A method and device for inverting forest height based on single baseline InSAR

Abstract

The present invention discloses a method and device for inverting forest height based on single-baseline InSAR. The method comprises: performing interference processing on single-baseline InSAR observation data to obtain an interference complex coherence coefficient and calculate a coherence compensation factor; calculating a local effective vertical wave number and a local incident angle; performing sub-aperture decomposition on a primary image and a secondary image to obtain multiple pairs of sub-view SLC images and perform interference processing; performing least squares coherence straight line fitting on multiple sub-view interference complex coherence coefficients and judging the surface phase; regarding the sub-view interference complex coherence coefficient farthest from the surface phase as the sub-view interference complex coherence coefficient #imgabs0# containing the minimum surface scattering, and performing a solution ignoring the surface scattering to obtain an initial forest height; judging the sub-view interference complex coherence coefficient farthest from #imgabs1#, and combining the above two sub-view interference complex coherence coefficients to invert the forest height, thereby realizing the inversion of forest height from single-baseline single-polarization InSAR data.

Description

Technical Field

[0001] The present invention relates to the field of forestry remote sensing using synthetic aperture radar interferometry (InSAR), and in particular to a method and device for forest height inversion based on synthetic aperture radar interferometry images. Background Art

[0002] Forest height plays a vital role in ecosystems and is a key factor in regulating carbon balance and climate. InSAR (Interferometric Synthetic Aperture Radar) technology offers continuous, cloud- and fog-penetrating, large-scale observations, while also being able to penetrate deep into forests, making it a superior observation method to optical remote sensing and LiDAR. Current spaceborne SAR systems mostly collect large-scale observation data using single-polarization, often employing dual-station observations or formation flight modes. Retrieving forest height using single-baseline, single-polarization InSAR has long been a technical challenge in forestry remote sensing.

[0003] The challenge of retrieving forest height using single-baseline, single-polarization InSAR data stems primarily from the limited observational information provided by the data. In single-polarization mode, interferometric processing provides only two observations: coherence amplitude and interferometric phase, making it difficult to solve complex physical models. For example, the widely used Random Volume Over Ground (RVoG) model requires simultaneous solution for four parameters: forest height, extinction coefficient, surface phase, and surface amplitude ratio. The two observations provided by single-baseline, single-polarization InSAR make it difficult to solve these four unknown parameters. Furthermore, InSAR DEMs generated from forested areas suffer from the problem of "impenetrability," resulting in an uncertain forest height signal in the DEM, making it impossible to translate the data into an accurate surface phase. Furthermore, even with an accurate surface phase, the complex coherence coefficient observed by single-baseline, single-polarization InSAR only provides two observations: coherence amplitude and interferometric phase, making it difficult to achieve a balance between solving for the three unknowns: forest height, surface amplitude ratio, and extinction coefficient.

[0004] In summary, it is necessary to develop a single-baseline single-polarization InSAR forest height inversion scheme that does not rely on external data assistance, can overcome the dependence on multi-polarization, multi-baseline observation data or observation data from other platforms, and realize the refined inversion of forest height under the constraints of physical models. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a method and device for inverting forest height based on single-baseline InSAR, so as to realize a forest height inversion method that does not rely on the assistance of multi-baseline, multi-polarization or other platform observation data.

[0006] First, a method for retrieving forest height based on single-baseline InSAR is provided, comprising the following steps:

[0007] S1: Acquire single-polarization single-baseline InSAR data of the observation area and perform interferometric processing to obtain the interferometric complex coherence coefficient and calculate the coherence compensation factor;

[0008] S2: Calculate the effective vertical wave number using the baseline parameter information and perform local correction to obtain the local incident angle and local effective vertical wave number;

[0009] S3: Perform azimuth sub-aperture decomposition on the primary and secondary images of the original InSAR data to obtain multiple pairs of sub-view SLC images;

[0010] S4: Multiple sub-view interferometric complex coherence coefficients are obtained by interferometric processing of multiple pairs of sub-view SLC images. The coherence amplitude of the multiple sub-view interferometric complex coherence coefficients is corrected based on the coherence compensation factor and then expanded to the complex plane. The least squares coherence straight line fitting is then performed on the multiple sub-view interferometric complex coherence coefficients. The surface phase is obtained by combining the positive and negative judgment of the effective vertical wave number.

[0011] S5: Find the sub-view interferometry complex coherence coefficient farthest from the ground phase within the unit circle of the complex plane, and regard it as the sub-view interferometry complex coherence coefficient with the minimum ground scattering. Based on the two-layer model of random ground scattering and ignoring the ground scattering, the initial forest height is obtained.

[0012] S6: Within the unit circle of the complex plane, find the sub-view interferometry complex coherence coefficient that is farthest from the sub-view interferometry complex coherence coefficient with the smallest surface scattering, combine the above two sub-view interferometry complex coherence coefficients, and obtain the final forest height by establishing a nonlinear iterative inversion framework based on the two-layer model of random ground scattering.

[0013] Furthermore, step S1 specifically includes:

[0014] Obtain single-polarization single-baseline InSAR data of the observation area and perform interferometric processing:

[0015] ;

[0016] Where, and represent the main image and the secondary image involved in the interference, Indicates the conjugate multiplication of the primary image and the secondary image. Indicates the expected value; interference complex coherence coefficient is a plural number, represented by , and are the interference phase and coherence amplitude, respectively;

[0017] Calculation includes signal-to-noise ratio decoherence factor and system quantized decoherence factor The coherence compensation factor is used to correct the coherence amplitude of the interferometric complex coherence coefficient. The expression of the coherence amplitude of the corrected observed interferometric complex coherence coefficient is: .

[0018] Furthermore, step S2 specifically includes:

[0019] The nominal incident angle of the electromagnetic wave signal is obtained through the baseline parameters and geometric relationships during InSAR data observation. , and calculate the effective vertical wave number , the expression is:

[0020] ;

[0021] Where, is the effective vertical wave number, represents the wavelength of radar waves, and Represents the slant range and vertical baseline length respectively; when the InSAR system is dual-station data, , when the InSAR system is heavy rail station data, ;

[0022] Calculate local slope using external DEM products , for the nominal angle of incidence Perform local correction to obtain the local incident angle :

[0023] ;

[0024] The effective vertical wave number is calculated based on the following formula: Perform local correction to obtain the local effective vertical wave number :

[0025] .

[0026] Furthermore, the main and secondary images of the original InSAR data are subjected to azimuth sub-aperture decomposition, specifically: the main and secondary images of the original InSAR data are subjected to azimuth fast Fourier transform respectively, and converted into the frequency domain; in the frequency domain, based on the preset sub-aperture segmentation strategy, the azimuth spectrum center of the image after sub-aperture decomposition is calculated. and spectrum bandwidth After windowing and filtering each sub-aperture spectrum, perform two-dimensional inverse fast Fourier transform to reconstruct multiple sub-view images of the main image and the secondary image respectively; if the final The main image and sub-viewing images, then by pairing them together we can generate A sub-view SLC image interference pair.

[0027] Furthermore, step S4 specifically includes:

[0028] After interferometric processing of multiple pairs of sub-view SLC images to obtain multiple sub-view interferometric complex coherence coefficients, the signal-to-noise ratio decoherence factor is used. and system quantized decoherence factor Performing coherence amplitude correction on multiple sub-view interferometric complex coherence coefficients;

[0029] Spreading the multiple sub-view interference complex coherence coefficients after the coherence amplitude correction onto the complex plane;

[0030] Using the total least squares constraint, the coherence line is fitted to the complex coherence coefficients of multiple sub-view interferences within the unit circle of the complex plane, and the phases of the two intersection points of the coherence line and the unit circle of the complex plane are obtained. and , through the relationship between the positive and negative values ​​of the effective vertical wave number and the phase of the two intersection points obtained above, it is determined that one of them is the surface phase point. The judgment criterion is: when >0, if ,but for ,like ,but for ;when 0 o'clock, if ,but for ,like ,but for ;in, represents the effective vertical wave number; It means taking the phase of a complex number; Indicates the surface phase; express and Conjugate multiplication, where i is the expression of the imaginary part.

[0031] Furthermore, step S5 specifically includes:

[0032] In the unit circle of the complex plane, find the phase point from the surface by traversing the search The farthest sub-view interference complex coherence coefficient , the expression is:

[0033] ;

[0034] Where, Indicates the Complex coherence coefficient of sub-view interference;

[0035] Sub-view interference complex coherence coefficient It is considered to be the sub-view interference complex coherence coefficient with the least surface scattering. At this time, the method of ignoring the amplitude ratio of the ground body is used to obtain an initial forest height, and the expression is:

[0036] ;

[0037] Where, represents the surface phase, express The corresponding model's theoretical coherence, It represents the corresponding ground amplitude ratio, which can be understood as the ratio of ground scattered energy to body scattered energy; Indicates forest height; represents the extinction coefficient; represents the local effective vertical wave number; represents the local angle of incidence;

[0038] Solve the above formula to get the initial forest height.

[0039] Furthermore, step S6 specifically includes:

[0040] The distance within the unit circle of the complex plane is determined by geometric relationships to obtain the sub-view interference complex coherence coefficient with the minimum surface scattering. Complex coherence coefficient of the farthest sub-view interference , the expression is as follows:

[0041] ;

[0042] Where, Indicates the Complex coherence coefficient of sub-viewing interference;

[0043] In the unit circle of the complex plane, the reference surface phase point and Since the distances from the complex coherence coefficients of different sub-view interference to the surface phase are different, their projection points on the coherence line can be considered to have different ground amplitude ratios. Complex coherence coefficient of sub-view interference Corresponding ground amplitude ratio It can be expressed as an expression that can realize the amplitude ratio of the ground body corresponding to different sub-views:

[0044] ;

[0045] Where, represent The complex coherence coefficient of the sub-view interference with the minimum surface scattering distance, for To surface phase distance; express To actual pure body scattering The distance between the ground and the ground can be compared by using geometric relationships. Established as expression.

[0046] For each sub-view complex interference coherence coefficient For example, the following functional relationship is established based on the two-layer model of random body scattering:

[0047] ;

[0048] Where, represents the surface phase, and are the local effective vertical wave number and the local incident angle respectively; so far, for any sub-view interference complex coherence , corresponding to three unknowns ; Indicates forest height; represents the extinction coefficient;

[0049] Combine the above two sub-view interference complex coherence coefficients and Based on the random ground scattering two-layer model, the forest height is inverted by establishing a nonlinear iterative inversion framework, and the expression is:

[0050] ;

[0051] The above equation is solved using a nonlinear iterative algorithm. The initial forest height obtained in step S5 is used as the initial value and input into the nonlinear iterative algorithm for solution to obtain the final forest height.

[0052] Secondly, a device for inverting forest height based on single-baseline InSAR is provided, comprising:

[0053] Interferometric data preprocessing module, used to obtain single-polarization single-baseline InSAR data of the observation area and perform interferometric processing to obtain the interferometric complex coherence coefficient and calculate the coherence compensation factor;

[0054] The vertical wave number calculation and correction module is used to calculate the effective vertical wave number through the baseline parameter information and perform local correction to obtain the local incident angle and local effective vertical wave number;

[0055] The sub-aperture decomposition module is used to perform azimuth sub-aperture decomposition of the primary and secondary images of the original InSAR data to obtain multiple pairs of sub-view SLC images;

[0056] The surface phase extraction module performs interferometric processing on multiple pairs of sub-view SLC images to obtain multiple sub-view interferometric complex coherence coefficients. Based on the coherence compensation factor, these multiple sub-view interferometric complex coherence coefficients are corrected for coherence amplitude and then expanded to the complex plane. The least squares coherence straight line fitting is then performed on the multiple sub-view interferometric complex coherence coefficients. The surface phase is then obtained by combining the positive and negative judgment of the effective vertical wave number.

[0057] The initial forest height estimation module is used to find the sub-view interferometry complex coherence coefficient farthest from the surface phase within the unit circle of the complex plane, and regard it as the sub-view interferometry complex coherence coefficient with the minimum surface scattering. The initial forest height is obtained based on the two-layer random ground scattering model and the surface scattering is ignored.

[0058] The forest height inversion module is used to find the sub-view interferometer complex coherence coefficient farthest from the sub-view interferometer complex coherence coefficient with the minimum surface scattering within the unit circle of the complex plane. The two sub-view interferometer complex coherence coefficients are combined and the final forest height is obtained by establishing a nonlinear iterative inversion framework based on the two-layer model of random ground scattering.

[0059] The present invention discloses a forest height inversion method and device based on single-baseline InSAR, and innovatively proposes a technical solution that can achieve forest height inversion only with single-baseline single-polarization InSAR data. The present invention breaks through the existing technology's reliance on high-precision prior information or multi-source auxiliary data (such as high-precision DTM data or observation data from other platforms). By deeply mining the scattering characteristics of the InSAR observation data itself, an innovative inversion framework is constructed, which significantly improves the inversion accuracy of the canopy height in the forest area. In terms of data requirements, the present invention has achieved a self-contained technological breakthrough, relying solely on single-baseline InSAR data to complete high-precision forest parameter inversion, without the need for auxiliary observation data from other platforms. In terms of technical performance, the present invention exhibits obvious accuracy advantages over traditional methods, providing a new high-precision solution for forest resource monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 A flow chart of a forest height inversion method based on single baseline InSAR provided in an embodiment of the present invention;

[0062] Figure 2 A schematic diagram of the sub-view interference complex coherence coefficient and coherence line fitting within the complex plane unit circle provided by the embodiment disclosed in the present invention;

[0063] Figure 3 Figure 1 shows the forest height inversion results obtained by the embodiment of the present invention; (a) shows the forest height inversion result obtained by the present invention, and (b) shows the airborne LiDAR CHM.

[0064] Figure 4 A scatter plot of forest height inversion results obtained from an example of the present invention and airborne LiDAR CHM accuracy verification is provided for the disclosed embodiments of the present invention. The color of the statistical scatter points represents the corresponding number of pixels. DETAILED DESCRIPTION

[0065] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other implementations obtained by those of ordinary skill in the art without inventive effort are within the scope of protection of the present invention.

[0066] To better illustrate the technical solution of the present invention, a test area is taken as an example. A scene of X-band dual-station InSAR data is selected, and airborne LiDAR DTM data is collected as the true value for verification. The present invention is further described in detail. Note that the specific implementation described here is only used to explain the present invention and is not intended to limit the present invention.

[0067] Among the data of the test area selected by the present invention, the airborne LiDAR DTM data has high precision and can be used as the true value to participate in the result verification of the technical method of the present invention.

[0068] The embodiment of the present invention provides a method for inverting forest height based on single baseline InSAR, the process is as follows: Figure 1 As shown, the following steps are included:

[0069] S1: Acquire single-polarization single-baseline InSAR data of the observation area and perform interferometric processing to obtain the interferometric complex coherence coefficient, calculate the coherence compensation factor and correct the coherence amplitude of the interferometric complex coherence coefficient.

[0070] Specifically, step S1 includes:

[0071] S11: Acquire single-polarization single-baseline InSAR data of the observation area and perform interferometric processing:

[0072] ;

[0073] Where, and represent the main image and the secondary image involved in the interference, Indicates the conjugate multiplication of the primary image and the secondary image. Indicates the expected value; interference complex coherence coefficient is a plural number, represented by , and are the interference phase and coherence amplitude, respectively, is a natural constant, i is the expression of the imaginary part; what is related to the terrain elevation is the interference phase , including the surface phase .

[0074] S12: Calculation including signal-to-noise ratio decoherence factor and the system quantitative decoherence factor introduced during data processing The coherence compensation factor is used to correct the coherence amplitude of the interferometric complex coherence coefficient. The expression of the coherence amplitude of the corrected observed interferometric complex coherence coefficient is: .

[0075] It is mainly caused by insufficient signal-to-noise ratio (SNR), which is manifested as reduced coherence and can be corrected by filtering. For InSAR images, there is an equivalent zero signal-to-noise ratio (Noise Equivalent SigmaZero, NESZ) in the distance upward, which is composed of polynomials. For the primary and secondary images, the backscatter coefficient Calculate the signal-to-noise ratio on each polarization channel using the estimated NESZ value , and then calculate the signal-to-noise ratio decoherence factor by the following formula:

[0076] ;

[0077] Where, and They represent the signal-to-noise ratio of the primary image and the secondary image respectively, and their expressions can be used as follows:

[0078] , ;

[0079] Where, and denote the equivalent zero signal-to-noise ratio of the primary image and the secondary image, respectively. and Represent the backscattering coefficients of the primary image and the secondary image respectively.

[0080] System Quantized Decoherence Factor Constant, usually =0.98 can meet the application requirements of most radar images. Of course, other constants close to 1 can also be used in other embodiments, such as 0.95-0.99.

[0081] S2: Calculate the effective vertical wave number using the baseline parameter information and perform local correction to obtain the local incident angle and local effective vertical wave number.

[0082] Specifically, step S2 includes:

[0083] S21: Obtain the nominal incident angle of the electromagnetic wave signal through the baseline parameters and geometric relationship during InSAR data observation , and calculate the effective vertical wave number , the expression is:

[0084] ;

[0085] Where, is the effective vertical wave number, which can characterize the height sensitivity of the InSAR signal; represents the wavelength of radar waves, and Represents the slant range and vertical baseline length respectively; when the InSAR system is dual-station data, , when the InSAR system is heavy rail station data, ;

[0086] S22: Calculate local slope using external DEM products , whose expression is:

[0087] ;

[0088] Where, It is in the pixel The local slope at The DEM product is in pixels The elevation value at is the pixel resolution in the range direction;

[0089] S23: Calculate the local slope Then, for the nominal incident angle Perform local correction to obtain the local incident angle :

[0090] ;

[0091] S24: Effective vertical wave number based on the following formula Perform local correction to obtain the local effective vertical wave number :

[0092] .

[0093] S3: Perform azimuth sub-aperture decomposition on the primary and secondary images of the original InSAR data to obtain multiple pairs of sub-view SLC images.

[0094] In this embodiment, the azimuth sub-aperture decomposition of the original InSAR primary and secondary images is performed, specifically: the azimuth fast Fourier transform of the primary and secondary images of the original InSAR data is performed respectively, and the images are converted into the frequency domain; in the frequency domain, based on the preset sub-aperture segmentation strategy, the azimuth spectrum center of the image after sub-aperture decomposition is calculated. and spectrum bandwidth After windowing and filtering each sub-aperture spectrum, perform two-dimensional inverse fast Fourier transform to reconstruct multiple sub-view images of the main image and the secondary image respectively; if the final Sub-view main image and sub-viewing images, then by pairing them together we can generate A sub-view SLC image interference pair.

[0095] The number and resolution of the segmented sub-view SLC images are determined by the segmentation bandwidth, so the spectrum center of the segmented sub-view SLC image is They are:

[0096] , ;

[0097] Where, represents the spectrum center of the d-th sub-view SLC image; N represents the number of sub-view SAR images split along the azimuth direction; Indicates the split bandwidth. In this example, =5, the split bandwidth is =0.5, the center frequencies of the bandwidth spectrum are: -0.250, -0.125, 0, 0.125, 0.250.

[0098] S4: Multiple sub-view interferometric complex coherence coefficients are obtained by interferometric processing of multiple pairs of sub-view SLC images. After coherence amplitude correction is performed on the multiple sub-view interferometric complex coherence coefficients, the points are spread to the complex plane (complex number plane). Then, the multiple sub-view interferometric complex coherence coefficients are fitted with the least squares coherence straight line. Combined with the positive and negative judgment of the effective vertical wavenumber, the surface phase is obtained.

[0099] Specifically, step S4 includes:

[0100] S41: After interfering multiple pairs of sub-view SLC images to obtain multiple sub-view interferometric complex coherence coefficients, use the signal-to-noise ratio decoherence factor and system quantized decoherence factor Performing coherence amplitude correction on multiple sub-view interferometric complex coherence coefficients;

[0101] S42: The multiple sub-view interference complex coherence coefficients after the above coherence amplitude correction are spread on the complex plane. For any sub-view interference complex coherence coefficient, the phase range is 0- , the amplitude range is 0-1;

[0102] S43: Using the total least squares constraint, perform coherence line fitting on multiple sub-view interference complex coherence coefficients within the complex plane unit circle, and obtain the phase of the two intersection points of the coherence line and the complex plane unit circle. and , Figure 2 The figure shows the sub-view interference complex coherence coefficient and the coherence line fitting diagram within the unit circle of the complex plane. The relationship between the positive and negative values ​​of the effective vertical wave number and the phase of the two intersection points obtained above is used to determine whether one of them is a surface phase point. The judgment criteria are:

[0103] ;

[0104] Specifically, it can be interpreted as: when >0, if ,but for ,like ,but for ;when 0 o'clock, if ,but for ,like ,but for ;in, represents the effective vertical wave number; It means taking the phase of a complex number; and They represent the phases corresponding to the two intersection points respectively; Indicates the surface phase; express and Conjugate multiplication, where i is the expression of the imaginary part.

[0105] S5: In the unit circle of the complex plane, find the sub-view interference complex coherence coefficient farthest from the surface phase, and regard it as the sub-view interference complex coherence coefficient with the minimum surface scattering. Based on the two-layer model of random ground scattering and ignoring the surface scattering, solve it to obtain the initial forest height.

[0106] Specifically, step S5 includes:

[0107] S51: Find the phase point from the surface by traversing the unit circle in the complex plane Complex coherence coefficient of the farthest sub-view interference , the expression is:

[0108] ;

[0109] Where, Indicates the Complex coherence coefficient of sub-viewing interference;

[0110] S52: The distance to the surface phase point found above Complex coherence coefficient of the farthest sub-view interference It is considered to contain the sub-view interference complex coherence coefficient with the least surface scattering. At this time, the method of ignoring the amplitude ratio of the ground body is used to calculate an initial forest height. The process of calculating the initial forest height is to solve the problem shown in the following expression:

[0111] ;

[0112] Where, Represents the surface phase information, express The corresponding model's theoretical coherence, It represents the corresponding ground amplitude ratio, which can be understood as the ratio of ground scattered energy to body scattered energy; Indicates forest height; represents the extinction coefficient; represents the local effective vertical wave number; represents the local incident angle; the model corresponding to the above equation is a two-layer random body scattering model, and the expression of the model is:

[0113] ;

[0114] Where, represents the surface phase, represents pure bulk decoherence, Represents the corresponding ground amplitude ratio, which can be understood as the ratio of ground scattered energy to body scattered energy; for any sub-viewing interference pair, pure body decorrelation can be expressed as forest height and extinction coefficient function.

[0115] S6: Within the unit circle of the complex plane, find the sub-view interferometry complex coherence coefficient that is farthest from the sub-view interferometry complex coherence coefficient with the smallest surface scattering, combine the above two sub-view interferometry complex coherence coefficients, and obtain the final forest height by establishing a nonlinear iterative inversion framework based on the two-layer model of random ground scattering.

[0116] Specifically, step S6 includes:

[0117] S61: Determine the distance within the unit circle of the complex plane by geometric relationships to determine the sub-view interference complex coherence coefficient with the minimum surface scattering The farthest sub-view interference complex coherence coefficient , the expression is as follows:

[0118] ;

[0119] Where, Indicates the Complex coherence coefficient of sub-view interference;

[0120] S62: In the unit circle of the complex plane, reference to the surface phase and Since the distances from the complex coherence coefficients of different sub-view interference to the surface phase are different, their projection points on the coherence line can be considered to have different ground amplitude ratios. Complex coherence coefficient of sub-view interference Corresponding ground amplitude ratio It can be expressed as an expression that can realize the amplitude ratio of the ground body corresponding to different sub-views:

[0121] ;

[0122] Where, represent The complex coherence coefficient of the sub-view interference with the minimum surface scattering distance, for To surface phase The distance between them can be calculated based on the above geometric relationship. express To actual pure body scattering The distance between the ground and the ground can be compared by using geometric relationships. Established as expression.

[0123] S63: For each sub-view interference complex coherence coefficient In terms of the two-layer model of random body scattering, the following functional relationship is established:

[0124] ;

[0125] Where, represents the surface phase, and are the local effective vertical wave number and the local incident angle respectively; so far, for any sub-view interference complex coherence , corresponding to three unknowns ;

[0126] S64: Combine the above two sub-view interference complex coherence coefficients and Based on the random ground scattering two-layer model, the forest height is inverted by establishing a nonlinear iterative inversion framework, and the expression is:

[0127] ;

[0128] In order to solve the above equation, a nonlinear iterative algorithm is used to solve the above equation. The initial forest height obtained in step S5 is used as the initial value and input into the nonlinear iterative algorithm for solution to obtain the final forest height.

[0129] In this example, the final forest height is as follows Figure 3 As shown, Figure 3 (a) is the forest height inversion result obtained by the present invention, Figure 3 (b) is the airborne LiDAR canopy height model (CHM, i.e., forest height). The LiDAR CHM is then used as the ground truth for accuracy evaluation, using the following metrics: (coefficient of determination), RMSE (root mean square error), and the statistical results of the accuracy indicators are as follows Figure 4 shown. The correlation coefficient is 0.77, indicating a high correlation, and the RMSE is 2.65 m, which proves that the technical method of the present invention is effective and can improve the forest height inversion accuracy of single-baseline InSAR.

[0130] An embodiment of the present invention further provides a device for inverting forest height based on a single baseline InSAR, comprising:

[0131] The interferometric data preprocessing module acquires the single-polarization single-baseline InSAR data of the observation area and performs interferometric processing to obtain the interferometric complex coherence coefficient and the corresponding coherence amplitude, calculates the coherence compensation factor and corrects the coherence amplitude;

[0132] The vertical wave number calculation and correction module is used to calculate the effective vertical wave number through the baseline parameter information and perform local correction to obtain the local incident angle and local effective vertical wave number;

[0133] The sub-aperture decomposition module is used to perform azimuth sub-aperture decomposition of the primary and secondary images of the original InSAR data to obtain multiple pairs of sub-view SLC images;

[0134] The surface phase extraction module performs interference processing on multiple pairs of sub-view SLC images to obtain multiple sub-view interferometric complex coherence coefficients. After performing coherence amplitude correction on these multiple sub-view interferometric complex coherence coefficients, the modules spread them to the complex plane. The modules then perform least squares coherence straight line fitting on these multiple sub-view interferometric complex coherence coefficients, and combine this with the positive and negative judgment of the effective vertical wavenumber to obtain the surface phase.

[0135] The initial forest height estimation module is used to find the sub-view interferometry complex coherence coefficient farthest from the surface phase within the unit circle of the complex plane, and regard it as the sub-view interferometry complex coherence coefficient with the minimum surface scattering. The initial forest height is obtained based on the two-layer random ground scattering model and the surface scattering is ignored.

[0136] The forest height inversion module is used to find the sub-view interferometer complex coherence coefficient farthest from the sub-view interferometer complex coherence coefficient with the minimum surface scattering within the unit circle of the complex plane. The two sub-view interferometer complex coherence coefficients are combined and the final forest height is obtained by establishing a nonlinear iterative inversion framework based on the two-layer model of random ground scattering.

[0137] It should be understood that the functional unit modules in various embodiments of the present invention can be concentrated in one processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated into one unit module, and can be implemented in the form of hardware or software.

[0138] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0139] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for inverting forest height based on single baseline InSAR, characterized by: The steps include: S1: Acquire single-polarization single-baseline InSAR data of the observation area and perform interferometric processing to obtain the interferometric complex coherence coefficient and calculate the coherence compensation factor; S2: Calculate the effective vertical wave number using the baseline parameter information and perform local correction to obtain the local incident angle and local effective vertical wave number; S3: Perform azimuth sub-aperture decomposition on the primary and secondary images of the original InSAR data to obtain multiple pairs of sub-view SLC images; S4: Multiple sub-view interferometric complex coherence coefficients are obtained by interferometric processing of multiple pairs of sub-view SLC images. The coherence amplitude of the multiple sub-view interferometric complex coherence coefficients is corrected based on the coherence compensation factor and then expanded to the complex plane. The least squares coherence straight line fitting is then performed on the multiple sub-view interferometric complex coherence coefficients. The surface phase is obtained by combining the positive and negative judgment of the effective vertical wave number. S5: Find the sub-view interferometry complex coherence coefficient farthest from the ground phase within the unit circle of the complex plane, and regard it as the sub-view interferometry complex coherence coefficient with the minimum ground scattering. Based on the two-layer model of random ground scattering and ignoring the ground scattering, the initial forest height is obtained. S6: Within the unit circle of the complex plane, find the sub-view interferometry complex coherence coefficient that is farthest from the sub-view interferometry complex coherence coefficient with the smallest surface scattering, combine the above two sub-view interferometry complex coherence coefficients, and obtain the final forest height by establishing a nonlinear iterative inversion framework based on the two-layer model of random ground scattering.

2. The method for inverting forest height based on single baseline InSAR according to claim 1, characterized in that: Step S1 specifically includes: Obtain single-polarization single-baseline InSAR data of the observation area and perform interferometric processing: ； Where, and represent the main image and the secondary image involved in the interference, Indicates the conjugate multiplication of the primary image and the secondary image. Indicates the expected value; interference complex coherence coefficient is a plural number, represented by , and are the interference phase and coherence amplitude, respectively; Calculation includes signal-to-noise ratio decoherence factor and system quantized decoherence factor The coherence compensation factor.

3. The method for inverting forest height based on single baseline InSAR according to claim 1, characterized in that: Step S2 specifically includes: The nominal incident angle of the electromagnetic wave signal is obtained through the baseline parameters and geometric relationships during InSAR data observation. , and calculate the effective vertical wave number , the expression is: ； Where, is the effective vertical wave number, represents the wavelength of radar waves, and Represents the slant range and vertical baseline length respectively; when the InSAR system is dual-station data, , when the InSAR system is heavy rail station data, ; Calculate local slope using external DEM products , for the nominal angle of incidence Perform local correction to obtain the local incident angle : ； The effective vertical wave number is calculated based on the following formula: Perform local correction to obtain the local effective vertical wave number : 。 4. The method for inverting forest height based on single baseline InSAR according to claim 1, characterized in that: In step S3, the main and secondary images of the original InSAR data are subjected to azimuth sub-aperture decomposition, specifically: the main and secondary images of the original InSAR data are subjected to azimuth fast Fourier transform respectively, and converted into the frequency domain; in the frequency domain, based on the preset sub-aperture segmentation strategy, the azimuth spectrum center of the image after sub-aperture decomposition is calculated. and spectrum bandwidth After windowing and filtering each sub-aperture spectrum, perform two-dimensional inverse fast Fourier transform to reconstruct multiple sub-view images of the main image and the secondary image respectively; if the final The main image and sub-viewing images, then by pairing them together we can generate A sub-view SLC image interference pair.

5. The method for inverting forest height based on single baseline InSAR according to claim 1, characterized in that: Step S4 specifically includes: After interferometric processing of multiple pairs of sub-view SLC images to obtain multiple sub-view interferometric complex coherence coefficients, the signal-to-noise ratio decoherence factor is used. and system quantized decoherence factor Performing coherence amplitude correction on multiple sub-view interferometric complex coherence coefficients; Spreading the multiple sub-view interference complex coherence coefficients after the coherence amplitude correction onto the complex plane; Using the total least squares constraint, the coherence line is fitted to the complex coherence coefficients of multiple sub-view interferences within the unit circle of the complex plane, and the phases of the two intersection points of the coherence line and the unit circle of the complex plane are obtained. and , through the relationship between the positive and negative values ​​of the effective vertical wave number and the phase of the two intersection points obtained above, it is determined that one of them is the surface phase point. The judgment criterion is: when >0, if ,but for ,like ,but for ;when 0 o'clock, if ,but for ,like ,but for ;in, represents the effective vertical wave number; It means taking the phase of a complex number; Indicates the surface phase; express and Conjugate multiplication, where i is the expression of the imaginary part.

6. The method for inverting forest height based on single baseline InSAR according to claim 1, characterized in that: Step S5 specifically includes: In the unit circle of the complex plane, find the phase point from the surface by traversing the search Complex coherence coefficient of the farthest sub-view interference , the expression is: ； Where, Indicates the Complex coherence coefficient of sub-viewing interference; Indicates the surface phase; Sub-view interference complex coherence coefficient It is considered to be the sub-view interference complex coherence coefficient with the least surface scattering. At this time, the method of ignoring the amplitude ratio of the ground body is used to obtain an initial forest height, and the expression is: ； Where, express the corresponding model-theoretical coherence; represents the corresponding ground amplitude ratio; Indicates forest height; represents the extinction coefficient; represents the local effective vertical wave number; represents the local angle of incidence; Solve the above formula to get the initial forest height.

7. The method for inverting forest height based on single baseline InSAR according to claim 1, characterized in that: Step S6 specifically includes: The distance within the unit circle of the complex plane is determined by geometric relationships to obtain the sub-view interference complex coherence coefficient with the minimum surface scattering. The farthest sub-view interference complex coherence coefficient , the expression is as follows: ； Where, Indicates the Complex coherence coefficient of sub-view interference; In the unit circle of the complex plane, the reference surface phase and Since the distances from the complex coherence coefficients of different sub-view interference to the surface phase are different, their projection points on the coherence line can be considered to have different ground amplitude ratios. Complex coherence coefficient of sub-view interference Corresponding ground amplitude ratio It can be expressed as an expression that can realize the amplitude ratio of the ground body corresponding to different sub-views: ； Where, represent The complex coherence coefficient of the sub-view interference with the minimum surface scattering distance, for To the surface phase point distance; express To actual pure body scattering The distance between the ground and the ground can be compared by using geometric relationships. Established as Expressions of For each sub-view interference complex coherence coefficient For example, the following functional relationship is established based on the two-layer model of random body scattering: ； Where, represents the surface phase, and are the local effective vertical wave number and the local incident angle respectively; so far, for any sub-view interference complex coherence , corresponding to three unknowns ; Indicates forest height; represents the extinction coefficient; Combine the above two sub-view interference complex coherence coefficients and Based on the random ground scattering two-layer model, the forest height is inverted by establishing a nonlinear iterative inversion framework, and the expression is: ； The above equation is solved using a nonlinear iterative algorithm. The initial forest height obtained in step S5 is used as the initial value and input into the nonlinear iterative algorithm for solution to obtain the final forest height.

8. A device for inverting forest height based on single baseline InSAR, characterized in that: include: Interferometric data preprocessing module, used to obtain single-polarization single-baseline InSAR data of the observation area and perform interferometric processing to obtain the interferometric complex coherence coefficient and calculate the coherence compensation factor; The vertical wave number calculation and correction module is used to calculate the effective vertical wave number through the baseline parameter information and perform local correction to obtain the local incident angle and local effective vertical wave number; The sub-aperture decomposition module is used to perform azimuth sub-aperture decomposition of the primary and secondary images of the original InSAR data to obtain multiple pairs of sub-view SLC images; The surface phase extraction module performs interferometric processing on multiple pairs of sub-view SLC images to obtain multiple sub-view interferometric complex coherence coefficients. Based on the coherence compensation factor, these multiple sub-view interferometric complex coherence coefficients are corrected for coherence amplitude and then expanded to the complex plane. The least squares coherence straight line fitting is then performed on the multiple sub-view interferometric complex coherence coefficients. The surface phase is then obtained by combining the positive and negative judgment of the effective vertical wave number. The initial forest height estimation module is used to find the sub-view interferometry complex coherence coefficient farthest from the surface phase within the unit circle of the complex plane, and regard it as the sub-view interferometry complex coherence coefficient with the minimum surface scattering. The initial forest height is obtained based on the two-layer random ground scattering model and the surface scattering is ignored. The forest height inversion module is used to find the sub-view interferometer complex coherence coefficient farthest from the sub-view interferometer complex coherence coefficient with the minimum surface scattering within the unit circle of the complex plane. The two sub-view interferometer complex coherence coefficients are combined and the final forest height is obtained by establishing a nonlinear iterative inversion framework based on the two-layer model of random ground scattering.

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