Adaptive multi-loop network insar phase unwrapping method and device

Abstract

This invention discloses an adaptive multi-ring network InSAR phase unwrapping method and apparatus, relating to the field of interferometric synthetic aperture radar measurement technology. The method includes: acquiring interferometric phase maps and topographic maps of complex terrain regions; calculating coherence, terrain gradient maps, phase gradient standard deviation, and residual density based on the interferometric phase maps and topographic maps; constructing a multi-ring network by dynamically selecting ring sizes through a decision function based on coherence, terrain gradient maps, phase gradient standard deviation, and residual density; determining the connectivity of the multi-ring network according to a bridging strategy and connectivity detection method, and outputting a globally connected network; performing hierarchical residual optimization using a two-layer strategy of local absorption and global planning based on the globally connected network, and outputting the optimized residuals; and unwrapping all rings in the globally connected network using a strategy of local unwrapping within the rings and global optimization, and outputting the unwrapping results. This invention can solve the network connectivity problem in complex regions.

Description

Technical Field

[0001] This invention relates to the field of interferometric synthetic aperture radar measurement technology, and in particular to an adaptive multi-ring network InSAR phase unwrapping method and apparatus. Background Technology

[0002] InSAR technology retrieves surface elevation or deformation information by analyzing the interferometric phase of two synthetic aperture radar (SAR) images from the same source. Phase unwrapping is a crucial step in the entire processing flow, aiming to extract information from the interferometric phase of two images. Recovering a continuous absolute phase field from a periodically constrained, wrapped phase. Theoretically, the absolute value of the true phase difference between adjacent pixels should be less than [value missing]. This ensures the continuity of the phase gradient. However, in practical applications, the widespread presence of system noise, such as complex terrain undulations, vegetated areas, water bodies, and densely built-up areas, as well as low-coherence regions, causes drastic changes in the local phase gradient, resulting in a large amount of phase residuals. If the unwrapping algorithm cannot effectively constrain and eliminate these residuals, the error will propagate and accumulate along the network structure, ultimately severely affecting the accuracy and reliability of the elevation or deformation inversion results.

[0003] Most existing mainstream phase unwrapping methods rely on pre-defined fixed network structures, exhibiting significant limitations in adaptability to complex scenarios. Typical four-neighbor or eight-neighbor regular mesh structures are simple and computationally efficient, achieving relatively stable unwrapping results in regions with gentle phase changes and good coherence. However, because their connection methods are entirely constrained by fixed topology, they are prone to generating high-density residuals in steep slopes or fault zones with drastic phase gradient changes. Furthermore, the residual propagation path is difficult to control, leading to widespread unwrapping errors. Unwrapping methods based on Delaunay triangulation enhance adaptability to terrain changes to some extent by constructing irregular triangular units; however, their triangle scale and connection patterns remain static designs, unable to be dynamically adjusted based on local coherence and residual distribution data characteristics. In low-coherence or noise-dominated regions, connection breaks or erroneous crossings easily occur, thus disrupting phase continuity.

[0004] Furthermore, traditional residual processing strategies, such as the branch-cutting method, typically eliminate inconsistencies by artificially severing the propagation paths between residuals. While this method can effectively suppress residual effects locally, it essentially sacrifices network connectivity for stability, often resulting in the loss of local phase information. In areas with complex terrain or dense residuals, this topological disruption can trigger unwrapping errors over a wider range, limiting the overall consistency and physical interpretability of the unwrapping results. Summary of the Invention

[0005] To address the technical problem of existing technologies failing to adapt to drastic local phase gradient changes, easily leading to entanglement error propagation and global unwrapping failure, this invention provides an adaptive multi-ring network InSAR phase unwrapping method and apparatus. The technical solution is as follows:

[0006] On the one hand, an adaptive multi-ring network InSAR phase unwrapping method is provided, which is implemented by an adaptive multi-ring network InSAR phase unwrapping device. The method includes:

[0007] S1. Obtain interferometric phase maps and topographic maps of complex terrain areas; calculate coherence, topographic gradient map, phase gradient standard deviation, and residual density based on the interferometric phase maps and topographic maps;

[0008] S2. Based on coherence, topographic gradient map, phase gradient standard deviation, and residual density, a multi-ring network is constructed by dynamically selecting the ring size through a decision function.

[0009] S3. Determine the connectivity of the multi-ring network based on the bridging strategy and connectivity detection method, and output the globally connected network;

[0010] S4. Based on the globally connected network, a two-layer strategy of local absorption and global path planning is adopted. The residuals in the local range are absorbed by adjusting the edge weights. The shortest path is searched based on Dijkstra's path algorithm. The residual points in the global range are matched based on the shortest path, and the residual point matching pairs are output.

[0011] S5. Based on residual point matching pairs, through the strategy of local unwrapping within the loop and global optimization, all loops in the globally connected network are unwrapped by constructing a weighted objective function within the loop to obtain the unwrapping results of each loop; based on the results of each loop, a global energy function is constructed for fusion, and the fused unwrapping result is output.

[0012] On the other hand, an adaptive multi-ring network InSAR phase unwrapping device is provided, which is applied to the adaptive multi-ring network InSAR phase unwrapping method. The device includes:

[0013] The computing unit is used to acquire interferometric phase maps and topographic maps of complex terrain areas; and to calculate coherence, topographic gradient map, phase gradient standard deviation, and residual density based on the interferometric phase maps and topographic maps.

[0014] The building unit is used to construct multi-ring networks by dynamically selecting the ring size through a decision function based on coherence, terrain gradient map, phase gradient standard deviation and residual density.

[0015] The output unit is used to determine the connectivity of the multi-ring network based on the bridging strategy and connectivity detection method, and output the globally connected network.

[0016] The local absorption and matching unit is used to absorb residuals in a local range by means of edge weight adjustment, based on a globally connected network and employing a two-layer strategy of local absorption and global path planning. It searches for the shortest path based on Dijkstra's path algorithm, matches residual points in the global range based on the shortest path, and outputs residual point matching pairs.

[0017] The unwrapping unit is used to unwrap all loops in the globally connected network based on residual point matching pairs, through local unwrapping within the loop and global optimization strategies, by constructing a weighted objective function within the loop to obtain the unwrapping results of each loop; based on the results of each loop, a global energy function is constructed to fuse them and output the fused unwrapping result.

[0018] On the other hand, an adaptive multi-ring network InSAR phase unwrapping device is provided, the adaptive multi-ring network InSAR phase unwrapping device comprising: a processor; a memory, the memory storing computer-readable instructions, which, when executed by the processor, implement any of the methods described above for adaptive multi-ring network InSAR phase unwrapping.

[0019] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to implement any of the above-described adaptive multi-ring network InSAR phase unwrapping methods.

[0020] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0021] This invention proposes a dynamic ring size selection mechanism based on local data features. By extracting three core features from sub-blocks—phase gradient standard deviation, average coherence, and residual density—it adaptively selects structures such as 3-point rings, 4-point rings, and 5-point or higher rings. Small ring structures are used to capture details in areas with drastic terrain changes, low coherence, and high residual density, while large ring structures are used in areas with gentle terrain and high coherence to improve computational efficiency. Simultaneously, virtual nodes and bridging strategies are employed to address network connectivity issues in complex regions, avoiding the connection breakage defects of traditional networks.

[0022] This invention proposes a hierarchical optimization strategy combining local residual absorption within a ring and global residual planning between rings. Within a single ring, weight adjustment and edge filtering absorb local residuals, preventing their global propagation. Then, residual point charge pairing, shortest path planning, and adaptive truncation strategies globally eliminate the impact of residuals. Compared to traditional branch truncation methods, this invention disperses remaining residuals using virtual nodes, without disrupting the network topology, ensuring the integrity of phase information and significantly reducing the risk of error propagation.

[0023] This invention constructs a dual-drive mechanism of "data features + terrain prior": dynamically adjusts the network structure using data features such as coherence, phase gradient and residual density to ensure that the network matches the local data characteristics; introduces a digital elevation model as an external terrain constraint to generate a reference phase to guide the unwrapping and repair of complex areas such as steep slopes, thereby improving terrain adaptability and unwrapping accuracy. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of an adaptive multi-ring network InSAR phase unwrapping method provided by an embodiment of the present invention;

[0026] Figure 2 This is a detailed flowchart of an adaptive multi-ring network InSAR phase unwrapping method provided in an embodiment of the present invention;

[0027] Figure 3 This is a block diagram of an adaptive multi-ring network InSAR phase unwrapping device provided in an embodiment of the present invention;

[0028] Figure 4 This is a schematic diagram of the structure of an adaptive multi-ring network InSAR phase unwrapping device provided in an embodiment of the present invention. Detailed Implementation

[0029] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0030] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0031] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0032] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0033] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0034] This invention provides an adaptive multi-ring network InSAR phase unwrapping method, which can be implemented by an adaptive multi-ring network InSAR phase unwrapping device, which can be a terminal or a server. Figure 1 The flowchart shown is for an adaptive multi-ring network InSAR phase unwrapping method. The processing flow of this method may include the following steps:

[0035] S1. Obtain interferometric phase maps and topographic maps of complex terrain areas; calculate coherence, topographic gradient map, phase gradient standard deviation, and residual density based on the interferometric phase maps and topographic maps.

[0036] in, Figure 2 This is a detailed flowchart of an adaptive multi-ring network InSAR phase unwrapping method provided in an embodiment of the present invention.

[0037] The coherence map is used to describe the reliability of phase observations. Its value ranges from [0,1]. The closer the coherence value is to 1, the more stable and reliable the phase information of the pixel is.

[0038] The terrain gradient map is determined by the spatial gradients of the interferometric phase in the horizontal and vertical directions; the process of calculating the terrain gradient map is expressed by the following formula (1):

[0039] (1)

[0040] in, Represents a topographic gradient map; Indicates elevation at x The rate of change of space in the direction; Indicates elevation at y The rate of change of space in a direction.

[0041] In one feasible implementation, the phase gradient standard deviation is calculated based on the terrain gradient map to characterize the dispersion or "chaos" of pixel phase changes. This index can comprehensively reflect the complexity of terrain undulations and the degree of influence of noise on phase observation within a local area. When the phase gradient changes within a sub-block are relatively gentle and concentrated, the phase gradient standard deviation is small, indicating that the phase structure in this area is relatively stable; conversely, when the phase gradient standard deviation is large, it indicates that the phase gradient differences within the sub-block are significant, usually corresponding to areas with severe terrain undulations, phase discontinuities, or strong noise. The phase gradient standard deviation is defined as the dispersion of the phase gradient relative to the mean of its sub-blocks, and is expressed by the following formula (2):

[0042] (2)

[0043] in, Indicates the standard deviation of the phase gradient; This represents the average value of the phase gradient within the sub-block; The size centered on the pixel to be calculated is k The blocks; Indicates a sub-block The number of pixels it contains;

[0044] The average value of the phase gradient within the sub-block is expressed by the following formula (3):

[0045] (3)

[0046] The residual density characterizes the concentration of residual points. A residual point is a pixel in a closed loop where the phase is discontinuous; its presence indicates local unwrapping or measurement errors. A smaller residual density indicates limited regional residual problems and a relatively smooth unwrapping process; conversely, a larger residual density indicates more severe local residual problems, which may lead to error propagation. In such cases, small loop structures or local constraints need to be introduced into the algorithm to control the impact of residuals on the global unwrapping result, ensuring overall accuracy and stability.

[0047] Among them, residual density Defined as the ratio of the number of residual points to the total number of pixels in the sub-block, it is expressed by the following formula (4):

[0048] (4)

[0049] The formula for calculating the residual value is as follows: , representing the phase gradient loop integral along the closed path. In actual calculations, a 2×2 pixel loop is used to sum the wrapped phase differences of four adjacent pixels, expressed by the following formula (5):

[0050] (5)

[0051] in, This represents the entanglement phase difference between adjacent pixels, with a value range of (-π, π]. This indicates a modulo operation, with the residual value taking the values ​​of 0, +1, or -1.

[0052] S2. Based on coherence, topographic gradient map, phase gradient standard deviation, and residual density, a multi-ring network is constructed by dynamically selecting the ring size through a decision function.

[0053] In one feasible implementation, the decision function is represented by the following formula (6):

[0054] (6)

[0055] in, Ring size; Indicates the standard deviation of the phase gradient; Indicates coherence; Represents the residual density; () represents the decision function for calculating the ring size. The ring size is calculated point-by-point on the two-dimensional image to determine the rings of the entire image. These rings are then connected to form a multi-ring network. The ring size can be a 3-point ring, a 4-point ring, or a 5-point or higher ring.

[0056] S3. Determine the connectivity of the multi-ring network based on the bridging strategy and connectivity detection method, and output the globally connected network.

[0057] To prevent multiple independent rings from forming isolated connected branches, bridging strategies and connectivity detection are used to ensure global network connectivity.

[0058] Optionally, the specific implementation process of S3 includes S31-S32:

[0059] S31. Determine whether the multi-ring network meets the direct bridging condition. If it does, perform direct bridging. The direct bridging condition is that if there are pixel pairs in the boundary cells of adjacent rings that meet the requirements of phase continuity and coherence reliability, then a connection can be directly established.

[0060] In one feasible implementation, the phase difference must be less than π, and the coherence product of the two pixels must be greater than a preset threshold to ensure the reliability of the connection. This method is suitable for regions with smooth boundary phases and high coherence, and can directly form network paths across sub-blocks.

[0061] Among them, the requirement that the pixel pair satisfies the phase continuity and coherence reliability is expressed by the following formulas (7)-(8):

[0062] (7)

[0063] (8)

[0064] in, Represents a cell The interference phase; Represents a cell The interference phase; Represents a cell Coherence; Represents a cell Coherence; This is the bridging threshold;

[0065] If the direct bridging condition is not met, virtual node bridging is performed by inserting a virtual node between two pixels as a transition and initializing the phase of the virtual node to complete the virtual node bridging.

[0066] To ensure connectivity, a dummy node is inserted between two pixels as a transition. The dummy node smooths phase jumps or low-coherence regions across sub-blocks, maintaining network connectivity. The phase of the dummy node is calculated by adding 2π to the average phase of adjacent pixels. k Initialization is performed to ensure that the phase of the virtual node is consistent with that of the pixel. p Pixel q The phase difference does not exceed π, thus maintaining phase continuity.

[0067] The process of initializing the phase of the virtual node is represented by the following formulas (9)-(10):

[0068] (9)

[0069] (10)

[0070] in, Indicates the phase of the virtual node; It is an integer; Represents a set of integers;

[0071] S32. Use a connectivity detection method to detect the global connectivity of the multi-ring network. If there are multiple branches, establish the shortest bridge edge based on coherence weight between the largest connected branch and the other branches to make the multi-ring network fully connected and output the globally connected network.

[0072] By establishing the shortest bridging edge, the entire network can be fully connected, improving the stability and accuracy of untangling. The process of establishing the shortest bridging edge based on coherence weights is expressed by the following formula (11):

[0073] (11)

[0074] in, Candidate bridging pixels p The coherence value; Candidate bridging pixels q The coherence value; the goal of bridging edge selection is to minimize the accumulation of unreliability, thereby prioritizing high-coherence pixels as connection nodes.

[0075] S4. Based on the globally connected network, a two-layer strategy of local absorption and global path planning is adopted. The residuals in the local range are absorbed by adjusting the edge weights. The shortest path is searched based on Dijkstra's path algorithm. The residual points in the global range are matched based on the shortest path, and the residual point matching pairs are output.

[0076] Optionally, the specific implementation process of S4 includes S41-S44:

[0077] S41. Calculate the loop integral residual and use the loop integral residual to detect problematic loops in the globally connected network;

[0078] In one feasible implementation, for a closed loop L composed of several adjacent pixels, the existence of a residual problem is determined by calculating the cumulative value of the phase increment along the loop direction. Ideally, the phase change within the closed loop should satisfy the closure constraint, i.e., the loop integral result is zero or an equivalent integer phase value. When the absolute value of the loop integral residual exceeds a preset threshold, a phase discontinuity exists within the loop, which can be identified as a problematic loop requiring further local optimization to absorb and eliminate the residual effect.

[0079] Wherein, let loop L be composed of It is formed by connecting 10 pixels in sequence, denoted as . p 1, p 2, …, p n ,in p n and p If the same 1 form a closed structure, then the loop integral residual is expressed by the following formula (12):

[0080] (12)

[0081] in, Represents the loop integral residual; Represents pixels Interference phase value at the location; n This indicates the number of pixels contained in the loop; ideally, It should be an integer multiple of 0 or 2π. When it satisfies... At that time, the loop was marked as a problem loop, indicating that there were residual points inside it.

[0082] S42. Based on the detected problem loops, the reliability of the edges between connected pixels is evaluated through an edge weighting mechanism based on coherence and phase difference. By reducing the priority of low-reliability edges in the network, residuals in the local range are absorbed.

[0083] In one feasible implementation, an iterative optimization strategy is adopted within the problem loop, prioritizing the removal of edges with lower weights to weaken the impact of noise or unstable observations on the loop constraints; after each edge removal, the loop integral residual is recalculated until the residual satisfies the closure constraint condition, thereby completing the local resolution of the residual.

[0084] S43. Detect the phase consistency within the closed loop. If the phase closure residual within the loop is not zero, then there is a residual point within the loop. Based on the obtained residual point, quantize the loop residual and convert the residual into several charges with positive and negative signs.

[0085] In this context, positive and negative charges cancel each other out physically, so they need to be properly paired on a global scale.

[0086] S44. Using the obtained residual points and their corresponding charges, search for the shortest path based on Dijkstra's path algorithm, match the positive and negative residual points based on the shortest path, and output the residual point matching pairs.

[0087] In one feasible implementation, a Dijkstra-based path algorithm is used. By introducing path weights constrained by coherence into the graph structure, connection paths that traverse highly coherent regions are preferentially selected. The Dijkstra-based path algorithm is existing technology and will not be described in detail in this embodiment of the invention.

[0088] In the pairing process, positive and negative residual points with equal absolute charge values ​​are prioritized for matching; when some residuals cannot be completely paired, their remaining charge will be distributed through virtual nodes in the future; the total path cost is expressed by the following formula (13):

[0089] (13)

[0090] in, This represents the total cost of the path, used as the basis for determining the shortest path in Dijkstra's algorithm. Indicates adjacent cell or node pairs in the path; Represents a node Coherence at the location; Represents a node Coherence at the location; It is a local minimum.

[0091] During path planning, if the residual pairing path traverses a low-coherence region, this path may become an error propagation channel. Therefore, an adaptive severing strategy is employed. Specifically, when the average coherence of the sub-blocks traversed by the path falls below a given threshold, the connection edge with the lowest coherence in the path is severed, thereby preventing residuals from spreading to low-quality regions. For residual charges that still cannot be paired, virtual nodes are introduced. These virtual nodes absorb and disperse the residuals; their phase values ​​are adjusted based on the residual charges, and by establishing connections with multiple adjacent ring structures, local errors are distributed over a larger area, thus reducing the error concentration in a single region.

[0092] S5. Based on residual point matching pairs, through the strategy of local unwrapping within the loop and global optimization, all loops in the globally connected network are unwrapped by constructing a weighted objective function within the loop to obtain the unwrapping results of each loop; based on the results of each loop, a global energy function is constructed for fusion, and the fused unwrapping result is output.

[0093] Optionally, the specific implementation process of S5 includes S51-S52:

[0094] S51. At the local level, each completed ring structure L is used as the basic processing unit to jointly optimize the pixel phase within the ring and construct a weighted least squares objective function. The local unwrapping within the ring is completed by solving the weighted least squares objective function, and the unwrapping results of all rings are output.

[0095] The weighted least squares objective function is expressed by the following formula (14):

[0096] (14)

[0097] in, Indicates the first ring within the ring The unwrapped phase of 1 pixel; Indicates the first ring within the ring The unwrapped phase of 1 pixel; This represents the observed phase difference between corresponding pixel pairs; For the edge The weights; The norm selection parameter is used to control the sensitivity of the objective function to noise;

[0098] Specifically, a weighted least-squares objective function is constructed to make the unwrapped phase difference as close as possible to the observed phase difference, while weights are introduced to reflect the reliability of different edges. The norm form can be adaptively selected according to the noise level to balance robustness and computational efficiency.

[0099] Among them, the norm selection parameter pThe choice is determined by the local noise level; low coherence regions should use [specific noise level]. p =1; High coherence region selected p =2. When selecting p When = 1, the objective function corresponds to the L1 norm form, which has a strong ability to suppress outliers and strong noise, and is suitable for low-coherence and high-noise regions. However, the solution process usually relies on linear programming or graph optimization methods, resulting in high computational complexity. When choosing p When = 2, the objective function corresponds to the L2 norm form, has an analytical solution, has high computational efficiency, and is suitable for regions with high coherence and low noise levels.

[0100] In one feasible implementation, let the pixel phase vector within the ring be... Define a phase difference matrix D, where each row corresponds to an edge. In the i The column takes a value of -1, in the [number]th [column]. j The column value is +1; define the observation phase difference vector. The weight matrix W is a diagonal matrix with diagonal elements of 1. The corresponding normal equation is expressed by the following formula (15):

[0101] (15)

[0102] The solution to the normal equation is expressed by the following formula (16):

[0103] (16)

[0104] in, This represents the pseudo-inverse of the matrix, used to handle the rank deficiency problem caused by the global phase translation invariance. In practical implementations, the uniqueness of the solution can be further constrained by fixing the phase of a certain pixel as an anchor point.

[0105] After unwrapping all local loops, a global energy function is constructed to unify and fuse the unwrapping results of each loop. By simultaneously introducing data fidelity constraints and virtual node smoothing constraints, the global phase is ensured to maintain spatial continuity while preserving observation consistency, thereby suppressing the propagation of local errors globally.

[0106] S52. Construct a global energy function, integrate all loop unwrapping results, and output the integrated loop unwrapping result.

[0107] The global energy function is expressed by the following formula (17):

[0108] (17)

[0109] in, Represents the global energy function; This is a data fidelity term used to ensure the consistency between the unwrapped phase and the observed phase difference; This is a virtual node smoothing term used to constrain the smooth transition between the phase of a virtual node and its adjacent real pixels; Represents virtual nodes The set of neighboring pixels; β For smoothing weight parameters; For the edge The weights are usually determined by coherence or quality indicators; Indicates the first ring within the ring The unwrapped phase of 1 pixel; Indicates the first ring within the ring The unwrapped phase of 1 pixel; This represents the observed phase difference between corresponding pixel pairs; Indicates the norm selection parameter; Represents virtual nodes The phase value; Represents virtual nodes domain pixel set The first in k The phase value of each pixel.

[0110] Among them, smoothing weight β Used to balance the relative importance of data fidelity and phase smoothing, its value is related to scene complexity. It is usually set to the reciprocal of the mean of the standard deviation of the sub-block phase gradient to enhance the smoothing constraint capability in complex regions.

[0111] In one feasible implementation, to address the potential phase jump problem remaining in the unwrapping results, this embodiment of the invention employs a phase jump detection and local repair process after unwrapping to accurately locate and correct abnormal regions, thereby improving the continuity and reliability of the overall phase field.

[0112] Optionally, after step S5, the steps also include: phase jump detection, phase constraint repair, virtual node fusion, and verification of untangling results;

[0113] The phase jump detection process includes: using a threshold detection method based on the phase difference between adjacent pixels to identify abnormal jump edges in the unwrapping result;

[0114] Optionally, a threshold detection method based on the phase difference between adjacent pixels is used to identify abnormal transition edges in the unwrapping result, including:

[0115] Calculate the unwrapped phase difference between adjacent pixel pairs and compare it with a preset threshold; when the unwrapped phase difference between adjacent pixel pairs exceeds the preset threshold, it is determined that there is a potential phase transition between the pixel pairs, and the corresponding boundary is marked as a transition edge.

[0116] In one feasible implementation, the unwrapped phase difference between adjacent pixels is expressed by the following formula (18):

[0117] (18)

[0118] in, Representing adjacent pixels The untangling phase difference at the location; Representing adjacent pixels The untangling phase difference at the location; This represents the unwrapping phase difference between adjacent pixels.

[0119] Among them, let The phase transition detection threshold is typically set to [value missing]. An edge is considered a jump edge when the following conditions are met: .

[0120] The phase constraint repair process includes: generating a reference phase based on the InSAR elevation inversion model to provide terrain prior constraints for the unwrapping results; and repairing the unwrapped phase based on the reference phase by constructing a constrained optimization objective function to obtain the repaired unwrapped phase.

[0121] In one feasible implementation, external DEM data is acquired, and a reference phase is generated based on the InSAR elevation inversion model to provide a priori terrain constraints for the unwrapping results. The reference phase reflects the theoretical interferometric phase components caused by the known terrain. The reference phase is represented by the following formula (19):

[0122] (19)

[0123] in, Indicates the reference phase; Indicates the length of the vertical baseline; This indicates the terrain elevation provided by the DEM; Indicates the slant range at which the radar reaches the target; Indicates the radar incident angle; Indicates the radar's operating wavelength.

[0124] In one feasible implementation, within the transition region Ω, the unwrapped phase is repaired by constructing a constrained optimization objective model, ensuring local smoothness while satisfying terrain priors. The objective function of the constrained optimization objective model is expressed by the following formula (20):

[0125] (20)

[0126] in, This indicates the untangled phase to be repaired; Represents the corresponding pixel The reference phase; Represents a cell The neighborhood set; Indicates neighborhood smoothing weights; Represents a cell i The untangled phase to be repaired; Represents a cell domain pixel set The first in j The unwrapping phase value of each pixel.

[0127] The quadratic optimization problem of the objective function can be transformed into a system of linear equations, the solution of which is expressed by the following formula (21):

[0128] (twenty one)

[0129] in, This represents the repaired phase vector; Represents the identity matrix; This represents a Laplace matrix constructed based on neighborhood relationships; This represents the reference phase vector.

[0130] The virtual node fusion process includes: using a combination of interpolation based on neighborhood information and phase correction to fuse the virtual node phase into the actual pixel space to obtain the corrected fused phase;

[0131] In this invention, virtual nodes do not directly correspond to actual pixel positions; their unwrapped phases need to be mapped onto the actual pixel grid. Therefore, this embodiment of the invention employs a combination of interpolation based on neighborhood information and phase correction to smoothly and consistently fuse the phases of virtual nodes into the actual pixel space.

[0132] Optionally, a combination of interpolation based on neighborhood information and phase correction is used to fuse the virtual node phase into the actual pixel space to obtain the corrected fused phase, including:

[0133] Set the four nearest actual pixel positions and corresponding unwrapped phases around any virtual node, and obtain the virtual node phase value at continuous spatial positions by weighted interpolation.

[0134] The weights are determined by the spatial distance between the virtual node and its surrounding pixels; the closer the distance, the greater the weight, thus ensuring the spatial continuity of the interpolation result. In one feasible implementation, the interpolation relationship is expressed by the following formula (22):

[0135] (twenty two)

[0136] in, This represents the phase value of the virtual node obtained through interpolation; This represents the unwrapping phase of adjacent actual pixels; Represents the spatial coordinates of the virtual node; Represents the spatial coordinates of the actual pixel; Indicates the weight.

[0137] Among them, weight From virtual node to the first The distance to each actual pixel is inversely proportional, as expressed by the following formula (23):

[0138] (twenty three)

[0139] in, Represents the spatial x-coordinate of the virtual node; Represents the horizontal coordinate of the actual pixel; Represents the spatial ordinate of the virtual node; Represents the spatial ordinate of the actual pixel.

[0140] Based on the obtained virtual node phase values, the fused phase is corrected by adjusting the interpolated phase to a position that is closest to an integer multiple of 2π of the phase difference of the nearest actual pixel.

[0141] In one feasible implementation, since the interpolation operation may introduce a phase shift that does not satisfy the 2π integer multiple relationship with adjacent actual pixels, an integer multiple correction is needed to the interpolated phase to avoid generating new phase jumps. This correction process is achieved by adjusting the interpolated phase to the position closest to the 2π integer multiple of the phase difference with the nearest actual pixel. The corrected fused phase is represented by the following formula (24):

[0142] (twenty four)

[0143] in, This represents the unwrapped phase value of the actual pixel closest to the virtual node; This is represented as the nearest integer operation; This represents the phase value of the virtual node obtained through interpolation; This represents the corrected fusion phase value.

[0144] Among these measures, correction ensures that the fused phase remains spatially consistent with the surrounding pixels.

[0145] The process of verifying the unwrapping results includes: multi-level verification of the unwrapping phase results based on the corrected fused phase, including: coherence consistency test, residual closure verification, and comparison verification with measured data.

[0146] In one feasible implementation, to further ensure the reliability and physical consistency of the unwrapping results, after the virtual node fusion is completed, multi-level verification is performed on the unwrapped phase results, including coherence consistency verification, residual closure verification, and measured data comparison verification. Coherence reflects the reliability of interferometric phase observations and can be used as a weighting factor for the consistency verification. The coherence consistency verification compares the differences between the unwrapped phase and the original wrapped phase in the complex plane and introduces coherence weighting to construct a global consistency index, which is used to evaluate the degree of matching between the unwrapped results and the original observations.

[0147] The consistency index is expressed by the following formula (25):

[0148] (25)

[0149] in, This represents the coherence value of the corresponding pixel; Indicates the original entanglement phase; This indicates the phase result after unwrapping; when the consistency index is lower than the preset threshold, the region is marked as a low-confidence region and requires local repair or re-unwrapping.

[0150] To verify whether the unwrapping result satisfies the basic phase closure constraint, several closed paths C are randomly selected in the global network, and the phase integral residuals along the paths are calculated. If the unwrapping is correct, the sum of the phase increments along the closed paths should be close to zero. The closure residuals are expressed by the following formula (26):

[0151] (26)

[0152] in, This represents a pair of adjacent pixels on path C; Represents a cell The untangling phase value; Represents a cell The unwrapping phase value; when |RC| exceeds the preset threshold, it is considered that there is still a residual problem in the region, triggering the re-unwrapping process of the local region.

[0153] This invention proposes a dynamic ring size selection mechanism based on local data features. By extracting three core features from sub-blocks—phase gradient standard deviation, average coherence, and residual density—it adaptively selects structures such as 3-point rings, 4-point rings, and 5-point or higher rings. Small ring structures are used to capture details in areas with drastic terrain changes, low coherence, and high residual density, while large ring structures are used in areas with gentle terrain and high coherence to improve computational efficiency. Simultaneously, virtual nodes and bridging strategies are employed to address network connectivity issues in complex regions, avoiding the connection breakage defects of traditional networks.

[0154] This invention proposes a hierarchical optimization strategy combining local residual absorption within a ring and global residual planning between rings. Within a single ring, weight adjustment and edge filtering absorb local residuals, preventing their global propagation. Then, residual point charge pairing, shortest path planning, and adaptive truncation strategies globally eliminate the impact of residuals. Compared to traditional branch truncation methods, this invention disperses remaining residuals using virtual nodes, without disrupting the network topology, ensuring the integrity of phase information and significantly reducing the risk of error propagation.

[0155] This invention constructs a dual-drive mechanism of "data features + terrain prior": dynamically adjusts the network structure using data features such as coherence, phase gradient and residual density to ensure that the network matches the local data characteristics; introduces a digital elevation model as an external terrain constraint to generate a reference phase to guide the unwrapping and repair of complex areas such as steep slopes, thereby improving terrain adaptability and unwrapping accuracy.

[0156] Figure 3 This is a block diagram of an adaptive multi-ring network InSAR phase unwrapping device provided in an embodiment of the present invention. This device is used in the adaptive multi-ring network InSAR phase unwrapping method. (Refer to...) Figure 3 The device includes a computing unit 310, a construction unit 320, an output unit 330, a local absorption and matching unit 340, and an untangling unit 350. Wherein:

[0157] The computing unit 310 is used to acquire interferometric phase maps and topographic maps of complex terrain areas; and to calculate coherence, topographic gradient map, phase gradient standard deviation, and residual density based on the interferometric phase maps and topographic maps.

[0158] Building unit 320 is used to construct a multi-ring network by dynamically selecting the ring size through a decision function based on coherence, topographic gradient map, phase gradient standard deviation and residual density.

[0159] Output unit 330 is used to determine the connectivity of the multi-ring network based on the bridging strategy and connectivity detection method, and output the globally connected network.

[0160] The local absorption and matching unit 340 is used to absorb residuals in a local range by means of edge weight adjustment, based on a global connected network and employing a two-layer strategy of local absorption and global path planning; it searches for the shortest path based on Dijkstra's path algorithm, matches residual points in the global range based on the shortest path, and outputs residual point matching pairs.

[0161] The unwrapping unit 350 is used to unwrap all loops in the globally connected network based on residual point matching pairs, through local unwrapping within the loop and global optimization strategies, by constructing a weighted objective function within the loop to obtain the unwrapping results of each loop; based on the results of each loop, a global energy function is constructed for fusion, and the fused unwrapping result is output.

[0162] Optionally, the output unit 330 is used for:

[0163] Determine whether the multi-ring network meets the direct bridging condition. If it does, then perform direct bridging. The direct bridging condition is that if there are pixel pairs in the boundary cells of adjacent rings that meet the requirements of phase continuity and coherence reliability, then a connection can be directly established.

[0164] Among them, the pixel pair satisfies the requirements of phase continuity and coherence reliability by the following formulas (1)-(2):

[0165] (1)

[0166] (2)

[0167] in, Represents a cell The interference phase; Represents a cell The interference phase; Represents a cell Coherence; Represents a cell Coherence; This is the bridging threshold;

[0168] If the direct bridging condition is not met, virtual node bridging is performed by inserting a virtual node between two pixels as a transition and initializing the virtual node phase to complete the virtual node bridging. The virtual node phase initialization process is represented by the following formulas (3)-(4):

[0169] (3)

[0170] (4)

[0171] in, Indicates the phase of the virtual node; It is an integer; Represents a set of integers;

[0172] A connectivity detection method is used to detect the global connectivity of a multi-ring network. If there are multiple branches, the shortest bridge edge based on coherence weight is established between the largest connected branch and the other branches to make the multi-ring network fully connected and output the globally connected network.

[0173] Optionally, the local absorption and matching unit 340 is used for:

[0174] Calculate the loop integral residual and use it to detect problematic loops in the globally connected network;

[0175] Based on the detected problem loops, the reliability of the edges between connected pixels is evaluated through an edge weighting mechanism based on coherence and phase difference. By reducing the priority of low-reliability edges in the network, residuals in the local range are absorbed.

[0176] Phase consistency within a closed loop is detected. If the phase closure residual within the loop is not zero, then a residual point exists within the loop. Based on the obtained residual point, the loop residual is quantized, and the residual is equivalent to several charges with positive and negative signs.

[0177] The obtained residual points and their corresponding charges are used to search for the shortest path based on Dijkstra's path algorithm. The positive and negative residual points are matched based on the shortest path, and the residual point matching pairs are output.

[0178] Optionally, the untangling unit 350 is used for:

[0179] At the local level, each completed ring structure L is used as the basic processing unit to jointly optimize the pixel phase within the ring and construct a weighted least squares objective function. The local unwrapping within the ring is completed by solving the weighted least squares objective function, and the unwrapping results of all rings are output.

[0180] The weighted least squares objective function is expressed by the following formula (5):

[0181] (5)

[0182] in, Indicates the first ring within the ring The unwrapped phase of 1 pixel; Indicates the first ring within the ring The unwrapped phase of 1 pixel; This represents the observed phase difference between corresponding pixel pairs; For the edge The weights; The norm selection parameter is used to control the sensitivity of the objective function to noise;

[0183] Construct a global energy function to uniformly merge all loop unwrapping results and output the merged loop unwrapping result;

[0184] The global energy function is expressed by the following formula (6):

[0185] (6)

[0186] in, Represents the global energy function; This is a data fidelity term used to ensure the consistency between the unwrapped phase and the observed phase difference; This is a virtual node smoothing term used to constrain the smooth transition between the phase of a virtual node and its adjacent real pixels; Represents virtual nodes The set of neighboring pixels; β For smoothing weight parameters; For the edge The weights are usually determined by coherence or quality indicators; Indicates the first ring within the ring The unwrapped phase of 1 pixel; Indicates the first ring within the ring The unwrapped phase of 1 pixel; This represents the observed phase difference between corresponding pixel pairs; Indicates the norm selection parameter; Represents virtual nodes The phase value; Represents virtual nodes domain pixel set The first in k The phase value of each pixel.

[0187] Optionally, after the steps of unwrapping all rings in the globally connected network based on residual point matching pairs, using a local unwrapping strategy within the ring and a global optimization strategy, constructing a weighted objective function within the ring to unwrap all rings and obtain unwrapping results for each ring; and fusion based on the results of each ring by constructing a global energy function to output the fused unwrapping result, the method further includes: phase jump detection, phase constraint repair, virtual node fusion, and unwrapping result verification.

[0188] The phase jump detection process includes: using a threshold detection method based on the phase difference between adjacent pixels to identify abnormal jump edges in the unwrapping result;

[0189] The phase constraint repair process includes: generating a reference phase based on the InSAR elevation inversion model to provide terrain prior constraints for the unwrapping results; and repairing the unwrapped phase based on the reference phase by constructing a constrained optimization objective function to obtain the repaired unwrapped phase.

[0190] The virtual node fusion process includes: using a combination of interpolation based on neighborhood information and phase correction to fuse the virtual node phase into the actual pixel space to obtain the corrected fused phase;

[0191] The process of verifying the unwrapping results includes: multi-level verification of the unwrapping phase results based on the corrected fused phase, including: coherence consistency test, residual closure verification, and comparison verification with measured data.

[0192] Optionally, the method of identifying abnormal transition edges in the unwrapping result using a threshold detection method based on the phase difference between adjacent pixels includes:

[0193] Calculate the unwrapped phase difference between adjacent pixel pairs and compare it with a preset threshold; when the unwrapped phase difference between adjacent pixel pairs exceeds the preset threshold, it is determined that there is a potential phase transition between the pixel pairs, and the corresponding boundary is marked as a transition edge.

[0194] Optionally, the step of combining interpolation based on neighborhood information with phase correction to fuse the virtual node phase into the actual pixel space to obtain the corrected fused phase includes:

[0195] Set the four nearest actual pixel positions and corresponding unwrapped phases around any virtual node, and obtain the virtual node phase value at continuous spatial positions by weighted interpolation.

[0196] Based on the obtained virtual node phase values, the fused phase is corrected by adjusting the interpolated phase to a position that is closest to an integer multiple of 2π of the phase difference of the nearest actual pixel.

[0197] This invention proposes a dynamic ring size selection mechanism based on local data features. By extracting three core features from sub-blocks—phase gradient standard deviation, average coherence, and residual density—it adaptively selects structures such as 3-point rings, 4-point rings, and 5-point or higher rings. Small ring structures are used to capture details in areas with drastic terrain changes, low coherence, and high residual density, while large ring structures are used in areas with gentle terrain and high coherence to improve computational efficiency. Simultaneously, virtual nodes and bridging strategies are employed to address network connectivity issues in complex regions, avoiding the connection breakage defects of traditional networks.

[0198] This invention proposes a hierarchical optimization strategy combining local residual absorption within a ring and global residual planning between rings. Within a single ring, weight adjustment and edge filtering absorb local residuals, preventing their global propagation. Then, residual point charge pairing, shortest path planning, and adaptive truncation strategies globally eliminate the impact of residuals. Compared to traditional branch truncation methods, this invention disperses remaining residuals using virtual nodes, without disrupting the network topology, ensuring the integrity of phase information and significantly reducing the risk of error propagation.

[0199] This invention constructs a dual-drive mechanism of "data features + terrain prior": dynamically adjusts the network structure using data features such as coherence, phase gradient and residual density to ensure that the network matches the local data characteristics; introduces a digital elevation model as an external terrain constraint to generate a reference phase to guide the unwrapping and repair of complex areas such as steep slopes, thereby improving terrain adaptability and unwrapping accuracy.

[0200] Figure 4 This is a schematic diagram of the structure of an adaptive multi-ring network InSAR phase unwrapping device provided in an embodiment of the present invention, as shown below. Figure 4 As shown, the adaptive multi-ring network InSAR phase unwrapping device may include the above-mentioned Figure 3 The adaptive multi-ring network InSAR phase unwrapping device 410 is shown. Optionally, the adaptive multi-ring network InSAR phase unwrapping device 410 may include a first processor 2001.

[0201] Optionally, the adaptive multi-ring network InSAR phase unwrapping device 410 may also include a memory 2002 and a transceiver 2003.

[0202] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0203] The following is combined with Figure 4 A detailed description of each component of the adaptive multi-ring network InSAR phase unwrapping device 410 is provided below:

[0204] The first processor 2001 is the control center of the adaptive multi-ring network InSAR phase unwrapping device 410. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0205] Optionally, the first processor 2001 can perform various functions of the adaptive multi-ring network InSAR phase unwrapping device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0206] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 4 CPU0 and CPU1 are shown in the diagram.

[0207] In a specific implementation, as one example, the adaptive multi-ring network InSAR phase unwrapping device 410 may also include multiple processors, for example... Figure 4 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor or a multi-core processor. Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0208] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0209] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently, and may be connected via the interface circuit of the adaptive multi-ring network InSAR phase unwrapping device 410. Figure 4 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0210] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0211] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 4(Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0212] Optionally, the transceiver 2003 can be integrated with the first processor 2001, or it can exist independently and be connected to the interface circuit of the adaptive multi-ring network InSAR phase unwrapping device 410. Figure 4 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0213] It should be noted that, Figure 4 The structure of the adaptive multi-ring network InSAR phase unwrapping device 410 shown does not constitute a limitation on the router. Actual adaptive multi-ring network InSAR phase unwrapping devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0214] Furthermore, the technical effect of the adaptive multi-ring network InSAR phase unwrapping device 410 can be referred to the technical effect of the adaptive multi-ring network InSAR phase unwrapping method described in the above method embodiments, and will not be repeated here.

[0215] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or it may be any conventional processor, etc.

[0216] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0217] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0218] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0219] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0220] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0221] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0222] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0223] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0224] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0225] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0226] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0227] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An adaptive multi-ring network InSAR phase unwrapping method, characterized in that, The method includes: S1. Obtain interferometric phase maps and topographic maps of complex terrain areas; calculate coherence, topographic gradient map, phase gradient standard deviation, and residual density based on the interferometric phase maps and topographic maps; S2. Based on coherence, topographic gradient map, phase gradient standard deviation, and residual density, a multi-ring network is constructed by dynamically selecting the ring size through a decision function. S3. Determine the connectivity of the multi-ring network based on the bridging strategy and connectivity detection method, and output the globally connected network; S4. Based on the globally connected network, a two-layer strategy of local absorption and global path planning is adopted. The residuals in the local range are absorbed by adjusting the edge weights. The shortest path is searched based on Dijkstra's path algorithm. The residual points in the global range are matched based on the shortest path, and the residual point matching pairs are output. Specifically, S4, based on a globally connected network, employs a two-layer strategy of local absorption and global path planning. It absorbs residuals within a local range through edge weight adjustment; it uses Dijkstra's path algorithm to search for the shortest path, matches residual points globally based on the shortest path, and outputs residual point matching pairs, including: S41. Calculate the loop integral residual and use the loop integral residual to detect problematic loops in the globally connected network; S42. Based on the detected problem loops, the reliability of the edges between connected pixels is evaluated through an edge weighting mechanism based on coherence and phase difference. By reducing the priority of low-reliability edges in the network, residuals in the local range are absorbed. S43. Detect the phase consistency within the closed loop. If the phase closure residual within the loop is not zero, then there is a residual point within the loop. Based on the obtained residual point, quantize the loop residual and convert the residual into several charges with positive and negative signs. S44. The obtained residual points and their corresponding charges are used to search for the shortest path based on Dijkstra's path algorithm. The positive and negative residual points are matched based on the shortest path, and the residual point matching pairs are output. S5. Based on residual point matching pairs, through the strategy of local unwrapping within the loop and global optimization, all loops in the globally connected network are unwrapped by constructing a weighted objective function within the loop to obtain the unwrapping results of each loop; based on the results of each loop, a global energy function is constructed for fusion, and the fused unwrapping result is output.

2. The adaptive multi-ring network InSAR phase unwrapping method according to claim 1, characterized in that, S3 determines the connectivity of the multi-ring network based on the bridging strategy and connectivity detection method, and outputs the globally connected network, including: S31. Determine whether the multi-ring network meets the direct bridging condition. If it does, perform direct bridging. The direct bridging condition is that if there are pixel pairs in the boundary cells of adjacent rings that meet the requirements of phase continuity and coherence reliability, then a connection can be directly established. Among them, the pixel pair satisfies the requirements of phase continuity and coherence reliability by the following formulas (1)-(2): （1） （2） in, Represents a cell The interference phase; Represents a cell The interference phase; Represents a cell Coherence; Represents a cell Coherence; This is the bridging threshold; If the direct bridging condition is not met, virtual node bridging is performed by inserting a virtual node between two pixels as a transition and initializing the virtual node phase to complete the virtual node bridging. The virtual node phase initialization process is represented by the following formulas (3)-(4): （3） （4） in, Indicates the phase of the virtual node; It is an integer; Represents a set of integers; S32. Use a connectivity detection method to detect the global connectivity of the multi-ring network. If there are multiple branches, establish the shortest bridge edge based on coherence weight between the largest connected branch and the other branches to make the multi-ring network fully connected and output the globally connected network.

3. The adaptive multi-ring network InSAR phase unwrapping method according to claim 1, characterized in that, S5, based on residual point matching pairs, uses a strategy of local unwrapping within the loop and global optimization. It constructs a weighted objective function within the loop to unwrap all loops in the globally connected network and obtains the unwrapping results of each loop. Based on the results of each ring, a global energy function is constructed for fusion, and the fused untangled results are output, including: S51. At the local level, each completed ring structure L is used as the basic processing unit to jointly optimize the pixel phase within the ring and construct a weighted least squares objective function. The local unwrapping within the ring is completed by solving the weighted least squares objective function, and the unwrapping results of all rings are output. The weighted least squares objective function is expressed by the following formula (5): （5） in, Indicates the first ring within the ring The unwrapped phase of 1 pixel; Indicates the first ring within the ring The unwrapped phase of 1 pixel; This represents the observed phase difference between corresponding pixel pairs; For the edge The weights; The norm selection parameter is used to control the sensitivity of the objective function to noise; S52. Construct a global energy function, integrate all loop unwrapping results, and output the integrated loop unwrapping result. The global energy function is expressed by the following formula (6): （6） in, Represents the global energy function; This is a data fidelity term used to ensure the consistency between the unwrapped phase and the observed phase difference; This is a virtual node smoothing term used to constrain the smooth transition between the phase of a virtual node and its adjacent real pixels; Represents virtual nodes The set of neighboring pixels; β For smoothing weight parameters; For the edge The weights are usually determined by coherence or quality indicators; Indicates the first ring within the ring The unwrapped phase of 1 pixel; Indicates the first ring within the ring The unwrapped phase of 1 pixel; This represents the observed phase difference between corresponding pixel pairs; Indicates the norm selection parameter; Represents virtual nodes The phase value; Represents virtual nodes domain pixel set The first in k The phase value of each pixel.

4. The adaptive multi-ring network InSAR phase unwrapping method according to claim 1, characterized in that, S5, based on residual point matching pairs, uses a strategy of local unwrapping within the loop and global optimization. It constructs a weighted objective function within the loop to unwrap all loops in the globally connected network and obtains the unwrapping results of each loop. Based on the results of each ring, after the steps of constructing a global energy function for fusion and outputting the fused untangling result, the following steps are also included: phase jump detection, phase constraint repair, virtual node fusion, and untangling result verification. The phase jump detection process includes: using a threshold detection method based on the phase difference between adjacent pixels to identify abnormal jump edges in the unwrapping result; The phase constraint repair process includes: generating a reference phase based on the InSAR elevation inversion model to provide terrain prior constraints for the unwrapping results; and repairing the unwrapped phase based on the reference phase by constructing a constrained optimization objective function to obtain the repaired unwrapped phase. The virtual node fusion process includes: using a combination of interpolation based on neighborhood information and phase correction to fuse the virtual node phase into the actual pixel space to obtain the corrected fused phase; The process of verifying the unwrapping results includes: multi-level verification of the unwrapping phase results based on the corrected fused phase, including: coherence consistency test, residual closure verification, and comparison verification with measured data.

5. The adaptive multi-ring network InSAR phase unwrapping method according to claim 4, characterized in that, The method for identifying anomalous transition edges in the unwrapping result using a threshold detection method based on the phase difference between adjacent pixels includes: Calculate the unwrapped phase difference between adjacent pixel pairs and compare it with a preset threshold; when the unwrapped phase difference between adjacent pixel pairs exceeds the preset threshold, it is determined that there is a potential phase transition between the pixel pairs, and the corresponding boundary is marked as a transition edge.

6. The adaptive multi-ring network InSAR phase unwrapping method according to claim 4, characterized in that, The method of combining interpolation based on neighborhood information with phase correction to fuse the phase of virtual nodes into the actual pixel space to obtain the corrected fused phase includes: Set the four nearest actual pixel positions and corresponding unwrapped phases around any virtual node, and obtain the virtual node phase value at continuous spatial positions by weighted interpolation. Based on the obtained virtual node phase values, the interpolated phase is adjusted to be closest to the phase difference of the nearest actual pixel by 2. The correction is performed using integer multiples of the position to obtain the corrected fused phase.

7. An adaptive multi-ring network InSAR phase unwrapping device, wherein the adaptive multi-ring network InSAR phase unwrapping device is used to implement the adaptive multi-ring network InSAR phase unwrapping method as described in any one of claims 1-6, characterized in that, The device includes: The computing unit is used to acquire interferometric phase maps and topographic maps of complex terrain areas; and to calculate coherence, topographic gradient map, phase gradient standard deviation, and residual density based on the interferometric phase maps and topographic maps. The building unit is used to construct multi-ring networks by dynamically selecting the ring size through a decision function based on coherence, terrain gradient map, phase gradient standard deviation and residual density. The output unit is used to determine the connectivity of the multi-ring network based on the bridging strategy and connectivity detection method, and output the globally connected network. The local absorption and matching unit is used to absorb residuals in a local range by means of edge weight adjustment, based on a globally connected network and employing a two-layer strategy of local absorption and global path planning. It searches for the shortest path based on Dijkstra's path algorithm, matches residual points in the global range based on the shortest path, and outputs residual point matching pairs. The unwrapping unit is used to unwrap all loops in the globally connected network based on residual point matching pairs, through local unwrapping within the loop and global optimization strategies, by constructing a weighted objective function within the loop to obtain the unwrapping results of each loop; based on the results of each loop, a global energy function is constructed to fuse them and output the fused unwrapping result.

8. An adaptive multi-ring network InSAR phase unwrapping device, characterized in that, The adaptive multi-ring network InSAR phase unwrapping device includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 6.

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