CN115880597B - Karst area water falling hole extraction method based on remote sensing technology - Google Patents
Karst area water falling hole extraction method based on remote sensing technology Download PDFInfo
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Abstract
The invention relates to a karst area water falling hole extraction method based on a remote sensing technology. The invention realizes the non-contact measurement of the falling hole, simultaneously realizes the rapid and accurate identification of the spatial distribution characteristics of the falling hole, improves the efficiency and saves a great deal of manpower.
Description
Technical Field
The invention belongs to the technical field of tunnel hydrogeology investigation, and particularly relates to a karst area water falling hole extraction method based on a remote sensing technology.
Background
The water burst problem in tunnel construction becomes a typical disaster of the tunnel engineering in the karst mountain area in the south, and has the characteristics of strong burst property, large water burst quantity, high hazard and the like, and once the water burst is generated, serious casualties and economic losses are caused. The interior structural features and water circulation conditions of the southern karst water system are complex, the surface karst depressions, karst troughs and water falling holes are widely distributed, underground karst pipelines, underground rivers, caverns and solution cavities are in dendritic distribution, and the space-time distribution of the karst water system is extremely complex. The water falling hole is mainly controlled by structural cracks, is in a slit shape and long strip shape, is not large in scale, is a main window for supplying groundwater to surface water body, and is important for finding out karst distribution; the water falling holes are distributed in karst depressions and are densely covered by vegetation and distributed in sporadic and punctiform form, the existing investigation means are mostly manual on-site investigation, the identification is difficult, the precision is low, the spatial distribution characteristics are difficult to find out, therefore, a karst water system cannot be finely depicted, and further, the quantitative evaluation of karst tunnel gushing water cannot be carried out.
The karst tunnel becomes the 'throat' of the line engineering, how to accurately identify and extract the water falling hole, accurately evaluate the karst water damage, ensure the engineering construction safety and become the 'neck' technology facing the line engineering construction in the karst area in the south.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a karst region water falling hole extraction method based on a remote sensing technology.
The invention solves the technical problems by adopting the following technical scheme:
a karst area water falling hole extraction method based on a remote sensing technology comprises the following steps:
step 1, acquiring remote sensing data and geological data of a comprehensive remote sensing data target acquisition area;
step 2, constructing a three-dimensional live-action remote sensing analysis model according to the remote sensing data and geological data of the comprehensive remote sensing data target acquisition area;
step 3, extracting a karst depression distribution range on the basis of the three-dimensional live-action remote sensing analysis model in the step 2;
and 4, identifying and extracting the water falling hole according to the three-dimensional live-action remote sensing analysis model for extracting the distribution range of the karst depression.
Furthermore, the step 1 includes the steps of:
step 1.1, defining a potential karst area as a comprehensive remote sensing data target acquisition area;
step 1.2, acquiring a remote sensing image of a comprehensive remote sensing data target acquisition area;
and 1.3, collecting geological data information of a comprehensive remote sensing data target acquisition area.
The specific implementation method of the step 1.2 is as follows: synchronously acquiring high-resolution optical images and LiDAR point cloud data of a comprehensive remote sensing data target acquisition area by adopting a high-altitude ground-imitation flight mode of the unmanned aerial vehicle; the specific implementation method of the step 1.3 is as follows: and collecting a comprehensive remote sensing data target acquisition area 1:5 geological map, and carrying out digital processing to obtain a soluble rock range.
Moreover, the step 2 includes the steps of:
step 2.1, manufacturing a digital orthophoto DOM according to the remote sensing data in the step 1;
step 2.2, manufacturing a digital surface model DSM according to the remote sensing data in the step 1;
step 2.3, processing the images and the models according to the step 2.1 and the step 2.2 to obtain a three-dimensional live-action remote sensing analysis general model;
step 2.4, manufacturing a refined digital elevation model DEM according to the remote sensing data in the step 1;
and 2.5, processing the elevation model in the step 2.4, and manufacturing a three-dimensional remote sensing analysis fine model.
The specific implementation method of the step 2.1 is as follows: performing geometric correction on the high-resolution optical image of the comprehensive remote sensing data target acquisition area obtained in the step 1 by using the internal and external azimuth elements and the image control points to generate a high-precision digital orthophoto DOM;
the specific implementation method of the step 2.2 is as follows: according to LiDAR point cloud data obtained in the step 1, the distance of 20cm is measuredThe grid of 20cm performs thinning on point cloud data to obtain plane and elevation coordinates, and performs spatial interpolation processing to obtain an engineering area digital surface model DSM containing vegetation information;
the specific implementation method of the step 2.3 is as follows: the digital orthophoto DOM in the step 2.1 and the digital surface model DSM in the step 2.2 are subjected to blocking, superposition, splicing and integration treatment to obtain a general model for three-dimensional real-scene remote sensing analysis of the comprehensive remote sensing data target acquisition area;
the specific implementation method of the step 2.4 is as follows: carrying out denoising pretreatment and quality inspection on the LiDAR point cloud data obtained in the step 1; based on a progressive encryption triangular network filtering algorithm, initially constructing a sparse irregular triangular network; setting an iteration distance and an iteration angle according to the fluctuation of the terrain of the area, and constructing an encrypted irregular triangular network; checking the automatic classification result by means of point cloud section analysis and point cloud color adding, removing residual vegetation points, finding out oversubscription ground points, and constructing a fine digital elevation model DEM for removing vegetation influence by a spatial interpolation method;
the specific implementation method of the step 2.5 is as follows: and (2) performing secondary treatment by adopting gradient, slope direction, contour line and mountain shadow analysis technology based on the refined digital elevation model DEM in the step (2.4), obtaining a corresponding grid analysis result, constructing a three-dimensional remote sensing analysis fine model for removing vegetation influence by utilizing a terrain rendering technology, and comprehensively presenting the detail information of the micro-landform of the target area.
Furthermore, the step 3 includes the steps of:
step 3.1, according to the three-dimensional live-action remote sensing analysis general model constructed in the step 2.3, according to the characteristics of the karst landform of the earth surface through a man-machine interaction form: rough ground, common peak cluster, peak forest, isolated peak and other forms on the ground surface; meanwhile, the karst development area is defined by combining the development range of the soluble rock;
step 3.2, processing the data of the refined digital elevation model DEM in the step 2.4 by using a GIS space analysis technology in a karst development area, judging the position of a depression of a water flow collecting area through the water flow direction, and obtaining a depression contribution area by taking a watershed as a boundary;
step 3.3, respectively extracting the highest point and the lowest point in the depression contribution area, and calculating the difference between the highest point and the lowest point to obtain the depression depth; regarding the contribution area with the depth less than 5m as a pseudo depression contribution area, performing elimination treatment;
step 3.4, obtaining the gradient through gradient analysisIs identified as a pseudo-depression contribution region, and is subjected to elimination processing to obtain a karst depression distribution range.
Moreover, the step 4 includes the steps of:
step 4.1, performing gradient analysis on the refined digital elevation model DEM with vegetation influence removed in step 2.5 to obtain a gradient rendering diagram, extracting circular topography with closure degree more than 80% and size less than 5m, and primarily judging as a water falling hole;
based on the annular target boundary vector, automatically extracting the elevation of an intersecting pixel of the refined digital elevation model DEM for removing vegetation influence, and marking the elevation as a vectorVector +.>The maximum and minimum squat marks of (1) are vector +.>And constructing a minimum rectangle enveloping the circular target according to the minimum rectangle, recording as Rect, calculating the plane coordinate of the geometric center of the rectangle Rect, and approximating the plane coordinate as the geometric center of the circular ring:
readingThe elevation at the location is denoted +.>Calculate vector +.>Average value of (2) is recorded as->Calculate->If the result is positive and is larger than 1m, judging that the circular ring is a water falling hole;
step 4.2, processing the LiDAR point cloud data obtained in step 1.2, and taking the geometric center of the ring in step 4.1 as the center, and performing every other timeCutting a section, adding 4 sections to obtain LiDAR point cloud data sections for removing vegetation, further judging the circular ring as a water falling hole, and simultaneously obtaining the space position coordinates (X, Y, Z) of the circular ring.
Step 4.3, determining the actual position of the water falling hole according to the space position coordinates (X, Y, Z), if the surface with good continuity is verified to be in an irregular round shape, a strip-shaped concave shape and a plane-shaped target with a large gradient, judging the surface to be the water falling hole, and if the surface is not verified to be in a general mountain slope;
step 4.4, measuring the determined water falling hole by using the LiDAR point cloud data section for removing vegetation obtained in the step 4.2 to obtain depth; and (5) obtaining the length and the width by using the water falling hole measurement obtained in the step 4.3.
The invention has the advantages and positive effects that:
according to the invention, remote sensing images and geological data of a target area are obtained, the remote sensing images are processed, a general and fine model for three-dimensional live-action remote sensing analysis is established, the distribution range of karst depressions is extracted, and the water falling holes are identified through gradient analysis and search, point cloud section rechecking and field verification, and finally, measurement analysis is carried out on the water falling holes. The invention realizes the non-contact measurement of the falling hole, simultaneously realizes the rapid and accurate identification of the spatial distribution characteristics of the falling hole, improves the efficiency and saves a great deal of manpower.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic illustration of a defined karst development area according to the present invention;
FIG. 3 is a schematic view of the extracted depression contribution area of the present invention;
FIG. 4 is a schematic view of the invention for obtaining karst depression distribution;
FIG. 5 is a schematic view illustrating the elevation point collection of a drop hole determination zone according to the present invention;
FIG. 6 is a schematic diagram of a grade analysis of the present invention;
FIG. 7 is a schematic view of a point cloud cross-section analysis of the present invention;
FIG. 8 is a field verification diagram of the present invention;
FIG. 9 is a diagram illustrating a measurement and analysis of a drain hole according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
A karst region water falling hole extraction method based on remote sensing technology, as shown in figure 1, comprises the following steps:
and step 1, acquiring remote sensing data and geological data of a comprehensive remote sensing data target acquisition area.
Step 1 comprises the following steps:
step 1.1, defining a potential karst area as a comprehensive remote sensing data target acquisition area;
step 1.2, acquiring a remote sensing image of a comprehensive remote sensing data target acquisition area; synchronously acquiring high-resolution optical images and LiDAR point cloud data of a comprehensive remote sensing data target acquisition area by adopting a high-altitude ground-imitation flight mode of the unmanned aerial vehicle; wherein the resolution of the high resolution optical image is not less than 5 cm; liDAR point cloud data density is not lower than 80 points per square meter, and laser intensity meets Class I (emission power is not higher than 0.4 mW).
Step 1.3, collecting geological data information of a comprehensive remote sensing data target acquisition area; and collecting a comprehensive remote sensing data target acquisition area 1:5 geological map, and carrying out digital processing to obtain a soluble rock range.
And 2, constructing a three-dimensional live-action remote sensing analysis model according to the remote sensing data and geological data of the comprehensive remote sensing data target acquisition area.
Step 2 comprises the following steps:
and 2.1, manufacturing a digital orthophoto DOM according to the remote sensing data in the step 1.
And (3) geometrically correcting the high-resolution optical image of the comprehensive remote sensing data target acquisition area obtained in the step (1) by using the internal and external azimuth elements and the image control points to generate a high-precision digital orthophoto DOM.
And 2.2, manufacturing a digital surface model DSM according to the remote sensing data in the step 1.
According to LiDAR point cloud data obtained in the step 1, the distance of 20cm is measuredAnd thinning the point cloud data by a 20cm grid, obtaining plane and elevation coordinates, and performing spatial interpolation processing to obtain the engineering area digital surface model DSM containing vegetation information.
And 2.3, processing according to the images and the models in the step 2.1 and the step 2.2 to obtain the three-dimensional live-action remote sensing analysis universal model.
And (3) performing blocking, superposition, splicing and integration processing on the digital orthophoto DOM in the step 2.1 and the digital surface model DSM in the step 2.2 to obtain the general model for three-dimensional real-scene remote sensing analysis of the comprehensive remote sensing data target acquisition area.
And 2.4, manufacturing a refined digital elevation model DEM according to the remote sensing data in the step 1.
Carrying out denoising pretreatment and quality inspection on the LiDAR point cloud data obtained in the step 1; based on a progressive encryption triangular network filtering algorithm, initially constructing a sparse irregular triangular network; setting an iteration distance and an iteration angle according to the fluctuation of the terrain of the area, and constructing an encrypted irregular triangular network; and checking the automatic classification result by means of point cloud section analysis and point cloud color adding, removing residual vegetation points, recovering overstocked ground points, and constructing a refined digital elevation model DEM for removing vegetation influence by a spatial interpolation method.
And 2.5, processing the elevation model in the step 2.4, and manufacturing a three-dimensional remote sensing analysis fine model.
And (2) performing secondary treatment by adopting gradient, slope direction, contour line and mountain shadow analysis technology based on the refined digital elevation model DEM in the step (2.4), obtaining a corresponding grid analysis result, constructing a three-dimensional remote sensing analysis fine model for removing vegetation influence by utilizing a terrain rendering technology, and comprehensively presenting the detail information of the micro-landform of the target area.
And 3, extracting a karst depression distribution range on the basis of the three-dimensional live-action remote sensing analysis model in the step 2.
Step 3 comprises the following steps:
step 3.1, according to the three-dimensional live-action remote sensing analysis general model constructed in the step 2.3, according to the characteristics of the karst landform of the earth surface through a man-machine interaction form: rough ground, common peak cluster, peak forest, isolated peak and other forms on the ground surface; as shown in fig. 2, the karst development area is delimited in combination with the easily soluble rock development range;
step 3.2, as shown in fig. 3, processing the data of the refined digital elevation model DEM in step 2.4 by using a GIS space analysis technology in a karst development area, judging the depression position of a water flow collecting area by the water flow direction, and obtaining a depression contribution area by taking a watershed as a boundary;
step 3.3, respectively extracting the highest point and the lowest point in the depression contribution area, and calculating the difference between the highest point and the lowest point to obtain the depression depth; regarding the contribution area with the depth less than 5m as a pseudo depression contribution area, performing elimination treatment;
step 3.4, obtaining the gradient through gradient analysis as shown in FIG. 4Is identified as a pseudo-depression contribution region, and is subjected to elimination processing to obtain a karst depression distribution range.
And 4, identifying and extracting the water falling hole according to the three-dimensional live-action remote sensing analysis model for extracting the distribution range of the karst depression.
Step 4 comprises the steps of:
step 4.1, gradient analysis and search: as shown in fig. 6, performing gradient analysis on the refined digital elevation model DEM with vegetation influence removed in the step 2.5 to obtain a gradient rendering diagram, and extracting a circular topography with the closure degree more than 80% and the size less than 5m to preliminarily determine as a water falling hole;
based on the annular target boundary vector, automatically extracting the elevation of an intersecting pixel of the refined digital elevation model DEM for removing vegetation influence, and marking the elevation as a vectorVector +.>The maximum and minimum squat marks of (1) are vector +.>As shown in fig. 5, a minimum rectangle enveloping a circular object is constructed accordingly, denoted as Rect, the plane coordinates of the geometric center of the rectangle Rect are calculated, and this is approximated as the geometric center of a circular ring:
readingThe elevation at the location is denoted +.>Calculate vector +.>Average value of (2) is recorded as->Calculate->If the result is positive and is more than 1m, judging that the circular ring is a water falling hole; this step is intended to exclude the same annular shape of the ground pits by comparing the magnitudes of the annular center and the boundary elevations.
And 4.2, rechecking the point cloud section: processing the LiDAR point cloud data obtained in the step 1.2, and centering on the geometric center of the ring in the step 4.1 every other timeA total of 4 sections are cut, the section length is 5m, and the section width is 50cm. The LiDAR point cloud data section with vegetation removed is obtained, the ring is further judged to be a water falling hole, and meanwhile, the space position coordinates (X, Y, Z) of the ring are obtained, as shown in fig. 7.
Step 4.3, field verification: as shown in fig. 8, the actual position of the water falling hole is determined according to the space position coordinates (X, Y, Z), if the surface with good continuity is verified to be in an irregular round shape and a strip-shaped concave shape, and the surface object with a large gradient (25 degrees or more) is judged to be the water falling hole, and if the surface object is not verified to be the general mountain slope;
step 4.4, measurement analysis: as shown in fig. 9, for the determined water falling hole, the depth is measured by using the LiDAR point cloud data section for removing vegetation obtained in step 4.2; and (5) obtaining the length and the width by using the water falling hole measurement obtained in the step 4.3.
It should be emphasized that the examples described herein are illustrative rather than limiting, and therefore the invention includes, but is not limited to, the examples described in the detailed description, as other embodiments derived from the technical solutions of the invention by a person skilled in the art are equally within the scope of the invention.
Claims (4)
1. A karst area water falling hole extraction method based on a remote sensing technology is characterized by comprising the following steps of: the method comprises the following steps:
step 1, acquiring remote sensing data and geological data of a comprehensive remote sensing data target acquisition area;
step 2, constructing a three-dimensional live-action remote sensing analysis model according to the remote sensing data and geological data of the comprehensive remote sensing data target acquisition area;
step 2.1, manufacturing a digital orthophoto DOM according to the remote sensing data in the step 1;
step 2.2, manufacturing a digital surface model DSM according to the remote sensing data in the step 1;
step 2.3, processing the images and the models according to the step 2.1 and the step 2.2 to obtain a three-dimensional live-action remote sensing analysis general model;
step 2.4, manufacturing a refined digital elevation model DEM according to the remote sensing data in the step 1;
step 2.5, processing the elevation model in the step 2.4 to manufacture a three-dimensional remote sensing analysis fine model;
step 3, extracting a karst depression distribution range on the basis of the three-dimensional live-action remote sensing analysis model in the step 2;
step 3.1, according to the three-dimensional live-action remote sensing analysis general model constructed in the step 2.3, according to the characteristics of the karst landform of the earth surface through a man-machine interaction form: rough ground, common peak cluster, peak forest and isolated peak form on the ground surface; meanwhile, the karst development area is defined by combining the development range of the soluble rock;
step 3.2, processing the data of the refined digital elevation model DEM in the step 2.4 by using a GIS space analysis technology in a karst development area, judging the position of a depression of a water flow collecting area through the water flow direction, and obtaining a depression contribution area by taking a watershed as a boundary;
step 3.3, respectively extracting the highest point and the lowest point in the depression contribution area, and calculating the difference between the highest point and the lowest point to obtain the depression depth; regarding the contribution area with the depth less than 5m as a pseudo depression contribution area, performing elimination treatment;
step 3.4, obtaining the gradient through gradient analysisIs identified as a pseudo-depression contribution area, and is arrangedRemoving treatment to obtain karst depression distribution range;
step 4, identifying and extracting the water falling hole according to a three-dimensional live-action remote sensing analysis model for extracting the distribution range of karst depressions;
step 4.1, performing gradient analysis on the refined digital elevation model DEM with vegetation influence removed in step 2.5 to obtain a gradient rendering diagram, extracting circular topography with closure degree more than 80% and size less than 5m, and primarily judging as a water falling hole;
based on the annular target boundary vector, automatically extracting the elevation of an intersecting pixel of the refined digital elevation model DEM for removing vegetation influence, and marking the elevation as a vectorVector +.>The maximum and minimum squat marks of (1) are vector +.>And constructing a minimum rectangle enveloping the circular target according to the minimum rectangle, recording as Rect, calculating the plane coordinate of the geometric center of the rectangle Rect, and approximating the plane coordinate as the geometric center of the circular ring: read->The elevation at the location is denoted +.>Calculate vector +.>Average value of (2) is recorded as->Calculation ofIf the result is positive and is larger than 1m, judging that the circular ring is a water falling hole;
step 4.2, processing the LiDAR point cloud data obtained in step 1.2, and taking the geometric center of the ring in step 4.1 as the center, and performing every other timeCutting a section, namely 4 sections in total, obtaining LiDAR point cloud data sections for removing vegetation, further judging that the circular ring is a water falling hole, and simultaneously obtaining the space position coordinates (X, Y, Z) of the circular ring;
step 4.3, determining the actual position of the water falling hole according to the space position coordinates (X, Y, Z), if the surface with good continuity is verified to be in an irregular round shape, a strip-shaped concave shape and a plane-shaped target with a large gradient, judging the surface to be the water falling hole, and if the surface is not verified to be in a general mountain slope;
step 4.4, measuring the determined water falling hole by using the LiDAR point cloud data section for removing vegetation obtained in the step 4.2 to obtain depth; and (5) obtaining the length and the width by using the water falling hole measurement obtained in the step 4.3.
2. The karst region water falling hole extraction method based on remote sensing technology as claimed in claim 1, wherein the method is characterized in that: the step 1 comprises the following steps:
step 1.1, defining a potential karst area as a comprehensive remote sensing data target acquisition area;
step 1.2, acquiring a remote sensing image of a comprehensive remote sensing data target acquisition area;
and 1.3, collecting geological data information of a comprehensive remote sensing data target acquisition area.
3. The karst region water falling hole extraction method based on remote sensing technology as claimed in claim 2, wherein the method is characterized in that: the specific implementation method of the step 1.2 is as follows: synchronously acquiring high-resolution optical images and LiDAR point cloud data of a comprehensive remote sensing data target acquisition area by adopting a high-altitude ground-imitation flight mode of the unmanned aerial vehicle; the specific implementation method of the step 1.3 is as follows: and collecting a comprehensive remote sensing data target acquisition area 1:5 geological map, and carrying out digital processing to obtain a soluble rock range.
4. The karst region water falling hole extraction method based on remote sensing technology as claimed in claim 1, wherein the method is characterized in that: the specific implementation method of the step 2.1 is as follows: performing geometric correction on the high-resolution optical image of the comprehensive remote sensing data target acquisition area obtained in the step 1 by using the internal and external azimuth elements and the image control points to generate a high-precision digital orthophoto DOM;
the specific implementation method of the step 2.2 is as follows: according to LiDAR point cloud data obtained in the step 1, the distance of 20cm is measuredThe grid of 20cm performs thinning on point cloud data to obtain plane and elevation coordinates, and performs spatial interpolation processing to obtain an engineering area digital surface model DSM containing vegetation information;
the specific implementation method of the step 2.3 is as follows: the digital orthophoto DOM in the step 2.1 and the digital surface model DSM in the step 2.2 are subjected to blocking, superposition, splicing and integration treatment to obtain a general model for three-dimensional real-scene remote sensing analysis of the comprehensive remote sensing data target acquisition area;
the specific implementation method of the step 2.4 is as follows: carrying out denoising pretreatment and quality inspection on the LiDAR point cloud data obtained in the step 1; based on a progressive encryption triangular network filtering algorithm, initially constructing a sparse irregular triangular network; setting an iteration distance and an iteration angle according to the fluctuation of the terrain of the area, and constructing an encrypted irregular triangular network; checking the automatic classification result by means of point cloud section analysis and point cloud color adding, removing residual vegetation points, finding out oversubscription ground points, and constructing a fine digital elevation model DEM for removing vegetation influence by a spatial interpolation method;
the specific implementation method of the step 2.5 is as follows: and (2) performing secondary treatment by adopting gradient, slope direction, contour line and mountain shadow analysis technology based on the refined digital elevation model DEM in the step (2.4), obtaining a corresponding grid analysis result, constructing a three-dimensional remote sensing analysis fine model for removing vegetation influence by utilizing a terrain rendering technology, and comprehensively presenting the detail information of the micro-landform of the target area.
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