CN111709432A - InSAR ground point extraction method, device, server and storage medium in complex urban environment - Google Patents

InSAR ground point extraction method, device, server and storage medium in complex urban environment Download PDF

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CN111709432A
CN111709432A CN202010564271.5A CN202010564271A CN111709432A CN 111709432 A CN111709432 A CN 111709432A CN 202010564271 A CN202010564271 A CN 202010564271A CN 111709432 A CN111709432 A CN 111709432A
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insar
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CN111709432B (en
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徐廷云
李振河
杨魁
徐骏千
李时博
邢恩文
孙铁
孙培周
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Tianjin North China Geological Exploration General Institute
Tianjin Binhai New Area Water Affairs Bureau
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Tianjin Binhai New Area Water Affairs Bureau
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Abstract

The embodiment of the invention discloses an InSAR ground point extraction method, a device, a server and a storage medium under a complex city environment, wherein the method comprises the following steps: carrying out filtering processing on the InSAR data by using DLG data; performing crude extraction of InSAR data based on low short feature characteristics and local elevation statistics; and performing fine extraction on the InSAR data after the crude extraction based on the topographic features and linear prediction. The method can realize the accurate extraction of InSAR ground points in a complex geographic environment, particularly in an urban environment.

Description

InSAR ground point extraction method, device, server and storage medium in complex urban environment
Technical Field
The invention relates to the technical field of geographic information, in particular to an InSAR ground point extraction method, device, server and storage medium in a complex urban environment.
Background
The radar satellite is a general name of a ground observation remote sensing satellite carrying a Synthetic Aperture Radar (SAR), the SAR is an active microwave sensor, and the SAR has the characteristics of all weather and all-day operation and is widely applied to many subject fields. The InSAR technology utilizes multiple-scene same-region SAR images, and searches for point targets which are not affected by time and space baseline decorrelation and atmospheric change through statistical analysis of returned phase and amplitude information.
However, in cities, a large number of land features, such as tall buildings and short vegetation, are distributed, which seriously affects the accuracy of the InSAR ground point extraction.
Disclosure of Invention
The embodiment of the invention provides an InSAR ground point extraction method, device, server and storage medium in a complex urban environment, aiming at solving the technical problem of low extraction precision of InSAR ground points in complex urban environments such as cities in the prior art.
In a first aspect, an embodiment of the present invention provides an InSAR ground point extraction method in a complex urban environment, including:
performing InSAR data filtering based on high terrestrial feature and DLG data;
coarse extraction of InSAR data is realized based on low short feature characteristics and local elevation system analysis;
and finely extracting the InSAR data after the crude extraction based on the topographic features and the linear prediction model.
Further, the InSAR data filtering based on high terrestrial feature and DLG data support comprises:
converting the DLG data and the InSAR data into the same coordinate system;
extracting effective surface element data in the DLG data;
and carrying out three-dimensional topology inspection on the point data in the effective surface element data and the InSAR data to determine whether the InSAR point data is a ground object point.
Further, the crude extraction of the InSAR data based on the low dwarf feature and the local high system analysis comprises:
calculating an elevation data statistic value within a preset range of data points in InSAR data;
and comparing the relationship between the data points of the elevation data statistic InSAR data and a preset threshold value by adopting a production formula rule, and determining the data points as the data points after the rough extraction if the relationship is judged to be smaller than the preset threshold value.
Further, in the above-mentioned case,
the calculation of the elevation data statistic value within the preset range of the data points in the InSAR data comprises the following steps:
and calculating the maximum elevation, the minimum elevation, the mean value and/or the mean square error statistic value through the elevation in the preset range of the data points in the InSAR data, and then obtaining the optimal statistic value through comprehensive weight analysis. Further, in the above-mentioned case,
the fine extraction of the InSAR data after the crude extraction based on the topographic features and linear prediction comprises the following steps:
determining the probability of ground points in InSAR data by using a linear prediction model;
selecting InSAR data points with higher ground point probability according to the probability;
and performing fine extraction based on topographic features according to the InSAR data points with higher ground point probability.
Further, the determining the probability of the ground point in the InSAR data by using the linear prediction model includes:
determining the ground points in InSAR data by the following method, and using STRepresents a set of ground point points, then STCan be expressed as:
Figure BDA0002547228480000031
and determining the probability of the ground points according to the elevation difference.
Furthermore, the selecting the InSAR data points with higher ground point probability according to the probability comprises:
selecting a point with higher ground point probability as a central point;
searching for nearby points according to a preset step length and a preset angle, and taking the searched points as non-ground points;
adjusting a preset angle according to a preset rule to continuously search nearby points, and taking the searched points as non-ground points;
and iterating the preset step length, and repeatedly searching for an adjacent point according to the preset step length and a preset angle until the preset step length after iteration reaches a preset distance threshold value.
In a second aspect, an embodiment of the present invention further provides an InSAR ground point extraction device in a complex city environment, including:
the filtering module is used for carrying out InSAR data filtering based on high terrestrial feature and DLG data support;
the rough extraction module is used for carrying out rough extraction on InSAR data based on low feature and local elevation statistical recognition algorithm;
and the fine extraction module is used for performing fine extraction on the InSAR data after the coarse extraction based on the topographic features and linear prediction.
Further, the filtering module includes:
the conversion unit is used for converting the DLG data and the InSAR data into the same coordinate system;
an extraction unit configured to extract the effective surface element data in the DLG data;
and the checking unit is used for carrying out three-dimensional topology checking on the point data in the effective surface element data and the InSAR data so as to determine whether the InSAR point data is a ground object point.
Further, the crude extraction module comprises: :
the calculation unit is used for calculating an elevation data statistic value within a preset range of data points in InSAR data;
and the judging unit is used for comparing the relationship between the elevation data statistic InSAR data point and a preset threshold value by adopting a generation formula rule, and determining the data point as a data point after rough extraction if the data point is judged to be smaller than the preset threshold value.
Further, the computing unit is configured to:
and calculating the maximum elevation, the minimum elevation, the mean value and/or the mean square error statistic value through the elevation in the preset range of the data points in the InSAR data, and then obtaining the optimal statistic value through comprehensive weight analysis.
Further, the fine extraction module comprises:
a determining unit, configured to determine a probability of a ground point in the InSAR data using a linear prediction model;
the selecting unit is used for selecting InSAR data points with higher ground point probability according to the probability;
and the extraction unit is used for performing fine extraction based on topographic features according to the InSAR data points with higher ground point probability.
Further, the determining unit is configured to:
determining the ground points in InSAR data by the following method, and using STRepresents a set of ground point points, then STCan be expressed as:
Figure BDA0002547228480000041
further, the extraction unit is configured to:
selecting a point with higher ground point probability as a central point;
searching for nearby points according to a preset step length and a preset angle, and taking the searched points as non-ground points;
adjusting a preset angle according to a preset rule to continuously search nearby points, and taking the searched points as non-ground points;
and iterating the preset step length, and repeatedly searching for an adjacent point according to the preset step length and a preset angle until the preset step length after iteration reaches a preset distance threshold value.
In a third aspect, an embodiment of the present invention further provides a server, where the server includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement any of the method for extracting InSAR ground points in a complex urban environment provided by the above embodiments.
In a fourth aspect, the present invention further provides a storage medium containing computer executable instructions, which when executed by a computer processor, is configured to perform the InSAR ground point extraction method in a complex urban environment as in any one of the above embodiments.
According to the method, the device, the server and the storage medium for extracting InSAR ground points in the complex urban environment, the InSAR data are filtered by utilizing DLG data; performing coarse extraction based on the InSAR data after the low short feature characteristic and the local elevation statistical filtering; and performing fine extraction on the InSAR data after the crude extraction based on the topographic features and linear prediction. The method can filter the reflected InSAR data of high-rise buildings such as buildings in the InSAR data by using DLG data, can eliminate the InSAR data of low and short objects by using local elevation, and can realize fine extraction of the InSAR data by setting a corresponding threshold through linear prediction. The method can realize the accurate extraction of InSAR ground points in a complex geographic environment, particularly in an urban environment.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:
fig. 1 is a schematic flow chart of an InSAR ground point extraction method in a complex city environment according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an InSAR ground point extraction method in a complex urban environment according to a second embodiment of the present invention;
fig. 3 is a schematic flow chart of an InSAR ground point extraction method in a complex urban environment according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an InSAR ground point extraction device in a complex urban environment according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a server according to a fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a schematic flow chart of an InSAR ground point extraction method in a complex urban environment according to an embodiment of the present invention, which is applicable to a complex geographic environment, particularly to an InSAR ground point extraction situation in an urban environment, and the method may be executed by an InSAR ground point extraction device in a complex urban environment, and may be integrated in a geographic information server, and specifically includes the following steps:
and S110, filtering the InSAR data by utilizing the DLG data.
DLG, namely a digital line map, is a vector data set stored hierarchically on the basis of geographic elements on the existing topographic map. The DLG includes both spatial information and attribute information.
The digital line drawing map (DLG) is a vectorized data file formed by vectorizing one or more map elements in each scanned and geometrically corrected map.
DLG adopts points, lines and surfaces to describe the geometric characteristics of ground elements, gives attributes of the points, the lines and the surfaces, outputs the attributes in a vector format, can accurately express the spatial position information and the attribute information of the ground element information, has the characteristic of no deformation when the DLG data is arbitrarily scaled, and can be effectively used as information data for extracting, retrieving and analyzing the spatial information of a geographic information system.
The InSAR monitoring point data comprises a plurality of point subsets with different spatial attributes, such as a ground point subset, a building point subset, a vegetation point subset, a noise point subset and the like. The dot density of InSAR dot data is typically on the order of meters.
The symbols representing the land topography in the DLG data include a dot element symbol, a line element symbol, a face element symbol, a character symbol, and the like. The point element symbol represents a single ground feature element, such as an electric pole, a single tree and the like; the line element symbols may represent various topographical features in a linear form, such as ridges, fences, etc.; and the face element symbol may represent a range of complex terrain, such as buildings, etc.
And the DLG data is used for assisting the InSAR data point to filter, the position precision of the DLG data plane is superior to the InSAR data point density, otherwise, the extraction effect is seriously influenced if the precision is too low. DLG data with a proper proportion is selected according to the position precision and the expression range of the data plane.
Corresponding point data may be determined using the surface elements by associating each point in the InSAR point data with a terrain or feature element represented in the DLG outcome data. By selecting the surface element in the element symbol, it is possible to represent feature information in a wide range and to be effective as auxiliary information for the filtering process. Filtering the InSAR data.
The filtering the InSAR data by using the DLG data may include:
converting the DLG data and the InSAR data into the same coordinate system;
extracting effective surface element data in the DLG data;
and carrying out three-dimensional topology inspection on the point data in the effective surface element data and the InSAR data to determine whether the InSAR point data is a ground object point.
The InSAR point data and the ground object surface represented by the multi-segment line have three-dimensional topological relations of point-in-plane, point-out-of-plane and point-in-plane boundaries in a two-dimensional plane. Therefore, the effective surface element data in the DLG data can be extracted first.
And all the surfaces of the DLG surface data which are in the same area, the same scale and the same mathematical basis as the InSAR point data form a surface set.
In a rectangular plane coordinate systemThe plane coordinates of all InSAR point data in a certain area form a set R2X, Y, plane coordinates (X) of each pointi,yi) Are all sets R2One element of (x)i,yi)∈{(x,y)|x∈X,y∈Y}(i＝1,2,…,n)。
All the surfaces of DLG surface data which have the same area, the same scale and the same mathematic basis with InSAR point data form a surface set S, and the laser foot point data (x) is judged by a certain methodi,yi) Inclusion relationship with the plane data S:
Figure BDA0002547228480000081
wherein k is 1, (x)i,yi) ∈ S indicates that InSAR point data is inside the face element data of DLG,
Figure BDA0002547228480000082
indicating that the InSAR dot data is outside the surface element data of the DLG.
Optionally, before the three-dimensional topology inspection is performed on the effective surface element data and the point data in the InSAR data, the following steps may be added to perform error correction on the point data in the InSAR data;
correspondingly, the performing three-dimensional topology inspection on the point data in the effective surface element data and the InSAR data may include: and carrying out topology check on the effective surface element data and the point data in the InSAR data after error correction.
Due to the fact that geographic coding errors exist in the PS points, in order to ensure the reliability of results, the positioning error threshold T of the target to be analyzed in the east-west direction is based on DLG dataxNorth-south positioning error threshold TyAnd performing topology analysis based on the space distance on the two-dimensional plane.
Optionally, the topology checking the point data in the effective surface element data and the error-corrected InSAR data may include: analyzing the spatial relationship between the point data and the surface of the terrain database by adopting superposition; and when the point data is located on the surface, judging whether the point data meets a preset relation.
The buffer area analysis commonly used in the GIS is consistent in the buffer radius of each direction and is not suitable for the spatial distance analysis of different directions. Therefore, the calculation method firstly adopts the superposition analysis to judge the reading points and the terrain database AnIf the spatial relationship of the plane is not contained, directly removing the plane; if yes, the chessboard distance calculation formula is used as a reference, and the PS point belongs to the terrain database AnThe plane should satisfy the following relationship in spatial distance:
Figure BDA0002547228480000091
and simultaneously importing the surface file and InSAR point data, traversing all the InSAR point data, sequentially judging the topological relation between each point and a surface element, if the point is in the surface, the point is an InSAR point reflected by a ground object represented by a surface element, and filtering the InSAR point to obtain effective ground InSAR point data.
And S120, performing coarse extraction based on the low feature and the InSAR data after the local elevation statistical filtering.
After the filtering is completed, although the high-rise buildings can be filtered, the low short objects still cannot be filtered, so that the InSAR data needs to be processed based on the characteristics of the low short objects, and the crude extraction of the InSAR data is realized.
For example, the performing the rough extraction based on the low short feature and the locally-elevation-statistically-filtered InSAR data may include: and calculating the elevation difference of the data points in the InSAR data within a preset range, and determining the data points as the data points after the rough extraction when the elevation difference is smaller than a preset threshold value.
Because the three-dimensional information of the PS point positions of the building is very visual, the elevation difference value of the vertical arrangement points corresponding to the side points is large, one horizontal direction can be selected as a square by taking the vertical arrangement points as the center for each PS point after geocoding, and small blocks are not limited in the elevation direction; and statistically analyzing the Z value difference of the PS point in the long and narrow rectangular box, and if the Z value difference is greater than a certain threshold value, the PS point is regarded as a side point to be removed.
And S130, finely extracting the InSAR data after the crude extraction based on the topographic features and linear prediction.
In special areas, such as partial areas of a multilayer covered river bank, the method still has difficulty in extracting ground points. The InSAR points are distributed in space and have the characteristics of high density and discreteness, and when the InSAR points are distributed on the ground objects with different heights, the InSAR points can show different elevation attributes. For example, when InSAR point data falls on a high-rise building, the elevation value of the foot point is obviously higher than that of the foot point on the ground. By utilizing the characteristics, the ground points can be finely extracted.
In the embodiment, the InSAR data is filtered by utilizing the DLG data; performing coarse extraction based on the InSAR data after the low short feature characteristic and the local elevation statistical filtering; and performing fine extraction on the InSAR data after the crude extraction based on the topographic features and linear prediction. The method can filter InSAR data of high-rise buildings such as buildings in the InSAR data by using DLG data, can eliminate the InSAR data of low and short objects by using local elevation, and can realize fine extraction of the InSAR data by setting corresponding threshold values through linear prediction. The method can realize the accurate extraction of InSAR ground points in a complex geographic environment, particularly in an urban environment.
Example two
Fig. 2 is a schematic flow chart of the method for extracting the InSAR ground points in the complex urban environment according to the second embodiment of the present invention. In this embodiment, the coarse extraction of the InSAR data based on the low and short feature and local elevation system analysis is specifically optimized as follows: calculating an elevation data statistic value within a preset range of data points in InSAR data; and comparing the relationship between the elevation data statistic and a preset threshold value by adopting a production formula rule, and determining the data point as the data point after rough extraction if the relationship is judged to be smaller than the preset threshold value.
Correspondingly, the method for extracting the InSAR ground points in the complex city environment provided by the embodiment specifically comprises the following steps:
and S210, filtering the InSAR data by utilizing the DLG data.
And S220, calculating an elevation data statistic value within a preset range of data points in InSAR data.
For example, the calculating the elevation data statistics within the preset range of the data points in the InSAR data may include: and calculating the maximum elevation, the minimum elevation, the mean value and/or the mean square error statistic value through the elevation in the preset range of the data points in the InSAR data, and then obtaining the optimal statistic value through comprehensive weight analysis.
In general, information of tall buildings and other buildings is included in data points in InSAR data, but there is a certain elevation difference between the data points and the ground. By utilizing the characteristic, coarse filtering of InSAR data can be realized. Illustratively, this may be accomplished using elevation data statistics. In this embodiment, the coarse filtering may be implemented using the optimal statistical values, and since the mean or mean square error reflects the average elevation of all InSAR data points, the maximum elevation may represent the highest elevation of the building, and the minimum elevation represents the elevation that may be sunk in the ground. According to the above features, one elevation data statistic can be obtained through comprehensive calculation, and for example, the elevation data statistic can be obtained through comprehensive calculation by setting corresponding weights for the above parameters according to actual conditions. The weights are all less than 1.
And S230, comparing the relationship between the elevation data statistic InSAR data point and a preset threshold value by adopting a production formula rule, and determining the data point as a data point after rough extraction if the data point is judged to be smaller than the relationship.
For example, the difference between the elevation data statistic obtained by the above calculation and the elevation value of the data point of the InSAR data may be calculated, to determine whether the difference is smaller than a preset threshold, and if the difference is smaller than the preset threshold, the data point is determined to be the data point after the rough extraction.
And S240, finely extracting the crude InSAR data based on the topographic features and the linear prediction model.
In this embodiment, the coarse extraction of the InSAR data is realized by analyzing the low-short-ground feature and the local elevation system, which is specifically optimized as follows: calculating an elevation data statistic value within a preset range of data points in InSAR data; and comparing the relationship between the elevation data statistic and a preset threshold value by adopting a production formula rule, and determining the data point as the data point after rough extraction if the relationship is judged to be smaller than the preset threshold value. By utilizing the elevation structure characteristics of the building, the data points in InSAR data can be coarsely filtered, and a foundation is provided for subsequent accurate identification. The subsequent operation amount is reduced.
EXAMPLE III
Fig. 3 is a schematic flow chart of the method for extracting the InSAR ground points in the complex urban environment according to the third embodiment of the present invention. In this embodiment, the coarse extracted InSAR data is finely extracted based on the topographic features and linear prediction, and specifically optimized as follows: determining the probability of ground points in InSAR data by using a linear prediction model; selecting InSAR data points with higher ground point probability according to the probability; and performing fine extraction based on topographic features according to the InSAR data points with higher ground point probability.
Correspondingly, the method for extracting the InSAR ground points in the complex city environment provided by the embodiment specifically comprises the following steps:
and S310, performing InSAR data filtering based on the high terrestrial feature and the DLG data.
And S320, realizing the rough extraction of InSAR data based on the low short feature and local elevation system analysis.
S330, determining the probability of the ground points in the InSAR data by using a linear prediction model.
In the embodiment, two modes of linear prediction and mutation prediction are mainly utilized to realize the accurate extraction of InSAR data ground points.
The linear prediction may be determined by:
determining the ground points in InSAR data by the following method, and using STRepresents a set of ground point points, then STCan be expressed as:
Figure BDA0002547228480000121
the specific meanings are as follows: given the range of the window(s),calculating piPoint on pjThe height difference between points, such as if the height difference is within the given threshold condition, is considered a ground point.
For example, the probability of belonging to the ground point can be determined according to different threshold values.
And S340, selecting InSAR data points with higher ground point probability according to the probability.
After the probability is determined, InSAR data points with higher probability can be selected to prepare for subsequent mutation prediction. The higher probability may be that the probability meets above a certain probability threshold.
And S350, performing fine extraction based on topographic features according to the InSAR data points with higher ground point probability.
Illustratively, the selecting an InSAR data point with a higher ground point probability according to the probability includes: selecting a point with higher ground point probability as a central point; searching for nearby points according to a preset step length and a preset angle, and taking the searched points as non-ground points; adjusting a preset angle according to a preset rule to continuously search nearby points, and taking the searched points as non-ground points; and iterating the preset step length, and repeatedly searching for an adjacent point according to the preset step length and a preset angle until the preset step length after iteration reaches a preset distance threshold value.
In fact, abrupt changes in ground elevation require both terrain itself and building height differences. If a strategy is used for filtering, errors are easily generated. In order to effectively distinguish the abrupt change caused by the terrain and the buildings to the maximum extent, the elevation abrupt change information needs to be redefined. Therefore, the present embodiment can perform accurate screening by a direction prediction method.
Illustratively, this can be achieved by: for an InSAR point classified as a ground point with a high probability, searching peripheral InSAR points according to a minimum iteration distance such as 2 meters and a maximum iteration angle such as 85 degrees, and if the InSAR points exist, identifying the searched points as non-ground points; if not, the next step is carried out; the iteration distance is unchanged, the iteration angle is reduced according to a certain value, then the peripheral InSAR points are continuously searched, and if the peripheral InSAR points exist, the searched points are identified as non-ground points; if not, continuously reducing until the iteration angle is smaller than a specified threshold (5 degrees); the points searched under the specified angle threshold value are regarded as ground points; the iteration distance is increased progressively according to a certain step length, and the step 1 and the step 2 are repeated until the iteration distance reaches a specified distance threshold value; the fine extraction can be realized by the above manner. And traversing all InSAR points in sequence as central points according to the method. And finishing the accurate extraction of all InSAR data points.
In this embodiment, the coarse extracted InSAR data is finely extracted based on the topographic features and linear prediction, which is specifically optimized as follows: determining the probability of ground points in InSAR data by using a linear prediction model; selecting InSAR data points with higher ground point probability according to the probability; and performing fine extraction based on topographic features according to the InSAR data points with higher ground point probability. By the method, whether the InSAR data belong to ground points or not can be accurately extracted, and the ground points with ground mutation can be effectively screened.
Example four
Fig. 4 is a schematic structural diagram of an InSAR ground point extraction device in a complex city environment according to a fourth embodiment of the present invention, and as shown in fig. 4, the device includes:
a filtering module 410, configured to filter the InSAR data by using the DLG data;
a rough extraction module 420, configured to perform rough extraction based on low feature and InSAR data after local elevation statistical filtering;
and a fine extraction module 430, configured to perform fine extraction on the coarse extracted InSAR data based on the topographic features and linear prediction.
In the device for extracting the InSAR ground points in the complex urban environment, the InSAR data is filtered by using the DLG data; performing coarse extraction based on the InSAR data after the low short feature characteristic and the local elevation statistical filtering; and performing fine extraction on the InSAR data after the crude extraction based on the topographic features and linear prediction. The method can filter the reflected InSAR data of high-rise buildings such as buildings in the InSAR data by using DLG data, can eliminate the InSAR data of low and short objects by using local elevation, and can realize fine extraction of the InSAR data by setting a corresponding threshold through linear prediction. The method can realize the accurate extraction of InSAR ground points in a complex geographic environment, particularly in an urban environment.
On the basis of the foregoing embodiments, the filtering module includes:
the conversion unit is used for converting the DLG data and the InSAR data into the same coordinate system;
an extraction unit configured to extract the effective surface element data in the DLG data;
and the checking unit is used for carrying out three-dimensional topology checking on the point data in the effective surface element data and the InSAR data so as to determine whether the InSAR point data is a ground object point.
On the basis of the above embodiments, the crude extraction module comprises: :
the calculation unit is used for calculating an elevation data statistic value within a preset range of data points in InSAR data;
and the judging unit is used for comparing the relationship between the elevation data statistic InSAR data point and a preset threshold value by adopting a generation formula rule, and determining the data point as a data point after rough extraction if the data point is judged to be smaller than the preset threshold value.
On the basis of the foregoing embodiments, the computing unit is configured to:
and calculating the maximum elevation, the minimum elevation, the mean value and/or the mean square error statistic value through the elevation in the preset range of the data points in the InSAR data, and then obtaining the optimal statistic value through comprehensive weight analysis.
On the basis of the above embodiments, the fine extraction module includes:
a determining unit, configured to determine a probability of a ground point in the InSAR data using a linear prediction model;
the selecting unit is used for selecting InSAR data points with higher ground point probability according to the probability;
and the extraction unit is used for performing fine extraction based on topographic features according to the InSAR data points with higher ground point probability.
On the basis of the foregoing embodiments, the determining unit is configured to:
determining the ground points in InSAR data by the following method, and using STRepresents a set of ground point points, then STCan be expressed as:
Figure BDA0002547228480000161
on the basis of the foregoing embodiments, the extracting unit is configured to:
selecting a point with higher ground point probability as a central point;
searching for nearby points according to a preset step length and a preset angle, and taking the searched points as non-ground points;
adjusting a preset angle according to a preset rule to continuously search nearby points, and taking the searched points as non-ground points;
and iterating the preset step length, and repeatedly searching for an adjacent point according to the preset step length and a preset angle until the preset step length after iteration reaches a preset distance threshold value.
The InSAR ground point extraction device under the complex urban environment provided by the embodiment of the invention can execute the InSAR ground point extraction method under the complex urban environment provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
EXAMPLE five
Fig. 5 is a schematic structural diagram of a server according to a fifth embodiment of the present invention. FIG. 5 illustrates a block diagram of an exemplary server 12 suitable for use in implementing embodiments of the present invention. The server 12 shown in fig. 5 is only an example, and should not bring any limitation to the function and the scope of use of the embodiment of the present invention.
As shown in FIG. 5, the server 12 is in the form of a general purpose computing device. The components of the server 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
The server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by server 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. The server 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, and commonly referred to as a "hard drive"). Although not shown in FIG. 5, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The server 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with the device/server 12, and/or with any devices (e.g., network card, modem, etc.) that enable the server 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the server 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 20. As shown, the network adapter 20 communicates with the other modules of the server 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the server 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by running the program stored in the system memory 28, for example, implementing the InSAR ground point extraction method in a complex city environment provided by the embodiment of the present invention.
EXAMPLE six
The sixth embodiment of the present invention further provides a storage medium containing computer-executable instructions, where the computer-executable instructions are executed by a computer processor to perform the method for extracting the InSAR ground points in the complex city environment as provided in the foregoing embodiments.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. An InSAR ground point extraction method under a complex urban environment is characterized by comprising the following steps:
performing InSAR data filtering based on high terrestrial feature and DLG data;
coarse extraction of InSAR data is realized based on low short feature characteristics and local elevation system analysis;
and finely extracting the InSAR data after the crude extraction based on the topographic features and the linear prediction model.
2. The method in claim 1, wherein the high terrestrial feature and DLG data support based InSAR data filtering comprises:
converting the DLG data and the InSAR data into the same coordinate system;
extracting effective surface element data in the DLG data;
and carrying out three-dimensional topology inspection on the point data in the effective surface element data and the InSAR data to determine whether the InSAR point data is a ground object point.
3. The method of claim 1, wherein the crude extraction of InSAR data based on low dwarf features and local high-level statistical analysis comprises:
calculating an elevation data statistic value within a preset range of data points in InSAR data;
and comparing the relationship between the data points of the elevation data statistic InSAR data and a preset threshold value by adopting a production formula rule, and determining the data points as the data points after the rough extraction if the relationship is judged to be smaller than the preset threshold value.
4. The method of claim 3, wherein the calculating the elevation data statistics for a predetermined range of data points in the InSAR data comprises:
and calculating the maximum elevation, the minimum elevation, the mean value and/or the mean square error statistic value through the elevation in the preset range of the data points in the InSAR data, and then obtaining the optimal statistic value through comprehensive weight analysis.
5. The method of claim 1, wherein the fine extracting of the crude extracted InSAR data based on topographic features and linear prediction comprises:
determining the probability of ground points in InSAR data by using a linear prediction model;
selecting InSAR data points with higher ground point probability according to the probability;
and performing fine extraction based on topographic features according to the InSAR data points with higher ground point probability.
6. The method of claim 5, wherein determining the probability of the ground points in the InSAR data using a linear predictive model comprises:
determining the ground points in InSAR data by the following method, and using STRepresents a set of ground point points, then STCan be expressed as:
Figure FDA0002547228470000021
and determining the probability of the ground points according to the elevation difference.
7. The method of claim 6 wherein said selecting InSAR data points with higher ground point probability based on said probability comprises:
selecting a point with higher ground point probability as a central point;
searching for nearby points according to a preset step length and a preset angle, and taking the searched points as non-ground points;
adjusting a preset angle according to a preset rule to continuously search nearby points, and taking the searched points as non-ground points;
and iterating the preset step length, and repeatedly searching for an adjacent point according to the preset step length and a preset angle until the preset step length after iteration reaches a preset distance threshold value.
8. An InSAR ground point extraction method device under a complex urban environment is characterized by comprising the following steps:
the filtering module is used for carrying out InSAR data filtering based on high terrestrial feature and DLG data support;
the rough extraction module is used for carrying out rough extraction on InSAR data based on low feature and local elevation statistical recognition algorithm;
and the fine extraction module is used for performing fine extraction on the InSAR data after the coarse extraction based on the topographic features and linear prediction.
9. A server, characterized in that the server comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the InSAR surface point extraction method in a complex urban environment as recited in any of claims 1-7.
10. A storage medium containing computer executable instructions for performing an InSAR terrestrial points extraction method in a complex urban environment as claimed in any one of claims 1 to 7 when executed by a computer processor.
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