Multi-baseline InSAR elevation fusion method and system for multi-scale geometric analysis

Abstract

The present invention provides a multi-baseline InSAR elevation fusion method and system for multi-scale geometric analysis, belonging to the field of radar signal processing technology, including: performing non-subsampled shearlet transform on elevation maps reconstructed from each interference pattern to obtain non-subsampled shearlet transform coefficients of each elevation map; decomposing the non-subsampled shearlet transform coefficients into a low-frequency coefficient and multiple high-frequency coefficients; performing weighted averaging on the low-frequency coefficients to obtain fused low-frequency coefficients; performing adaptive selection on the high-frequency coefficients to obtain fused high-frequency coefficients; performing non-subsampled shearlet inverse transform on the fused coefficients to obtain a fused elevation map. The present invention realizes feature extraction of the elevation map and can effectively retain texture details.

Description

Technical Field

[0001] The invention belongs to the technical field of radar signal processing, and in particular relates to a multi-baseline InSAR elevation fusion method and system for multi-scale geometric analysis. Background Art

[0002] Both Digital Elevation Model (DEM) and Digital Surface Model (DSM) are physical ground models that use ordered numerical arrays to represent ground elevation. The difference between the two is that DEM mainly reflects the terrain elevation information of the surface, that is, terrain features such as mountains, valleys, and rivers, while DSM reflects the elevation information of the surface and the objects above it, including the elevation information of objects such as buildings and trees, as well as the topography and landforms. DEM and DSM are widely used in surveying and mapping, hydrology, meteorology, landforms, geology, soil, communications, military, engineering construction, and other national economy and national defense construction, as well as humanities and natural sciences. Therefore, it is of great significance to obtain high-resolution and high-precision DEM / DSM on a global scale.

[0003] Interferometric synthetic aperture radar (InSAR) is one of the effective means to obtain DEM and DSM. Its basic principle is to use the interferometric phase information between two SAR images with a small viewing angle difference to achieve elevation inversion. Multi-baseline InSAR is an extension of the traditional single-baseline InSAR. It observes the target scene more times, and its implementation methods include multi-antenna, multi-flight, distributed and multi-frequency. Compared with the traditional single-baseline InSAR, multi-baseline InSAR has two main advantages. One is that it can improve the accuracy of elevation measurement, and the other is that it can improve the accuracy of phase unwrapping.

[0004] Multi-baseline InSAR can obtain multiple interferograms, each of which can be used to obtain a reconstructed elevation map. Fusion of the elevation maps reconstructed from the interferograms can further improve the accuracy of elevation measurement. However, most of the existing multi-baseline InSAR elevation fusion algorithms adopt a weighted average processing strategy, with differences in the selection of weighting coefficients. Commonly used weighting coefficients include coherence coefficient, relative elevation error, and effective baseline.

[0005] The weighted average-based algorithm has the advantages of being easy to implement and having high computational efficiency, but its disadvantage is that it does not consider the connection between adjacent pixels and the intrinsic characteristics of the elevation map, resulting in poor accuracy of the fused elevation map. Therefore, a high-precision, high-performance multi-baseline InSAR elevation fusion algorithm needs further research and development. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a multi-baseline InSAR elevation fusion method and system based on multi-scale geometric analysis.

[0007] To achieve the above object, the present invention adopts the following technical solution:

[0008] A multi-baseline InSAR elevation fusion method for multi-scale geometric analysis includes the following steps:

[0009] Performing non-subsampled shearlet transform on the elevation map reconstructed from each interference pattern to obtain non-subsampled shearlet transform coefficients of each elevation map; the non-subsampled shearlet transform coefficients are decomposed into a low-frequency coefficient and a plurality of high-frequency coefficients;

[0010] Perform weighted averaging on the low-frequency coefficients to obtain the fused low-frequency coefficients;

[0011] Adaptively select the high-frequency coefficients to obtain the fused high-frequency coefficients;

[0012] The fused low-frequency coefficients and the fused high-frequency coefficients are subjected to non-subsampled shearlet inverse transform to obtain a fused elevation map.

[0013] Furthermore, the non-subsampled shearlet transform includes: performing multi-scale decomposition on the input image to obtain a low-frequency coefficient and multiple high-frequency coefficients after decomposition; performing band-pass filtering on all high-frequency coefficients to obtain multiple high-frequency directional coefficients.

[0014] Furthermore, the time domain convolution kernel corresponding to the frequency domain bandpass filter for bandpass filtering is called shear wave, and the expression of shear wave is:

[0015] ;

[0016] ;

[0017] ;

[0018] in, represents the set of non-subsampled shearlets, is the shear wave function, satisfying , represents the class of square integrable functions, is the anisotropic dilation matrix associated with the scale transformation, represents the scale parameter, which governs the frequency refinement and redundancy of basic elements, is the shear matrix associated with the geometric transformation, is the shear parameter, which constrains the direction of each shear element. represents the displacement parameter for locating distribution discontinuities in the spatial domain, represents an integer, The scale parameter is , the shearing parameters are , the displacement parameters are The shear wave function, is the independent variable of the function.

[0019] Furthermore, the low-frequency coefficients are weighted averaged to obtain the fused low-frequency coefficients, which are recorded as include:

[0020] The weight of each low-frequency coefficient is set to the inverse of its corresponding elevation variance. The calculation formula for the fused low-frequency coefficient is:

[0021] ;

[0022] in, is the low-frequency coefficient after fusion, is the number of elevation maps, For the The low frequency coefficient of the elevation map, is the weight, and its calculation formula is:

[0023] ;

[0024] in, For the The standard deviation of the elevation error of an elevation map is calculated using fuzzy height, coherence coefficient, and multi-look number.

[0025] Furthermore, the adaptive selection of high-frequency coefficients to obtain fused high-frequency coefficients includes:

[0026] Use Laplace energy and describe the edge, texture and directional features of the image; Elevation map Scale, The high-frequency directivity coefficient in each direction is For the Elevation map Scale, High-frequency directivity coefficient in each direction , denoted by the high frequency directivity coefficient At any coordinate The value at , coordinate point The value of the Laplace energy and It is centered on this coordinate point Variable step size Laplacian value in the window The sum of squares, where the variable step size Laplace operator value Contains discrete gradient components in the horizontal direction and discrete gradient components in the vertical direction. and are the vertical window size and the horizontal window size respectively. , is the intermediate parameter.

[0027] Furthermore, the value of the Laplace energy and The calculation formula is:

[0028] ;

[0029] ;

[0030] in, is a variable step size, , is a temporary variable; is the variable step size Laplace operator value in the coordinate The value at , , and They are In coordinates , , and The value at .

[0031] For the high-frequency coefficients of each scale and each direction, the value SML of the sum of the Laplace energy is calculated, and an adaptive selection strategy is adopted to retain the high-frequency directivity coefficient with the maximum value SML of the sum of the Laplace energy.

[0032] The present invention also provides a multi-baseline InSAR elevation fusion system for multi-scale geometric analysis, comprising the following modules:

[0033] The transformation module performs non-subsampled shearlet transformation on the elevation map reconstructed from each interference pattern to obtain non-subsampled shearlet transformation coefficients of each elevation map; the non-subsampled shearlet transformation coefficients are decomposed into a low-frequency coefficient and a plurality of high-frequency coefficients;

[0034] The low-frequency fusion module performs weighted averaging on the low-frequency coefficients to obtain the fused low-frequency coefficients;

[0035] The high-frequency fusion module adaptively selects the high-frequency coefficients to obtain the fused high-frequency coefficients;

[0036] The inverse transformation module performs non-subsampled shearlet inverse transformation on the fused low-frequency coefficients and the fused high-frequency coefficients to obtain a fused elevation map.

[0037] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the multi-baseline InSAR elevation fusion method for multi-scale geometric analysis are implemented.

[0038] The present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the multi-baseline InSAR elevation fusion method for multi-scale geometric analysis are implemented.

[0039] Beneficial effects:

[0040] 1. The present invention utilizes non-subsampled shearlet transform to realize feature extraction of elevation map, so that texture details can be effectively preserved in the fused elevation map.

[0041] 2. The present invention can effectively improve the accuracy of the fused elevation map by weighted averaging the low-frequency NSST (non-subsampled shearlet transform) coefficients and adaptively selecting the high-frequency NSST coefficients. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of the multi-baseline InSAR elevation fusion method based on multi-scale geometric analysis of the present invention;

[0043] Figure 2 Schematic diagram of NSST transformation;

[0044] Figure 3a The radar domain elevation map inverted from the interferogram generated for satellites A and B. The numbers in the color bar on the right indicate the altitude;

[0045] Figure 3b The radar domain elevation map inverted from the interferogram generated by satellites A and C. The numbers in the color bar on the right indicate the altitude;

[0046] Figure 3c The radar domain elevation map inverted from the interferogram generated by satellites A and D. The numbers in the color bar on the right indicate the altitude;

[0047] Figure 3d The radar domain elevation map inverted from the interferogram generated for satellites B and C. The numbers in the color bar on the right indicate the altitude;

[0048] Figure 3e The radar domain elevation map inverted from the interferogram generated by satellites B and D. The numbers in the color bar on the right indicate the altitude;

[0049] Figure 3f The radar domain elevation map inverted from the interferogram generated by satellites C and D. The numbers in the color bar on the right indicate the altitude;

[0050] Figure 4 It is a radar domain elevation map obtained by using the present invention after fusion, and the numbers in the color bar on the right side thereof represent the height;

[0051] Figure 5 It is a rendering of a DSM product obtained by geocoding the fused radar domain elevation map obtained by the method proposed by the present invention, and the numbers in the color bar on the right side represent the altitude;

[0052] Figure 6 The reconstructed DSM and ICESat-2 lidar point cloud obtained by the present invention are Figure 5 Histogram of elevation differences for the flat area shown;

[0053] Figure 7 The reconstructed DSM and ICESat-2 lidar point cloud obtained by the present invention are Figure 5 Histogram of elevation differences for the mountainous region shown. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0055] In the description of the present invention, it should be understood that the terms "upper", "lower", "top", "bottom", "inner", "outer", etc. used in the present invention to indicate the orientation or position relationship is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0056] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0057] like Figure 1As shown, the multi-baseline InSAR elevation fusion method for multi-scale geometric analysis of the present invention comprises the following steps:

[0058] Step 1: Perform non-subsampled shearlet transform (NSST) on the elevation map reconstructed from each interference pattern to obtain the non-subsampled shearlet transform coefficients of each elevation map.

[0059] Figure 2 The flowchart of NSST transformation is given. NSST is a multi-scale geometric analysis theory with a compact support framework and is sensitive to the directionality of the image. NSST transformation is divided into the following two steps:

[0060] (1) Decompose the input image into multiple scales. The decomposition is performed recursively to obtain a low-frequency coefficient and multiple high-frequency coefficients.

[0061] (2) Perform bandpass filtering on all high-frequency coefficients to obtain multiple high-frequency directional coefficients. Specifically, the time domain convolution kernel corresponding to the frequency domain bandpass filter is called shearlet, which is constructed by affine transformations such as scaling, shearing and translation of basic functions, reflecting the directionality, scale and oscillation of the function. The specific expression of shearlet is:

[0062] ;

[0063] ;

[0064] ;

[0065] in, represents the set of non-subsampled shearlets, , is the anisotropic dilation matrix associated with the scale transformation, represents the scale parameter, which governs the frequency refinement and redundancy of basic elements, is the shear matrix associated with the geometric transformation, is the shear parameter, which constrains the direction of each shear element. represents the displacement parameter for locating distribution discontinuities in the spatial domain, The scale parameter is , the shearing parameters are , the displacement parameters are The shear wave function, is the independent variable of the function.

[0066] Record The low frequency coefficient of the elevation map is , No. Elevation map Scale, The high-frequency directivity coefficient in each direction is .

[0067] Step 2: Perform weighted averaging on the low-frequency coefficients to obtain the fused low-frequency coefficients.

[0068] The weight setting criterion is the inverse weight of variance, that is, the weight of each low-frequency coefficient is set to the inverse of its corresponding elevation variance, so that data points with smaller variance can play a greater role in fitting. The specific calculation formula is:

[0069] ;

[0070] in, is the low-frequency coefficient after fusion, is the number of elevation maps, is the weight, and its calculation formula is:

[0071] ;

[0072] in, For the The standard deviation of the elevation error of an elevation map can be calculated using parameters such as fuzzy height, coherence coefficient, and multi-look number.

[0073] Step 3: Adaptively select the high-frequency coefficients to obtain the fused high-frequency coefficients.

[0074] The sum of modified Laplacian (SML) can be used to describe the edge, texture, and directional features of an image. Elevation map Scale, High-frequency directivity coefficient in each direction , the coordinate point of any position is , coordinate point The SML value is centered at this point The sum of the squares of the variable-step Laplacian values ​​ML within the window, where the variable-step Laplacian values ​​ML contain discrete gradient components in both horizontal and vertical directions. and are the window sizes in the vertical and horizontal directions respectively. The specific calculation formula of SML is:

[0075] ;

[0076] ;

[0077] in, is a variable step size, and The vertical and horizontal windows are

[0078] size, , is a temporary variable, For point centered Variable-step Laplacian values ​​within the window.

[0079] For the high-frequency directivity coefficient at each scale and in each direction, its SML value is calculated, and then an adaptive selection strategy is adopted, specifically, the high-frequency directivity coefficient with the largest SML value is retained.

[0080] Step 4: Perform NSST inverse transform on the fused non-subsampled shearlet transform coefficients, that is, recombine the coefficients obtained by the non-subsampled shearlet transform to restore the original signal and obtain the fused elevation map.

[0081] The technical solution of the present invention is further described in detail below in conjunction with specific embodiments.

[0082] Example 1

[0083] The present invention is verified by using measured data of a multi-satellite distributed InSAR system, which includes four satellites A, B, C, and D. Figure 3a-3f The radar domain elevation map obtained by inverting each interferogram is shown in Figure 1. Figure 3a-3f These are the elevation maps inverted by the interference pairs AB, AC, AD, BC, BD, and CD. Figure 3a-3f It can be seen that the area has a large range of elevation fluctuations and has many mountains. Figure 4 The fused elevation map is obtained by using the present invention. The fused elevation map is geocoded to generate the final DSM product such as Figure 5 shown.

[0084] In order to quantitatively evaluate the improvement of elevation accuracy achieved by the method proposed in this invention, the elevation values ​​of the lidar point cloud of the ICESat-2 satellite are used as reference values ​​to evaluate the accuracy of the DSM before and after fusion. Figure 6 The reconstructed DSM and ICESat-2 lidar point cloud are given in Figure 5 A histogram of elevation differences over the flat area shown. Figure 7 The reconstructed DSM and ICESat-2 lidar point cloud are given in Figure 5 Table 1 shows the histogram of elevation differences in the mountainous area. Table 1 shows the reconstructed DSM and fused DSM of each interferometric pair calculated with ICESat-2 lidar point cloud as reference. Figure 5The mean error table of the flat area and the mountainous area is shown in FIG. It can be seen that the method proposed in the present invention can effectively improve the accuracy of elevation reconstruction.

[0085] Table 1

[0086]

[0087] The present invention also provides a multi-baseline InSAR elevation fusion system for multi-scale geometric analysis, comprising the following modules:

[0088] The transformation module performs non-subsampled shearlet transformation on the elevation map reconstructed from each interference pattern to obtain non-subsampled shearlet transformation coefficients of each elevation map; the non-subsampled shearlet transformation coefficients are decomposed into a low-frequency coefficient and a plurality of high-frequency coefficients;

[0089] The low-frequency fusion module performs weighted averaging on the low-frequency coefficients to obtain the fused low-frequency coefficients;

[0090] The high-frequency fusion module adaptively selects the high-frequency coefficients to obtain the fused high-frequency coefficients;

[0091] The inverse transformation module performs a non-subsampled shearlet inverse transformation on the fused non-subsampled shearlet transform coefficients to obtain a fused elevation map.

[0092] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned multi-baseline InSAR elevation fusion method for multi-scale geometric analysis when executing the program.

[0093] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the multi-baseline InSAR elevation fusion method for multi-scale geometric analysis are implemented.

[0094] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0095] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0096] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0097] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0098] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0099] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A multi-baseline InSAR elevation fusion method for multi-scale geometric analysis, characterized in that: The steps include: Performing non-subsampled shearlet transform on the elevation map reconstructed from each interference pattern to obtain non-subsampled shearlet transform coefficients of each elevation map; the non-subsampled shearlet transform coefficients are decomposed into a low-frequency coefficient and a plurality of high-frequency coefficients; Perform weighted averaging on the low-frequency coefficients to obtain the fused low-frequency coefficients; Adaptively select the high-frequency coefficients to obtain the fused high-frequency coefficients; Performing non-subsampled shearlet inverse transform on the fused low-frequency coefficients and the fused high-frequency coefficients to obtain a fused elevation map; The non-subsampled shearlet transform comprises: performing multi-scale decomposition on the input image to obtain a low-frequency coefficient and multiple high-frequency coefficients after decomposition; performing band-pass filtering on all high-frequency coefficients to obtain multiple high-frequency directional coefficients; The time domain convolution kernel corresponding to the frequency domain bandpass filter for bandpass filtering is called shear wave, and the expression of shear wave is: ； ； ； in, represents the set of non-subsampled shearlets, is the shear wave function, satisfying , represents the class of square integrable functions, is the anisotropic dilation matrix associated with the scale transformation, represents the scale parameter, which governs the frequency refinement and redundancy of basic elements, is the shear matrix associated with the geometric transformation, is the shear parameter, which constrains the direction of each shear element. represents the displacement parameter for locating distribution discontinuities in the spatial domain, represents an integer, The scale parameter is , the shearing parameters are The displacement parameters are The shear wave function, is the independent variable of the function; The low-frequency coefficients are weighted averaged to obtain the fused low-frequency coefficients, which are recorded as include: The weight of each low-frequency coefficient is set to the inverse of its corresponding elevation variance. The calculation formula for the fused low-frequency coefficient is: ； in, is the low-frequency coefficient after fusion, is the number of elevation maps, For the The low frequency coefficient of the elevation map, is the weight, and its calculation formula is: ； in, For the The standard deviation of the elevation error of the elevation map is calculated by fuzzy height, coherence coefficient and multi-look number; The adaptive selection of high-frequency coefficients to obtain fused high-frequency coefficients includes: Use Laplace energy and describe the edge, texture and directional features of the image; Elevation map Scale, The high-frequency directivity coefficient in each direction is For the Elevation map Scale, High-frequency directivity coefficient in each direction , denoted by the high frequency directivity coefficient At any coordinate The value at , coordinate point The value of the Laplace energy and It is centered on this coordinate point Variable step size Laplacian value in the window The sum of squares, where the variable step size Laplace operator value Contains discrete gradient components in the horizontal direction and discrete gradient components in the vertical direction. and are the vertical window size and the horizontal window size respectively. , is the intermediate parameter.

2. The multi-baseline InSAR elevation fusion method for multi-scale geometric analysis according to claim 1, characterized in that: The value of the Laplace energy sum The calculation formula is: ； ； Among them, s is a variable step size, , is a temporary variable; is the variable step size Laplace operator value in the coordinate The value at , , and They are In coordinates , , and The value at For the high-frequency coefficients of each scale and each direction, the value SML of the sum of the Laplace energy is calculated, and an adaptive selection strategy is adopted to retain the high-frequency directivity coefficient with the maximum value SML of the sum of the Laplace energy.

3. A multi-baseline InSAR elevation fusion system for multi-scale geometric analysis using a multi-baseline InSAR elevation fusion method for multi-scale geometric analysis according to any one of claims 1 to 2, characterized in that: Includes the following modules: The transformation module performs non-subsampled shearlet transformation on the elevation map reconstructed from each interference pattern to obtain non-subsampled shearlet transformation coefficients of each elevation map; the non-subsampled shearlet transformation coefficients are decomposed into a low-frequency coefficient and a plurality of high-frequency coefficients; The low-frequency fusion module performs weighted averaging on the low-frequency coefficients to obtain the fused low-frequency coefficients; The high-frequency fusion module adaptively selects the high-frequency coefficients to obtain the fused high-frequency coefficients; The inverse transformation module performs non-subsampled shearlet inverse transformation on the fused low-frequency coefficients and the fused high-frequency coefficients to obtain a fused elevation map.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of a multi-baseline InSAR elevation fusion method for multi-scale geometric analysis as described in any one of claims 1 to 2 are implemented.

5. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a multi-baseline InSAR elevation fusion method for multi-scale geometric analysis as claimed in any one of claims 1 to 2 are implemented.

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