According to the present invention, a method for evaluating the morphological characteristics of a Martian rampart crater is provided. Compared with the current method of identifying the morphological characteristics of Martian rampart craters by human eyes, the present invention obtains the position information of the target rampart crater in response to the morphological characteristic evaluation signal of the target rampart crater; and based on the position information, determines the remote sensing image to which the target rampart crater belongs and matches it with a digital elevation model homologous to the remote sensing image; then, based on the remote sensing image and the digital elevation model, segments the contour area of ​​the target rampart crater and its corresponding ejecta in the remote sensing image; finally, determines the geometric characteristic parameters of the contour area, and finally, evaluates the morphological characteristics of the target rampart crater based on the geometric characteristic parameters. Thus, the target rampart crater and its corresponding ejecta contour area are segmented in the remote sensing image using the remote sensing image and the digital elevation model to which the target rampart crater belongs, and finally, the morphological characteristics of the target rampart crater are evaluated based on the geometric characteristic parameters of the contour area. This method can realize the automated evaluation of the morphological characteristics of the rampart crater, avoid manual intervention, and thus improve the efficiency and accuracy of the evaluation of the morphological characteristics of Martian rampart craters.

[0087] Furthermore, in order to better illustrate the above process of evaluating the morphological characteristics of the Martian rampart impact crater, as a refinement and extension of the above embodiment, the embodiment of the present invention provides another method for evaluating the morphological characteristics of the Martian rampart impact crater, such as Figure 2 As shown, the method includes:

[0088] 201. In response to a morphological feature evaluation signal of a target barrier impact crater, obtain position information of the target barrier impact crater.

[0089] Specifically, when a morphological feature evaluation signal of a target barrier crater is received, the latitude and longitude coordinate information of the target barrier crater is acquired.

[0090] 202. Based on the location information, determine the remote sensing image to which the target barrier crater belongs, and match the digital elevation model with the remote sensing image.

[0091] Specifically, based on the latitude and longitude coordinates of the target rampart crater, a remote sensing image that can cover the target rampart crater is searched in a preset remote sensing image database, and then the remote sensing image is preprocessed. Based on this, the method includes: filtering the remote sensing image using a preset filter, and projecting the filtered remote sensing image to the Martian geographic coordinate system where the target rampart crater is located to obtain the projected remote sensing image; determining the center position and extension range of the target rampart crater, and based on the center position and the extension range, cropping the target remote sensing image corresponding to the target rampart crater from the projected remote sensing image. Finally, matching the digital elevation model with the same source as the target remote sensing image.

[0092] The preset filter may be at least one of a low-pass filter, a high-pass filter, and a band-pass filter. Specifically, before filtering, the remote sensing image is preprocessed, including removing redundant information and correcting geometric distortion. The preprocessed remote sensing image is then filtered using the preset filter. The coordinate systems of the remote sensing images from different data sources are then unified into a common coordinate system. Conformal projection is used, using the Mars radius datum as the unified datum, to project the remote sensing image onto the Martian geographic coordinate system to obtain a projected remote sensing image. For example, the gdalwarp tool (a command-line tool of GDAL) of the Geospatial Data Abstraction Library (a geospatial data conversion library) can be used for image reprojection, and the pyproj library (a library for coordinate projection and conversion of geospatial data) of Python (a programming language for coordinate conversion) can be used to complete the coordinate conversion. The cropping boundary is calculated using the center coordinates and extended range of the rampart crater, with reference to the center position of the rampart crater in the rampart crater database file, and the outermost visual boundary of the rampart crater is used as the starting boundary. For example, the length and width directions are expanded outward by about 3 times the maximum distance of the splash, and the rampart crater is ensured to be located in the center of the image, so as to crop the target remote sensing image corresponding to the target rampart crater in the projected remote sensing image. This method is conducive to reducing image deformation, especially in high latitudes and image edge areas, and can effectively reduce the residual error caused by projection. For example, the long side range of the cropping boundary L=2×(3×R), where L is the long side range and R is the maximum splash distance of the splash of the target rampart crater. The wide side range of the cropping boundary W=2×(3×R), where W is the wide side range and R is the maximum splash distance of the splash of the target rampart crater. The coordinates of the upper left corner of the target remote sensing image are , the coordinates of the lower right corner are ,in, is the horizontal coordinate of the center of the target barrier crater, is the vertical coordinate of the center of the target barrier crater, is the long side of the clipping boundary, The width of the cropping boundary is defined as the cropped image. Based on this, a valid cropped remote sensing image of the target Rampart crater is obtained, i.e., the target remote sensing image corresponding to the target Rampart crater. An initial digital elevation model (DEM) is then obtained based on the latitude and longitude coordinates of the target Rampart crater. The overlapping region with the target remote sensing image is determined within the initial DEM, and the elevation model corresponding to this overlapping region is determined as the DEM of the same origin as the target remote sensing image.

[0093] 203. Obtain prompt information for segmenting the remote sensing image.

[0094] Among them, the remote sensing image is the target remote sensing image referred to in step 202; the prompt information refers to the guidance information for accurately segmenting the remote sensing image. For example, the prompt information can be information such as one or more points, annotation boxes, texts, etc. that the user terminal clicks on on the remote sensing image to mark the representative pixel positions of the spatter area. During the segmentation process, the guidance starts from the marked points and extracts the spatter area related to these points. The one or more points can be the center of the impact crater and the edge of the spatter, so as to prompt the distinction between the impact crater body and the spatter during the image segmentation process. By introducing the prompt information, the image segmentation accuracy can be improved, thereby improving the estimation accuracy of the morphological characteristics of the barrier impact crater.

[0095] 204. Obtain a large remote sensing model, wherein the large remote sensing model includes a multi-head self-attention mechanism for extracting crater features, a feedforward neural network for transforming crater features, an encoder for encoding prompt information, and an image segmentation network for segmenting remote sensing images.

[0096] 205. The remote sensing image, digital elevation model, and prompt information are input into the remote sensing large model. The crater features are extracted from the remote sensing image and the digital elevation model through a multi-head self-attention mechanism to obtain the crater features. The crater features are nonlinearly transformed through a feedforward neural network to obtain nonlinear crater features. The prompt information is encoded through an encoder to obtain encoded features. The nonlinear crater features and the encoded features are image segmented through an image segmentation network to obtain the contour area of ​​the target barrier crater and its corresponding spatter.

[0097] Specifically, the remote sensing large model is composed of a multi-head self-attention mechanism, a feedforward neural network, an encoder, an image segmentation network, etc. In order to improve the segmentation accuracy of the remote sensing large model, it is first necessary to train and test the initial remote sensing large model using a sample data set consisting of remote sensing images and digital elevation models with segmentation labels, so as to obtain a remote sensing large model with segmentation accuracy that meets the requirements. Furthermore, image enhancement, resolution optimization, and standardization operations are performed on the remote sensing images and digital elevation models to improve the large model's parsing ability and visual effects for remote sensing images and digital elevation models. Afterwards, the processed remote sensing images, digital elevation models, and prompt information are input into the remote sensing large model together, and the remote sensing images and digital elevation models are feature extracted through the multi-head self-attention mechanism in the remote sensing large model to obtain the image features corresponding to the remote sensing images and the elevation features corresponding to the digital elevation models. The feature extraction formula of the multi-head self-attention mechanism is as follows:

[0098]

[0099] Among them, Q, K, and V represent the query vector, key vector, and value of the remote sensing image and digital elevation model, respectively. is the dimension of the key, T represents the transpose of K, is the remote sensing feature corresponding to the remote sensing image, or the elevation feature corresponding to the digital elevation model, is the feature extraction function. Through the above formula, the remote sensing large model can focus on different parts of the remote sensing image and digital elevation model, thereby achieving more accurate feature extraction.

[0100] Furthermore, the remote sensing features and elevation features are fused to obtain the impact crater features, which are then input into the feedforward neural network for feature transformation to obtain nonlinear impact crater features. The transformation formula for feature transformation is as follows:

[0101]

[0102] in, is the activation function of the feedforward neural network layer, and are the weight matrices of the feedforward neural network layer, and are the bias terms of the feedforward neural network layer, Characteristic of impact craters. The nonlinear crater features are transformed by feedforward neural networks to enhance the expressive power of large remote sensing models and thus improve image segmentation accuracy.

[0103] At the same time, the prompt information is encoded by an encoder in the remote sensing large model to obtain encoded features. Specifically, the encoder converts the information such as points, boxes, or text in the prompt information into a feature display. The encoded features are then fused with the nonlinear impact crater features to obtain fused features. Ultimately, the fused features are input into an image segmentation network, which segments the target barrier impact crater and its corresponding spatter contour area. This embodiment of the present invention introduces prompt information into the image segmentation process, allowing high-level image features to receive directional guidance from user input, thereby achieving accurate segmentation of the barrier impact crater and its corresponding spatter contour.

[0104] Furthermore, after the target barrier crater and its corresponding sputtering object are segmented in the remote sensing image, in order to improve the estimation accuracy of the morphological characteristics of the target barrier crater, it is also necessary to verify whether the segmentation of the contour area is accurate or not. Based on this, the method includes: taking the contour area as the contour area to be verified, obtaining the reference contour area of ​​the target barrier crater and its corresponding sputtering object segmented in the remote sensing image, and determining the reference contour area and reference contour perimeter of the reference contour area, the contour area to be verified and the contour perimeter to be verified of the contour area to be verified; calculating the reference contour area; The area overlap between the region and the contour area to be verified, and based on the reference contour perimeter and the contour perimeter to be verified, the perimeter detection parameters are determined; based on the reference contour area and the contour area to be verified, the area detection parameters are determined; based on the area overlap, the perimeter detection parameters, and the area detection parameters, whether the contour area to be verified meets the segmentation requirements is judged; if so, the morphological characteristics of the target barrier impact crater are determined based on the contour area to be verified; otherwise, the contour area of ​​the target barrier impact crater and its corresponding spatter are re-segmented in the remote sensing image.

[0105] Specifically, the contour area is used as the contour area to be verified, and the reference contour area of ​​the target barrier impact crater and its corresponding sputtering product is obtained at the same time. The reference contour area can be manually drawn by an expert. Then the reference contour area of ​​the reference contour area is determined. and the reference contour perimeter , the contour area to be verified and the perimeter of the contour to be verified , and calculate the regional overlap according to the following formula :

[0106]

[0107] in, is the overlapping area between the reference contour area and the contour area to be verified, is the area of ​​the union of the reference contour area and the contour area to be verified. At the same time, the perimeter detection parameter is calculated according to the following formula :

[0108]

[0109] At the same time, the area detection parameters are calculated according to the following formula :

[0110]

[0111] Furthermore, it is determined whether the regional overlap is less than a preset overlap threshold, whether the perimeter detection parameter is less than a preset perimeter threshold, and whether the area detection parameter is less than a preset area threshold; if the regional overlap is less than the preset overlap threshold, and / or the perimeter detection parameter is less than the preset perimeter threshold, and / or the area detection parameter is less than the preset area threshold, then it is determined that the contour area extraction of the target barrier impact crater and its corresponding spatter does not meet the requirements, and it is necessary to re-extract the contour area of ​​the target barrier impact crater and its corresponding spatter in the remote sensing image; if the regional overlap is greater than or equal to the preset overlap threshold, and the perimeter detection parameter is greater than or equal to the preset perimeter threshold, and the area detection parameter is greater than or equal to the preset area threshold, then it is determined that the contour area extraction of the target barrier impact crater and its corresponding spatter meets the requirements, and it is not necessary to re-extract the contour area, wherein the preset overlap threshold, the preset perimeter threshold, and the preset area threshold are all values ​​set according to actual needs. By determining whether the contour area extraction meets the requirements, the embodiment of the present invention can ensure the extraction accuracy of the contour area, thereby improving the assessment accuracy of the morphological characteristics of the barrier impact crater.

[0112] 206. Determine the geometric characteristic parameters of the contour area, and based on the geometric characteristic parameters, evaluate the morphological characteristics of the target barrier crater.

[0113] The geometric characteristic parameters include the crater area and radius of the target barrier impact crater in the contour area, and the sputtering perimeter and area of ​​the sputtering mat formed by each sputtering object. In the embodiment of the present invention, the geometric characteristic parameters of the contour area can be extracted from the remote sensing image and the digital elevation model. Then, the morphological characteristics of the target barrier impact crater need to be evaluated based on the geometric characteristic parameters. Based on this, step 206 specifically includes: determining the flow coefficient of the sputtering object corresponding to the target barrier impact crater based on the crater area, the sputtering area, and the crater radius; determining the lobation coefficient of the target barrier impact crater based on the sputtering perimeter and the sputtering area; and determining the morphological characteristics of the target barrier impact crater based on the flow coefficient and the lobation coefficient.

[0114] Specifically, the flow coefficient of the sputtering material corresponding to the target barrier impact crater is calculated according to the following formula: :

[0115]

[0116] in, is the sputtering area of ​​the sputtering seat (the total area of ​​each sputtered object), is the crater area of ​​the target barrier impact crater, is the radius of the target barrier crater. The flow distance of continuous ejecta in the flow coefficient may be affected by the slope / roughness of the surface before impact, the viscosity of the ejecta, the size distribution of debris in the ejecta, and the velocity of the crater, leading to uneven diffusion of continuous ejecta. To ensure the scientific and reliable calculation of the flow coefficient, direct contour extraction is used to calculate the area of ​​the barrier crater and its ejecta, reducing errors caused by errors in the average distance calculation. The batch calculation process can be implemented in a programming language, with multi-threaded computing improving processing efficiency and generating visual results, including flow area distribution maps and parameter statistics tables.

[0117] At the same time, the lobate coefficient of the target barrier crater is calculated according to the following formula: :

[0118]

[0119] in, is the splashing perimeter of the splashing seat, is the sputtering area of ​​the sputtering mat. Ultimately, the morphological characteristics of the target barrier impact crater are determined based on the flow coefficient and lobation coefficient. For example, a larger flow coefficient indicates greater sputtering fluidity and a wider distribution, while a smaller lobation coefficient indicates restricted sputtering flow. A lobation coefficient closer to 1 indicates a more regular, nearly circular pattern of continuous sputtering; a larger lobation coefficient indicates a more irregular appearance of continuous sputtering.

[0120] Furthermore, if the flow coefficients and lobation coefficients of multiple target barrier craters are determined, it is possible to plot the variation patterns of the flow coefficients and lobation coefficients for all target barrier craters, and generate statistical reports to analyze the regular characteristics of the flow coefficients and lobation coefficients. This can reveal the radial and circumferential expansion and distribution patterns of the ejecta from barrier craters, providing basic data for subsequent simulations of impact events.

[0121] According to another method for evaluating the morphological characteristics of a Martian rampart crater provided by the present invention, compared to the current method of identifying the morphological characteristics of a Martian rampart crater by the naked eye, the present invention obtains the position information of the target rampart crater in response to a morphological characteristic evaluation signal of the target rampart crater; and based on the position information, determines the remote sensing image to which the target rampart crater belongs and matches it with a digital elevation model homologous to the remote sensing image; then, based on the remote sensing image and the digital elevation model, segments the contour area of ​​the target rampart crater and its corresponding ejecta in the remote sensing image; finally, determines the geometric characteristic parameters of the contour area, and finally, evaluates the morphological characteristics of the target rampart crater based on the geometric characteristic parameters. Thus, by segmenting the contour area of ​​the target rampart crater and its corresponding ejecta in the remote sensing image using the remote sensing image and the digital elevation model to which the target rampart crater belongs, and finally, evaluating the morphological characteristics of the target rampart crater based on the geometric characteristic parameters of the contour area, the present invention can realize automated evaluation of the morphological characteristics of the rampart crater, avoid manual intervention, and thus improve the efficiency and accuracy of the evaluation of the morphological characteristics of Martian rampart craters.

[0122] Further, as Figure 1 In a specific implementation, an embodiment of the present invention provides a device for evaluating the morphological characteristics of a Martian rampart impact crater, such as Figure 3 As shown, the device includes: an acquisition unit 31, a matching unit 32, a segmentation unit 33, and an evaluation unit 34.

[0123] The acquisition unit 31 may be configured to acquire position information of the target barrier impact crater in response to a morphological feature evaluation signal of the target barrier impact crater.

[0124] The matching unit 32 may be configured to determine the remote sensing image to which the target barrier crater belongs based on the position information, and to match a digital elevation model having the same origin as the remote sensing image.

[0125] The segmentation unit 33 may be configured to segment the target barrier impact crater and its corresponding sputtering object contour area in the remote sensing image based on the remote sensing image and the digital elevation model.

[0126] The evaluation unit 34 may be configured to determine geometric characteristic parameters of the contour area, and evaluate the morphological characteristics of the target barrier crater based on the geometric characteristic parameters.

[0127] In a specific application scenario, the location information is the latitude and longitude coordinates of the target barrier crater. In order to determine the remote sensing image to which the target barrier crater belongs, such as Figure 4 As shown, the matching unit 32 includes a coordinate conversion module 321 and a search module 322 .

[0128] The coordinate conversion module 321 can be used to convert the latitude and longitude coordinates of the target barrier crater into the coordinates of the Martian sphere where the target barrier crater is located, so as to obtain the converted coordinates of the target barrier crater.

[0129] The search module 322 can be used to determine the map range of the target barrier impact crater on the Martian sphere based on the converted coordinates, and search for remote sensing images that match the map range in a preset remote sensing image database, wherein the preset remote sensing image database stores remote sensing images corresponding to different map ranges.

[0130] In a specific application scenario, in order to match a digital elevation model that is homologous to a remote sensing image, the matching unit 32 further includes a first determination module 323 , a processing module 324 , and a matching module 325 .

[0131] The first determining module 323 may be configured to determine an initial digital elevation model corresponding to the target barrier crater based on the location information of the target barrier crater.

[0132] The processing module 324 can be used to project the remote sensing image and the initial digital elevation model onto the same coordinate system, and process the projected remote sensing image and the projected initial digital elevation model to the same resolution to obtain the processed remote sensing image and the processed initial digital elevation model.

[0133] The matching module 325 can be used to match the feature points of the target barrier crater in the processed remote sensing image and the processed initial digital elevation model, and based on the matching results, identify the digital elevation model in the initial digital elevation model that is homologous to the remote sensing image.

[0134] In a specific application scenario, in order to segment the target barrier impact crater and its corresponding sputtering object contour area in the remote sensing image, the segmentation unit 33 includes an acquisition module 331 and a segmentation module 332 .

[0135] The acquisition module 331 may be used to acquire prompt information for segmenting the remote sensing image.

[0136] The acquisition module 331 can also be used to acquire a large remote sensing model, wherein the large remote sensing model includes a multi-head self-attention mechanism for extracting crater features, a feedforward neural network for transforming crater features, an encoder for encoding prompt information, and an image segmentation network for segmenting remote sensing images.

[0137] The segmentation module 332 can be used to input the remote sensing image, the digital elevation model, and the prompt information into the remote sensing large model, extract the impact crater features of the remote sensing image and the digital elevation model through the multi-head self-attention mechanism to obtain the impact crater features, perform nonlinear transformation on the impact crater features through the feedforward neural network to obtain nonlinear impact crater features, encode the prompt information through the encoder to obtain encoding features, and perform image segmentation on the nonlinear impact crater features and the encoding features through the image segmentation network to obtain the contour area of ​​the target barrier impact crater and its corresponding spatter.

[0138] In a specific application scenario, in order to convert the file format of the contour area, the device further includes a file conversion unit 35 .

[0139] The file conversion unit 35 can be used to convert the label mask file into a vector file containing the geometric feature parameters of the contour area using a preset conversion method if the contour area is a label mask file, wherein the method of converting the label mask file into a vector file containing the geometric feature parameters of the contour area using a preset conversion method includes: converting the label mask file into a binary image, and processing the binary image into a raster image; determining the geographic transformation matrix of the raster image, and extracting an affine matrix for converting from pixel coordinates to geographic coordinates from the geographic transformation matrix; calculating the inverse matrix of the affine matrix, and based on the inverse matrix, converting the Martian geographic coordinates of the contour area into pixel coordinates, and determining the vector file containing the geometric feature parameters of the contour area based on the contour area after the pixel coordinates are converted.

[0140] In a specific application scenario, the geometric feature parameters include the pit area and pit radius of the target barrier impact crater in the contour area, and the sputtering perimeter and sputtering area of ​​the sputtering seat formed by each of the sputtering objects; in order to evaluate the morphological characteristics of the target barrier impact crater, the evaluation unit 34 can be specifically used to determine the flow coefficient of the sputtering object corresponding to the target barrier impact crater based on the pit area, the sputtering area, and the pit radius; determine the lobate coefficient of the target barrier impact crater based on the sputtering perimeter and the sputtering area; and determine the morphological characteristics of the target barrier impact crater based on the flow coefficient and the lobate coefficient.

[0141] In a specific application scenario, in order to pre-process the remote sensing image, the device further includes a pre-processing unit 36 ​​.

[0142] The pre-processing unit 36 ​​can be specifically used to filter the remote sensing image using a preset filter, and project the filtered remote sensing image to the Martian geographic coordinate system where the target barrier crater is located to obtain the projected remote sensing image; determine the center position and expansion range of the target barrier crater, and based on the center position and the expansion range, crop the target remote sensing image corresponding to the target barrier crater from the projected remote sensing image.

[0143] The matching unit 32 may also be used to match a digital elevation model that is homologous to the target remote sensing image.

[0144] In a specific application scenario, in order to verify the segmentation accuracy of the contour area, the device further includes a segmentation verification unit 37 .

[0145] The segmentation verification unit 37 can be used to take the contour area as the contour area to be verified, obtain the reference contour area of ​​the target barrier impact crater and its corresponding spatter segmented in the remote sensing image, and determine the reference contour area and reference contour perimeter of the reference contour area, the contour area to be verified and the contour perimeter to be verified of the contour area to be verified; calculate the area overlap between the reference contour area and the contour area to be verified, and determine the perimeter detection parameters based on the reference contour perimeter and the contour perimeter to be verified, and determine the area detection parameters based on the reference contour area and the contour area to be verified; based on the area overlap, the perimeter detection parameters, and the area detection parameters, judge whether the contour area to be verified meets the segmentation requirements; if so, determine the morphological characteristics of the target barrier impact crater based on the contour area to be verified; otherwise, re-segment the contour area of ​​the target barrier impact crater and its corresponding spatter in the remote sensing image.

[0146] It should be noted that for other corresponding descriptions of the functional modules involved in the device for evaluating the morphological characteristics of a Martian rampart impact crater provided by the embodiment of the present invention, reference can be made to Figure 1 The corresponding description of the method shown will not be repeated here.

[0147] Based on the above Figure 1The method shown, accordingly, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the following steps when executed by a processor: in response to a morphological feature evaluation signal of a target barrier crater, obtaining the position information of the target barrier crater; based on the position information, determining the remote sensing image to which the target barrier crater belongs, and matching a digital elevation model homologous to the remote sensing image; based on the remote sensing image and the digital elevation model, segmenting the contour area of ​​the target barrier crater and its corresponding spatter in the remote sensing image; determining the geometric feature parameters of the contour area, and based on the geometric feature parameters, evaluating the morphological features of the target barrier crater.

[0148] Based on the above Figure 1 The method shown and Figure 3 The embodiment of the device shown in the figure, the embodiment of the present invention also provides a physical structure diagram of a computer device, such as Figure 5 As shown, the computer device includes: a processor 41, a memory 42, and a computer program stored in the memory 42 and executable on the processor, wherein the memory 42 and the processor 41 are both arranged on a bus 43. When the processor 41 executes the program, the following steps are implemented: in response to a morphological feature evaluation signal of a target barrier crater, position information of the target barrier crater is acquired; based on the position information, a remote sensing image to which the target barrier crater belongs is determined, and a digital elevation model homologous to the remote sensing image is matched; based on the remote sensing image and the digital elevation model, a contour area of ​​the target barrier crater and its corresponding sputtering material is segmented in the remote sensing image; geometric feature parameters of the contour area are determined, and based on the geometric feature parameters, the morphological features of the target barrier crater are evaluated.

[0149] Through the technical solution of the present invention, the present invention obtains the position information of the target barrier crater by responding to the morphological feature evaluation signal of the target barrier crater; and based on the position information, determines the remote sensing image to which the target barrier crater belongs and matches the digital elevation model homologous to the remote sensing image; then, based on the remote sensing image and the digital elevation model, segments the contour area of ​​the target barrier crater and its corresponding ejecta in the remote sensing image; finally, determines the geometric feature parameters of the contour area, and finally, evaluates the morphological features of the target barrier crater based on the geometric feature parameters. Thus, by segmenting the contour area of ​​the target barrier crater and its corresponding ejecta in the remote sensing image using the remote sensing image and the digital elevation model to which the target barrier crater belongs, and finally, evaluating the morphological features of the target barrier crater based on the geometric feature parameters of the contour area, the present invention can realize the automated evaluation of the morphological features of the barrier crater, avoid manual intervention, and thus improve the efficiency and accuracy of the evaluation of the morphological features of the Martian barrier crater.

[0150] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, can be centralized on a single computing device, or can be distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, and thus, can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than herein, or can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0151] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the morphological characteristics of a Martian rampart impact crater, characterized in that: include: In response to a morphological characteristic evaluation signal of a target barrier impact crater, obtaining position information of the target barrier impact crater; Based on the location information, determining the remote sensing image to which the target barrier crater belongs, and matching a digital elevation model homologous to the remote sensing image; Based on the remote sensing image and the digital elevation model, segmenting the target barrier impact crater and the corresponding sputtering area in the remote sensing image; determining geometric characteristic parameters of the contour area, and evaluating morphological characteristics of the target barrier impact crater based on the geometric characteristic parameters; The geometric characteristic parameters include the pit area and pit radius of the target barrier impact crater in the contour area, and the sputtering perimeter and sputtering area of ​​the sputtering seat formed by each of the sputtering objects; The step of evaluating the morphological characteristics of the target barrier crater based on the geometric characteristic parameters includes: Determining a flow coefficient of the sputtered material corresponding to the target barrier impact crater based on the crater area, the sputtering area, and the crater radius; determining a lobation coefficient of the target barrier impact crater based on the sputtering perimeter and the sputtering area; and determining a morphological feature of the target barrier impact crater based on the flow coefficient and the lobation coefficient; Before matching the digital elevation model homologous to the remote sensing image, the method further includes: Filtering the remote sensing image using a preset filter, and projecting the filtered remote sensing image to the Martian geographic coordinate system where the target rampart crater is located to obtain the projected remote sensing image; determining the center position and extension range of the target rampart crater, and based on the center position and the extension range, cropping a target remote sensing image corresponding to the target rampart crater from the projected remote sensing image; The matching of the digital elevation model having the same source as the remote sensing image includes: Matching a digital elevation model that is homologous to the target remote sensing image.

2. The method according to claim 1, characterized in that The location information is the latitude and longitude coordinates of the target barrier crater; Determining the remote sensing image to which the target barrier crater belongs based on the position information includes: Converting the latitude and longitude coordinates of the target rampart impact crater to the coordinates of the Martian sphere where the target rampart impact crater is located to obtain the converted coordinates of the target rampart impact crater; Based on the converted coordinates, the map range of the target barrier crater on the Martian sphere is determined, and a remote sensing image matching the map range is searched in a preset remote sensing image database, wherein the preset remote sensing image database stores remote sensing images corresponding to different map ranges.

3. The method according to claim 1, characterized in that The matching of the digital elevation model having the same source as the remote sensing image includes: Determining an initial digital elevation model corresponding to the target barrier crater based on the position information of the target barrier crater; Projecting the remote sensing image and the initial digital elevation model onto the same coordinate system, and processing the projected remote sensing image and the projected initial digital elevation model to the same resolution to obtain the processed remote sensing image and the processed initial digital elevation model; Feature point matching of the target barrier crater is performed in the processed remote sensing image and the processed initial digital elevation model, and based on the matching result, a digital elevation model homologous to the remote sensing image is identified in the initial digital elevation model.

4. The method according to claim 1, wherein The step of segmenting the target barrier impact crater and its corresponding sputtering area in the remote sensing image based on the remote sensing image and the digital elevation model includes: Obtaining prompt information for segmenting the remote sensing image; Obtaining a large remote sensing model, wherein the large remote sensing model includes a multi-head self-attention mechanism for extracting impact crater features, a feedforward neural network for transforming impact crater features, an encoder for encoding prompt information, and an image segmentation network for segmenting remote sensing images; The remote sensing image, the digital elevation model, and the prompt information are inputted into the remote sensing large model, the remote sensing image and the digital elevation model are subjected to impact crater feature extraction by the multi-head self-attention mechanism to obtain impact crater features, the impact crater features are subjected to nonlinear transformation by the feedforward neural network to obtain nonlinear impact crater features, the prompt information is encoded by the encoder to obtain encoding features, the nonlinear impact crater features and the encoding features are subjected to image segmentation by the image segmentation network to obtain the contour area of ​​the target barrier impact crater and its corresponding spatter; After segmenting the target barrier impact crater and its corresponding sputtering area in the remote sensing image based on the remote sensing image and the digital elevation model, the method further includes: If the contour area is a label mask file, the label mask file is converted into a vector file containing geometric feature parameters of the contour area using a preset conversion method, wherein the method of converting the label mask file into a vector file containing geometric feature parameters of the contour area using the preset conversion method includes: Converting the label mask file into a binary image, and processing the binary image into a raster image; Determining a geographic transformation matrix of the raster image, and extracting an affine matrix for converting from pixel coordinates to geographic coordinates from the geographic transformation matrix; The inverse matrix of the affine matrix is ​​calculated, and based on the inverse matrix, the Martian geographic coordinates of the contour area are converted into pixel coordinates. Based on the contour area after the pixel coordinates are converted, a vector file containing the geometric feature parameters of the contour area is determined.

5. The method according to claim 1, wherein After segmenting the target barrier impact crater and its corresponding sputtering area in the remote sensing image based on the remote sensing image and the digital elevation model, the method further includes: Taking the contour area as the contour area to be verified, obtaining a reference contour area of ​​the target barrier impact crater and its corresponding sputtering material segmented in the remote sensing image, and determining a reference contour area and a reference contour perimeter of the reference contour area, and a contour area to be verified and a contour perimeter to be verified of the contour area to be verified; Calculating the degree of area overlap between the reference contour area and the contour area to be verified, and determining a perimeter detection parameter based on the reference contour perimeter and the contour perimeter to be verified, and determining an area detection parameter based on the reference contour area and the contour area to be verified; Based on the area overlap, the perimeter detection parameters, and the area detection parameters, determine whether the contour area to be verified meets the segmentation requirements. If so, determine the morphological characteristics of the target barrier impact crater based on the contour area to be verified. Otherwise, re-segment the contour area of ​​the target barrier impact crater and its corresponding spatter in the remote sensing image.

6. A device for evaluating the morphological characteristics of a Martian rampart impact crater, characterized in that: include: an acquisition unit, configured to acquire position information of the target barrier impact crater in response to a morphological characteristic evaluation signal of the target barrier impact crater; a matching unit, configured to determine, based on the position information, the remote sensing image to which the target barrier crater belongs, and to match a digital elevation model homologous to the remote sensing image; a segmentation unit, configured to segment the target barrier impact crater and its corresponding sputtering material contour area in the remote sensing image based on the remote sensing image and the digital elevation model; an evaluation unit, configured to determine geometric characteristic parameters of the contour area, and evaluate morphological characteristics of the target barrier impact crater based on the geometric characteristic parameters; the geometric characteristic parameters include the crater area and crater radius of the target barrier impact crater in the contour area, and the sputtering perimeter and sputtering area of ​​the sputtering seat formed by each of the sputtering objects; Determining a flow coefficient of the sputtered material corresponding to the target barrier impact crater based on the crater area, the sputtering area, and the crater radius; determining a lobation coefficient of the target barrier impact crater based on the sputtering perimeter and the sputtering area; and determining a morphological feature of the target barrier impact crater based on the flow coefficient and the lobation coefficient; a preprocessing unit configured to filter the remote sensing image using a preset filter, and project the filtered remote sensing image to a Martian geographic coordinate system where the target rampart crater is located to obtain the projected remote sensing image; determine the center position and extension range of the target rampart crater, and based on the center position and extension range, crop a target remote sensing image corresponding to the target rampart crater from the projected remote sensing image; The matching unit is further configured to match a digital elevation model that is homologous to the target remote sensing image.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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