CN117853924B - Landslide area identification method and system based on inSAR technology - Google Patents
Landslide area identification method and system based on inSAR technology Download PDFInfo
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Abstract
The invention belongs to the landslide identification field, and discloses a landslide area identification method and a landslide area identification system based on inSAR technology, wherein the method comprises the following steps of S1, acquiring an interference pattern corresponding to an SAR image containing an area to be identified; s2, detecting target pixel points of the interference pattern to obtain a set A of target pixel points; s3, calculating a judgment value of each pixel point in the A; s4, storing the pixel points with the judging value larger than the set judging threshold value in the A into a set A1, and storing the rest pixel points in the A into a set A2; s5, respectively filtering the pixel points in the A1 and the A2 by adopting two different filtering algorithms to obtain a filtered interference pattern; and S6, identifying the filtered interferogram, and judging whether the area to be identified is a potential landslide area. The invention is beneficial to obtaining the interference pattern after filtering with higher quality and improving the accuracy of the result obtained by phase unwrapping the interference pattern.
Description
Technical Field
The invention relates to the landslide identification field, in particular to a landslide area identification method and system based on inSAR technology.
Background
InSAR is a newly developed space-to-earth observation technique, and is a product of combining a traditional SAR remote sensing technique with a radio astronomical interference technique. The method comprises the steps of transmitting microwaves to a target area by using a radar, receiving echoes reflected by the target to obtain SAR complex image pairs imaged by the same target area, obtaining an interference pattern by conjugate multiplication of the SAR complex image pairs if coherent conditions exist between the complex image pairs, and obtaining the distance difference of the microwaves in two imaging according to the phase value of the interference pattern, so as to calculate the topography, the landform and the tiny change of the surface of the target area, and the method can be used for digital elevation model establishment, crust deformation detection and the like.
In the process of potential landslide identification by using inSAR technology, the steps of registration, interferogram generation, phase unwrapping, ellipsoid fitting, deformation calculation and the like are generally carried out on inSAR images. Due to the influence of factors such as errors in the registration process, system noise and the like, more noise usually exists in the interference pattern, so that interference fringes are not clear enough, and the accuracy of a result obtained by subsequent phase unwrapping is influenced. Therefore, the interferogram is usually required to be filtered, in the prior art, the same filtering mode is usually used for filtering all pixels in the interferogram, but noise may exist in both a high-frequency part (a region with a relatively fast image change) and a low-frequency part (a region with a relatively gentle image change) in the image, which results in that more noise still exists after the interferogram is filtered, and the quality of the image obtained by filtering is still not high enough, so that the accuracy of the result obtained by phase unwrapping is affected.
Disclosure of Invention
The invention aims to disclose a landslide area identification method and system based on inSAR technology, which solve the problem of how to further improve the accuracy of a phase unwrapping result when an image obtained by filtering an interference pattern is subjected to phase unwrapping.
In order to achieve the above purpose, the present invention provides the following technical solutions:
In a first aspect, the present invention provides a landslide area identifying method based on inSAR technology, including:
S1, acquiring an interferogram corresponding to an SAR image containing a region to be identified;
s2, detecting target pixel points of the interference pattern to obtain a set A of target pixel points;
s3, calculating a judgment value of each pixel point in the A;
S4, storing the pixel points with the judging value larger than the set judging threshold value in the A into a set A1, and storing the rest pixel points in the A into a set A2;
S5, respectively filtering the pixel points in the A1 and the A2 by adopting two different filtering algorithms to obtain a filtered interference pattern;
And S6, identifying the filtered interferogram, and judging whether the area to be identified is a potential landslide area.
Optionally, S1 includes:
S11, two SAR images P1 and P2 with different imaging visual angles are obtained, wherein the P1 and the P2 both contain areas to be identified;
s12, registering the P1 and the P2 to obtain a registered image;
s13, acquiring an interference image based on the registration image.
Optionally, S2 includes:
For a pixel b in the interferogram, if the pixel value of the pixel b satisfies value b-valuebnei,max > valthre, the pixel b is taken as a target pixel, bnei is a set of pixels in a square area of d×d with the pixel b as a center, value b represents the pixel value of b, value bnei,max represents the maximum value of the pixel values of the pixels in bnei, and valthre represents a preset pixel value threshold.
Optionally, S3 includes:
the judgment value is calculated using the following formula:
judval b denotes a judgment value of the pixel point b, numnei b denotes a pixel point bnei in which an absolute value of a difference from the pixel value b is smaller than Distedg b denotes a distance between b and the center of the connected domain in bnei, madistpix b denotes a maximum value of the distances between b and the pixel in bnei, α1 denotes a weight on the number, and α2 denotes a weight on the distance.
Optionally, the acquiring process of the connected domain in bnei includes:
Respectively taking each pixel point in bnei as a calculation starting point, and carrying out neighborhood judgment in a D multiplied by D square area taking a pixel point b as a center to obtain a plurality of local areas;
The local region with the largest area is used as the connected region.
Optionally, for the pixel point c in bnei, the process of obtaining the corresponding local area includes:
s31, taking the pixel point c as a calculation starting point;
s32, deleting the calculation starting point from bnei, and storing the calculation starting point into the local area set
S33, acquiring the minimum value of the absolute value of the difference value of the pixel value between the pixel point c in all pixel points which are adjacent to the pixel point c and belong to bnei;
s34, judging whether the minimum value obtained in S31 is smaller than If yes, go to S35; if not, entering S36;
S35, judging whether the number of pixel points in bnei is larger than 0, if so, taking the pixel point belonging to bnei corresponding to the minimum value as a next calculation starting point, and entering S32; if not, entering S36;
s36, forming a local area by the pixel points in the local area set.
Optionally, S5 includes:
If the number of the pixel points in the A1 is greater than or equal to the number of the pixel points in the A2, filtering the pixel points in the A1 in the interference diagram to obtain a filtered intermediate image;
then, in the filtered intermediate image, filtering the pixel points in the A2 to obtain a filtered interference pattern;
If the number of the pixel points in the A1 is smaller than that of the pixel points in the A2, filtering the pixel points in the A2 in the interference diagram to obtain a filtered intermediate image;
and then in the filtered intermediate image, carrying out filtering treatment on the pixel points in the A1 to obtain a filtered interference image.
Optionally, the pixel point in A1 is filtered, and the algorithm used is as follows:
Storing the interferograms and the filtered intermediate images in a set lwPU;
for the image lwp, lwp ε lwPU, if lwp is an interferogram, the pixel point in A1 is stored in the set A11; if lwp is the filtered intermediate image, acquiring a set A11 of pixel points with the same coordinates as the pixel points in A1 in the filtered intermediate image;
Respectively calculating a first influence coefficient of each pixel point in A11;
And sequentially filtering each pixel point in the A11 according to the sequence of the first influence coefficient from high to low to obtain a filtered first image, wherein the filtered first image is used as a filtering intermediate image if lwp is an interference image, and the filtered first image is used as the filtering interference image if lwp is the filtering intermediate image.
Optionally, the pixel point in A2 is filtered, and the algorithm used is as follows:
Storing the interferograms and the filtered intermediate images in a set lwPU;
For the image lwp, lwp e lwPU, if lwp is an interferogram, storing the pixel points in A2 into the set A12; if lwp is the filtered intermediate image, acquiring a set A12 of pixel points with the same coordinates as the pixel points in A2 in the filtered intermediate image;
respectively calculating a second influence coefficient of each pixel point in A12;
And sequentially filtering each pixel point in the A12 according to the sequence from high to low of the second influence coefficient to obtain a filtered second image, wherein the filtered second image is used as a filtering intermediate image if lwp is an interference image, and the filtered second image is used as the filtering interference image if lwp is the filtering intermediate image.
In a second aspect, the invention provides a landslide area identification system based on inSAR technology, which comprises an acquisition module, a detection module, a calculation module, a storage module, a filtering module and an identification module;
The acquisition module is used for acquiring an interferogram corresponding to the SAR image containing the area to be identified;
the detection module is used for detecting the target pixel points of the interference pattern to obtain a set A of the target pixel points;
The calculating module is used for calculating the judgment value of each pixel point in the A;
The storage module is used for storing the pixel points of which the judging values in A are larger than the set judging threshold values in the set A1, and storing the rest pixel points in A in the set A2;
the filtering module is used for filtering the pixel points in the A1 and the A2 respectively by adopting two different filtering algorithms to obtain a filtered interference pattern;
The identification module is used for identifying the filtered interferogram and judging whether the area to be identified is a potential landslide area.
The beneficial effects are that:
According to the invention, the noise in the high-frequency part (namely the pixel point in A1) and the noise in the low-frequency part (namely the pixel point in A2) in the interference pattern are filtered respectively, so that the noise in the filtered image is more effectively removed, and the filtered interference pattern with higher quality is obtained, thereby being beneficial to improving the accuracy of the result obtained by phase unwrapping the interference pattern.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a landslide area identification method based on inSAR technology according to the present invention.
Fig. 2 is a schematic diagram of a landslide area recognition system based on inSAR technology according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
As shown in an embodiment of fig. 1, the present invention provides a landslide area identifying method based on inSAR technology, including:
S1, acquiring an interferogram corresponding to an SAR image containing a region to be identified;
s2, detecting target pixel points of the interference pattern to obtain a set A of target pixel points;
s3, calculating a judgment value of each pixel point in the A;
S4, storing the pixel points with the judging value larger than the set judging threshold value in the A into a set A1, and storing the rest pixel points in the A into a set A2;
S5, respectively filtering the pixel points in the A1 and the A2 by adopting two different filtering algorithms to obtain a filtered interference pattern;
And S6, identifying the filtered interferogram, and judging whether the area to be identified is a potential landslide area.
By filtering the noise in the high frequency part (i.e. the pixel point in A1) and the noise in the low frequency part (i.e. the pixel point in A2) of the interference pattern respectively, the noise in the filtered image is removed more effectively, and a higher quality filtered interference pattern is obtained, so that the accuracy of the result obtained by phase unwrapping the interference pattern is improved.
Because the prior art generally carries out filtering processing on the interference pattern according to only one filtering algorithm, the invention respectively adopts two different algorithms for filtering, can realize processing on more noise and improves the image quality.
The area to be identified may be a hillside.
Optionally, S1 includes:
S11, two SAR images P1 and P2 with different imaging visual angles are obtained, wherein the P1 and the P2 both contain areas to be identified;
s12, registering the P1 and the P2 to obtain a registered image;
s13, acquiring an interference image based on the registration image.
Specifically, after registration, the secondary image is converted into a coordinate system of the primary image, resampling is performed on the primary image and the secondary image or only the secondary image, and then conjugate multiplication is performed on pixels corresponding to the primary image and the secondary image, so that an interference image is obtained.
Here, the main image refers to any one of P1 and P2, and the sub image refers to another image other than the main image among P1 and P2.
Two SAR images with different imaging visual angles can be obtained by adopting a single-rail double-antenna mode or a repeated-rail single-antenna mode.
When image registration is performed, an algorithm based on feature matching, an algorithm based on region matching and the like can be adopted to calculate P1 and P2, and a registration result is obtained.
Optionally, S2 includes:
For a pixel b in the interferogram, if the pixel value of the pixel b satisfies value b-valuebnei,max > valthre, the pixel b is taken as a target pixel, bnei is a set of pixels in a square area of d×d with the pixel b as a center, value b represents the pixel value of b, value bnei,max represents the maximum value of the pixel values of the pixels in bnei, and valthre represents a preset pixel value threshold.
Specifically, the method and the device for detecting the target pixel point can avoid executing filtering operation on all the pixel points, reduce the number of the pixel points needing filtering, and improve the filtering efficiency.
When the target detection is carried out, the pixel value of the pixel point b is compared with the pixel points in bnei, when the pixel value of the pixel point b is larger than that of each pixel point in bnei, the probability that the pixel point belongs to noise is very large, so that the pixel points are screened out, the pixel points not belonging to noise are prevented from being filtered, and the number of the pixel points needing to be filtered is reduced.
Alternatively, D has a value of 7.
Optionally, if the several graphs are RGB color modes, the pixel value threshold is 10.
If the interferogram is Lab color mode, the pixel value threshold is 5.
Optionally, S3 includes:
the judgment value is calculated using the following formula:
judval b denotes a judgment value of the pixel point b, numnei b denotes a pixel point bnei in which an absolute value of a difference from the pixel value b is smaller than Distedg b denotes a distance between b and the center of the connected domain in bnei, madistpix b denotes a maximum value of the distances between b and the pixel in bnei, α1 denotes a weight on the number, and α2 denotes a weight on the distance.
Specifically, the judgment value is obtained by comprehensively calculating the number of pixels close to the pixel value b in bnei and the distance between the pixel point b and the possible image edge, the larger the number of the pixel points with similarity and the smaller the distance between the pixel point b and the possible image edge are, the larger the judgment value is, the more likely the pixel point b is the noise point on the edge of the image, namely the noise belonging to the high-frequency part, if the pixel point is in the low-frequency part, the difference of the pixel values between the pixel point and the surrounding pixel points is very large, so the value of numnei b is very small, the judgment value is relatively small, and therefore, the larger the judgment value is, the larger the pixel point in the A is in the high-frequency part or the low-frequency part, the later targeted filtering of the pixel points with different positions in the A is facilitated, and the noise in the high-frequency part is effectively filtered while more details are reserved.
Alternatively, the weights of α1 and α2 are 0.6 and 0.4, respectively.
Specifically, the set judgment threshold value is 0.8.
Optionally, the acquiring process of the connected domain in bnei includes:
Respectively taking each pixel point in bnei as a calculation starting point, and carrying out neighborhood judgment in a D multiplied by D square area taking a pixel point b as a center to obtain a plurality of local areas;
The local region with the largest area is used as the connected region.
Specifically, when the pixel is in the high frequency portion of the image, the surrounding connected domain is only the adjacent region belonging to the edge of the image, and therefore, the image edge around the pixel can be identified by selecting the region having the largest area from the plurality of partial regions as the connected domain.
Optionally, for the pixel point c in bnei, the process of obtaining the corresponding local area includes:
s31, taking the pixel point c as a calculation starting point;
s32, deleting the calculation starting point from bnei, and storing the calculation starting point into the local area set
S33, acquiring the minimum value of the absolute value of the difference value of the pixel value between the pixel point c in all pixel points which are adjacent to the pixel point c and belong to bnei;
s34, judging whether the minimum value obtained in S31 is smaller than If yes, go to S35; if not, entering S36;
S35, judging whether the number of pixel points in bnei is larger than 0, if so, taking the pixel point belonging to bnei corresponding to the minimum value as a next calculation starting point, and entering S32; if not, entering S36;
s36, forming a local area by the pixel points in the local area set.
Specifically, when the local area is acquired, the threshold value for judging the absolute value is set to be very small when the local area is acquired, so that the problem that numnei b is larger when the pixel point in A is positioned in a low-frequency part and classification of the pixel point in A is influenced is avoided, and only the very similar image edge part meets the threshold value, so that the large probability of the identified connected domain is the edge in the image is ensured.
Optionally, S5 includes:
If the number of the pixel points in the A1 is greater than or equal to the number of the pixel points in the A2, filtering the pixel points in the A1 in the interference diagram to obtain a filtered intermediate image;
then, in the filtered intermediate image, filtering the pixel points in the A2 to obtain a filtered interference pattern;
If the number of the pixel points in the A1 is smaller than that of the pixel points in the A2, filtering the pixel points in the A2 in the interference diagram to obtain a filtered intermediate image;
and then in the filtered intermediate image, carrying out filtering treatment on the pixel points in the A1 to obtain a filtered interference image.
In order to keep more boundary information, the prior art generally uses a guard edge filtering algorithm to filter the interferogram, but the guard edge filtering algorithm has insufficient filtering capability on a high-frequency part in an image due to the requirement of guard edge, so that more pixel points still exist in the image obtained after filtering.
In addition, the invention also determines the filtering sequence of A1 and A2 according to the number of the pixel points in A1 and A2, and because the filtering is performed on the basis of the previous filtering result, in order to fully utilize the filtering result of the previous type of pixel points, the invention preferentially filters the pixel points in the set with more numbers in A1 and A2, thus, when the filtering is performed on the other type of pixel points later, the filtering is based on the image with better quality, and the previous filtering result can be fully utilized to obtain a more accurate filtering result.
Optionally, the pixel point in A1 is filtered, and the algorithm used is as follows:
Storing the interferograms and the filtered intermediate images in a set lwPU;
for the image lwp, lwp ε lwPU, if lwp is an interferogram, the pixel point in A1 is stored in the set A11; if lwp is the filtered intermediate image, acquiring a set A11 of pixel points with the same coordinates as the pixel points in A1 in the filtered intermediate image;
Respectively calculating a first influence coefficient of each pixel point in A11;
And sequentially filtering each pixel point in the A11 according to the sequence of the first influence coefficient from high to low to obtain a filtered first image, wherein the filtered first image is used as a filtering intermediate image if lwp is an interference image, and the filtered first image is used as the filtering interference image if lwp is the filtering intermediate image.
Specifically, besides sorting the filtering sequences of the pixel points of different types, the invention sorts the pixel points of the same type, and the larger the first influence coefficient is, the greater the filtering influence degree of the pixel point on surrounding pixel points is, the more the filtering is preferentially performed, so that the prior filtering result of the invention can fully play a role, and the accuracy of the result obtained by the subsequent filtering is improved.
Optionally, the calculation formula of the first influence coefficient is:
firimp d denotes a first influence coefficient of the pixel point D in a11, neifa d denotes the number of pixel points belonging to a11 contained in a square region of d×d centered on D in lwp; dnei denotes a set of pixel points belonging to a11 included in a d×d square area centering around D in lwp, dist d,u denotes a distance between D and u, u denotes a pixel point in dnei, dist d,max denotes a maximum value of a distance between a pixel point in dnei and D, and δ1 and δ2 denote preset similar pixel point weights and distribution density degree weights, respectively.
Specifically, the first influence coefficient considers the number of pixels belonging to the same type in the neighborhood of the specified size, and considers the distribution situation of the pixels of the same type, the larger the number is, the smaller the average distance between the pixels of the same type and d is, at this time, the larger the first influence coefficient is, the more likely the pixels in dnei are filtered based on the result obtained by filtering d, so that the influence degree of the pixels is comprehensively represented from two different aspects, and the influence degree of the pixels can be more accurately represented.
Optionally, the pixel point weight and the distribution density degree weight of the same kind are respectively 0.5 and 0.5.
Optionally, filtering each pixel point in a11 sequentially according to the order of the first influence coefficient from high to low to obtain a filtered image, including:
each subsequent filtering is performed on the image resulting from the previous filtering, except that the first filtering is performed on the basis of lwp.
Specifically, the filtering of different pixel points is not performed based on the same image, but is performed on the image obtained by the previous filtering except the first filtering, so that as the number of the filtered pixel points is increased, the correct pixel values which can be referred to by the pixel points subjected to the subsequent filtering are increased, and the accuracy of the filtering result is effectively improved.
Optionally, the pixel points in a11 are filtered, and a median filtering algorithm is adopted.
Specifically, the median filtering algorithm is adopted to filter the pixel points in the A11, so that the blurring of edges in the image can be avoided, and the pixel points in the high-frequency part can be effectively filtered. Since if a guard algorithm is used, the filtering of the pixels of the high frequency part may not be complete enough in order to preserve the edges of the image.
Optionally, the pixel point in A2 is filtered, and the algorithm used is as follows:
Storing the interferograms and the filtered intermediate images in a set lwPU;
For the image lwp, lwp e lwPU, if lwp is an interferogram, storing the pixel points in A2 into the set A12; if lwp is the filtered intermediate image, acquiring a set A12 of pixel points with the same coordinates as the pixel points in A2 in the filtered intermediate image;
respectively calculating a second influence coefficient of each pixel point in A12;
And sequentially filtering each pixel point in the A12 according to the sequence from high to low of the second influence coefficient to obtain a filtered second image, wherein the filtered second image is used as a filtering intermediate image if lwp is an interference image, and the filtered second image is used as the filtering interference image if lwp is the filtering intermediate image.
Specifically, the principle of filtering the pixel point in the A2 is the same as the principle of filtering the pixel point in the A1, and the achieved effect and principle are not repeated in the invention.
Optionally, the calculation formula of the second influence coefficient is:
firimp h denotes a first influence coefficient of the pixel point h in a12, neifa h denotes the number of pixel points belonging to a12 contained in a square region of d×d centered on h in lwp; hnei denotes a set of pixel points belonging to a12 included in a square region of d×d centering on h in lwp, dist h,v denotes a distance between h and v, v denotes a pixel point in dnei, and dist h,max denotes a maximum value of the distance between a pixel point in hnei and h.
Optionally, filtering each pixel point in a12 sequentially according to the order of the second influence coefficient from high to low to obtain a filtered image, including:
each subsequent filtering is performed on the image resulting from the previous filtering, except that the first filtering is performed on the basis of lwp.
Optionally, filtering is performed on the pixel points in the A12, and a guard filtering algorithm is adopted.
Specifically, the edge protection filtering algorithm comprises a guiding filtering algorithm, a bilateral filtering algorithm, a weighted least square smoothing filtering algorithm and the like.
Optionally, identifying the filtered interferogram, and determining whether the area to be identified is a potential landslide area includes:
And calculating the filtered interferogram by adopting an SBAS algorithm or a DINSAR algorithm to obtain the deformation rate of the region to be identified, and if the deformation rate is greater than a set deformation rate threshold, determining the region to be identified as a potential landslide region.
Specifically, the deformation rate threshold was 10mm/a.
Embodiment two:
the invention provides a landslide area identification system based on inSAR technology, which comprises an acquisition module, a detection module, a calculation module, a storage module, a filtering module and an identification module, wherein the acquisition module is used for acquiring landslide area information;
The acquisition module is used for acquiring an interferogram corresponding to the SAR image containing the area to be identified;
the detection module is used for detecting the target pixel points of the interference pattern to obtain a set A of the target pixel points;
The calculating module is used for calculating the judgment value of each pixel point in the A;
The storage module is used for storing the pixel points of which the judging values in A are larger than the set judging threshold values in the set A1, and storing the rest pixel points in A in the set A2;
the filtering module is used for filtering the pixel points in the A1 and the A2 respectively by adopting two different filtering algorithms to obtain a filtered interference pattern;
The identification module is used for identifying the filtered interferogram and judging whether the area to be identified is a potential landslide area.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functions of some or all of the components in a hardware configuration device for a request message according to an embodiment of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (7)
1. A landslide area identification method based on inSAR technology is characterized by comprising the following steps:
S1, acquiring an interferogram corresponding to an SAR image containing a region to be identified;
s2, detecting target pixel points of the interference pattern to obtain a set A of target pixel points;
s3, calculating a judgment value of each pixel point in the A;
S4, storing the pixel points with the judging value larger than the set judging threshold value in the A into a set A1, and storing the rest pixel points in the A into a set A2;
S5, respectively filtering the pixel points in the A1 and the A2 by adopting two different filtering algorithms to obtain a filtered interference pattern;
S6, identifying the filtered interferogram, and judging whether the area to be identified is a potential landslide area or not;
S5 comprises the following steps:
If the number of the pixel points in the A1 is greater than or equal to the number of the pixel points in the A2, filtering the pixel points in the A1 in the interference diagram to obtain a filtered intermediate image;
then, in the filtered intermediate image, filtering the pixel points in the A2 to obtain a filtered interference pattern;
If the number of the pixel points in the A1 is smaller than that of the pixel points in the A2, filtering the pixel points in the A2 in the interference diagram to obtain a filtered intermediate image;
then, in the filtered intermediate image, filtering the pixel points in the A1 to obtain a filtered interference pattern;
the pixel point in A1 is subjected to filtering processing, and the algorithm is as follows:
Storing the interferograms and the filtered intermediate images in a set lwPU;
for the image lwp, lwp ε lwPU, if lwp is an interferogram, the pixel point in A1 is stored in the set A11; if lwp is the filtered intermediate image, acquiring a set A11 of pixel points with the same coordinates as the pixel points in A1 in the filtered intermediate image;
Respectively calculating a first influence coefficient of each pixel point in A11;
Sequentially filtering each pixel point in A11 according to the sequence of the first influence coefficient from high to low to obtain a filtered first image, wherein if lwp is an interferogram, the filtered first image is used as a filtering intermediate image, and if lwp is the filtering intermediate image, the filtered first image is used as the filtering interferogram;
the pixel point in A2 is subjected to filtering treatment, and the algorithm is as follows:
Storing the interferograms and the filtered intermediate images in a set lwPU;
For the image lwp, lwp e lwPU, if lwp is an interferogram, storing the pixel points in A2 into the set A12; if lwp is the filtered intermediate image, acquiring a set A12 of pixel points with the same coordinates as the pixel points in A2 in the filtered intermediate image;
respectively calculating a second influence coefficient of each pixel point in A12;
And sequentially filtering each pixel point in the A12 according to the sequence from high to low of the second influence coefficient to obtain a filtered second image, wherein the filtered second image is used as a filtering intermediate image if lwp is an interference image, and the filtered second image is used as the filtering interference image if lwp is the filtering intermediate image.
2. The landslide area recognition method of claim 1 wherein S1 comprises:
S11, two SAR images P1 and P2 with different imaging visual angles are obtained, wherein the P1 and the P2 both contain areas to be identified;
s12, registering the P1 and the P2 to obtain a registered image;
s13, acquiring an interference image based on the registration image.
3. The landslide area recognition method of claim 1, wherein S2 comprises:
for the pixel point b in the interference image, if the pixel value of the pixel point b meets the requirement
Value b-valuebnei,max > valthre, the pixel b is taken as the target pixel, bnei is a set of pixels in a square area of d×d centered on the pixel b, value b represents the pixel value of b, value bnei,max represents the maximum value of the pixel values of the pixels in bnei, and valthre represents the preset pixel value threshold.
4. A landslide area recognition method based on inSAR technology of claim 3 wherein S3 comprises:
the judgment value is calculated using the following formula:
judval b denotes a judgment value of the pixel point b, numnei b denotes a pixel point bnei in which an absolute value of a difference from the pixel value b is smaller than Distedg b denotes a distance between b and the center of the connected domain in bnei, madistpix b denotes a maximum value of the distances between b and the pixel in bnei, α1 denotes a weight on the number, and α2 denotes a weight on the distance.
5. The landslide area recognition method of claim 4 wherein the communicating area of bnei is obtained by:
Respectively taking each pixel point in bnei as a calculation starting point, and carrying out neighborhood judgment in a D multiplied by D square area taking a pixel point b as a center to obtain a plurality of local areas;
The local region with the largest area is used as the connected region.
6. The method for identifying a landslide area based on inSAR technique of claim 5 wherein for pixel point c of bnei, obtaining a corresponding local area comprises:
s31, taking the pixel point c as a calculation starting point;
s32, deleting the calculation starting point from bnei, and storing the calculation starting point into the local area set
S33, acquiring the minimum value of the absolute value of the difference value of the pixel value between the pixel point c in all pixel points which are adjacent to the pixel point c and belong to bnei;
s34, judging whether the minimum value obtained in S31 is smaller than If yes, go to S35; if not, entering S36;
S35, judging whether the number of pixel points in bnei is larger than 0, if so, taking the pixel point belonging to bnei corresponding to the minimum value as a next calculation starting point, and entering S32; if not, entering S36;
s36, forming a local area by the pixel points in the local area set.
7. The landslide area identification system based on inSAR technology is characterized by comprising an acquisition module, a detection module, a calculation module, a storage module, a filtering module and an identification module;
The acquisition module is used for acquiring an interferogram corresponding to the SAR image containing the area to be identified;
the detection module is used for detecting the target pixel points of the interference pattern to obtain a set A of the target pixel points;
The calculating module is used for calculating the judgment value of each pixel point in the A;
The storage module is used for storing the pixel points of which the judging values in A are larger than the set judging threshold values in the set A1, and storing the rest pixel points in A in the set A2;
the filtering module is used for filtering the pixel points in the A1 and the A2 respectively by adopting two different filtering algorithms to obtain a filtered interference pattern;
The identification module is used for identifying the filtered interferogram and judging whether the area to be identified is a potential landslide area or not;
two different filtering algorithms are adopted to filter the pixel points in A1 and A2 respectively, and a filtered interference diagram is obtained, which comprises the following steps:
If the number of the pixel points in the A1 is greater than or equal to the number of the pixel points in the A2, filtering the pixel points in the A1 in the interference diagram to obtain a filtered intermediate image;
then, in the filtered intermediate image, filtering the pixel points in the A2 to obtain a filtered interference pattern;
If the number of the pixel points in the A1 is smaller than that of the pixel points in the A2, filtering the pixel points in the A2 in the interference diagram to obtain a filtered intermediate image;
then, in the filtered intermediate image, filtering the pixel points in the A1 to obtain a filtered interference pattern;
the pixel point in A1 is subjected to filtering processing, and the algorithm is as follows:
Storing the interferograms and the filtered intermediate images in a set lwPU;
for the image lwp, lwp ε lwPU, if lwp is an interferogram, the pixel point in A1 is stored in the set A11; if lwp is the filtered intermediate image, acquiring a set A11 of pixel points with the same coordinates as the pixel points in A1 in the filtered intermediate image;
Respectively calculating a first influence coefficient of each pixel point in A11;
Sequentially filtering each pixel point in A11 according to the sequence of the first influence coefficient from high to low to obtain a filtered first image, wherein if lwp is an interferogram, the filtered first image is used as a filtering intermediate image, and if lwp is the filtering intermediate image, the filtered first image is used as the filtering interferogram;
the pixel point in A2 is subjected to filtering treatment, and the algorithm is as follows:
Storing the interferograms and the filtered intermediate images in a set lwPU;
For the image lwp, lwp e lwPU, if lwp is an interferogram, storing the pixel points in A2 into the set A12; if lwp is the filtered intermediate image, acquiring a set A12 of pixel points with the same coordinates as the pixel points in A2 in the filtered intermediate image;
respectively calculating a second influence coefficient of each pixel point in A12;
And sequentially filtering each pixel point in the A12 according to the sequence from high to low of the second influence coefficient to obtain a filtered second image, wherein the filtered second image is used as a filtering intermediate image if lwp is an interference image, and the filtered second image is used as the filtering interference image if lwp is the filtering intermediate image.
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