CN114201839A - Micro-terrain automatic identification method based on surface flowing water physical simulation analysis principle - Google Patents
Micro-terrain automatic identification method based on surface flowing water physical simulation analysis principle Download PDFInfo
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Abstract
The invention discloses a micro-terrain automatic identification method based on the surface flowing water physical simulation analysis principle, which overcomes the defects of the prior art and comprises the following steps: step 1, acquiring area elevation data; step 2, determining the type of a terrain area, wherein the type of the terrain area comprises a positive terrain area and a negative terrain area; step 3, filling the depression based on the area elevation data; step 4, calculating the flow and the flow zero value through flow analysis; step 5, identifying ridge lines and valley lines according to the content of the substep 4; and 6, identifying the bealock and the mountain vertex.
Description
Technical Field
The invention relates to the technical field of automatic image identification, in particular to an automatic micro-terrain identification method based on a surface flowing water physical simulation analysis principle.
Background
For the study of the hydrophysical process, the ridges and valleys represent the water diversion property and the water collection property, respectively, and the extraction of the ridge lines and the valley lines is also the extraction of the water diversion lines and the water collection lines in essence. Therefore, the ridge line and the valley line can be extracted by using a hydrological analysis method.
However, in the existing extraction method for ridge lines and valley lines by using hydrological analysis, the error is large, the ridge lines and the valley lines cannot be accurately and effectively extracted, the error rate of automatic identification of the microtopography is high, and the identification efficiency is low.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an automatic micro-terrain identification method based on the surface flowing water physical simulation analysis principle.
The purpose of the invention is realized by the following technical scheme:
the micro-terrain automatic identification method based on the surface water physical simulation analysis principle comprises the following steps:
step 1, acquiring area elevation data;
step 2, determining the type of a terrain area, wherein the type of the terrain area comprises a positive terrain area and a negative terrain area;
step 3, filling the depression based on the area elevation data;
step 4, calculating the flow and the flow zero value through flow analysis;
step 5, identifying ridge lines and valley lines according to the content of the substep 4;
and 6, identifying the bealock and the mountain vertex.
The principle of the surface flowing water physical simulation analysis based on the DEM is as follows: for the ridge line, since it is also the water diversion line, for those grids on the water diversion line, since the nature of the water diversion line is the origin of the water flow, the water flow directions of these grids after the surface runoff simulation calculation should only have the outflow direction and not have the inflow direction, that is, the confluence accumulation amount of the grids is zero. By extracting the grids of the zero-value convergence accumulated value, a water diversion line can be obtained, and a ridge line is also obtained; for the valley line, due to the property of water collection, the feature of inverse terrain can be utilized for extracting the valley line, namely, the original DEM data is subtracted by a larger value, so that the terrain data completely opposite to the original terrain is obtained, namely, the ridge in the original DEM becomes the valley of the negative terrain, and the valley in the original DEM becomes the ridge in the negative terrain, so that the extraction of the valley line can be carried out in the negative terrain by utilizing the method for extracting the ridge line.
The main extraction positions of the bealock landform are concentrated at the intersection of characteristic lines formed by a mountain vertex, a valley point, a ridge line and a valley line. I.e. the orthogonal concave-convex focal point positions between the landform connecting lines such as ridges are mostly positioned in the catchment surface areas opposite to the ridges. Kindling et al define bealock topography as being located at the intersection of orthogonal convex-concave lines, also called intersection points. Thus, the bealock can be considered to be located in a relatively catchment area on the ridge.
Preferably, the step 2 specifically comprises:
firstly, DEM focus statistics is carried out, the DEM focus statistics is used for calculating neighborhood operation of output raster data, the value of each output pixel is a function of all input pixel values in a specified neighborhood range, the pixel to be calculated and counted is called a pixel to be processed, and the value of the pixel to be processed and all pixel values in an identified neighborhood are included in the calculation of neighborhood statistical data;
and then, carrying out subtraction operation on the original DEM data and the data after neighborhood analysis, and subdividing the operation result into two stages, wherein the classification boundary line is 0, so that the area larger than 0 is a positive terrain area on the original DEM, and the area smaller than 0 is a negative terrain area on the original DEM.
Preferably, the step 5 specifically comprises:
opening the attribute information of the data processed in the step 4, reclassifying, setting classification levels into two types, continuously adjusting the size of boundary data, and taking an isopleth chart and a shaded image generated by the DEM as auxiliary judgment data; the grid whose data attribute value is closer to 1 is more likely to be the position of the ridge line; for the valley line, due to the property of water collection, the feature of inverse terrain is utilized for extracting the valley line, namely, original DEM data is subtracted by a larger value, so that terrain data completely opposite to the original terrain is obtained, namely, ridges in the original DEM become valleys of negative terrain, and valleys in the original DEM become ridges in the negative terrain, so that the valley line can be extracted in the negative terrain by utilizing a method for extracting the ridge line.
Preferably, the process of identifying the bealock in step 6 specifically includes:
carrying out focus statistics of the maximum value on the original DEM, wherein the point which is subtracted from the original DEM to be zero is a mountain top point; and multiplying the extracted ridge line data and valley line data by using a grid calculator tool, and multiplying the obtained result by the positive terrain data to obtain grid form data of the bealock point.
Preferably, the process of identifying the mountain vertex in step 6 specifically includes:
and carrying out focus statistics on the DEM, calculating a maximum value by using a neighborhood analysis method and a 11 multiplied by 11 grid, naming an analysis result as max, comparing the obtained max data with the original DEM to obtain an equal part of the max data, naming the equal part as max _ data, then reclassifying the max _ data, assigning a value of 0 to be Nodata, assigning a value of 2 to be 1, and outputting and obtaining mountain top grid data.
Preferably, the micro-terrain automatic identification method based on the surface water physical simulation analysis principle further comprises an accuracy verification step, wherein the automatic identification of the base tower of the sample is compared with the base tower identified by naked eyes, and if the identification accuracy is greater than a set threshold value, the automatic identification is judged to be successful.
The invention has the beneficial effects that: the micro-terrain automatic identification method based on the surface water physical simulation analysis principle can effectively and rapidly and accurately identify the micro-terrain, and provides a favorable basis for the subsequent analysis and research by utilizing the micro-terrain.
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FIG. 1 is an algorithmic schematic of the present invention.
Detailed Description
The invention is further described below with reference to the figures and examples.
Example (b):
the micro-terrain automatic identification method based on the surface water physical simulation analysis principle comprises the following steps:
step 1, acquiring area elevation data;
step 2, determining the type of a terrain area, wherein the type of the terrain area comprises a positive terrain area and a negative terrain area;
step 3, filling the depression based on the area elevation data;
step 4, calculating the flow and the flow zero value through flow analysis;
step 5, identifying ridge lines and valley lines according to the content of the substep 4;
and 6, identifying the bealock and the mountain vertex.
The principle of the surface flowing water physical simulation analysis based on the DEM is as follows: for the ridge line, since it is also the water diversion line, for those grids on the water diversion line, since the nature of the water diversion line is the origin of the water flow, the water flow directions of these grids after the surface runoff simulation calculation should only have the outflow direction and not have the inflow direction, that is, the confluence accumulation amount of the grids is zero. By extracting the grids of the zero-value convergence accumulated value, a water diversion line can be obtained, and a ridge line is also obtained; for the valley line, due to the property of water collection, the feature of inverse terrain can be utilized for extracting the valley line, namely, the original DEM data is subtracted by a larger value, so that the terrain data completely opposite to the original terrain is obtained, namely, the ridge in the original DEM becomes the valley of the negative terrain, and the valley in the original DEM becomes the ridge in the negative terrain, so that the extraction of the valley line can be carried out in the negative terrain by utilizing the method for extracting the ridge line. The technical process of extracting ridge lines and valleys by utilizing a hydrological analysis method based on DEM is shown in figure 1.
The main extraction positions of the bealock landform are concentrated at the intersection of characteristic lines formed by a mountain vertex, a valley point, a ridge line and a valley line. I.e. the orthogonal concave-convex focal point positions between the landform connecting lines such as ridges are mostly positioned in the catchment surface areas opposite to the ridges. Kindling et al define bealock topography as being located at the intersection of orthogonal convex-concave lines, also called intersection points. Thus, the bealock can be considered to be located in a relatively catchment area on the ridge.
The step 2 specifically comprises the following steps:
firstly, DEM focus statistics is carried out, the DEM focus statistics is used for calculating neighborhood operation of output raster data, the value of each output pixel is a function of all input pixel values in a specified neighborhood range, the pixel to be calculated and counted is called a pixel to be processed, and the value of the pixel to be processed and all pixel values in an identified neighborhood are included in the calculation of neighborhood statistical data;
and then, carrying out subtraction operation on the original DEM data and the data after neighborhood analysis, and subdividing the operation result into two stages, wherein the classification boundary line is 0, so that the area larger than 0 is a positive terrain area on the original DEM, and the area smaller than 0 is a negative terrain area on the original DEM.
In the step 3: the depression is filled based on the area elevation data. The depressions (and bumps) are common errors due to the resolution of the data or rounding of the elevation to the nearest integer value, so the raw DEM data needs to be dimpled to ensure that basins and rivers are correctly demarcated. If the depression is not filled, the resulting water-based network may exhibit discontinuities. And then, the D8 algorithm is adopted to realize the flow direction analysis of the grid, and the water flow flowing into the grid is distributed according to the maximum downstream gradient of the adjacent units, so that the D8 algorithm is provided. Both algorithms assume that the water flow in a single grid flows into one of the 8 adjacent grids in the steepest slope direction, thereby determining the water flow direction.
The flow direction working principle is as follows: the distance between the centers of the pixels is calculated, and if the pixel size is 1, the distance between two orthogonal pixels is 1 and the distance between two diagonal pixels is 1.414 (square root of 2). If the maximum falling directions of a plurality of picture elements are all the same, the range of adjacent picture elements is expanded until the steepest falling direction is found. After finding the steepest descent direction, the output pixel will be encoded with a value representing this direction. If all the adjacent pixels are higher than the pixel to be processed, the pixel to be processed is regarded as noise and is filled with the lowest value of the adjacent pixels, and the pixel to be processed has a flow direction towards the pixel to be processed. However, if a single pel sink is located near the actual edge of the grid or has at least one NoData pel as a neighbor pel, it may not be filled because of insufficient neighborhood information. To treat a pel as a true single pel sink, all neighborhood information must exist.
In the step 4, after the flow direction analysis, the flow rate accumulation is calculated according to the flow direction of the water flow. The method comprises the following steps: the flow accumulation in the present study is not the river flow in an actual river, and is a spatial concept, the flow accumulation is determined by the direction of the water level in the rainfall runoff process, and is abstracted into individual lattices according to the grid DEM, and the accumulated amount of the flow is the accumulated amount generated when one lattice flows into another lattice. The obtained confluence accumulation zero-value data are generally disordered, and some grids are not ridge lines and are processed. The processing process can utilize a neighborhood analysis method to perform 3 x 3 neighborhood analysis on the extracted data with the confluence cumulant equal to zero value for smoothing.
The step 5 specifically comprises the following steps:
opening the attribute information of the data processed in the step 4, reclassifying, setting classification levels into two types, continuously adjusting the size of boundary data, and taking an isopleth chart and a shaded image generated by the DEM as auxiliary judgment data; the grid whose data attribute value is closer to 1 is more likely to be the position of the ridge line; for the valley line, due to the property of water collection, the feature of inverse terrain is utilized for extracting the valley line, namely, original DEM data is subtracted by a larger value, so that terrain data completely opposite to the original terrain is obtained, namely, ridges in the original DEM become valleys of negative terrain, and valleys in the original DEM become ridges in the negative terrain, so that the valley line can be extracted in the negative terrain by utilizing a method for extracting the ridge line.
The process of identifying the bealock in the step 6 specifically comprises the following steps:
carrying out focus statistics of the maximum value on the original DEM, wherein the point which is subtracted from the original DEM to be zero is a mountain top point; and multiplying the extracted ridge line data and valley line data by using a grid calculator tool, and multiplying the obtained result by the positive terrain data to obtain grid form data of the bealock point.
The process of identifying the mountain top in the step 6 specifically comprises the following steps:
and carrying out focus statistics on the DEM, calculating a maximum value by using a neighborhood analysis method and a 11 multiplied by 11 grid, naming an analysis result as max, comparing the obtained max data with the original DEM to obtain an equal part of the max data, naming the equal part as max _ data, then reclassifying the max _ data, assigning a value of 0 to be Nodata, assigning a value of 2 to be 1, and outputting and obtaining mountain top grid data.
The method for automatically identifying the microtopography based on the surface flow physical simulation analysis principle further comprises an accuracy verification step, wherein the automatic identification of the base tower of the sample is compared with the base tower identified by naked eyes, and if the identification accuracy is greater than a set threshold value, the automatic identification is judged to be successful. Because the base tower is located at the ridge line position, whether the ridge line is successfully identified can be reflected to the base tower, and whether the micro-terrain automatic identification is successful can be further judged.
In the embodiments provided in this application, it should be understood that the described structures and methods may be implemented in other ways. For example, the above-described embodiments with respect to structures are merely illustrative, and for example, a module or a unit may be divided into only one logic function, and may have another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another structure, or some features may be omitted or not executed.
Units described as separate parts may or may not be physically separate, and parts displayed as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed to a plurality of different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partially contributed to by the prior art, or all or part of the technical solutions may be embodied in the form of a software product, where the software product is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.
Claims (6)
1. The micro-terrain automatic identification method based on the surface water physical simulation analysis principle is characterized by comprising the following steps of:
step 1, acquiring area elevation data;
step 2, determining the type of a terrain area, wherein the type of the terrain area comprises a positive terrain area and a negative terrain area;
step 3, filling the depression based on the area elevation data;
step 4, calculating the flow and the flow zero value through flow analysis;
step 5, identifying ridge lines and valley lines according to the content of the substep 4;
and 6, identifying the bealock and the mountain vertex.
2. The method for automatically identifying the microtopography based on the surface water physics simulation analysis principle according to claim 1, wherein the step 2 is specifically as follows:
firstly, DEM focus statistics is carried out, the DEM focus statistics is used for calculating neighborhood operation of output raster data, the value of each output pixel is a function of all input pixel values in a specified neighborhood range, the pixel to be calculated and counted is called a pixel to be processed, and the value of the pixel to be processed and all pixel values in an identified neighborhood are included in the calculation of neighborhood statistical data;
and then, carrying out subtraction operation on the original DEM data and the data after neighborhood analysis, and subdividing the operation result into two stages, wherein the classification boundary line is 0, so that the area larger than 0 is a positive terrain area on the original DEM, and the area smaller than 0 is a negative terrain area on the original DEM.
3. The method for automatically identifying the microtopography based on the surface water physics simulation analysis principle according to claim 1, wherein the step 5 is specifically as follows:
opening the attribute information of the data processed in the step 4, reclassifying, setting classification levels into two types, continuously adjusting the size of boundary data, and taking an isopleth chart and a shaded image generated by the DEM as auxiliary judgment data; the grid whose data attribute value is closer to 1 is more likely to be the position of the ridge line; for the valley line, due to the property of water collection, the feature of inverse terrain is utilized for extracting the valley line, namely, original DEM data is subtracted by a larger value, so that terrain data completely opposite to the original terrain is obtained, namely, ridges in the original DEM become valleys of negative terrain, and valleys in the original DEM become ridges in the negative terrain, so that the valley line can be extracted in the negative terrain by utilizing a method for extracting the ridge line.
4. The method for automatically identifying the microtopography based on the surface water physical simulation analysis principle according to claim 1, wherein the process of identifying the bealock in the step 6 specifically comprises the following steps: carrying out focus statistics of the maximum value on the original DEM, wherein the point which is subtracted from the original DEM to be zero is a mountain top point; and multiplying the extracted ridge line data and valley line data by using a grid calculator tool, and multiplying the obtained result by the positive terrain data to obtain grid form data of the bealock point.
5. The method for automatically identifying the microtopography based on the surface water physics simulation analysis principle according to claim 1, wherein the process of identifying the mountain top in the step 6 is specifically as follows: and carrying out focus statistics on the DEM, calculating a maximum value by using a neighborhood analysis method and a 11 multiplied by 11 grid, naming an analysis result as max, comparing the obtained max data with the original DEM to obtain an equal part of the max data, naming the equal part as max _ data, then reclassifying the max _ data, assigning a value of 0 to be Nodata, assigning a value of 2 to be 1, and outputting and obtaining mountain top grid data.
6. The method for automatically identifying the microtopography based on the surface water physical simulation analysis principle as claimed in claim 1, further comprising an accuracy verification step of comparing the base tower automatic identification of the sample with a base tower identified by naked eyes, and judging that the automatic identification is successful if the identification accuracy is greater than a set threshold value.
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| CN114463564A (en) * | 2022-04-12 | 2022-05-10 | 西南石油大学 | Ridge line extraction method combining morphological characteristics and runoff simulation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114463564A (en) * | 2022-04-12 | 2022-05-10 | 西南石油大学 | Ridge line extraction method combining morphological characteristics and runoff simulation |
| CN114463564B (en) * | 2022-04-12 | 2022-06-28 | 西南石油大学 | Ridge line extraction method combining morphological characteristics and runoff simulation |
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