309 results about "Elevation data" patented technology

The invention relates to the technical field of agricultural data analysis, in particular to a land reclamation suitability evaluation and cultivated land quality improvement method based on big data, which comprises the following steps of: acquiring and superposing land utilization, soil and elevation data to construct a multi-source land attribute evaluation data set, calculating the distance from a pattern spot to a water source, reconstructing a weight through Gaussian attenuation, and calculating the land utilization, soil and elevation data; the method comprises the following steps: forming a spatial distance weighted feature matrix, extracting a gradient and an irrigation guarantee rate, performing weight reduction on an overrun part of a conflict threshold value, performing linear weighting calculation on a comprehensive score after normalization, dividing a regulation priority area, matching quality improvement measures, and forming a cultivated land quality improvement project layout map. A pattern spot level evaluation basis is formed through multi-source attribute space superposition, water source distance attenuation calculation is introduced to enable weight to change along with reachability, irrigation difference response is enhanced, conflict constraint correction is carried out on gradient and irrigation guarantee, adverse combination influence is inhibited, stable sorting is formed through uniform scale weighting, and the method is applied to the field of irrigation. And improvement priority area identification and quality improvement measure directional configuration are supported.

The invention relates to a soil erosion amount dynamic monitoring method and system based on multi-source remote sensing data fusion. The method comprises the following steps: acquiring multi-source remote sensing data and auxiliary data, and performing geometric fine correction and atmospheric correction on an image based on high-resolution digital elevation data to obtain a data set with consistent space; performing inversion based on the data set to obtain a corrected terrain factor and a water and soil conservation measure factor; generating a time sequence rainfall erosivity factor and a time sequence vegetation coverage factor in combination with the multi-temporal satellite rainfall data; and calculating a preliminary soil erosion modulus in combination with the soil type map, and correcting by using multi-stage high-resolution digital elevation data to obtain a dynamic monitoring result of the soil erosion amount. By adopting the method, the problem of multi-source data fusion matching can be solved, and the factor inversion precision is improved, so that the accuracy of a dynamic monitoring result of the soil erosion amount is improved.

The invention belongs to the technical field of image processing, and particularly relates to a mountain comprehensive ecological condition monitoring system based on remote sensing intelligent interpretation. The system comprises a remote sensing data processing unit, an object-oriented interpretation unit, an ecological patch diagram construction unit, a connectivity evaluation unit and an ecological output unit. The remote sensing data processing unit obtains a target mountain region remote sensing image and digital elevation data and generates a fusion base map; an object-oriented interpretation unit performs object-oriented segmentation on the fusion base map to generate an ecological patch object set; the ecological plaque graph construction unit constructs an ecological plaque graph through the ecological plaque object set; the connectivity evaluation unit generates an object state record and generates a connectivity anchor point object set; and the ecological output unit generates a comprehensive ecological condition score based on the connectivity enhanced ecological plaque object set and generates a comprehensive ecological condition assessment report. The spatial relation between the hydrological process and the ecological plaque can be stably expressed under the complex mountain terrain condition, and the influence of the initial interpretation error on the result is reduced.

The invention discloses a hyperspectral image and laser radar data fusion classification method based on an improved attention mechanism. The method comprises the following steps: obtaining hyperspectral image data and laser radar elevation data to be classified; processing the hyperspectral image data through a spectral channel attention module to extract spectral features; processing the laser radar elevation data through an elevation space attention module to extract spatial features; inputting the spectral features and the spatial features into a double-feature fusion module for coupling to generate fusion features; image classification is completed based on the fusion features; according to the method, the image features of the hyperspectral image in the spectral dimension and the elevation information of the radar image in the spatial dimension can be fully extracted, and the representation capability of the model for the spatial features and the spectral features is enhanced.

The invention provides a coastal zone land coverage type classification method based on a random forest and a topology rule, and belongs to the technical field of coastal zone land coverage type classification. The method comprises the following steps: based on a GEE cloud platform, integrating a multispectral remote sensing image and elevation data, extracting spectral features and geographic features, and realizing pixel-level classification of random forest machine learning; generating a clustering unit through a simple non-iterative clustering method, and performing object-oriented special classification on the water body and the aquaculture pond in combination with spectral and morphological characteristics of the clustering unit; spatial filtering is introduced to remove salt and pepper noise, a coastal zone ground feature topology rule base is established to carry out spatial logic verification, and coastal zone land coverage classification with high precision and reasonable spatial layout is realized. According to the method, the multi-source remote sensing data and the multi-level classification strategy are fused, the problems of coastal zone ground feature spectrum confusion, boundary fuzziness and space logic inconformity are effectively solved, the classification precision and practicability are remarkably improved, and the method is suitable for land coverage monitoring and management of the complex coastal zone environment.

The invention relates to the technical field of continuous casting billet detection, and discloses a continuous casting billet surface image defect detection method based on image processing, and the method comprises the following steps: collecting a continuous casting billet surface multispectral image sequence, separating an oxide scale and a matrix metal area by means of spectral reflectivity difference, and building a surface material distribution map; constructing a dynamic threshold segmentation model, generating an adaptive segmentation interval according to gray value fluctuation characteristics, and extracting a contour boundary of a potential defect area; establishing a defect morphological feature library, and screening candidate defect areas through multi-scale matching and geometric constraint; performing spectrum verification, and comparing the similarity between the reflectivity mutation curve and a preset defect characteristic curve; generating a three-dimensional shape reconstruction instruction, and triggering a laser displacement sensor to acquire elevation data; and establishing a defect classification decision tree, inputting elevation and multispectral features, and outputting a final defect category according to a material type and deformation degree combination rule.

The invention provides an urban flood disaster dynamic risk assessment method under multi-scale model coupling, and relates to the technical field of urban flood disaster risk prevention and control and emergency risk avoidance. The method comprises the following steps: evaluating the urban flood disaster risk according to urban underlying surface type data, vertical elevation data, drainage pipe network data, hydrogeological data, actually measured rainfall data, hydrological monitoring data and disaster-bearing system instability failure threshold data; constructing a multi-agent system model covering a disaster-inducing factor agent, a disaster-pregnant environment agent and a disaster-bearing body agent, and outputting a dynamic risk value of a disaster-bearing body; constructing a dynamic model of a disaster-affected body disaster-affected system by using a system dynamic modeling technology, outputting a risk change process of a disaster-affected body and quantifying a risk value; the flood disaster emergency prevention and control effect is evaluated by combining engineering and non-engineering measures, and emergency risk avoiding measures are optimized. According to the flood disaster assessment method and device, the flood disaster assessment can be more accurate through the combined action of the macroscale, the mesoscale and the microscale.

The invention relates to the technical field of satellite navigation terminals, and discloses a Beidou terminal capable of intelligently evaluating multiple states. Comprising a Beidou positioning module used for acquiring a Beidou signal corresponding to the Beidou terminal; the control module is configured to execute the following steps: performing time sequence modeling on the propagation time delay, the geometric distribution factor, the ionosphere influence parameter and the troposphere influence parameter of the Beidou signal to generate a positioning reliability index so as to realize positioning state evaluation; performing conjoint analysis on the Beidou signal, and outputting a data credibility grade; processing the inertial navigation data, the terrain elevation data, the sky satellite geometric distribution factor and the multi-terminal altitude difference data corresponding to the Beidou terminal through a convolutional neural network to generate a three-dimensional environment interference coefficient; and fusing the channel fading parameter, the network topology data, the communication antenna signal-to-noise ratio, the transmission rate change parameter and the data integrity parameter corresponding to the Beidou terminal, and calculating a communication link stable value to realize communication state evaluation.

The invention relates to the technical field of water ecological environment remote sensing monitoring, in particular to a river total nitrogen concentration remote sensing estimation method based on multi-source data fusion, and the method comprises the following steps: S1, obtaining and processing total nitrogen measured data, and unifying time and a coordinate reference system; s2, processing the Landsat 8 / 9 image, and extracting water body reflectivity and remote sensing indexes; s3, ERA5-Land meteorological data are acquired, and derivation indexes are calculated; s4, DEM elevation data are extracted; s5, fusing the multi-source data to generate a table type data set; s6, screening sensitive indexes based on SHAP and correlation; s7, constructing and evaluating an inversion model; s8, extracting an MNDWI water body mask; s9, generating a feature stack and inverting and outputting a total nitrogen concentration diagram; according to the method, through fusion of multi-source data, construction of a high-precision inversion model and a cloud platform automatic processing flow, high-temporal-spatial-resolution, stable and reproducible remote sensing estimation of the total nitrogen concentration of the river is realized, and the monitoring efficiency and the application value are effectively improved.

The invention discloses a river water level video image non-contact measurement method and system, and relates to the technical field of hydrological image processing. Comprising the following steps: acquiring a monitoring video frame sequence and physical elevation data of preset feature points; extracting the pixel coordinates of the feature points of the current frame to solve a dynamic homography matrix; extracting water and land boundary candidate edges of the current frame under each scale, and outputting optimal water line pixel coordinates and detection confidence; motion vectors are extracted from continuous frame time sequence differences, and floating object interference is eliminated according to translation features; mapping the interference-removed water level coordinates into an observation water level elevation through a dynamic homography matrix, and calculating a change rate of the observation water level elevation and a correction value at a previous moment; and judging whether the change rate is in a preset hydrological change rate interval, if so, smoothly outputting a final water level value according to the confidence coefficient, otherwise, extrapolating and correcting the water level value and reversely adjusting the next frame of edge search area. According to the invention, self-adaptive and high-robustness non-contact measurement of the river water level in a complex environment is realized.

The application belongs to the technical field of height data analysis, and provides a DEM space model height point automatic extraction method, the application carries out clustering on the height data of sampling points by setting different clustering radii, judges the terrain integrity of each clustering range, and divides subspaces; analyzes the reference value of different subspaces on the height interpolation of the projection position point, obtains the reference height value of the projection position point; then analyzes the distance interval condition of each height data in the same region in different subspaces, adjusts the coordinate position of the projection position point, and obtains the corrected DEM space model; finally, the difference condition between the water flow trend and the actual terrain in the corrected DEM space model is analyzed, the reference height value of part of the projection position points is adjusted, and the twice corrected DEM space model is obtained. The accuracy of the interpolation height points of different positions is improved by the method, and the accuracy of the DEM space model construction is improved.

This application relates to an IoT-based intelligent land remediation monitoring method and system. The monitoring method includes: acquiring satellite remote sensing elevation data and historical soil composition data of the land area to be monitored; dividing the land area to be monitored into multiple grid units and assigning grid identifiers; deploying fixed soil sensor nodes and mobile drone nodes to generate a sensor network topology; collecting real-time soil parameters of each grid unit and performing noise filtering and anomaly detection to generate a soil composition dataset; inputting the soil composition dataset and historical soil composition data into a cloud-based prediction model to output a land degradation risk score and remediation strategy; controlling the execution equipment to perform land remediation operations and monitoring changes in soil parameters after execution in real time; updating the parameters of the cloud-based prediction model and optimizing the sensor network topology based on the deviation between the changes in soil parameters after execution and the expected values. This application improves agricultural production efficiency and soil health.

This invention proposes a method for observing ocean waves based on a binocular camera, belonging to the field of image processing technology. The method includes: S1: The binocular camera captures ocean wave images, generating ocean wave point cloud data at the current time t; S2: The ocean wave point cloud data in S1 is converted to ocean wave point cloud data in a geodetic coordinate system; S3: The ocean wave data in S2 undergoes quality screening and optimization, including: selecting the region of interest, quality screening, converting noisy data points to null values, null value filling, and Gaussian filtering; the quality screening classifies the ocean wave data into three levels: excellent, good, and poor; S4: Based on the excellent and good quality ocean wave point cloud data, the wave height, wavelength, and period at that time are calculated; S5: S1-S4 are repeated to obtain multiple frames of ocean wave point cloud data at different times, and the effective wave height, wavelength, period, and wave spectrum are calculated. This method can fill in null values, making the overall data more reasonable and the obtained elevation data more accurate.

The application relates to a river flood discharge capacity dynamic evaluation and hydrological early warning method. The method comprises the following steps: based on a parameter sensitive observation subset, sequentially updating an initial hidden variable in a low-dimensional hidden space through an unscented particle filter algorithm to obtain an optimized hidden variable, and inputting the optimized hidden variable into a river dynamic resistance field generator based on an initial dynamic roughness field to perform decoding processing to obtain a dynamic roughness field of a current period; based on the dynamic roughness field and a state sensitive observation subset, performing analysis and update processing on a state variable field of a hydrodynamic model through a deterministic ensemble Kalman filter algorithm to obtain an assimilated water level field and a flow velocity field; according to the water level field, the flow velocity field and the dynamic roughness field, the maximum flood discharge capacity of a section is calculated in combination with dike top elevation data, and a flood discharge capacity dynamic index is calculated based on the maximum flood discharge capacity of the section and a current actual flow. The method can realize real-time dynamic quantification of river flood discharge capacity and predictive evaluation of flood risk.

The application discloses a kind of based on multi-angle weighting correction's road surface flatness measurement method and system, it is related to data measurement technical field, method includes: obtaining to be measured section and measurement route, obtain laser sensor, the elevation data of measurement point in measurement route is collected;Obtain initial weight coefficient, obtain the reflection intensity of laser sensor, according to the reflection intensity of the laser sensor corresponding to measurement point the correction coefficient of the laser sensor corresponding to measurement point is obtained;According to the correction coefficient of the laser sensor corresponding to measurement point and initial weight coefficient, the target weight coefficient of the laser sensor corresponding to measurement point is obtained;According to the target weight coefficient of the laser sensor corresponding to measurement point and elevation data, the target elevation data corresponding to measurement point is obtained, according to the target elevation data of measurement point in measurement route, the road surface flatness of to be measured section is obtained.The application has the advantages of dynamic self-adaptability, weighted fusion and tolerance expansion optimization.

The invention relates to a video and map data mapping method based on three-dimensional geographic information, and the method comprises the steps: (1) obtaining the three-dimensional geographic coordinates of a starting point and a target point, respectively including the longitude, latitude and elevation data of each point, picking up the video pixel coordinates and the three-dimensional geographic coordinates of the target point, and constructing a serialized feature point pair; (2) according to the serialized feature point pairs, camera geographic calibration based on the feature point pairs is carried out; and step (3), mapping the three-dimensional geographic coordinates of all points in the geographic space in the video coverage area into video pixel coordinates based on a rotation matrix constructed after camera geographic calibration, or mapping the video pixel coordinates of all points in a camera picture into three-dimensional geographic coordinates.

The invention provides a shared vehicle control method and a server. The method comprises the following steps: acquiring positioning data and elevation data of a target shared vehicle; wherein the elevation data represents a first elevation of the target shared vehicle; according to the positioning data, whether the target shared vehicle is located in a dangerous area is determined, and the dangerous area is an area of at least two road sections with horizontal space overlapping; when it is determined that the target shared vehicle is located in the dangerous area, determining whether the target shared vehicle is located in a forbidden road section where the target shared vehicle is forbidden to travel in the dangerous area according to the elevation data; and under the condition that the target shared vehicle is in the forbidden road section, carrying out safety management and control on the target shared vehicle so as to guide a user to drive the target shared vehicle to safely leave the forbidden road section.

This invention belongs to the field of building engineering quality inspection technology, and discloses a defect detection method for aluminum-magnesium-manganese metal roofs. This method is designed for phased construction and acceptance scenarios of metal roofs. Based on the design documents, it establishes inspection benchmarks such as panel type parameters, vertical joint parameters, purlin axis parameters, fixing seat arrangement parameters, moisture-proof layer overlap parameters, and insulation layer laying parameters. It integrates exposed base images during the construction phase, visible light images and infrared temperature images or elevation data during the sealing phase, and completes registration mapping, measured parameter extraction, abnormal area projection overlap analysis, and consistency verification under a unified roof coordinate system, realizing traceable output of defect type, defect coordinates, and defect level.

The application discloses a kind of building contour regularization-oriented polygon generation methods for remote sensing, it is related to remote sensing image recognition technical field, including: through the image instance segmentation model of training completion, real-time high-resolution remote sensing image is carried out sliding window prediction, generates candidate building instance mask, and according to candidate building instance mask calculates center point coordinate and mask area;According to mask area, generate adaptive center point distance threshold, and combine center point coordinate and mask area to exclude duplicate candidate building instance mask;Obtain elevation data that is spatially aligned with real-time high-resolution remote sensing image, carry out spatial superposition calculation to non-duplicate candidate building instance mask and elevation data, obtain elevation statistical characteristics;The application realizes the deduplication processing of candidate building instance mask, and under the unified spatial reference, combine elevation data to complete building floor value calculation and regularized building polygon generation.

The invention provides a bird's-eye view orthographic image generation method and device and a computer readable storage medium. The method comprises the following steps: generating a three-dimensional ground triangulation network model according to three-dimensional ground point cloud data; penetrating dotting is conducted on the three-dimensional ground triangulation network model, multi-layer tile data are generated, the position information of tiles in the multi-layer tile data comprises row numbers, column numbers and elevation values, the row numbers and the column numbers indicate the horizontal positions of the tiles, and the elevation values indicate the vertical heights of the tiles; and according to the multi-layer tile data, generating a hierarchical aerial view orthographic image corresponding to the elevation data of the track points. Through the three-dimensional ground triangulation network model, penetrating dotting is carried out, the multi-layer tile data including the row number, the column number and the elevation value can be generated, the information capable of covering the multi-layer road surface can be generated, the aerial view orthographic image of the level corresponding to the elevation data of the track point can be generated according to the multi-layer tile data, and the aerial view orthographic image can be conveniently displayed to a user.

The invention discloses a vehicle road level determination method and device, and the method comprises the steps: obtaining the communication signal intensity between a vehicle and all other vehicles in a multi-layer stereoscopic model if the vehicle is located in the multi-layer stereoscopic model; dividing all the vehicles in the multi-layer stereoscopic model into a plurality of vehicle groups according to the communication signal intensity between the vehicle and all other vehicles in the multi-layer stereoscopic model; wherein the number of the vehicle groups is equal to the number of layers of the multi-layer three-dimensional model; determining at least one to-be-determined road level of the vehicle based on the number of layers of the multi-layer stereoscopic model and the plurality of vehicle groups; and determining the road level of the vehicle based on the to-be-determined road level of the vehicle. According to the method, cognition of a road space structure is actively constructed through collaborative perception of a vehicle group, and accurate positioning of a road level where a vehicle is located is achieved by fusing multi-source information on the premise that accurate elevation data does not need to be obtained.

The invention relates to a rapid high-precision scanning type laser fuze data processing method, and belongs to the technical field of laser detection and fuze control. According to the method, an echo signal of a line scanning laser radar is received, after shaping, multi-echo screening, distance calculation, speed correction and elevation conversion are carried out, an improved one-dimensional clustering algorithm is adopted to carry out rapid analysis on elevation data, and a target elevation and a confidence coefficient thereof are output for detonation moment prediction. The method abandons a traditional three-dimensional point cloud reconstruction process, has the advantages of being high in processing speed, high in anti-interference capability, small in resource occupation and the like, and is suitable for real-time target detection and fuze control of a high-dynamic missile-borne platform in a complex environment.

The application relates to the field of agricultural and rural technologies, and particularly relates to a method for extracting waterlogging disaster of northern spring maize unmanned aerial vehicle image based on multi-source data fusion. The application provides a method for extracting waterlogging disaster of northern spring maize unmanned aerial vehicle image based on multi-source data fusion. Multi-source data of unmanned aerial vehicle multispectral image, elevation data, meteorological data and soil data are fused, and finally a waterlogging disaster risk distribution map is generated, thereby providing decision support for agricultural production. The method comprises the following steps: acquiring unmanned aerial vehicle multispectral image, meteorological data, elevation data and soil data of a waterlogging disaster area to be evaluated; calculating a result file of a current pixel; calculating weight coefficients of the data through an MLP improved model; constructing a comprehensive evaluation formula; according to the comprehensive evaluation formula, each pixel is traversed, and waterlogging disaster risk is extracted according to a waterlogging disaster risk value of each pixel, and a waterlogging disaster evaluation image is generated.

The invention relates to the technical field of silo conical shell construction, in particular to a silo wall perpendicularity and elevation detection device and method for silo slip form construction, and the device comprises a reference module which comprises a central reference point and an infrared ground reference point, the central reference point is arranged at the central position of the bottom of a silo and is used for providing a space coordinate origin, and the infrared ground reference point is arranged at the central position of the bottom of the silo; the infrared ground datum point is arranged at the bottom edge of the silo and is used for providing an elevation datum; the three-dimensional data acquisition module is suspended in the central area of the sliding formwork operation platform through the leveling mechanism and is used for scanning and acquiring three-dimensional point cloud data and elevation data of the inner wall of the warehouse wall in real time; the control center is in communication connection with the three-dimensional data acquisition module and the leveling mechanism; and the leveling mechanism adjusts the spatial position of the three-dimensional data acquisition module based on the correction instruction, so that the measurement center of the three-dimensional data acquisition module is centered at the central reference point. And through real-time data acquisition, accurate deviation analysis and automatic position calibration, the construction quality of the silo is ensured.

The present application relates to the technical field of reservoir capacity curve reconstruction, in particular to a reservoir capacity curve reconstruction method and system based on multi-source data fusion, comprising the following steps: obtaining multi-source data fusion elevation information, reconstructing the water level-storage capacity relationship curve, and outputting the reservoir capacity curve reconstruction graphical expression result. In the present application, the water body boundary surface information is obtained by fusing the unmanned aerial vehicle remote sensing image to identify the boundary pixel area, the abnormal water level line number is identified by combining the difference index comparison, the complete three-dimensional fusion data set is constructed by matching the water point cloud and the underwater elevation data, the grid network is generated according to the unified projection and the abnormal elevation points are corrected, the continuous trajectory line sequence is established and integrated to form the two-dimensional boundary expression, which can quickly identify the reservoir capacity change in the dynamic environment, effectively avoid the incomplete boundary extraction and the data precision decline caused by the complex terrain and the tedious operation in the traditional measurement method, and improve the comprehensive performance of the reservoir capacity curve reconstruction in the spatial coverage, data updating and expression precision.

This application discloses a method, apparatus, storage medium, and processor for processing floodplain data. The method includes: monitoring river flow and determining the water level at various points along the riverbank in a watershed model based on the flow at the monitoring points. The watershed model is composed of elevation data within a target watershed, which includes at least a river and a riverbank. The method further includes determining floodplain points based on the water level and elevation of each point along the riverbank, resulting in at least one floodplain point. The method also includes determining floodplain links based on the water level and elevation of the surrounding terrain at each floodplain point, resulting in at least one floodplain link, where each floodplain link contains multiple points. Finally, the method involves simulating floodplain flow in the watershed model based on the at least one floodplain link. This application solves the problems of high computational load and demanding performance requirements for computing equipment in simulating floodplain conditions in related technologies.

The present application relates to the technical field of power transmission line construction simulation, and discloses a GIS-based power transmission line pay-off simulation construction visual simulation method, comprising: acquiring line elevation data and calculating a terrain fingerprint sequence, and dividing a risk section according to a fingerprint value; generating a basic dynamic envelope for a medium-risk area, and superimposing an asymmetric compensation envelope driven by the fingerprint on a high-risk area; and performing spatial overlay analysis in GIS to identify collision risks, wherein the present application realizes accurate optimization configuration of computing resources through a hierarchical response mechanism driven by the terrain fingerprint, simultaneously fuses multi-source data features to construct an adaptive safety boundary, and improves the accuracy and reliability of construction safety prediction under complex terrain.

This disclosure relates to the field of remote sensing and mapping technology, and provides an adaptive distributed scatterer interferometry method, apparatus, and medium for mountainous areas. The method includes: acquiring radar image data and digital elevation model (DEM) data of the target monitoring mountainous area; calculating the radar vegetation index of each pixel in the dual-polarization synthetic aperture radar (DAP) image sequence and classifying it into multiple vegetation coverage levels to obtain a vegetation coverage grading atlas; identifying statistically homogeneous pixels in each image scene under the spatial constraints of vegetation coverage levels according to the grading atlas to determine a candidate set of distributed scatterers; further, using the grading atlas, performing adaptive coherence threshold filtering on each candidate point to obtain multiple distributed scatterers; and determining the long-term surface deformation results of the mountainous area based on the multiple distributed scatterers, the DAP image sequence, and the DEM data. This embodiment effectively improves the accuracy and reliability of distributed scatterer extraction and is suitable for deformation monitoring in complex mountainous areas.

The invention provides a ground penetrating radar surface clutter separation method based on cross correlation, which can be applied to the technical field of radars. The method comprises the steps that actually-measured echo signals and surface layer elevation data of a target object are acquired, second clutter signals are constructed based on the surface layer elevation data, the actually-measured echo signals comprise target echo signals representing internal structure information of the target object and first clutter signals representing surface layer topography of the target object, and the second clutter signals comprise the target echo signals representing internal structure information of the target object and the first clutter signals representing surface layer topography of the target object; the surface elevation data comprises image data with position information obtained by detecting the surface terrain of the target object by using the ground penetrating radar; performing signal processing on the actually measured echo signal and the second clutter signal to obtain an actually measured echo image and a clutter image; performing cross-correlation operation on the actually measured echo imaging and clutter imaging, and adjusting the position and amplitude of clutter imaging based on the result of the cross-correlation operation; and separating the adjusted clutter image from the actually measured echo image to obtain a target echo image.

The application relates to the technical field of automatic control, in particular to a hilly and mountainous area seeder depth control system and method. The system comprises: a visual module configured to collect visual images of a to-be-seeded area at a first preset frequency; a data processing module configured to convert the visual images into depth maps by using an image processing model, perform grid processing on the visual images, determine at least one piece of target ground elevation data corresponding to each column of grids based on the depth maps, and determine at least one first adjustment instruction based on a preset seeding depth and the at least one piece of target ground elevation data; and a control instruction output module configured to issue the at least one first adjustment instruction corresponding to each column of grids to a corresponding depth limiting adjustment mechanism. The system can realize independent and accurate control of the ditching depth of each ditching device by using visual preview and intelligent control technology, so as to improve the uniformity of seedling emergence and crop yield.