A Method and Device for Detecting the Horizon of Cultivated Land Soil Layers Based on Ground Penetrating Radar and Markov Random Field

Abstract

The present invention discloses a method and device for detecting the horizons of cultivated land soil based on ground penetrating radar and Markov random field. By acquiring the B-SCAN data of cultivated land through ground penetrating radar, preprocessing and selecting horizon seed points are carried out. Based on the undirected graph model and the initial horizon tracking marker field, a Markov random field model is constructed; by integrating the posterior probability energy field of horizon points and the dominant term of horizon envelope characteristics, the optimal horizon points are calculated; finally, the horizon depth value is calculated according to the soil dielectric constant, and whether the thickness of the tillage layer and the effective soil layer meets the standard is evaluated. The present invention has higher accuracy and stability in complex soil environments, can achieve efficient and accurate horizon tracking without relying on large-scale high-quality data, is suitable for the rapid processing of large-scale cultivated land soil data, and meets the requirements of soil layer thickness measurement and evaluation in land consolidation and high-standard farmland construction, providing an innovative technical solution for farmland management and construction.

Description

Technical Field

[0001] The present application belongs to the technical field of ground penetrating radar echo data interpretation, and specifically relates to a method and device for automatic layer detection of cultivated land soil echo data. Background Art

[0002] Land consolidation refers to the consolidation, reclamation, and development of unused or underutilized land to increase effective arable land area and improve its quality. It is the foundation of high-standard farmland development. However, the quality control of land consolidation projects is difficult, with potential risks such as low acceptance standards, a lack of acceptance methods, and fraud. To address these issues, developing efficient and accurate land consolidation project quality acceptance techniques has become an important means of ensuring the quality of newly reclaimed farmland.

[0003] The detection of cultivated land soil layer is an important link to ensure the quality of land consolidation and high-standard farmland construction. Its focus is on accurately measuring the thickness of the tillage layer and the anti-seepage layer, which constitute the effective soil layer thickness. Existing detection technologies include profile method, hole rotation method, geophysical method, etc. Ground penetrating radar is a type of geophysical method with the characteristics of high efficiency, non-destructiveness, and continuity. In recent years, with the rapid development of ground penetrating radar technology, this technology has gradually been applied to the field of soil detection. When using ground penetrating radar to detect the thickness of cultivated land soil layers, automatic layer tracking of ground penetrating radar data is a key step, but there are often the following difficulties: (1) The nearby rocks look very similar to the soil layer under consideration, and the tracking algorithm can easily jump to the wrong layer; (2) Due to the structural deformation of the cultivated land underground environment, the layer may be discontinuous; (3) The appearance of a layer may be different at different locations, and nearby layers may appear bifurcated or disappear.

[0004] Currently, most existing layer tracking methods focus on signal correlation analysis, with limited research specifically targeting ground-penetrating radar soil echo data. For example, correlation-based sliding window algorithms (CN111562620A, CN109886989A, and CN118795468A) rapidly identify appropriate peak targets by calculating correlations between waveforms. These methods offer consistent tracking results and rapid computation. However, these methods are susceptible to local noise, making it difficult to distinguish rock signals near the layer. The tracking algorithm often jumps to the wrong soil layer and is inadequate for soil environments with variable reflection signals. While some methods incorporating deep learning offer improved intelligence, they often require high-quality data to adapt to seasonal and regional variations in soil properties, presenting significant challenges in practical product applications. Therefore, in the interpretation of cultivated land soil data, a stable, efficient, and widely applicable automatic layer detection algorithm is urgently needed to accurately calculate the thickness of the tillage layer and impermeable layer, providing an important basis for scientifically assessing cultivated land quality. Summary of the Invention

[0005] The object of the present invention is to provide a method and device for detecting the soil layer position of cultivated land based on ground penetrating radar and Markov random field in view of the deficiencies of the prior art, aiming to solve the problem that the thickness of the tillage layer and the anti-seepage layer cannot be calculated quickly, non-destructively and accurately in the prior art.

[0006] The principle of ground penetrating radar is that when high-frequency electromagnetic waves propagate underground and encounter interfaces with different dielectric constants, reflections will occur. By receiving these reflected waves and analyzing their travel time, amplitude and waveform, the structure, shape and size of underground media can be inferred. The transmitting antenna of the ground penetrating radar emits high-frequency broadband electromagnetic wave pulses underground, and the receiving antenna receives the reflected waves from the underground media interface. The echo data obtained at each detection point is called A-SCAN data, and the echoes of multiple detection points are superimposed to form B-SCAN data.

[0007] To achieve the above object, the object of the present invention is realized through the following technical solutions: In the first aspect, the present invention provides a method for detecting the soil layer position of cultivated land based on ground penetrating radar and Markov random field, and the method includes the following steps:

[0008] Step 1: Use ground penetrating radar to obtain B-SCAN data of the underground soil layer of cultivated land on the arranged sampling line, and then perform preprocessing;

[0009] Step 2: Select layer seed points, construct an undirected graph model on the B-SCAN data according to the same polarity principle, calculate the shortest path, and determine the initial layer tracking marker field;

[0010] Step 3: Establish a Markov random field model MRF according to the undirected graph model and the initial layer tracking marker field, and calculate the posterior probability energy field of the layer points;

[0011] Step 4: Determine the dominant term according to the layer envelope characteristics, construct a layer point energy objective function by integrating the posterior probability energy field and the dominant term, and optimize and calculate the optimal layer points;

[0012] Step 5: Calculate the layer depth value according to the soil dielectric constant;

[0013] Step 6: Obtain data of different survey lines on the sampling line, repeat steps 1 to 5 until all data is processed, and evaluate whether the thickness of the tillage layer and the effective soil layer of the cultivated land meet the standards.

[0014] Further, in step 1, the sampling line is several radar survey lines arranged according to the size and shape of the cultivated land. The preprocessing is to preprocess the B-SCAN data of the underground soil layer of the cultivated land in the order of removing the DC component, band-pass filtering, removing background noise, initial maximum phase calibration, zero point adjustment and linear gain.

[0015] Further, in Step 2, the process of constructing the undirected graph model is to expand new nodes forward and backward along the horizontal position of the survey line from the horizon seed points according to the principle of the same polarity, and connect them with edges. Among them, each node represents a data point, and the edge represents the adjacent relationship between these points. The starting point is the horizon seed point, and the end point is the point with the strongest envelope feature in the last column along the expansion direction. The undirected graph is represented as ; where represents the node set, , represents the nth node; represents the edge set, , represents the edge connecting node and node ; The expansion according to the principle of the same polarity means that the positive and negative polarities of the amplitude of the new node are the same as those of the horizon seed point, and each time new nodes are expanded on the A-SCAN data, starting from the nearest position in sequence; the edges in the undirected graph model use the amplitude correlation of adjacent nodes as the weight.

[0016] Further, the initial horizon tracking marker field marks the horizon points and non-horizon points with different numbers, and the marked position is the nearest extreme value position of the waveform in the current period.

[0017] Further, in Step 3, the Markov random field model needs to define the dependence relationship between nodes and construct an energy function:

[0018]

[0019]

[0020]

[0021] Among them, the prior term , is the feature of node , is the corresponding horizon point marking label, is the probability that node is given a certain mark; the feature is a multi-dimensional vector composed of the envelope feature, cosine phase feature, frequency domain feature and entropy feature of the node, and the is calculated by the multivariate Gaussian probability distribution, refers to the parameters of the multivariate Gaussian distribution , , is the mean vector, is the covariance matrix; the neighborhood constraint term , is the intensity parameter of the smoothing term, is the scale parameter in the distance metric, used to control the influence range of the distance, is the Euclidean distance of the features of adjacent nodes; takes values according to the mean and standard deviation of the Euclidean distances of all adjacent nodes.

[0022] Furthermore, the Markov random field model is solved according to the Expectation-Maximization (EM) algorithm. The EM algorithm includes an E-step and an M-step. In the E-step, the posterior probability of the node labels is calculated based on the observed data and the current model parameters. In the M-step, the model parameters are updated ; if the absolute error before and after using the EM algorithm is less than or the number of iterations is greater than N times, the calculation is completed, and the maximum a posteriori probability energy field of the layer sites is obtained. The maximum a posteriori probability energy field of the layer sites refers to the probability matrix of each node belonging to the layer sites.

[0023] Furthermore, in step four, the dominance term is , where is the converted value of the envelope feature intensity at node , is the weight coefficient; the instantaneous amplitude of node is calculated using a two-dimensional window. After sorting the instantaneous amplitudes in these regions from largest to smallest and taking the top several points, is obtained, and the corresponding . At the remaining nodes , b is a constant greater than 0; the objective function is , and the shortest path of the layer label field energy is calculated using the graph theory model. This path is the final layer label result.

[0024] Furthermore, in step five, the effective soil layer thickness refers to the total thickness of the soil body above the parent material layer that can be utilized by crops, including the plow layer and the anti-seepage layer; when there is an obstacle layer, it is the soil layer thickness above the obstacle layer. The layer positions in paddy fields include the layer positions of the plow layer, the anti-seepage layer, and the percolation layer; the method for evaluating whether the plow layer and the effective soil layer thickness of the cultivated land meet the standards is to determine the confidence intervals of the plow layer and the effective soil layer thickness of the cultivated land respectively , , where is the mean, is the standard deviation, is the sample size, is the distribution critical value; if meets the relevant standard or specification requirements within the error , then the plow layer thickness and the effective soil layer thickness of the cultivated land meet the standards.

[0025] In a second aspect, the present invention further provides a device for detecting the horizons of cultivated land soil layers based on ground penetrating radar and Markov random field, including a memory and one or more processors. Executable code is stored in the memory, and when the processor executes the executable code, a method for detecting the horizons of cultivated land soil layers based on ground penetrating radar and Markov random field as described above is implemented.

[0026] In a third aspect, the present invention further provides a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, the method for detecting the horizons of cultivated land soil layers based on ground penetrating radar and Markov random field as described above is implemented.

[0027] Compared with the prior art, the present invention has at least the following beneficial effects:

[0028] Based on further analysis and research of the problems in the prior art, the present invention realizes that the current methods of applying correlation and deep learning in other media are difficult to effectively track the horizons of cultivated land soil layers. Through a method for detecting the thickness of the cultivated layer and effective soil layer of cultivated land based on Markov random field provided by the present invention, an undirected graph model is constructed in the B-SCAN data of cultivated land, and a posterior probability energy field model is established in combination with the Markov random field (MRF), which can effectively cope with the complex and diverse reflection signals in the cultivated land soil environment.

[0029] Compared with the traditional correlation-based method, the method of the present invention can more accurately distinguish the signals near the soil horizons and avoid tracking and jumping to the wrong horizons. By introducing horizon envelope features and dominant terms to construct a horizon point energy objective function and integrating various feature information into the optimization objective, the robustness and anti-noise performance of the algorithm are effectively improved. Even in the case of low signal quality or significant changes in soil properties, a high tracking accuracy can be guaranteed. In addition, the method of the present invention does not rely on high-quality data-driven, but is based on physical characteristics and a stable horizon tracking algorithm, avoiding the dependence of deep learning methods on large-scale and high-quality labeled data, and is applicable to scenarios where the data acquisition cost is high and the spatio-temporal variation of soil properties is significant in practical applications. Through the optimization calculation of the graph theory model and Markov random field, the initial horizon tracking marker field can be quickly determined and the optimal horizon points can be optimized. The tracking result has good continuity and high calculation efficiency, and can meet the rapid processing requirements of large-scale cultivated land soil data.

[0030] The present invention can quickly, non-destructively and accurately detect the cultivated layer and anti-seepage layer, provides a method for evaluating whether the thickness of the cultivated layer and effective soil layer of cultivated land meets the standard, can well meet the measurement and evaluation requirements of the thickness of the cultivated layer and effective soil layer in land improvement and high-standard farmland construction, and provides an innovative technical solution for farmland management. Description of the Drawings

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0032] Figure 1 Schematic flow chart of a method for detecting the soil layer position of cultivated land based on ground penetrating radar and Markov random field provided by an embodiment of the present application.

[0033] Figure 2 Schematic diagram of a survey line for cultivated land provided by an embodiment of the present application (top view).

[0034] Figure 3 Schematic diagram of constructing an undirected graph theory model provided by an embodiment of the present application, where (a) is a schematic diagram of B-SCAN waveform and (b) is a B-SCAN grayscale image.

[0035] Figure 4 Schematic diagram of an initial layer position marking field provided by an embodiment of the present application.

[0036] Figure 5 Schematic diagram of constructing layer position features provided by an embodiment of the present application.

[0037] Figure 6 Schematic diagram of Markov random field energy superposition provided by an embodiment of the present application.

[0038] Figures 7 - 12 Schematic diagrams of layer position tracking results of 6 groups of tillage layers and anti-seepage layers provided by an embodiment of the present application.

[0039] Figure 13 Structural diagram of a device for detecting the soil layer position of cultivated land based on ground penetrating radar and Markov random field of the present application. Detailed implementation manners

[0040] To more clearly illustrate the technical problems to be solved, technical solutions, and beneficial effects of the present application, the following will further elaborate on the present application in combination with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0041] The following will be further detailed through specific implementation manners:

[0042] In an embodiment of the present invention, a method for detecting the horizons of cultivated land soil layers based on ground penetrating radar and Markov random field is provided. As Figure 1 shown, it includes the following steps:

[0043] Step 1: Use ground penetrating radar to obtain B-SCAN data of the underground soil layer of cultivated land on the arranged sampling line and perform preprocessing. The arrangement method of the sampling line in this embodiment is as Figure 2 shown. The survey lines include at least 3 transverse lines and 3 longitudinal lines, in a "grid" shape, with a certain distance between each survey line, such as 2.5 m. Then, the obtained B-SCAN data of the underground soil layer of cultivated land is preprocessed in the order of removing the DC component, band-pass filtering, removing background noise, initial maximum phase calibration, zero adjustment, and linear gain.

[0044] Step 2: Select horizon seed points, construct an undirected graph model on the B-SCAN data, calculate the shortest path, and determine the initial horizon tracking marker field.

[0045] Taking the B-SCAN data of survey line 1 in this embodiment as an example, Figure 3 it is a schematic diagram of the corresponding seed points, where Figure 3 in (a) is the waveform diagram of the B-SCAN data, Figure 3 in (b) is Figure 3 the two-dimensional grayscale diagram converted from the waveform diagram in (a) of Figure 3 in (b). Each column in (b) represents a waveform data, and each row represents a data sampling point. As Figure 3 shown in (a), select the horizon seed point position [104, 164]. 104 refers to the position of the seed point at the 104th sampling point, and 164 refers to the seed point on the 164th trace. Then, construct an undirected graph model according to the same-polarity principle. The undirected graph model means expanding new nodes from the horizon seed point to the forward and backward directions of the horizontal position (X-axis) of the survey line and connecting them with edges. Among them, each node represents a data point, and the edge represents the adjacent relationship between these points. The starting point is the horizon seed point, and the end point is the point with the strongest envelope feature in the last column along the expansion direction. The undirected graph , , , where represents the node set, represents the edge set, represents the node and the edge connecting the node and the node represents the nth node.

[0046] The expansion according to the same-polarity principle means that the positive and negative polarities of the amplitude of the new node are the same as those of the horizon seed point, and each time new nodes are expanded on the A-SCAN data. In this example Start searching sequentially in the vertical direction from the position closest to the previous node. In the undirected graph model, the edges use the correlation between adjacent nodes as weights, and the correlation refers to the similarity of nodes. In this embodiment, the amplitude features of the node regions are used to calculate the correlation, which is extracted with a one-dimensional window, and the window size is column vectors. The correlation calculation formula for two adjacent nodes is as follows:

[0047]

[0048] where, and are the amplitudes of the th column and the th column waveforms of the th adjacent nodes, is the window length, which takes the value of 14 here.

[0049] In this embodiment, two undirected graph models are constructed from the seed point forward and backward. The weight of the edge takes the reciprocal of the correlation coefficient, and the Dijkstra algorithm is used to solve the shortest path, and the results are merged.

[0050] The Dijkstra algorithm gradually constructs the shortest path of the entire graph by gradually selecting the shortest path nodes that have not been visited currently and updating the shortest paths of their neighbors. The node positions in the shortest path are recorded as layer points, the layer points are marked as 2, the non-layer points are marked as 1, and the marked positions are the nearest extreme value positions of the periodic waveforms where they are located. Finally, the initial layer position marking field is obtained. As shown in the matrix of Figure 4 , the positions of the pentagrams are the peak layer points, marked as 2, and the non-layer points are marked as 1. According to the initial layer position marking field, the initial parameters of the model are calculated. The model parameters in this embodiment are the mean vector and the covariance matrix .

[0051] Step 3: Establish a Markov Random Field (MRF) model based on the undirected graph model and the initial layer position tracking marking field, and calculate the posterior probability energy field of the layer points. The Markov Random Field model needs to define the dependence relationship between nodes and introduce an energy function to describe the change of node states. The energy function is constructed as follows:

[0052]

[0053] ,

[0054] where the prior term , is the feature of node , is the corresponding layer site marking label, is a node According to the feature Assign a label The probability of. Such as Figure 5 As shown, Contains the instantaneous amplitude, cosine phase, frequency domain feature, and entropy feature of the node Together, they form a 4D vector, Then it is calculated according to the multivariate Gaussian distribution.

[0055]

[0056] Among them, Refers to the parameters of the multivariate Gaussian distribution , , Is the covariance matrix, Is the mean vector, Is the dimension.

[0057] Neighborhood constraint term , Is the intensity parameter of the smoothing term, Is the scale parameter in the distance metric, used to control the influence range of the distance; Is the Euclidean distance of the features of adjacent nodes. If The labels are the same, The value is 0. If The labels are different, then . According to the mean and standard deviation of the Euclidean distances of all adjacent nodes, it can be further optimized through experiments. In this embodiment Take 0.5, Take 0.8.

[0058] In the Bayesian framework, the Markov random field model is solved using the Iterative Conditional Mode (ICM) and Expectation-Maximization (EM) algorithms to predict the label field. ICM calculates the conditional probability of each node according to the initial parameters of the model, updates the label of the node, and makes the energy function locally minimum, which can be expressed as:

[0059]

[0060] Among them, Is the neighborhood of node , and the meaning of the formula is to calculate the local energy under different labels. The label of node Is updated to the label that makes the energy function locally minimum 。

[0061] When using the EM algorithm to solve, in the E step, according to the observed data and the current model parameters, calculate the posterior probability of the node label. The posterior probability , where ; Also use the multivariate Gaussian function model for calculation; represents the node labeled as , represents the sum of the posterior probabilities that the nodes are respectively labeled as 1 and 2; represents the conditional probability that it is the node , represents the set of node labels after removing the element new set, The calculation formula is as follows:

[0062]

[0063] where is the neighborhood of the node , that is, the connected nodes.

[0064] In the M step, according to the conditional probability of the E step, update the model parameters , which can be expressed as , is the number of iterations, and the parameter includes the covariance matrix , the mean vector , and the update formula is as follows:

[0065] ,

[0066] where is the total number of nodes.

[0067] The present invention uses ICM conditional iteration according to the new parameters to obtain the posterior probability that each node belongs to the layer site, and then calculates , if the absolute error before and after using the EM algorithm is less than (the preferred value is ) or the number of iterations is greater than N (the preferred value is 40), the calculation is completed. According to the final result, the present invention calls the posterior probability of all nodes the maximum posterior probability energy field.

[0068] Step 4: Determine the dominant terms based on the horizon envelope features, construct the horizon point energy objective function by integrating the posterior probability energy field and the dominant terms, and optimize to calculate the optimal horizon points, as Figure 6 shown. The dominant term is , where is the converted value of the envelope feature intensity at node , and is the weight coefficient. Calculate the average instantaneous amplitude of the node area according to the two-dimensional window of size , sort the instantaneous amplitudes in these areas from large to small and take the first several points, denoted as . For these nodes, , and for the remaining nodes, , where b takes the value of 3. The objective function is . In this embodiment, the weight coefficient and the intensity parameter of the smoothing term in the objective function are determined by the grid search method, , . Evaluate the model performance for each combination of candidate values in the validation set, and finally determine the optimal parameters , . Then, with the help of the previously constructed graph theory model, solve it using the Dijkstra algorithm to obtain the set of optimal horizon points .

[0069] Step 5: Calculate the horizon depth value according to the soil dielectric constant. Repeat the above steps to process all the data and evaluate whether the thickness of the tillage layer and the effective soil layer meets the standards. This embodiment can be used for newly reclaimed cultivated land for planting aquatic plants. The horizons include the tillage layer and the anti-seepage layer. The effective soil layer thickness refers to the sum of the thicknesses of the tillage layer and the anti-seepage layer. Each B-SCAN data tracks 2 horizons, and the horizon tracking results are as Figures 7 - 12 shown. There are a total of 6 groups of graphs. The upper side of each graph is the horizon tracking result graph, and the lower side is the absolute deviation of the corresponding horizon points. After obtaining the horizon points in Step 4, calculate the horizon point depth value , the dielectric constant of the tillage layer , the dielectric constant of the anti-seepage layer , is the time at the horizon point position in the ground penetrating radar B-SCAN data, and is the speed of light.

[0070] In order to better reflect the beneficial effects of the present application, this embodiment provides the absolute average deviation of the cultivated layer and the anti-seepage layer in these 6 sets of B-SCAN data, and compares it with the traditional layer tracking method based on waveform correlation. The calculation results of the absolute average deviation of the cultivated layer, the anti-seepage layer and all layers of the present application are better than the traditional layer tracking method based on waveform correlation; the layer tracking method based on waveform correlation starts from the seed point, calculates the correlation between the subsequent waveform and the reference waveform column by column, marks the position with the highest correlation as the layer position of the current column, and updates the reference waveform.

[0071] Among them, the absolute mean deviation , is the actual average layer thickness of the tillage layer and the anti-seepage layer, which is measured by cutting open the cultivated soil layer. It is the average thickness calculated based on B-SCAN data.

[0072] The method for evaluating whether the thickness of the cultivated layer and the effective soil layer meets the standard is to determine the confidence interval of the thickness of the cultivated layer and the effective soil layer respectively. , ,like ,in, is the mean, is the standard deviation, is the sample size, and an average of 200 groups of layer results are sampled in each B-SCAN data, forming a total of 1200 groups of samples. for Distribution critical value. If the relevant standards or specifications are met, such as the requirements of GB / T 30600-2022 "General Rules for High-standard Farmland Construction", the cultivated layer thickness and effective soil layer thickness of the cultivated land meet the standards. In this embodiment, the cultivated layer thickness should be greater than 20 cm, and the effective layer thickness should be greater than 60 cm. Take 8cm. After calculation, we get the 95% confidence interval of the cultivated layer. , the plow bottom layer is calculated with a 95% confidence interval , unit cm, , It can be basically determined that the thickness of the cultivated layer and the effective soil layer of the newly reclaimed land meet the requirements for high-standard farmland construction.

[0073] Corresponding to the aforementioned embodiment of a method for detecting cultivated land soil layer positions based on ground penetrating radar and Markov random fields, the present invention also provides an embodiment of a device for detecting cultivated land soil layer positions based on ground penetrating radar and Markov random fields.

[0074] See also Figure 13An embodiment of the present invention provides a device for detecting cultivated land soil layers based on ground penetrating radar and Markov random fields, comprising a memory and one or more processors. The memory stores executable code, and when the processor executes the executable code, it is used to implement a method for detecting cultivated land soil layers based on ground penetrating radar and Markov random fields in the above embodiment.

[0075] The embodiment of the cultivated land soil layer detection device based on ground penetrating radar and Markov random field provided by the present invention can be applied to any device with data processing capability, and the device with data processing capability can be a device or apparatus such as a computer. The device embodiment can be implemented through software, or through hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, it is formed by the processor of any device with data processing capability in which it is located reading the corresponding computer program instructions in the non-volatile memory into the memory for execution. From the hardware level, if Figure 13 As shown in the figure, a hardware structure diagram of a device for detecting soil layer of cultivated land based on ground penetrating radar and Markov random field provided by the present invention is provided, in addition to Figure 13 In addition to the processor, memory, network interface, and non-volatile memory shown, any device with data processing capabilities in which the apparatus in the embodiment is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0076] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0077] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and 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 modules may be selected according to actual needs to achieve the purpose of the present invention. A person of ordinary skill in the art can understand and implement the present invention without inventive work.

[0078] An embodiment of the present invention also provides a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a method for detecting cultivated land soil layers based on ground penetrating radar and Markov random fields in the above embodiment is implemented.

[0079] The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium may also be an external storage device of any device with data processing capabilities, such as a plug-in hard disk, a Smart Media Card (SMC), an SD card, a Flash Card, etc. equipped on the device. Further, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capabilities. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and may also be used to temporarily store data that has been output or is to be output.

[0080] The present invention also provides a computer program product, including a computer program, which when executed by a processor, implements the method for detecting the soil layer position of cultivated land based on ground penetrating radar and Markov random field described above.

[0081] The above description only represents a preferred embodiment of the present application, and does not limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for detecting the horizons of cultivated land soil layers based on ground penetrating radar and Markov random field, characterized in that The method comprises the following steps: Step 1: Use ground penetrating radar on the arranged sampling lines to obtain B-SCAN data of the underground soil layer of the cultivated land, and then perform preprocessing. Step 2: Select horizon seed points, construct an undirected graph model on the B-SCAN data according to the same-polarity principle, calculate the shortest path, and determine the initial horizon tracking marker field; the process of constructing the undirected graph model is to expand new nodes forward and backward along the horizontal position of the survey line from the horizon seed points according to the same-polarity principle and connect them with edges. Among them, each node represents a data point, and the edge represents the adjacent relationship between these points. The starting point is the horizon seed point, and the end point is the point with the strongest envelope feature in the last column along the expansion direction. The undirected graph is represented as ; where represents the node set, , represents the nth node; represents the edge set, , represents the edge connecting node and node ; the expansion according to the same-polarity principle means that the positive and negative polarities of the amplitudes of the new nodes are the same as those of the horizon seed points, and each time new nodes are expanded on the A-SCAN data and searched starting from the nearest position in sequence; the edges in the undirected graph model use the amplitude correlation of adjacent nodes as weights; Step 3: Establish a Markov random field model MRF according to the undirected graph model and the initial layer tracking marker field, and calculate the posterior probability energy field of the layer points; the Markov random field model needs to define the dependence relationship between nodes and construct an energy function: Among them, the prior term , is the feature of node , is the corresponding layer site marking label, is the probability that node is given a certain mark; the feature is a multi-dimensional vector composed of the envelope feature, cosine phase feature, frequency domain feature and entropy feature containing the node, and the is calculated by the multivariate Gaussian probability distribution, refers to the parameters , of the multivariate Gaussian distribution, is the mean vector, is the covariance matrix; the neighborhood constraint term , is the intensity parameter of the smoothing term, is the scale parameter in the distance metric, used to control the influence range of the distance, is the Euclidean distance of the features of adjacent nodes; takes values according to the mean and standard deviation of the Euclidean distances of all adjacent nodes; Step 4: Determine the dominant terms according to the horizon envelope features, construct the horizon point energy objective function by integrating the posterior probability energy field and the dominant terms, and optimize to calculate the optimal horizon points; the dominant terms are , where is the converted value of the envelope feature strength at node , is the weight coefficient; calculate the instantaneous amplitude of node using a two-dimensional window, sort the instantaneous amplitudes in these regions from largest to smallest and take the first several points to obtain , the corresponding , and at the remaining nodes , where b is a constant greater than 0; the objective function is , calculate the shortest path of the horizon label field energy with the help of the graph theory model, and this path is the final horizon labeling result; Step 5: Calculate the layer depth value according to the soil dielectric constant. Step 6: Obtain data of different survey lines on the sampling lines, and repeat Steps 1 to 5 until all data are processed, and evaluate whether the thickness of the cultivated layer and the effective soil layer of the cultivated land meet the standards.

2. The method for detecting the horizons of cultivated land soil layers based on ground penetrating radar and Markov random field according to claim 1, wherein, In Step 1, the sampling lines are several radar survey lines arranged according to the size and shape of the cultivated land; the preprocessing is to preprocess the B-SCAN data of the underground soil layer of the cultivated land in the order of removing the DC component, band-pass filtering, removing background noise, initial maximum phase calibration, zero-point adjustment, and linear gain.

3. A method for detecting the horizon of cultivated land soil layers based on ground penetrating radar and Markov random field according to claim 1, characterized in that, The initial layer tracking marker field marks layer points and non-layer points with different numbers, and the marked positions are the nearest extreme positions of the periodic waveforms.

4. A method for detecting the horizon of cultivated land soil layers based on ground penetrating radar and Markov random field according to claim 1, characterized in that Solve the Markov random field model according to the Expectation-Maximization (EM) algorithm. The EM algorithm includes an E-step and an M-step. In the E-step, calculate the posterior probability of the node labels based on the observed data and the current model parameters. In the M-step, update the model parameters ; If The absolute error before and after using the EM algorithm is less than Or the number of iterations is greater than N times, the calculation is completed, and the maximum a posteriori probability energy field of the layer sites is obtained. The maximum a posteriori probability energy field of the layer sites refers to the probability matrix of each node belonging to the layer sites.

5. A method for detecting the soil layer position of cultivated land based on ground penetrating radar and Markov random field according to claim 1, characterized in that, In step 5, the effective soil layer thickness refers to the total thickness of the soil above the parent material layer that can be used by crops, including the cultivated layer and the anti-seepage layer; when there is a barrier layer, it refers to the thickness of the soil layer above the barrier layer, and the layer in paddy field farmland includes the cultivated layer, anti-seepage layer and infiltration layer; the method for evaluating whether the cultivated layer and effective soil layer thickness of cultivated land meet the standards is to determine the confidence interval of the cultivated layer and effective soil layer thickness respectively. , ,in, is the mean, is the standard deviation, is the sample size, for Distribution critical value; if In the error If the relevant standards or specifications are met, the thickness of the cultivated layer and the effective soil layer of the cultivated land are up to standard.

6. An apparatus for detecting the horizon of cultivated land soil layers based on ground penetrating radar and Markov random field, comprising a memory and one or more processors, wherein executable code is stored in the memory, characterized in that, When the processor executes the executable code, it implements a method for detecting the layer position of cultivated land soil based on ground penetrating radar and Markov random field as described in any one of Claims 1-5.

7. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements a method for detecting the layer position of cultivated land soil based on ground penetrating radar and Markov random field as described in any one of Claims 1-5.

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