CN107064933B - SAR chromatography building height method based on cyclic spectrum estimation - Google Patents

SAR chromatography building height method based on cyclic spectrum estimation Download PDF

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CN107064933B
CN107064933B CN201710140792.6A CN201710140792A CN107064933B CN 107064933 B CN107064933 B CN 107064933B CN 201710140792 A CN201710140792 A CN 201710140792A CN 107064933 B CN107064933 B CN 107064933B
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李新武
彭星
梁雷
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Institute of Remote Sensing and Digital Earth of CAS
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Abstract

The invention discloses a method for SAR chromatography building height based on cyclic spectrum estimation, which comprises the following steps: an SAR data acquisition step, wherein N scene SAR data are acquired; a main image selection step, namely performing pre-treatment on the acquired N-scene SAR data to obtain N-scene single-view complex data images, and selecting one scene image as a common main image according to a joint correlation function of a vertical baseline and a Doppler mass center frequency difference; a data preprocessing step, namely processing the acquired N scene SAR data into a data mode required by SAR chromatography; and a step of estimating the height position by using the cyclic spectrum, wherein the position of the building in the height direction is estimated by using the cyclic spectrum. The invention divides the signal into the interesting signal and the noise by using the cyclostationarity of the signal, can well inhibit the influence of the noise and can obtain a good result even under the condition of low signal-to-noise ratio. Compared with the conventional spectrum estimation method, the method has higher precision.

Description

SAR chromatography building height method based on cyclic spectrum estimation
Technical Field
The invention relates to a microwave remote sensing technology, in particular to a method for SAR chromatography of building height, and especially relates to a method for SAR chromatography of building height based on cyclic spectrum estimation.
Background
With the rapid development of Chinese economy, the urbanization process is also rapidly accelerated, and unprecedented challenges are brought to city dynamic monitoring and resource management. In the face of the rapid urbanization process, digital city construction is carried forward. An important content of digital city construction is to establish a digital three-dimensional model of a large-scale city scene. It is an essential basic element for establishing digital cities, and various analog simulations of the digital cities and vivid display of urban scenes are performed on the basis of the digital cities. The content plays an important role in the fields of urban planning, intelligent transportation, three-dimensional maps, emergency management of accidents such as natural disasters and terrorist attacks, simulation and emulation of pollutant diffusion, cultural heritage protection in cities, realistic games, urban monitoring, building design and the like.
The multi-baseline Synthetic Aperture Radar (multi-baseline Synthetic Aperture Radar) Tomography technology utilizes a Synthetic Aperture Radar (SAR) system to fly for multiple times of near-parallel flight at different heights, forms a Synthetic Aperture in the height direction for the change of the observation visual angle of the same target, and realizes the imaging of the height direction of the target. The method overcomes the defect that the conventional synthetic aperture radar interferometry (InSAR) technology cannot distinguish the height distribution of different scatterers in the same resolution unit, and has real height dimension geometric resolution. And the problem of image interpretation blurring caused by overlapping and perspective shrinkage can be effectively solved, and the method has great potential in urban area monitoring and forest biomass estimation particularly for high-resolution synthetic aperture radar image building dense areas.
At present, the multi-baseline SAR tomography altitude imaging algorithm mainly comprises the following steps: conventional spectrum estimation algorithms such as beamforming (beamforming) method and compressed sensing method generally require the known noise signal characteristics of multi-baseline acquired data or independent and identically distributed gaussian process, and the resolution and estimation accuracy are often poor in case of low signal-to-noise ratio. Although the compressed sensing method can obtain higher estimation accuracy, the limited equidistant condition is difficult to meet in the current data acquisition mode.
Disclosure of Invention
In order to solve the problems, the invention divides the signals to be processed into two types of interesting signals and noise on a circular correlation function or a circular spectrum by utilizing the circular stationary characteristic of the signals based on the characteristic that the statistical characteristic parameters of the signals are periodically changed along with time, and further can achieve the purposes of only processing the interesting signals and effectively inhibiting interference because the noise does not have the circular stationary characteristic.
The invention provides a method for SAR chromatography building height based on cyclic spectrum estimation, which comprises the following steps:
an SAR data acquisition step, wherein N scene SAR data are acquired;
a main image selection step, namely, for the acquired N-scene SAR data, obtaining an N-scene single-view complex data (SLC) image through early-stage preprocessing, and selecting a scene image as a common main image according to a joint correlation function of a vertical baseline and a Doppler mass center frequency difference;
a data preprocessing step, namely processing the acquired N scene SAR data into a data mode required by SAR chromatography; and
and a step of estimating the height position by using the cyclic spectrum, wherein the position of the building in the height direction is estimated by using the cyclic spectrum.
In the method for SAR tomography of building height based on cyclic spectrum estimation according to the present invention, preferably, the function is expressed as
Figure BDA0001242754380000021
Wherein, γmIn order to integrate the correlation coefficients,
Figure BDA0001242754380000022
Tk,m、
Figure BDA0001242754380000023
the effective spatial baseline, temporal baseline, and Doppler centroid frequency difference of the interference pair formed for images k and m, respectively, and function g expresses the correlation of the individual factors, defined as
g(x,c)＝1-|x|/c(x<c)，g(x,c)＝0(x≥c)，
When integrating the correlation coefficient gammamWhen the maximum value is reached, the group of parameters is the optimal solution of the model, and the corresponding image is the common main image to be selected.
In the method for SAR chromatography of building height based on cyclic spectrum estimation of the present invention, preferably, the data preprocessing step comprises the following substeps:
registering all the slave images to the public master image to enable the same-name pixel points in the image sequence to correspond to the same ground objects;
conjugate multiplying the master image and the slave image to obtain N-1 interference pairs;
removing phases caused by a flat ground effect from the N-1 interference images; and
and compensating all the flat ground phases extracted from the image to obtain data required by SAR tomography.
In the method for SAR chromatography of building height based on cyclic spectrum estimation of the present invention, preferably, the step of cyclic spectrum estimation of height-to-position comprises the following substeps:
a deskew sub-step of removing phase changes caused by a center slope establishing an internal relationship between the observation data and the target slope vertical structure information;
a signal dividing sub-step of dividing the signal into a signal of interest and noise using a cyclostationary property of the signal; and
and an elevation direction imaging substep of obtaining the position of the building in the elevation direction by using a cyclic spectrum estimation algorithm with respect to the signal of interest.
In the method for SAR tomography of building height based on cyclic spectrum estimation according to the present invention, preferably, the signal dividing sub-step specifically includes the following sub-steps:
the signal measurement for any pixel is expressed as:
Figure BDA0001242754380000031
wherein A is0(t) a complex random variable which varies with time, c is the speed of light, f0Is the carrier frequency, fdcIs the Doppler center, R0Is the zero Doppler distance, η, of the sensor to the point target0Is a reference time of the azimuth direction,
assuming that the neighboring pixels around the pixel are of the same type as they are, the SAR signals of the central pixel as well as the neighboring pixels can be considered as time-varying signals, thereby separating the signals into signals of interest and noise.
In the method for SAR chromatography building height based on cyclic spectrum estimation, preferably, the presentation period of the conjugate correlation function of the SAR signal model along with the time t is 2f0The periodic variation of (a) can be expressed as:
Figure BDA0001242754380000041
in the method for SAR tomography of building height based on cyclic spectrum estimation of the present invention, preferably, the height direction imaging sub-step specifically includes the following sub-steps:
a pixel value obtaining sub-step of obtaining a certain azimuth-distance pixel (x) in the nth scene data for the tomographic SAR signal0,y0) Is/are as followsThe complex measurement values are:
Figure BDA0001242754380000042
where γ(s) represents the complex scattering function in the elevation direction, Δ s is the imaging range in the elevation direction, ξn＝-2bn/(λr0) For spatial sampling frequency, with a vertical base bnCenter slant distance r0Related to the wavelength λ;
discretization sub-step, discretizing the continuous reflection function along the height direction s, the tomographic model approximation becomes:
Figure BDA0001242754380000043
wherein L is the number of discrete intervals,sΔ s/(L-1) is a discrete interval,
removing discontinuous spacing constantssThen the scattering model becomes
g＝Rγ+ζ (6)
Where ζ is noise, g is a vector composed of N observations, and R dimension of the mapping matrix is NxLn×l＝exp(-j2πξnsl) Gamma is a vector consisting of L discretized complex scattering coefficients; and
a sub-step of the cyclic spectrum algorithm,
the cycle-beamforming algorithm is represented as:
Figure BDA0001242754380000044
wherein
Figure BDA0001242754380000045
The cycle-multiplex signal classification algorithm is represented as:
Figure BDA0001242754380000046
wherein
Figure BDA0001242754380000047
Is a conjugate correlation matrix CggThe eigenvalues of (a) decompose the subspace represented by the noise.
Drawings
Fig. 1 is a flow chart of a method for SAR tomographic building height estimation based on cyclic spectrum estimation.
FIG. 2 is a sub-flow diagram of the data pre-processing step.
FIG. 3 is a sub-flow chart of the step of cyclic spectrum estimation of altitude position.
Fig. 4 is a schematic diagram of the signal over time.
Fig. 5 is a flow chart of the elevation-wise imaging sub-step.
FIG. 6(a) is an intensity map of an ATC tower on a SAR image; (b) is a three-dimensional model of the ATC tower on a google map.
Fig. 7 is the estimation result of the different methods in the distance direction: (a) multiple information classification, (b) beamforming, (c) round-robin information classification, (d) round-robin beamforming.
FIG. 8 is the estimation results in the azimuth direction for different methods: (a) multiple information classification, (b) beamforming, (c) round-robin information classification, (d) round-robin beamforming.
Fig. 9 is a comparison of the accuracy of the SAR tomography method based on the cyclic spectrum estimation and the conventional spectrum estimation method: (a) is a comparison of the results of the estimates of beamforming and round-robin beamforming in the range direction, (b) is a comparison of the results of the estimates of the multiple information classification and round-robin multiple information classification in the range direction, (c) is a comparison of the results of the estimates of beamforming and round-robin beamforming in the azimuth direction, and (d) is a comparison of the results of the estimates of the multiple information classification and round-robin multiple information classification in the azimuth direction.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly and completely understood, the technical solutions in the embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention, and it should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention. The described embodiments are only some embodiments of the invention, not all 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.
Fig. 1 is a flow chart of a method for SAR tomographic building height estimation based on cyclic spectrum estimation. The method for SAR chromatography of building height based on cyclic spectrum estimation is explained in detail below with reference to FIG. 1. As shown in fig. 1, the method for building height chromatography based on cyclic spectrum estimation comprises the following steps: an SAR data acquisition step S1, a main image selection step S2, a data preprocessing step S3 and a cyclic spectrum estimation height position step S4.
Specifically, in the SAR data acquisition step S1, N-view SAR data is acquired. In the main image selection step S2, the N-view single view complex data (SLC) image is obtained by preprocessing the acquired N-view SAR data, and a view image is selected as a common main image according to a joint correlation function of the vertical baseline and the difference between the doppler centroid frequencies as shown in formula (1)
Figure BDA0001242754380000061
Wherein the function g expresses the relevance of the monomeric factors, which is defined as
g(x,c)＝1-|x|/c(x<c)
g(x,c)＝0(x≥c)
In the formula of gammamIn order to integrate the correlation coefficients,
Figure BDA0001242754380000062
Tk,m、
Figure BDA0001242754380000063
the effective spatial baseline, temporal baseline, and doppler centroid frequency difference of the interference pair formed by images k and m, respectively. When the comprehensive correlation coefficient is maximum, the group of parameters is the optimal solution of the model, and the corresponding image is the common main image which is selected by people.
Next, in a data preprocessing step S3, the acquired N view SAR data is processed into a data pattern required for SAR tomography. A sub-flow diagram of the data pre-processing step is shown in figure 2. As shown in fig. 2, the method specifically includes the following sub-steps:
in the registration substep S31, all the slave images are registered to a common master image, so that the same-name pixel points in the image sequence correspond to the same ground object;
in the interference substep S32, conjugate multiplication is performed on the master image and the slave image to obtain N-1 interference pairs;
in the flat ground phase compensation substep S33, due to the side view imaging characteristics of the SAR images, the phases caused by the flat ground effect are removed from the N-1 interference images;
in the SAR tomographic data acquisition substep S34, the flat phase extracted from the image compensation is all corrected to obtain a data pattern required for the SAR tomographic processing.
Finally, in the step S4 of estimating the altitude position by cyclic spectrum, the position of the building in the altitude direction is estimated by using a beam forming (cyclic-beamforming) method based on cyclic spectrum and a multiple signal classification (cyclic-MUSIC) method based on cyclic spectrum. A sub-flow diagram of the step of cyclic spectral estimation of height position is shown in fig. 3. As shown in fig. 3, the method specifically includes the following sub-steps:
in the declivity substep S41, the intrinsic relationship between the observed data and the target slope vertical (nsr) structural information is established by the center slope, and therefore the phase change caused by this center slope needs to be removed.
In the signal dividing sub-step S42, the signal is divided into the signal of interest and the noise using the cyclostationary property of the signal. Specifically, for a certain SAR image, the signal measurement value of any pixel can be expressed as the following formula:
Figure BDA0001242754380000071
wherein A is0(t) is a time-varying complex random variable, c is the speed of light, f0Is the carrier frequency, fdcIs the Doppler center, R0Is the zero Doppler distance, η, of the sensor to the point target0Is the reference time of the azimuth.
It is assumed that the neighboring pixels around the pixel are of the same type as the pixel, i.e., the neighboring pixels are of the same ground target. Then, given the above assumptions, knowing the resolution in the distance direction and the azimuth direction, the values of the pixels in the four directions of the field around the pixel can be derived from equation (2), as shown in fig. 4. From the above analysis, it can be seen that the change in the phase value of the signal of the domain pixel is related only to the relative position of the central pixel signal, and not to the start-stop time of the central pixel signal. Generally, if the acquisition time of the signal of the central pixel is 0 and the initial value of the phase change is 0, the SAR signals of the central pixel and the neighboring pixels can be regarded as signals that change with time.
Second order statistical properties of the signal model-the conjugate correlation function can be expressed as:
Figure BDA0001242754380000072
then, the period of the conjugate correlation function over time t is 2f as shown in equation (3)0The period is changed.
In the elevation direction imaging sub-step S43, the position of the building in the elevation direction is estimated using a cyclic spectrum based beamforming algorithm and a cyclic spectrum based multiple signal classification algorithm. Specifically, as shown in fig. 5, the method includes the following steps:
in the sub-step S431, for the SAR tomographic signal, the complex measurement value of an azimuth-azimuth pixel (x0, y0) in the nth view data is:
Figure BDA0001242754380000081
where γ(s) represents the complex scattering function in the elevation direction, Δ s is the imaging range in the elevation direction, ξn＝-2bn/(λr0) For spatial sampling frequency, with a vertical base bnCenter slant distance r0And wavelength lambda.
In the discretization sub-step S432, the continuous reflection function is discretized along the height direction S, and the tomographic model approximation becomes:
Figure BDA0001242754380000082
wherein L is the number of discrete intervals,sΔ s/(L-1) is a discrete interval.
Removing discontinuous spacing constantssThen the scattering model becomes
g＝Rγ+ζ (6)
Where, ζ is noise, g is vector composed of N observation values, R dimension of mapping matrix is NxL, and Rn×l＝exp(-j2πξnsl) And gamma is a vector consisting of L discretized complex scattering coefficients.
In the sub-step S433 of the cyclic spectrum algorithm, a beam forming algorithm based on the cyclic spectrum and a multiple signal classification algorithm based on the cyclic spectrum are used to solve the problem.
The beam forming algorithm based on the cyclic spectrum is expressed as:
Figure BDA0001242754380000083
Figure BDA0001242754380000084
wherein the content of the first and second substances,
Figure BDA0001242754380000085
the multiple signal classification algorithm based on the cyclic spectrum is represented as:
Figure BDA0001242754380000086
Figure BDA0001242754380000087
wherein the content of the first and second substances,
Figure BDA0001242754380000091
is a conjugated phaseRelation matrix CggThe eigenvalues of (a) decompose the subspace represented by the noise.
The SAR chromatography building height method based on the cyclic spectrum estimation utilizes the cyclostationarity of the signal to divide the signal into the signal of interest and the noise, can well inhibit the influence of the noise, and can obtain a good result even under the condition of low signal-to-noise ratio. Compared with the conventional spectrum estimation method, the method has higher precision. In order to show the effect of the present invention more clearly, the comparison between the SAR tomographic building height method based on the cyclic spectrum estimation and the conventional spectrum estimation method is further illustrated in the following set of comparative experimental results.
Selecting an Air Traffic Control (ATC) tower on an airport as a typical target by utilizing high-resolution SAR data under a mode of TerrasAR-X bunching (Spotlight) of 9 Jingdeland covering the hong Kong international airport between 2008 and 2009. SAR chromatography result analysis is carried out on the azimuth direction and the distance direction respectively based on a beam forming method (cyclic-beamforming) of a cyclic spectrum, a multiple signal classification method (cyclic-MUSIC) based on the cyclic spectrum and a spectrum estimation method (beam forming and multiple signal classification). Fig. 6(a) and 6(b) show an intensity map of the ATC tower on the SAR image and a three-dimensional model of the ATC tower on a google (google) map, respectively. In FIG. 6(a), aa 'represents a distance direction, and bb' represents an orientation direction. The results of SAR tomography on ATC tower azimuth and range directions using the cyclic spectrum estimation method and the spectrum estimation method are shown in FIGS. 7 and 8, respectively. Among them, fig. 7(a) is an estimation result of the multiple information classification method in the distance direction, fig. 7(b) is an estimation result of the beam forming method in the distance direction, fig. 7(c) is an estimation result of the multiple information classification algorithm based on the cyclic spectrum in the distance direction, and fig. 7(d) is an estimation result of the beam forming algorithm based on the cyclic spectrum in the distance direction. Fig. 8(a) is an estimation result of the multiple information classification algorithm in the azimuth direction, (b) is an estimation result of the beamforming algorithm in the azimuth direction, (c) is an estimation result of the multiple information classification algorithm based on the loop in the azimuth direction, and (d) is an estimation result of the beamforming algorithm based on the spectrum loop in the azimuth direction.
Finally, the position of the strongest spectrum is extracted, and the estimation accuracy of the cyclic spectrum estimation method (cyclic-beam forming and cyclic-multiple information classification) and the conventional spectrum estimation method (beam forming and multiple signal classification) is analyzed, as shown in fig. 9. Fig. 9(a) is a comparison of the results of the estimation of beamforming with the round-robin beamforming in the range direction, fig. 9(b) is a comparison of the results of the estimation of the multiple information classification with the round-robin multiple information classification in the range direction, fig. 9(c) is a comparison of the results of the estimation of beamforming with the round-robin beamforming in the azimuth direction, and fig. 9(d) is a comparison of the results of the estimation of the multiple information classification with the round-robin multiple information classification in the azimuth direction. On two sections of the azimuth direction and the slant range direction, the curve obtained by the circulation-wave beam forming method has more severe fluctuation than that of the wave beam forming method, and the curve obtained by the circulation-multiple information classification method has more severe fluctuation than that of the multi-signal classification method, which indicates that the resolution capability of the circulation spectrum estimation method in the height direction is higher. Therefore, the method has higher precision compared with the conventional spectrum estimation method.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention 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 invention are included in the scope of the present invention.

Claims (5)

1. A method for SAR chromatography building height based on cyclic spectrum estimation is characterized in that,
the method comprises the following steps:
an SAR data acquisition step S1, acquiring N scene SAR data;
a main image selecting step S2, for the acquired N-scene SAR data, obtaining N-scene monoscopic complex data images through pre-processing, and selecting one scene image as a common main image according to a joint correlation function of a vertical baseline and a Doppler centroid frequency difference;
a data preprocessing step S3, processing the acquired N scene SAR data into a data mode required by SAR chromatography; and
the step of estimating the height direction position by using the cyclic spectrum S4, which estimates the height direction position of the building by using the cyclic spectrum, comprises the following substeps:
a deskew sub-step S41 of removing phase changes caused by center slope establishing an internal relationship between the observation data and the target slope vertical structure information;
a signal division substep S42, which considers the SAR signals of the central pixel and the neighboring pixels as time-varying signals, and expresses the signal measurement of any pixel as:
Figure FDA0002651068220000011
wherein A is0(t) a complex random variable which varies with time, c is the speed of light, f0Is the carrier frequency, fdcIs the Doppler center, R0Is the zero Doppler distance, η, of the sensor to the point target0Is a reference time of the azimuth direction,
assuming that the neighboring pixels around the pixel are the same as the type of the pixel, the SAR signals of the central pixel and the neighboring pixels can be regarded as signals varying with time, thereby dividing the signals into a signal of interest and noise; and
in the elevation direction imaging substep S43, the position of the building in the elevation direction is obtained for the signal of interest by using a cyclic spectrum estimation algorithm.
2. The SAR tomography building height method based on cyclic spectrum estimation according to claim 1,
in the main picture selection step S2, the function is expressed as
Figure FDA0002651068220000012
Wherein, γmIn order to integrate the correlation coefficients,
Figure FDA0002651068220000021
Tk,m、
Figure FDA0002651068220000022
the effective spatial baseline, temporal baseline, and Doppler centroid frequency difference of the interference pair formed for images k and m, respectively, and function g expresses the correlation of the individual factors, defined as
g(x,c)＝1-|x|/c(x＜c)，g(x,c)＝0(x≥c)，
When integrating the correlation coefficient gammamWhen the maximum value is reached, the group of parameters is the optimal solution of the model, and the corresponding image is the common main image to be selected.
3. The SAR tomography building height method based on cyclic spectrum estimation according to claim 1,
the data preprocessing step S3 includes the following sub-steps:
a registration substep S31, registering all the slave images to the common master image, so that the same pixel points in the image sequence correspond to the same ground object;
an interference substep S32, which is used for carrying out conjugate multiplication on the master image and the slave image to obtain N-1 interference pairs;
a flat ground phase compensation substep S33 for removing phases due to the flat ground effect from the N-1 interference images; and
the SAR tomography data acquisition substep S34 compensates all the extracted flat phase from the image to obtain the data pattern required by SAR tomography.
4. The SAR tomography building height method based on cyclic spectrum estimation according to claim 1,
the conjugate correlation function of the SAR signal model has a period of 2f along time t0The periodic variation of (a) can be expressed as:
Figure FDA0002651068220000023
5. the SAR tomography building height method based on cyclic spectrum estimation according to claim 1,
the height direction imaging sub-step S43 specifically includes the following sub-steps:
a pixel complex measurement value obtaining substep S431 for the tomographic SAR signal, an azimuth-azimuth pixel (x) in the nth scene data0,y0) The complex measurement values are:
Figure FDA0002651068220000031
where γ(s) represents the complex scattering function in the elevation direction, Δ s is the imaging range in the elevation direction, ξn＝-2bn/(λr0) For spatial sampling frequency, with a vertical base bnCenter slant distance r0Related to the wavelength λ;
discretization substep S432 discretizes the continuous reflection function along the height direction S, and the tomographic model approximation becomes:
Figure FDA0002651068220000032
wherein L is the number of discrete intervals,sΔ s/(L-1) is a discrete interval,
removing discontinuous spacing constantssThen the scattering model becomes
g＝Rγ+ζ (6)
Where ζ is the noise, g is a vector of N observations, and the R dimension of the mapping matrix is NxL, where R isn×l＝exp(-j2πξnsl) Gamma is a vector consisting of L discretized complex scattering coefficients; and
the cyclic spectrum algorithm sub-step S433,
the beam forming algorithm based on the cyclic spectrum is expressed as:
Figure FDA0002651068220000033
wherein
Figure FDA0002651068220000034
The multiple signal classification algorithm based on the cyclic spectrum is represented as:
Figure FDA0002651068220000035
wherein
Figure FDA0002651068220000036
Is a conjugate correlation matrix CggThe eigenvalues of (a) decompose the subspace represented by the noise.
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