CN113642544B - InSAR deformation information-based method and system for automatically extracting suspected disaster hidden danger area - Google Patents
InSAR deformation information-based method and system for automatically extracting suspected disaster hidden danger area Download PDFInfo
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Abstract
The application discloses a method and a system for automatically extracting a suspected ground disaster hidden danger area based on InSAR deformation information. And extracting a stable point target in the monitoring area by utilizing the time sequence SAR satellite image, acquiring the earth surface deformation information of the stable point target, and acquiring the gradient information of the stable point target by adopting digital elevation model data. And according to the gradient information, setting a deformation center threshold value and a deformation noise threshold value, identifying an outlier target in a deformation result, setting the outlier target as a noise point and removing the outlier target. And extracting the interest point target from the deformation result after the noise point is removed according to the gradient threshold value and the deformation center threshold value. And constructing a Delaunay triangulation network for the interest point target. And applying side length constraint to the triangulation network to obtain a local self-adaptive spatial neighborhood. And automatically generating a vector outline of the suspected ground disaster hidden danger area by the point target cluster in the spatial neighborhood. According to the scheme, the early identification of the large-range potential ground disaster hidden danger is realized, the interference of noise information is avoided, and the ground disaster hidden danger identification efficiency of the relevant area is improved.
Description
Technical Field
The application relates to the technical field of geological disaster prevention, in particular to a method and a system for automatically extracting a suspected ground disaster hidden danger area based on InSAR deformation information.
Background
Geological disasters, abbreviated as ground disasters, refer to natural disasters mainly caused by geological dynamic activities or abnormal geological environment, which are formed under the action of natural or human factors, and have geological effects or geological phenomena of loss to human life and property and damage to the environment. The loss caused by landslide, debris flow, collapse and other geological disasters is countless every year. Therefore, the method develops large-scale geological disaster hidden danger identification and has important research value and practical significance for scientific prevention and control of the ground disasters.
Early ground disaster identification mainly adopts manual field investigation, statistics and sketch of corresponding drawings, but the efficiency is low. Although the accuracy of the manual visual interpretation is high, the workload is large, the processing speed is very slow, and the applicability is not high. To solve the above problems, some scholars have tried a semi-automatic or automatic computer identification method to detect the area of ground disaster. In addition, some scholars also try to extract a landslide region by using a region growing algorithm, but all select seed points through human-computer interaction, and full-automatic identification is not achieved. In recent years, with the development of the interferometric Synthetic Aperture Radar (sar) technology, identification of a potential disaster area using information of a large-scale surface deformation becomes a new research direction. The InSAR technology has the advantages of wide coverage range, high monitoring precision, quick time sampling and the like, can quickly acquire large-range ground surface deformation information, provides powerful data support for identifying hidden dangers of geological disasters, and is still influenced by factors such as noise information, topographic relief, atmospheric delay and the like in practical application.
At present, the identification of the ground disaster hidden danger areas mainly comprises the steps of analyzing the image spot characteristics, the deformation characteristics and the like, and identifying typical ground disaster hidden danger areas by a method which is universal and takes noise information into consideration, so that the identification automation degree is low, and the accuracy is low. How to set a high-automation-degree and high-precision method for identifying a potential disaster area can scientifically identify and remove noise information, which is a key problem to be solved urgently by technical personnel in the field.
Disclosure of Invention
The embodiment of the invention provides an InSAR deformation information-based suspected ground disaster hidden danger area automatic extraction method and system, and aims to solve the problems that in the prior art, the identification automation degree is weak, the accuracy is low, and the noise information and the slope cannot be fully considered for identifying the ground disaster hidden danger area, so that the high-automation suspected ground disaster hidden danger area identification is realized, the safety guarantee is provided for the human activities in a monitoring area, the information such as geological disaster evolution characteristics and distribution rules is obtained, and the disaster prevention and reduction work is assisted.
On one hand, the application provides an InSAR deformation information-based method for automatically extracting a suspected ground disaster hidden danger area, which comprises the following steps:
extracting a stable point target in a monitoring area by utilizing a time sequence SAR satellite image, and acquiring the earth surface deformation information of the stable point target;
acquiring gradient information of the stable point target by adopting digital elevation model data;
according to the gradient information, setting a deformation center threshold value and a deformation noise threshold value, identifying an outlier target in a deformation result, setting the outlier target as a noise point and removing the outlier target, wherein the deformation center threshold value represents a deformation rate mean value of a local area, and the deformation noise threshold value represents a difference value between the deformation rate of any point target in the local area and a deformation rate mean value of a target adjacent to the local area;
extracting an interest point target from the deformation result after the noise point is removed according to a gradient threshold value and a deformation center threshold value;
constructing a Delaunay triangulation network for the interest point target;
applying side length constraint to the Delaunay triangulation network to obtain a local self-adaptive spatial neighborhood, wherein the step of applying the side length constraint is to apply hierarchical interruption to the side length of a triangulation network in the Delaunay triangulation network to obtain a reasonable spatial adjacency relation;
and automatically generating a vector outline of the suspected ground disaster hidden danger area by the point target cluster in the spatial neighborhood.
Further, in the process of extracting a stable point target of a monitoring area by utilizing a time sequence SAR satellite image and acquiring the earth surface deformation information of the stable point target, the earth surface deformation information is acquired by a time sequence synthetic aperture radar interferometry, and the time sequence synthetic aperture radar interferometry comprises the following steps: image registration, interferogram generation, terrain phase removal, phase filtering, stable point target extraction, point target deformation information inversion and geocoding.
Furthermore, the surface deformation should meet the deformation processing requirements of the time sequence InSAR technology, and a stable point target can be extracted from the monitoring area.
Further, according to gradient information, a deformation center threshold value and a deformation noise threshold value are set, an outlier target is identified in a deformation result, the outlier target is set as a noise point and is removed, the noise point is identified and removed in a manner of constructing a Delaunay triangulation network, the definition of the local area is determined according to the spatial adjacency relation of the Delaunay triangulation network, and the local area is set as a first-order spatial neighborhood.
Furthermore, the Delaunay triangulation network can be constructed in a sub-region blocking mode, and then noise information can be removed in parallel.
Further, the identification of outliers is determined according to the following equation:
wherein,
for the surface deformation rate of the point target to be judged,
is the surface deformation rate mean value of the target point in the first-order spatial neighborhood of the target point Delaunay triangulation network,
in order to be the deformation center threshold value,
is the deformation noise threshold. When the target point meets the above-mentioned discrimination condition, the target point is identified as an outlier, and is set as a noise point and then rejected.
Further, applying a side length constraint to the Delaunay triangulation network to obtain a locally adaptive spatial neighborhood, including:
applying side length hierarchical constraint to the Delaunay triangulation network, and interrupting that the average value of all the side lengths in the Delaunay triangulation network is larger than that of the triangulation network in which the side lengths are positioned exceeds
Edge of multiple standard deviation:
wherein,
is the side length of the ith side in the triangular net,
is the average value of all side lengths in the triangulation,
is the standard deviation of all side lengths in the triangulation,
representing the side length constraint parameter.
On the other hand, the application also provides an automatic suspected ground disaster hidden danger area extraction system based on InSAR deformation information, which comprises:
the earth surface deformation information acquisition module is used for extracting a stable point target in a monitoring area by utilizing a time sequence SAR satellite image and acquiring earth surface deformation information of the stable point target;
the gradient information acquisition module is used for acquiring gradient information of the stable point target by adopting digital elevation model data;
the noise point removing module is used for setting a deformation center threshold value and a deformation noise threshold value according to the gradient information, identifying an outlier target in a deformation result, setting the outlier target as a noise point and removing the outlier target, wherein the deformation center threshold value represents a local area deformation rate mean value, and the deformation noise threshold value represents a difference value between the deformation rate of any point target in a local area and a local adjacent point target deformation rate mean value;
the interest point target extraction module is used for extracting an interest point target from the deformation result after the noise point is removed according to a gradient threshold value and a deformation center threshold value;
the Delaunay triangulation network construction module is used for constructing a Delaunay triangulation network for the interest point target;
the spatial neighborhood acquisition module is used for applying side length constraint to the Delaunay triangulation network to acquire a local self-adaptive spatial neighborhood, wherein the step of applying the side length constraint is to apply level interruption to the side length of a network in the Delaunay triangulation network to acquire a reasonable spatial adjacency relation;
and the suspected ground disaster hidden danger area vector outline generating module is used for automatically generating the suspected ground disaster hidden danger area vector outline by the point target cluster in the spatial neighborhood.
In the earth surface deformation information acquisition module, earth surface deformation information is acquired by a time series synthetic aperture radar interferometry technique, and the time series synthetic aperture radar interferometry technique comprises the following steps: image registration, interferogram generation, terrain phase removal, phase filtering, stable point target extraction, point target deformation information inversion and geocoding.
In the noise point removing module, noise points are identified and removed in a mode of constructing a Delaunay triangulation network, the definition of a local area is determined according to the spatial adjacency relation of the Delaunay triangulation network, and the local area is set as a first-order spatial neighborhood.
In the noise point eliminating module, the identification of outliers is judged according to the following formula:
wherein,
for the surface deformation rate of the point target to be judged,
is the surface deformation rate mean value of the target point Delaunay first-order spatial neighborhood point target,
in order to be the deformation center threshold value,
is the deformation noise threshold.
In the space neighborhood acquisition module, side length hierarchical constraint is applied to the Delaunay triangulation network, and the condition that the average value of all the side lengths in the Delaunay triangulation network is larger than that of the triangulation network in which the side lengths exceed the average value of all the side lengths in the Delaunay triangulation network is interrupted
Edge of multiple standard deviation:
wherein,
is the side length of the ith side in the triangular net,
is the average value of all side lengths in the triangulation,
is the standard deviation of all side lengths in the triangulation,
representing the side length constraint parameter.
According to the method and the system for automatically extracting the suspected ground disaster hidden danger areas based on the InSAR deformation information, the performance characteristics of geological disasters on gradient and deformation characteristics are fully considered, the interference of noise information is avoided, the application advantages of the InSAR technology in the aspects of intelligent disaster prevention and reduction are exerted, the large-range potential ground disaster hidden danger areas can be automatically identified with high precision, the labor and material cost and the time consumption are reduced, and the ground disaster hidden danger identification efficiency of related areas is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a suspected ground disaster area automatic extraction method based on InSAR deformation information disclosed in the present application;
fig. 2 is a detailed flowchart of an automatic extraction method for suspected disaster area disclosed in the present application;
fig. 3(a) and fig. 3(b) are comparison graphs of experimental region noise information rejection results provided by the embodiment of the present application; wherein, fig. 3(a) is a point target distribution diagram before the noise information of the experimental region is removed; fig. 3(b) is a point target distribution diagram after the noise information in the experimental region is removed.
FIGS. 4(a) and 4(b) are diagrams of spatiotemporal distribution thermodynamic diagrams of interest targets provided by examples of implementations of the present application; wherein FIG. 4(a) is a lift region interest point target distribution map; FIG. 4(b) is a target distribution diagram of interest points in a subsidence area;
5(a) -5 (f) are hierarchical constraint graphs of the interest point target Delaunay triangulation network provided by the embodiment of the application; FIG. 5(a) is a primary networking graph of a lift area point of interest target; FIG. 5(b) is a lifting area interest point target Delaunay triangulation network edge length constraint diagram; FIG. 5(c) is a quadratic side length constraint graph of a Delaunay triangulation network for the interest point target of the elevation region; FIG. 5(d) is a primary network construction diagram of interest point targets in a subsidence area; FIG. 5(e) is a boundary length constraint graph of a subsidence area interest point target Delaunay triangulation network; FIG. 5(f) is a secondary side length constraint diagram of a subsidence area interest point target Delaunay triangulation network;
fig. 6(a) and fig. 6(b) are diagrams illustrating a result of a suspected disaster area convex hull boundary provided in an embodiment of the present application; fig. 6(a) is a diagram of a lifting area suspected ground disaster potential area convex hull boundary result; fig. 6(b) is a result diagram of a convex hull boundary in a suspected disaster area of a subsidence area;
fig. 7 is a diagram illustrating automatic extraction visualization of a suspected disaster area according to an embodiment of the present application;
fig. 8 is a block diagram of a system for automatically extracting a suspected ground disaster hidden danger area based on InSAR deformation information disclosed in the present application.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present application provides an automatic suspected disaster area extraction method based on InSAR deformation information. Fig. 1 is a flowchart of an automatic suspected ground disaster area extraction method based on InSAR deformation information, please refer to fig. 2, and fig. 2 is a detailed flowchart of the automatic suspected ground disaster area extraction method disclosed in the present application, where the method disclosed in the present application mainly includes the following steps 100 to 700. The steps 100 to 200 are data preparation stages, the step 300 is a noise processing stage, the step 400 is a region identification stage, and the steps 500 to 700 are boundary generation stages.
Step 100, extracting a stable point target in a monitoring area by utilizing a time sequence SAR satellite image, and acquiring the earth surface deformation information of the stable point target.
The earth surface deformation information is acquired by a time series synthetic aperture radar interferometry, and the time series synthetic aperture radar interferometry comprises the following steps: image registration, interferogram generation, terrain phase removal, phase filtering, stable point target extraction, point target deformation information inversion and geocoding.
Synthetic Aperture Radar interferometry (InSAR for short) is a remote sensing technology capable of obtaining surface centimeter or even millimeter-scale deformation. InSAR is a remote sensing technology which mainly carries out interference imaging processing on two or more Synthetic Aperture Radar (SAR for short) satellite images and then inverts terrain height and elevation change information by combining Radar system parameters and Radar platform geometric position information. The time-series SAR satellite images are generally free data provided by a Sentinel-1 satellite or commercial data provided by a Radarsat-2 satellite.
Furthermore, by utilizing the same region time sequence SAR image and adopting a time sequence InSAR technology to obtain large-range ground surface deformation data, the ground surface deformation information containing five-dimensional attributes of ID (serial number), X (X coordinate), Y (Y coordinate), V (annual average deformation rate) and S (gradient) can be obtained. The surface deformation information can be acquired by software such as GDEMSI, SARscape, GAMMA and the like. The acquired surface deformation information needs to meet the current InSAR surface deformation monitoring data processing standard of time series, a stable point target can be extracted from a monitoring area, and the deformation result precision is generally better than 10 mm/year.
And 200, acquiring gradient information of the stable point target by adopting Digital Elevation Model (DEM) data.
The Digital Elevation Model (DEM) is a solid ground Model which expresses the ground Elevation in a group of ordered numerical array forms, is the Digital expression of the topographic surface form information and mainly describes the spatial distribution of the topographic form of an area. Here, the grade information for each stable point target is extracted from Digital Elevation Model (DEM) data for the monitored area.
And 300, setting a deformation center threshold value and a deformation noise threshold value according to the gradient information, identifying an outlier target in a deformation result, setting the outlier target as a noise point and removing the outlier target. The deformation center threshold value represents the average value of the deformation rate of the local area, and the deformation noise threshold value represents the difference value of the deformation rate of any point target in the local area and the deformation rate of the targets adjacent to the local area.
The method and the device assume that inevitable noise information exists in the deformation information, usually expressed as outliers and having abnormal deformation attributes, and if the noise information is not identified, the noise information will interfere with the extraction of the suspected ground disaster hidden danger area. Therefore, a deformation center threshold value and a deformation noise threshold value are needed to be set to distinguish noise information from a suspected ground disaster hidden danger area.
Further, in the method, the noise points are identified and removed in a manner of constructing the Delaunay triangulation network. In order to increase the operation speed of the algorithm, a triangular net can be constructed in a sub-area blocking mode, and then noise information is removed in parallel. The definition of the local area is determined according to the spatial adjacency relation of the Delaunay triangulation network, and the local area is set as a first-order spatial neighborhood.
Further, the identification of outliers is determined according to the following equation:
wherein,
for the surface deformation rate of the point target to be judged,
is the surface deformation rate mean value of the target point Delaunay first-order spatial neighborhood point target,
in order to be the deformation center threshold value,
is the deformation noise threshold. When the target point meets the judgment condition, the target point is identified as an outlier, namely a noise point, and the outlier is eliminated.Generally, the deformation center threshold and the deformation noise threshold are respectively 30 mm/year and 10 mm/year.
And 400, extracting an interest point target from the deformation result after the noise point is removed according to a gradient threshold value and a deformation center threshold value.
The extracted interest point target is a point target of a suspected ground disaster hidden danger area and needs to meet a certain local deformation rate and a certain gradient value. Generally, the threshold value is set empirically.
Further, according to the gradient threshold value and the deformation center threshold value, the interest point target is subjected to region identification and can be divided into a lifting area interest point target and a settling area interest point target. The interest point target of the lifting area is the interest point target deformed to be close to the satellite direction, and the interest point target of the settling area is the interest point target deformed to be far away from the satellite direction.
And 500, constructing a Delaunay triangulation network for the interest point target.
Wherein the point of interest target needs to satisfy the constraints of the gradient threshold and the deformation center threshold.
Further, Delaunay triangulation network construction is respectively carried out on the interest point target of the uplifting area and the interest point target of the subsidence area.
Further, side length constraints are respectively applied to the lifting area Delaunay triangulation network and the subsidence area Delaunay triangulation network, and a local self-adaptive space neighborhood of the lifting area and a local self-adaptive space neighborhood of the subsidence area are obtained.
The method is characterized in that a side length hierarchical constraint is applied to the Delaunay triangulation network, and the fact that the average value of all the side lengths in the Delaunay triangulation network is larger than that of the triangulation network is broken
Edge of multiple standard deviation:
wherein,
is the side length of the ith side in the triangular net,
is the average value of all side lengths in the triangulation,
is the standard deviation of all side lengths in the triangulation,
representing a side length constraint parameter, typically having a value of 3.
Further, by applying hierarchical constraints, a more reasonable spatial neighborhood may be obtained. Generally, the 2 layers of hierarchical constraint can obtain ideal separation sub-clusters, and then the separation sub-clusters can be regarded as independent suspected disaster area.
And the vector outline of the suspected ground disaster hidden danger area is the minimum circumscribed convex hull of the rear point target cluster of the hierarchy constraint.
Further, a lifting area suspected ground disaster hidden danger area boundary deformed towards the satellite direction and a settling area suspected ground disaster hidden danger area boundary deformed away from the satellite direction are automatically generated by using the point target clusters in the lifting area spatial neighborhood and the point target clusters in the settling area spatial neighborhood respectively.
In the embodiment of the invention, a Zhoushan area is selected as an experimental area. The above technical solutions of the embodiments of the present invention are described in detail below with reference to application examples:
(1) and extracting a large-range stable point target in the monitoring area by using the same-area time sequence SAR image and acquiring earth surface deformation information. The SAR image used in this embodiment is derived from 68 sentinel images acquired from the navicular region from 2019, month 1 to 2021, month 3, from which 247710 stable point targets are extracted.
(2) And extracting the gradient information of each stable point target from the DEM data by means of an ArcGIS tool.
(3) And according to the gradient information, setting a deformation center threshold value and a deformation noise threshold value, identifying an outlier target in a deformation result, setting the outlier target as a noise point and removing the noise point. In this embodiment, 3087 outliers, that is, noise points, are removed from 247710 point targets in a monitoring area, where the outliers include lifting noise and deformation noise, and a part of the outlier removing effect is as shown in fig. 3(a) and fig. 3(b), fig. 3(a) is a distribution diagram of the point targets before removing noise information in an experimental area, and fig. 3(b) is a distribution diagram of the point targets after removing noise information in the experimental area, and colors represent different deformation levels.
(4) And extracting an interest point target from the deformation result after the noise point is removed according to a gradient threshold value and a deformation center threshold value. The point of interest targets are shown in fig. 4(a) and 4(b), wherein fig. 4(a) is a lift region point of interest target distribution map; FIG. 4(b) is a distribution diagram of interest point targets in the subsidence area.
(5) And constructing a Delaunay triangulation network for the interest point target.
(6) And applying side length constraint to the Delaunay triangulation network to obtain a local self-adaptive spatial neighborhood. The interest point target Delaunay triangulation network hierarchical constraint process is shown in fig. 5(a) -5 (f), wherein fig. 5(a) is a primary network construction diagram of the interest point target in the lifting area; FIG. 5(b) is a lifting area interest point target Delaunay triangulation network edge length constraint diagram; FIG. 5(c) is a quadratic side length constraint graph of a Delaunay triangulation network for the interest point target of the elevation region; FIG. 5(d) is a primary network construction diagram of interest point targets in a subsidence area; FIG. 5(e) is a boundary length constraint graph of a subsidence area interest point target Delaunay triangulation network; fig. 5(f) is a secondary side length constraint diagram of a subsidence region interest point target Delaunay triangulation network.
(7) And automatically generating a vector outline of the suspected ground disaster hidden danger area by the point target cluster in the spatial neighborhood. The identification result of the ground disaster area is shown in fig. 6(a) and fig. 6(b), where fig. 6(a) is a diagram of a result of a boundary of a convex hull in a suspected ground disaster area in a lifted area, and fig. 6(b) is a diagram of a result of a boundary of a convex hull in a suspected ground disaster area in a settled area.
Through the implementation of the above steps, the sentinel images acquired from the navicular area during the period from 2019 month 1 to 2021 month 3 are automatically extracted, and a vector outline of the suspected ground disaster potential area is generated, please refer to fig. 7, and fig. 7 is a visual diagram of the suspected ground disaster potential area provided by the implementation example of the present application. The method and the system for automatically extracting the suspected ground hazard areas based on the InSAR deformation information fully consider the performance characteristics of the geological disaster on the gradient and the deformation characteristic, avoid the interference of noise information, exert the application advantages of the InSAR technology in the aspects of intelligent disaster prevention and reduction, realize high-precision automatic identification of large-range potential ground hazard areas, reduce the cost of manpower and material resources and time consumption, and improve the identification efficiency of the ground hazard areas in related areas.
Referring to fig. 8, fig. 8 is a block diagram of a system for automatically extracting a suspected disaster area based on InSAR deformation information according to the present disclosure. The application also provides an automatic suspected ground disaster hidden danger area extraction system based on InSAR deformation information, which comprises:
the earth surface deformation information acquisition module 21 is configured to extract a stable point target in a monitoring area by using a time sequence SAR satellite image, and acquire earth surface deformation information of the stable point target;
the gradient information acquisition module 22 is configured to acquire gradient information for the stable point target by using digital elevation model data;
the noise point removing module 23 is configured to set a deformation center threshold and a deformation noise threshold according to the gradient information, identify an outlier target in a deformation result, set the outlier target as a noise point, and remove the outlier target, where the deformation center threshold represents a mean deformation rate of a local area, and the deformation noise threshold represents a difference between a deformation rate of any point target in the local area and a mean deformation rate of a target adjacent to the local area;
the interest point target extraction module 24 is configured to extract an interest point target according to a gradient threshold and a deformation center threshold in the deformation result from which the noise point is removed;
the Delaunay triangulation network building module 25 is configured to build a Delaunay triangulation network for the interest point target;
the spatial neighborhood acquiring module 26 is configured to apply a side length constraint to the Delaunay triangulation network to acquire a locally adaptive spatial neighborhood, where the application of the side length constraint is to apply a level break to the side length of a network in the Delaunay triangulation network to acquire a reasonable spatial adjacency relationship;
and a suspected ground disaster hidden danger area vector outline generating module 27, configured to automatically generate a suspected ground disaster hidden danger area vector outline by using the point target cluster in the spatial neighborhood.
In the earth surface deformation information obtaining module 21, the earth surface deformation information is obtained by a time series synthetic aperture radar interferometry, and the time series synthetic aperture radar interferometry includes the following steps: image registration, interferogram generation, terrain phase removal, phase filtering, stable point target extraction, point target deformation information inversion and geocoding.
Furthermore, by utilizing the same region time sequence SAR image and adopting a time sequence InSAR technology to obtain large-range ground surface deformation data, the ground surface deformation information containing five-dimensional attributes of ID (serial number), X (X coordinate), Y (Y coordinate), V (annual average deformation rate) and S (gradient) can be obtained. The surface deformation information can be acquired by software such as GDEMSI, SARscape, GAMMA and the like. The acquired surface deformation information needs to meet the current InSAR surface deformation monitoring data processing standard of time series, a stable point target can be extracted from a monitoring area, and the deformation result precision is generally better than 10 mm/year.
In the noise point removing module 23, noise points are identified and removed by constructing a Delaunay triangulation network, the definition of the local region is determined according to the spatial adjacency relationship of the Delaunay triangulation network, and the local region is set as a first-order spatial neighborhood.
The method and the device assume that inevitable noise information exists in the deformation information, usually expressed as outliers and having abnormal deformation attributes, and if the noise information is not identified, the noise information will interfere with the extraction of the suspected ground disaster hidden danger area. Therefore, a deformation center threshold value and a deformation noise threshold value are needed to be set to distinguish noise information from a suspected ground disaster hidden danger area.
In the noise point eliminating module 23, the identification of outliers is determined according to the following formula:
wherein,
for the surface deformation rate of the point target to be judged,
is the surface deformation rate mean value of the target point Delaunay first-order spatial neighborhood point target,
in order to be the deformation center threshold value,
is the deformation noise threshold. When the target point meets the judgment condition, the target point is identified as an outlier, namely a noise point, and the outlier is eliminated. Generally, the deformation center threshold and the deformation noise threshold are respectively 30 mm/year and 10 mm/year.
In the spatial neighborhood obtaining module 26, a side length hierarchical constraint is applied to the Delaunay triangulation network, and it is interrupted that all the side length mean values in the Delaunay triangulation network are larger than those in the triangulation network where the side length mean values exceed
Edge of multiple standard deviation:
wherein,
is the side length of the ith side in the triangular net,
is the average value of all side lengths in the triangulation,
is the standard deviation of all side lengths in the triangulation,
representing a side length constraint parameter, typically having a value of 3.
According to the method and the system for automatically extracting the suspected ground disaster hidden danger area based on InSAR deformation information, input data are encoded deformation rate and gradient values obtained by InSAR, and the encoded deformation rate and gradient values comprise five-dimensional attributes of ID (serial number), X (X coordinate), Y (Y coordinate), V (annual average deformation rate) and S (gradient). The output data is vector lines of suspected ground disaster hidden danger areas respectively extracted from a lifting area (namely the deformation direction towards the satellite) and a settling area (namely the deformation direction away from the satellite). The method and the system for automatically extracting the suspected ground disaster hidden danger area based on InSAR deformation information can be developed based on various software platforms: including but not limited to Matlab, PyCharm.
According to the method and the system for automatically extracting the suspected ground disaster hidden danger areas based on the InSAR deformation information, the performance characteristics of geological disasters on gradient and deformation characteristics are fully considered, the interference of noise information is avoided, the application advantages of the InSAR technology in the aspects of intelligent disaster prevention and reduction are exerted, the large-range potential ground disaster hidden danger areas can be automatically identified with high precision, the labor and material cost and the time consumption are reduced, and the ground disaster hidden danger identification efficiency of related areas is improved.
The division of modules, units or sub-units herein is merely a division of logical functions and other divisions may be made in an actual implementation, for example, a plurality of modules and/or units may be combined or integrated in another system. Modules, units, and sub-units described as separate components may or may not be physically separate. The components displayed as cells may or may not be physical cells, and may be located in a specific place or distributed in grid cells. Therefore, some or all of the units can be selected according to actual needs to implement the scheme of the embodiment.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. The method for automatically extracting the suspected ground disaster hidden danger area based on InSAR deformation information is characterized by comprising the following steps:
extracting a stable point target in a monitoring area by utilizing a time sequence SAR satellite image, and acquiring the earth surface deformation information of the stable point target, wherein the earth surface deformation information is acquired by time sequence synthetic aperture radar interferometry;
acquiring gradient information of the stable point target by adopting digital elevation model data;
according to the gradient information, identifying outlier targets in the earth surface deformation information by setting a deformation center threshold value and a deformation noise threshold value, setting the outlier targets as noise points and removing the outlier targets, wherein the deformation center threshold value represents a local area deformation rate mean value, and the deformation noise threshold value represents a difference value between the deformation rate of any point target in a local area and a local adjacent point target deformation rate mean value; identifying outlier targets in the ground surface deformation information, setting the outlier targets as noise points and rejecting the outlier targets specifically comprises identifying the outlier targets in the ground surface deformation information in a manner of constructing a Delaunay triangulation network, setting the outlier targets as noise points and rejecting the outlier targets; the definition of the local area is determined according to the spatial adjacency relation of a Delaunay triangulation network, and the local area is set as a first-order spatial neighborhood;
the identification of the outliers is determined according to the following formula:
wherein,
for the surface deformation rate of the point target to be judged,
the mean value of the earth surface deformation rate of each point target in the first-order space neighborhood of the point target to be judged in the Delaunay triangulation network,
is the deformation center threshold value, and is,
is the deformation noise threshold;
extracting an interest point target from the earth surface deformation information after the noise point is removed according to a gradient threshold value and a deformation center threshold value;
constructing a Delaunay triangulation network for the interest point target;
applying side length constraint to the Delaunay triangulation network to obtain a local self-adaptive spatial neighborhood, wherein the step of applying the side length constraint is to apply hierarchical interruption to the side length of a triangulation network in the Delaunay triangulation network to obtain a reasonable spatial adjacency relation;
and automatically generating a vector outline of the suspected ground disaster hidden danger area by the point target cluster in the spatial neighborhood.
2. The method for automatically extracting the suspected ground disaster hidden danger area based on the InSAR deformation information as claimed in claim 1, wherein the applying the side length constraint to the Delaunay triangulation network to obtain the locally adaptive spatial neighborhood includes:
applying a side length hierarchical constraint to the Delaunay triangulation network asBreaking the mean value of all the side lengths in the Delaunay triangulation network larger than the triangulation network
Edge of multiple standard deviation:
wherein,
is the side length of the ith side in the triangular net,
is the average value of all side lengths in the triangulation,
is the standard deviation of all side lengths in the triangulation,
representing the side length constraint parameter.
3. Suspected ground disaster hidden danger area automatic extraction system based on InSAR deformation information is characterized by comprising:
the system comprises a surface deformation information acquisition module, a time sequence SAR satellite image acquisition module and a monitoring module, wherein the surface deformation information acquisition module is used for extracting a stable point target in a monitoring area by utilizing a time sequence SAR satellite image and acquiring the surface deformation information of the stable point target, and the surface deformation information is acquired by time sequence synthetic aperture radar interferometry;
the gradient information acquisition module is used for acquiring gradient information of the stable point target by adopting digital elevation model data;
the noise point removing module is used for identifying an outlier target in the ground surface deformation information by setting a deformation center threshold and a deformation noise threshold according to the gradient information, setting the outlier target as a noise point and removing the outlier target, wherein the deformation center threshold represents a mean value of deformation rates of local areas, and the deformation noise threshold represents a difference value between the deformation rate of any point target in the local areas and a mean value of deformation rates of local adjacent point targets; identifying outlier targets in the ground surface deformation information, setting the outlier targets as noise points and rejecting the outlier targets specifically comprises identifying the outlier targets in the ground surface deformation information in a manner of constructing a Delaunay triangulation network, setting the outlier targets as noise points and rejecting the outlier targets; the definition of the local area is determined according to the spatial adjacency relation of a Delaunay triangulation network, and the local area is set as a first-order spatial neighborhood;
the identification of the outliers is determined according to the following formula:
wherein,
for the surface deformation rate of the point target to be judged,
the mean value of the earth surface deformation rate of each point target in the first-order space neighborhood of the point target to be judged in the Delaunay triangulation network,
is the deformation center threshold value, and is,
is the deformation noise threshold;
the interest point target extraction module is used for extracting an interest point target from the earth surface deformation information after the noise point is removed according to a gradient threshold value and a deformation center threshold value;
the Delaunay triangulation network construction module is used for constructing a Delaunay triangulation network for the interest point target;
the spatial neighborhood acquisition module is used for applying side length constraint to the Delaunay triangulation network to acquire a local self-adaptive spatial neighborhood, wherein the side length constraint is applied to apply level interruption to the side length of a network in the Delaunay triangulation network to acquire a reasonable spatial adjacency relation;
and the suspected ground disaster hidden danger area vector outline generating module is used for automatically generating the suspected ground disaster hidden danger area vector outline by the point target cluster in the spatial neighborhood.
4. The InSAR deformation information-based suspected ground disaster potential area automatic extraction system as claimed in claim 3, wherein the applying a side length constraint to the Delaunay triangulation network to obtain a local adaptive spatial neighborhood comprises:
applying a side length hierarchical constraint to the Delaunay triangulation network, and interrupting that the average value of all the side lengths in the Delaunay triangulation network is larger than that of the triangulation network where the side lengths exceed
Edge of multiple standard deviation:
wherein,
is the side length of the ith side in the triangular net,
is the average value of all side lengths in the triangulation,
is the standard deviation of all side lengths in the triangulation,
representing the side length constraint parameter.
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