Satellite remote sensing data collection and preprocessing method and system

Abstract

The application discloses a satellite remote sensing data collection and preprocessing method and system, and relates to the technical field of satellite navigation. The method specifically comprises the following steps: collecting remote sensing images of a semiarid region through a satellite, analyzing pixel radiation and brightness gradient to identify bare land water features, constructing a leading atlas, extracting a boundary and registering to generate a standard structure, combining a climate model to correct to generate a reflectivity layer, transferring instructions when parameters are insufficient, reconstructing a queue by using a shortest algorithm to complete radiation calibration, atmospheric correction and geometric registration, and forming a preprocessing layer set. In the application, multi-band division is combined with radiation gradient calculation to enhance the separation degree of ground objects, boundary feature extraction and geometric registration are used to eliminate imaging distortion, a dynamic radiation model is fused with climate parameters to correct reflectivity, a double-threshold value is monitored to optimize a transmission strategy and avoid interruption, a priority algorithm is used to reconstruct a queue to reduce data delay, multi-level quality control is used to cooperatively process to form standard data, and the temporal and spatial reliability and application value are improved.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation technology, and in particular to a method and system for satellite remote sensing data collection and preprocessing. Background Technology

[0002] The field of satellite navigation technology encompasses global positioning technology based on a constellation of space satellites, ground monitoring networks, and user terminal equipment. This field is grounded in satellite orbital dynamics models, generating time reference signals through onboard atomic clocks, and performing three-dimensional positioning calculations using pseudorange measurements and carrier phase observations. Core technologies involve key elements such as satellite signal modulation scheme design, ionospheric delay dual-frequency correction models, multipath effect suppression algorithms, and receiver clock bias compensation mechanisms. It provides real-time, centimeter-level positioning services for mobile devices in fields such as marine mapping, intelligent transportation, meteorological observation, and agriculture, while also supporting applications such as geological disaster early warning and urban 3D modeling spatial information.

[0003] Among them, the satellite remote sensing data collection and preprocessing methods and systems refer to the end-to-end quality control and standardized conversion system for optical / radar satellite downlink data. Technical aspects include a remote sensing data receiving station site selection optimization model, raw data block verification and validation mechanisms, multi-payload sensor radiometric calibration parameter fusion methods, and automatic data transmission interruption resumption protocols. This is achieved by constructing a dynamic monitoring model for the signal-to-noise ratio of the satellite-to-ground link, establishing a multi-temporal image geometric registration control point library, developing an engine for converting unstructured data to NetCDF format, and designing data integrity assessment methods based on metadata verification.

[0004] Existing data preprocessing technologies employ a linear processing structure without a dynamic task priority adjustment mechanism. When sudden data transmission interruptions occur, the entire data processing chain must be restarted, reducing the timeliness of acquiring high-value data. Geometric correction relies on a pre-set control point library for registration, lacking real-time compensation for dynamic distortions during imaging, which easily leads to positioning errors in topographical areas. Radiometric calibration uses general atmospheric model parameters without regional adaptation to the aerosol concentration fluctuations in semi-arid regions, affecting the accuracy of reflectance data inversion. The data transmission protocol uses a fixed retransmission interval mechanism, which easily leads to the accumulation of duplicate data packets in scenarios with drastic network bandwidth fluctuations, increasing the load on the satellite-to-ground link. The multi-payload sensor data fusion process uses a serial processing mode, failing to fully utilize the parallel processing potential of heterogeneous computing resources, resulting in limited efficiency in large-scale remote sensing data processing. In flood disaster emergency monitoring, existing technologies struggle to prioritize image processing of disaster-stricken areas, delaying the golden window for disaster assessment. Summary of the Invention

[0005] To address the technical problems existing in the prior art, embodiments of the present invention provide a method and system for satellite remote sensing data collection and preprocessing. The technical solution is as follows:

[0006] On the one hand, a method for satellite remote sensing data collection and preprocessing is provided, including the following steps: S1: Remote sensing images of the watershed in the semi-arid region are collected by satellite multi-source sensors. Multiple channels are divided according to the image bands and the radiation intensity and spatial brightness gradient of the corresponding pixels are calculated. The gradient characteristics of bare land and water bodies under arid environment are identified, and the response areas are extracted to construct the dominant response map. S2: Extract the boundary structure of water bodies and bare land in the dominant response map, identify the distortion caused by the water body edge and bare area in the remote sensing image and perform geometric position registration to generate a standardized boundary structure; S3: Based on the standardized boundary structure, obtain the center frequency and incident angle information of the sensor band, combine it with the semi-arid climate, correct the image through the atmospheric radiative transfer model, restore the real reflectivity of the ground object, and generate a reflectivity layer. S4: Obtain the bandwidth and delay parameters of the reflectivity layer in the transmission link. When the bandwidth is less than the bandwidth required for the task data or the delay exceeds the specified threshold, generate a layer transfer instruction. S5: Based on the layer transfer instruction, reconstruct the layer processing queue using the shortest job first algorithm, and perform radiometric calibration, atmospheric correction, and geometric registration according to the layer processing queue to generate a sorted and preprocessed layer set.

[0007] As a further aspect of the present invention, the dominant response map includes water body boundary features, bare land boundary features, and brightness gradient distribution; the standardized boundary structure includes registration edge lines, distortion information, and boundary geometric labels; the reflectance layer results include reflectance distribution, pixel spectral response, and atmospheric correction parameters; the layer transfer instruction is a task transmission node identifier and transmission priority parameters; and the sorted preprocessed layer set is a radiometric calibration layer, an atmospheric correction layer, and a spatial consistency layer.

[0008] As a further aspect of the present invention, the steps of acquiring remote sensing images of a semi-arid watershed using a satellite multi-source sensor, dividing the images into multiple channels according to image bands and calculating the radiant intensity and spatial brightness gradient of corresponding pixels, identifying the gradient characteristics of bare land and water bodies under arid conditions, and extracting response regions to construct a dominant response map are as follows: S101: Acquire remote sensing image data collected by satellite multi-source sensors, classify them according to band channels and extract spectral radiance values, call the spectral radiance values ​​in the image as the dominant parameter and group the pixels, calculate the normalized ratio of spectral reflectance to solar zenith angle of each group of pixels, and generate multi-band spectral intensity values. S102: Based on the multi-band spectral intensity, perform gray-level difference on pixels of the same geographical location under multiple channels according to the column direction and row direction respectively, extract the horizontal gradient and vertical gradient and calculate the spatial gradient of each pixel to obtain the brightness gradient of the whole image. S103: Based on the brightness gradient of the entire image, compare the gradient distribution of water bodies and bare land areas to determine the frequency gradient interval. Based on the pixel ratio within the interval, extract areas with similar gradient characteristics to water bodies or bare land and overlay band response layers to generate a dominant response map.

[0009] As a further aspect of the present invention, the steps of extracting the boundary structures of water bodies and bare land in the dominant response map, identifying the distortions caused by the edges of water bodies and exposed areas in remote sensing imaging, performing geometric position registration, and generating standardized boundary structures are as follows: S201: Obtain the exposed area and water body area layers in the dominant response map, calculate the contour change of the corresponding area boundary and extract the edge coordinate lines to generate a boundary coordinate line sequence; S202: Obtain the curve direction of the exposed area and the edge of the water body in the boundary coordinate line sequence, calculate the coordinate offset value between the curve and the surrounding pixel position and perform spatial offset analysis to generate a position offset value interval table. S203: Based on the coordinate offset values ​​in the position offset value interval table, correct the boundary direction and pixel pattern of the corresponding region, reconstruct the spatial position distribution of the edge contour lines on the raster image and perform alignment processing to obtain a standardized boundary structure layer.

[0010] As a further aspect of the present invention, the steps of obtaining the center frequency and incident angle information of the sensor based on the standardized boundary structure, combining it with the semi-arid climate, correcting the image through an atmospheric radiative transfer model, restoring the true reflectivity of ground objects, and generating a reflectivity layer are as follows: S301: Extract the band identifier corresponding to the layer based on the spatial position index in the standardized boundary structure layer, obtain the center frequency of the corresponding band through the band identifier index sensor, and extract the scanning angle recorded during image imaging to generate a dataset of band center frequency and incident angle. S302: Based on the center frequency and incident angle dataset of the band, combined with the surface temperature and water vapor content of the semi-arid climate zone where the image acquisition area is located, calculate the atmospheric attenuation value of the image signal and generate the radiation attenuation correction coefficient. The structure of the atmospheric radiative transfer model includes atmospheric transmittance, atmospheric upward radiative transfer and downward radiative transfer, and the original pixel radiative values ​​are corrected by substituting them into the radiative transfer equation. S303: Based on the radiation attenuation correction coefficient and the band pixels of the original remote sensing image, the reflectance value of each pixel after atmospheric influence is calculated pixel by pixel, and the reflectance layer result is generated.

[0011] As a further aspect of the present invention, the calculation of atmospheric attenuation of the image signal... The formula used is: ; in, Represents standardized surface temperature. The index represents the standardized atmospheric water vapor content, and is set based on the nonlinear decay law of water vapor absorption characteristics in semi-arid climate zones. Represents the upward radiation intensity, a normalized dimensionless value. Represents the downward radiation intensity, a normalized dimensionless value. Represents standardized atmospheric pressure, dimensionless. Represents standardized atmospheric temperature, dimensionless. Represents the thermal radiation absorption coefficient, which is dimensionless.

[0012] As a further aspect of the present invention, the step of obtaining the bandwidth and delay parameters of the reflectivity layer in the transmission link, and generating a layer transfer instruction when the bandwidth is less than the bandwidth required for the task data volume or the delay exceeds the specified threshold for the response, specifically includes: S401: Based on the reflectance layer results, obtain the bandwidth and delay information required for the image data in the transmission link, and obtain a bandwidth and delay parameter dataset; S402: Based on the bandwidth and latency parameter dataset, compare the bandwidth required for image transmission with the current link bandwidth value to determine whether the image transmission requirements are met. For the latency parameter, obtain the transmission latency of the image data and compare it with the specified threshold of the response to generate a performance evaluation result. The response time threshold is set based on expected latency requirements, quality assurance requirements, and network capacity. S403: Based on the performance evaluation results, if the bandwidth is insufficient or the latency exceeds the standard, initiate a link reselection that meets the bandwidth and latency requirements and perform a transfer, generating a layer transfer instruction.

[0013] As a further aspect of the present invention, the steps of reconstructing the layer processing queue based on the layer transfer instruction using the shortest job first algorithm, and performing radiometric calibration, atmospheric correction, and geometric registration according to the layer processing queue to generate a sorted preprocessed layer set are as follows: S501: Based on the layer transfer instruction, extract the processing duration and start timestamp of all layer tasks, calculate the task scheduling priority value of the layer using the shortest job first algorithm, reconstruct the scheduling list and adjust the execution order to obtain the layer update scheduling sequence; S502: Call the layer update scheduling sequence, read the layer image data and sensor response parameters item by item and calculate the layer radiation intensity, calculate the deviation between the radiation intensity and the ideal response value set by the sensor and statistically analyze the numerical distribution range to obtain the radiation calibration deviation distribution statistical results. The ideal response value is set by calibration using a standard radiation source integrating sphere and a standard lamp under controlled conditions. S503: Based on the statistical results of the radiation calibration deviation distribution, extract the ground control point coordinates, attitude records and imaging time of the layer image, adjust the layer radiation intensity, spatial position and coordinate position, and generate a sorted preprocessed layer set.

[0014] As a further aspect of the present invention, the task scheduling priority value of the layer is calculated using the shortest job first algorithm. The formula used is: ; in, This represents the processing duration of the j-th layer task, in seconds. This represents the arithmetic mean of the duration of all layer task processing, in seconds. This represents the waiting time for the j-th layer task, in seconds. This represents the resource urgency coefficient of the j-th layer task, with a value ranging from 0.1 to 1.0. This represents the data dependency weight factor of the j-th layer. This represents the real-time memory usage of the j-th layer task, which is normalized by dividing by 100.

[0015] On the other hand, a satellite remote sensing data collection and preprocessing system is provided. This system is applied to satellite remote sensing data collection and preprocessing methods, and includes: The image acquisition module is used to acquire remote sensing images of watersheds in semi-arid regions through satellite multi-source sensors, divide multiple channels according to image bands and calculate the radiation intensity and spatial brightness gradient of corresponding pixels, identify the gradient characteristics of bare land and water bodies in arid environments, extract response areas to construct dominant response maps and transfer them to the boundary extraction module. The boundary extraction module is used to extract the boundary structure of water bodies and bare land in the dominant response map, identify the distortion caused by the water body edge and bare area in the remote sensing image and perform geometric position registration, generate a standardized boundary structure and transfer it to the image correction module. The image correction module is used to obtain the center frequency and incident angle information of the sensor through the standardized boundary structure, combine it with the semi-arid climate, correct the image through the atmospheric radiative transfer model, restore the real reflectivity of the ground objects, generate a reflectivity layer and transfer it to the bandwidth analysis module. The bandwidth analysis module is used to obtain the bandwidth and delay parameters of the reflectivity layer in the transmission link. When the bandwidth is less than the bandwidth required for the task data or the delay exceeds the specified threshold, a layer transfer instruction is generated and transmitted to the queue reconstruction module. The queue reconstruction module is used to reconstruct the layer processing queue using the shortest job first algorithm based on the layer transfer instruction, and to perform radiometric calibration, atmospheric correction, and geometric registration based on the layer processing queue to generate a sorted and preprocessed layer set.

[0016] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: In this embodiment of the invention, a multi-channel image processing mechanism is used to identify ground features. Remote sensing images are divided by band and the radiometric intensity and spatial brightness gradients are calculated, effectively enhancing the feature separation between water bodies and bare land in semi-arid regions. Gradient feature-based boundary structure extraction technology, combined with geometric location registration methods, eliminates the impact of imaging distortion on ground feature outlines and improves the spatial consistency of watershed boundary data. Atmospheric correction incorporates regional climate parameters and sensor physical characteristics to establish a dynamic radiative transfer model, solving the reflectivity distortion problem caused by fixed parameters in traditional methods. A dual-threshold monitoring mechanism for bandwidth and latency is implemented in the transmission link to optimize data scheduling strategies in real time and avoid data processing interruptions caused by network fluctuations. The shortest job first algorithm is used to reconstruct the task queue, optimize the allocation logic of computing resources, and shorten the processing latency of high-value data. A multi-level quality control system covers the entire process from data reception to preprocessing. Through the coordinated operation of radiometric calibration, geometric correction, and format conversion, standardized data output capabilities are formed, improving the spatiotemporal comparability and application reliability of watershed monitoring data in semi-arid regions. Attached Figure Description

[0017] 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.

[0018] Figure 1 This is a schematic diagram of the workflow of the present invention; Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0020] In embodiments of the present invention, words such as "exemplarily" and "comprising" 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.

[0021] 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.

[0022] 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.

[0023] 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.

[0024] Please see Figure 1 This invention provides a technical solution: a method for collecting and preprocessing satellite remote sensing data, comprising the following steps: S1: Remote sensing images of the watershed in the semi-arid region are collected by satellite multi-source sensors. Multiple channels are divided according to the image bands and the radiation intensity and spatial brightness gradient of the corresponding pixels are calculated. The gradient characteristics of bare land and water bodies under arid environment are identified, and the response areas are extracted to construct the dominant response map. S2: Extract the boundary structure of water bodies and bare land in the dominant response map, identify the distortion caused by the water body edge and bare area in the remote sensing image and perform geometric position registration to generate a standardized boundary structure; S3: Based on the standardized boundary structure, the center frequency and incident angle information of the sensor are obtained. Combined with the semi-arid climate, the image is corrected by the atmospheric radiative transfer model to restore the real reflectivity of the ground objects and generate a reflectivity layer. S4: Obtain the bandwidth and latency parameters of the reflectivity layer in the transmission link. When the bandwidth is less than the bandwidth required for the task data or the latency exceeds the specified threshold in the response, generate a layer transfer instruction. S5: Based on the layer transfer instruction, the layer processing queue is reconstructed using the shortest job first algorithm. Radiometric calibration, atmospheric correction, and geometric registration are performed according to the layer processing queue to generate a sorted and preprocessed layer set.

[0025] The dominant response map includes water body boundary features, bare land boundary features, and brightness gradient distribution. The standardized boundary structure includes registration edge lines, distortion information, and boundary geometric labels. The reflectance layer results include reflectance distribution, pixel spectral response, and atmospheric correction parameters. The layer transfer instructions are task transfer node identifier and transfer priority parameters. The sorted and preprocessed layer set consists of a radiometric calibration layer, an atmospheric correction layer, and a spatial consistency layer.

[0026] Furthermore, the specific steps for acquiring remote sensing images of the semi-arid watershed using satellite multi-source sensors, dividing the images into multiple channels according to image bands, calculating the radiant intensity and spatial brightness gradient of corresponding pixels, identifying the gradient characteristics of bare land and water bodies under arid conditions, and extracting response regions to construct dominant response maps are as follows: S101: Acquire remote sensing image data collected by satellite multi-source sensors, classify and extract spectral radiance values ​​according to band channels, call the spectral radiance values ​​in the image as the dominant parameter and group the pixels, calculate the spectral reflectance and solar zenith angle of each group of pixels, perform normalization processing and calculate the ratio between the two to generate multi-band spectral intensity values. Acquire images of wetlands in Yancheng, Jiangsu Province, from the Landsat 8 satellite OLI sensor, and select... (Blue light) (Green light) (Red light) (Near-infrared) Four bands were used to establish a correspondence table between the bands and the center wavelength (see Table 1). Multi-band radiometric calibration coefficients were read from image metadata, and radiometric calibration conversion was performed on the DN values, including... Band DN value Gain coefficient and offset Calculated radiation value Perform the same calculation for each pixel, store the four band radiation values ​​in a four-dimensional array, and select... The spectral radiometric value is used as the dominant parameter to divide the image into... Pixel blocks, spaced according to radiance values ​​within each block. Grouping, calculating the average radiometric value of pixels within each group, and combining this with the image acquisition time. and geographic coordinates Calculate the solar zenith angle using a solar position algorithm. For multiple sets of radiation values Normalization process Including when the radiation value hour Calculate multiple sets and ratio Four bands Values ​​by weight ( ), ( ), ( ), ( The spectral intensity values ​​are obtained by weighted summation. .

[0027] Table 1 Landsat 8 Band Parameters S102: Based on multi-band spectral intensity, pixels at the same geographical location under multiple channels are subjected to gray-level difference according to column and row directions, the horizontal gradient and vertical gradient are extracted, and the spatial gradient of each pixel is calculated to obtain the brightness gradient of the entire image. Selecting from images of Taihu Lake Pixel area, for Band spectral intensity matrix Window scan, calculate lateral gradient including when , hour, longitudinal gradient ,when , hour, Spatial gradient After traversing all pixels, the gradient distribution is statistically analyzed, and the gradient interval division criteria are set: low gradient... Medium gradient High gradient Statistics on water area Pixel gradient values ​​are distributed in Interval.

[0028] S103: Based on the brightness gradient of the entire image, compare the gradient distribution of water bodies and bare land areas to determine the frequency gradient interval. Based on the pixel ratio within the interval, extract areas with similar gradient characteristics to water bodies or bare land and overlay band response layers to generate dominant response maps. In the imagery of the Poyang Lake area, the frequency distribution curve of the gradient value across the entire map was obtained, and the characteristic gradient interval of the water body was determined. (include Water body pixels), bare land feature range is (include (bare ground pixels), for gradient values ​​in the overlapping region The pixels, superimposed with near-infrared ( ) and shortwave infrared ( Band response difference ,when The area was initially identified as bare land, including single pixels. , but Marked as bare land feature regions, those conforming to the gradient interval and Perform a logical AND operation between the pixels and the original gradient map to generate a result including... The dominant response map of each feature pixel, where the average gradient of pixels in the water feature region is... Average gradient of bare land feature area .

[0029] Furthermore, the specific steps for extracting the boundary structures of water bodies and bare land in the dominant response map, identifying distortions caused by water body edges and exposed areas in remote sensing images, performing geometric registration, and generating standardized boundary structures are as follows: S201: Obtain the exposed area and water body area layers in the dominant response map, calculate the contour change of the corresponding area boundary and extract the edge coordinate lines to generate a boundary coordinate line sequence; Based on the dominant response map of the Poyang Lake region, the bare map layer is extracted. (include (each pixel) and water layer ( (pixels), for Perform an 8-neighbor connectivity scan on the layer boundary and record the coordinate sequence of consecutive boundary points. Calculate the change in distance between adjacent points Set a threshold for sudden changes in spacing. Pixel (1 pixel = 30 meters), when The time marker is used as the break point, including the point With the next node spacing If the number of pixels is greater than 5, insert the center point. Complete the boundary, Perform the same operation on the layer to generate a sequence of bare ground boundary coordinates. (length Points and water body boundary sequences (length (points), among which The largest continuous segment contains 892 coordinate points, and the smallest segment contains 3 points.

[0030] Table 2 Statistical Table of Boundary Coordinate Sequence As shown in Table 2, the average spacing between bare land boundaries is 32.4 meters (corresponding to...). (pixels), by detecting abrupt changes in spacing, 23 breakpoints were filled in, improving the boundary continuity to 98.7%.

[0031] S202: Obtain the curve direction of the exposed area and the edge of the water body in the boundary coordinate line sequence, calculate the coordinate offset value between the curve and the surrounding pixel position and perform spatial offset analysis to generate a position offset value interval table. exist Selecting continuous boundary segments in the sequence Calculate the curve direction angle , including points , , hour, Calculate the lateral offset of multiple points relative to the curve's direction. ,in To fit a straight line using the least squares method, when , At time, point Fitted values ,but Pixels, Vertical Offset Statistical analysis of all points and Set the offset determination threshold: lateral offset Pixels, vertical offset Pixels, of which 83% of the bare land boundary Distributed in to Pixel range.

[0032] S203: Based on the coordinate offset values ​​in the position offset value interval table, correct the boundary direction and pixel pattern of the corresponding area, reconstruct the spatial position distribution of the edge contour lines on the raster image and perform alignment processing to obtain a standardized boundary structure layer. right Coordinate correction is performed on points in the sequence where the offset exceeds the limit. When pixels, Coordinates adjusted to The average coordinate points The pixel is the average offset of 10 adjacent points, including the point. Original After correction For longitudinal offset Pixels, by Smoothing was performed (coefficient 0.7, experimentally determined), including points. of pixels, then Rounded to 76 pixels, the corrected boundary coordinates are mapped to the raster image, aligned with 5-pixel intervals (150 meters), including the original coordinates. Align to Generate a standardized boundary layer Among them, the length error of bare land boundary decreased from 4.7% to 1.2%, and the number of topological error points of water body boundary decreased from 15 to 2.

[0033] Furthermore, based on the standardized boundary structure, the sensor's band center frequency and incident angle information are obtained. Combined with the semi-arid climate, the image is corrected using an atmospheric radiative transfer model to restore the true reflectivity of ground objects and generate a reflectivity layer. The specific steps are as follows: S301: Based on the spatial location index in the standardized boundary structure layer, extract the band identifier corresponding to the layer, obtain the center frequency of the corresponding band through the band identifier index sensor, and extract the scanning angle recorded during image imaging to generate a dataset of band center frequency and incident angle. Based on standardized boundary layer Spatial location index, extract the band identifier associated with each cell in the layer. The center frequency of the corresponding band can be queried through the sensor parameter database. (include Band center frequency Read the scan angle from the image header file. Build a dataset, where Band data recorded as , Precision is retained to one decimal place, generating a number including... A dataset of records, each record in the following format: .

[0034] Table 3. Example table of center frequency and scanning angle of the band. As shown in Table 3, the center frequency is obtained from the sensor's factory calibration parameters, and the scanning angle is obtained from the image acquisition time. Extracted from satellite attitude data, with an accuracy error of less than .

[0035] S302: Based on the band center frequency and incident angle dataset, combined with the surface temperature and water vapor content of the semi-arid climate zone where the image acquisition area is located, calculate the atmospheric attenuation value of the image signal and generate the radiation attenuation correction coefficient. The structure of the atmospheric radiative transfer model includes atmospheric transmittance, upward atmospheric radiation, and downward atmospheric radiation, and the original pixel radiation values ​​are corrected by substituting them into the radiative transfer equation. Monitoring points were selected in the semi-arid region of Inner Mongolia to obtain measured values ​​of surface temperature. (Convert to Kelvin) Calculate standardized land surface temperature (Based on the ratio of the measured value of 315.45K to the standard reference temperature of 288.15K), atmospheric water vapor content was collected using a radiosonde. ,calculate Use a radiometer to measure the upward radiation intensity (Normalized value) and downward radiation intensity Calculate the ratio term Set a standardized atmospheric pressure (Based on measured values ​​at an altitude of 1200 meters) Compared with sea level standard value ratio (corrected to 0.92 after temperature compensation) Standardized atmospheric temperature (Measured temperature) Compared with standard temperature ratio (Adjusted to 1.05 after humidity compensation) Thermal radiation absorption coefficient (Calibrated through laboratory blackbody radiation experiments), substituting into the formula: The results showed that the atmospheric attenuation value It will be used for radiation correction, with a magnitude consistent with typical atmospheric optical thickness values ​​(0.8-1.5) in semi-arid regions.

[0036] S303: Based on the radiation attenuation correction coefficient and the band pixels of the original remote sensing image, the reflectance value of each pixel after atmospheric influence is calculated pixel by pixel, and the reflectance layer result is generated. right Band pixels Original radiation value Application of attenuation coefficient Calculate reflectivity Generate a reflectance layer by traversing all pixels. The average reflectance of the water body area bare land area The data is consistent with the measured data on the ground (2.1-2.5 for water bodies and 12.0-13.5 for bare land). The standard deviation of the image pixel value after correction decreased from 18.7 to 5.2.

[0037] Furthermore, the steps for obtaining the bandwidth and latency parameters of the reflectivity layer in the transmission link, and generating a layer transfer instruction when the bandwidth is less than the bandwidth required for the task data volume or the latency exceeds the specified threshold in the response, are as follows: S401: Based on the reflectance layer results, obtain the bandwidth and delay information required for image data in the transmission link, and obtain the bandwidth and delay parameter dataset; Based on reflectivity layer (size Pixels × Pixel, single-image data volume ), calculate the total data volume Measure the current transmission link bandwidth using a network probe. Round trip delay Recorded as a dataset ,in (Allowed transmission time) Calculated ), delay threshold The real-time transmission protocol is set to 200ms.

[0038] Table 4 Example of Transmission Parameter Dataset As shown in Table 4, the required bandwidth is obtained through... The result is rounded up to 153.6 Mbps, and the latency threshold is set according to the ITU-TG.114 standard.

[0039] S402: Based on the bandwidth and latency parameter dataset, compare the bandwidth required for image transmission with the current link bandwidth value to determine whether the image transmission requirements are met. For the latency parameter, obtain the transmission latency of image data and compare it with the specified threshold of response to generate a performance evaluation result. The response time threshold is set based on expected latency requirements, quality assurance requirements, and network capacity. Compare current link bandwidth With required bandwidth Calculate the bandwidth difference (Insufficient percentage: 38.2%), compared to latency and Exceeding the limit (10% over the limit), set the evaluation rule: insufficient bandwidth is judged as... (Threshold 138.24Mbps), latency exceeding the standard is judged as... (210ms) The current link simultaneously triggers insufficient bandwidth (95<138.24) and excessive latency (220>210), generating the evaluation result R={bandwidth status: insufficient, latency status: excessive}.

[0040] S403: Based on the performance evaluation results, if the bandwidth is insufficient or the latency exceeds the standard, initiate a link reselection that meets the bandwidth and latency requirements and perform a transfer, generating a layer transfer instruction; Select candidate links from the available link pool (Bandwidth 160Mbps, latency 180ms) (Bandwidth 200Mbps, latency 240ms), excluded (Lapse exceeded the limit), for Verify bandwidth margin (Reserve a threshold) Generate a transfer instruction Actual bandwidth measured after switching Delay Time required to complete layer transfer ,satisfy Require.

[0041] Furthermore, based on the layer transfer instructions, the layer processing queue is reconstructed using the shortest job first algorithm. The specific steps for generating a sorted preprocessed layer set, including radiometric calibration, atmospheric correction, and geometric registration based on the layer processing queue, are as follows: S501: Based on the layer transfer instruction, extract the processing duration and start timestamp of all layer tasks, calculate the task scheduling priority value of the layer through the shortest job first algorithm, reconstruct the scheduling list and adjust the execution order to obtain the layer update scheduling sequence. Based on layer transfer instruction set (Including 3 tasks to be scheduled), extract the multi-task processing time. ( , , ), calculate average duration Get waiting time ( , , Set resource urgency coefficient (Based on task priority:) , , ), data dependency weight ( , , Real-time memory usage ( , , Substitute into the formula to calculate the priority value. : , , ,according to Ascending sort (Task 1) 325.76 < Task 2 1016.2 <Task 3> 1686.4), generating scheduling sequences .

[0042] Table 5 Task Scheduling Parameter Table As shown in Table 5, the resource urgency coefficient The weights are manually set based on the urgency of the task (0.1-1.0), and the data depends on the weights. Calculated using the task-related graph (+0.2 for each dependent sublayer).

[0043] S502: Call the layer update scheduling sequence, read the layer image data and sensor response parameters item by item and calculate the layer radiometric intensity, calculate the deviation between the radiometric intensity and the ideal response value set by the sensor and statistically analyze the numerical distribution range to obtain the radiometric calibration deviation distribution statistical results. The ideal response value is set by calibration using a standard radiation source integrating sphere and a standard lamp under controlled conditions. For scheduling sequence Medium task B5 band image pixels Radiation intensity Call the ideal value of the standard radiation source calibration data Calculate the deviation The overall image deviation distribution is statistically analyzed: deviation ≤ 0.02: 15% of pixels; deviation < 0.02: 70% of pixels; deviation > 0.05: 15% of pixels. Statistical results are then generated. .

[0044] S503: Based on the statistical results of the radiometric calibration deviation distribution, extract the ground control point coordinates, attitude records and imaging time of the layer image, adjust the layer radiometric intensity, spatial position and coordinate position, and generate a sorted preprocessed layer set; extract Ground control points (latitude and longitude) (elevation 12m), based on imaging time attitude angle (pitch angle) Yaw angle Adjusting radiation intensity Spatial position correction offset , Generate a preprocessed layer ,include One correction pixel.

[0045] In this embodiment of the invention, a multi-channel image processing mechanism is used to identify ground features. Remote sensing images are divided by band and the radiometric intensity and spatial brightness gradients are calculated, effectively enhancing the feature separation between water bodies and bare land in semi-arid regions. Gradient feature-based boundary structure extraction technology, combined with geometric location registration methods, eliminates the impact of imaging distortion on ground feature outlines and improves the spatial consistency of watershed boundary data. Atmospheric correction incorporates regional climate parameters and sensor physical characteristics to establish a dynamic radiative transfer model, solving the reflectivity distortion problem caused by fixed parameters in traditional methods. A dual-threshold monitoring mechanism for bandwidth and latency is implemented in the transmission link to optimize data scheduling strategies in real time and avoid data processing interruptions caused by network fluctuations. The shortest job first algorithm is used to reconstruct the task queue, optimize the allocation logic of computing resources, and shorten the processing latency of high-value data. A multi-level quality control system covers the entire process from data reception to preprocessing. Through the coordinated operation of radiometric calibration, geometric correction, and format conversion, standardized data output capabilities are formed, improving the spatiotemporal comparability and application reliability of watershed monitoring data in semi-arid regions.

[0046] Please see Figure 2 This invention also provides a satellite remote sensing data collection and preprocessing system. This system is used to execute the aforementioned satellite remote sensing data collection and preprocessing method. The system includes: The image acquisition module is used to acquire remote sensing images of watersheds in semi-arid regions through satellite multi-source sensors, divide multiple channels according to image bands and calculate the radiation intensity and spatial brightness gradient of corresponding pixels, identify the gradient characteristics of bare land and water bodies in arid environments, extract response areas to construct dominant response maps and transfer them to the boundary extraction module. The boundary extraction module is used to extract the boundary structure of water bodies and bare land in the dominant response map, identify the distortion caused by the water body edge and bare area in the remote sensing image and perform geometric position registration, generate a standardized boundary structure and transfer it to the image correction module. The image correction module is used to obtain the center frequency and incident angle information of the sensor band through a standardized boundary structure, combine it with the semi-arid climate, correct the image through the atmospheric radiative transfer model, restore the real reflectivity of the ground object, generate a reflectivity layer and transfer it to the bandwidth analysis module. The bandwidth analysis module is used to obtain the bandwidth and latency parameters of the reflectivity layer in the transmission link. When the bandwidth is less than the bandwidth required for the task data or the latency exceeds the specified threshold in the response, a layer transfer instruction is generated and passed to the queue reconstruction module. The queue reconstruction module is used to reconstruct the layer processing queue using the shortest job first algorithm based on the layer transfer command. Then, it performs radiometric calibration, atmospheric correction, and geometric registration based on the layer processing queue to generate a sorted and preprocessed layer set.

[0047] For ease of explanation, Figure 2 Only the main components of the system are shown. The system of this embodiment can be used to perform... Figure 1 The technical solutions of the method embodiments shown are similar in principle and in effect, and will not be described again here.

[0048] 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: A and / or B, which 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 may also indicate an "and / or" relationship; please refer to the context for a more accurate understanding.

[0049] 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. "At least one of a, b, or c" can be represented as: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0050] It should be understood that, in various embodiments of the present invention, the order of the sequence numbers of the multiple processes does not imply the order of execution. The execution order of the multiple processes should be determined by their functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0051] Those skilled in the art will recognize that the various example units and algorithm steps 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.

[0052] 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.

[0053] 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. This includes the fact that the apparatus embodiments described above are merely illustrative, and that the division of units is only a logical functional division; in actual implementation, there may be other division methods, including the combination or integration of multiple units or components into another device, or the omission or non-execution of some features. Furthermore, the displayed or discussed mutual couplings, direct couplings, or communication connections can be through some interfaces; indirect couplings or communication connections between devices or units can be electrical, mechanical, or other forms.

[0054] 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.

[0055] In addition, the multifunctional units in various embodiments of the present invention can be integrated into one processing unit, or multiple units can exist physically separately, or two or more units can be integrated into one unit.

[0056] 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 the present invention, or the part that contributes to the prior art, or a portion 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 the present 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.

[0057] 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. A method for satellite remote sensing data collection and preprocessing, characterized in that, The method includes: S1: Remote sensing images of the watershed in the semi-arid region are collected by satellite multi-source sensors. Multiple channels are divided according to the image bands and the radiation intensity and spatial brightness gradient of the corresponding pixels are calculated. The gradient characteristics of bare land and water bodies under arid environment are identified, and the response areas are extracted to construct the dominant response map. S2: Extract the boundary structure of water bodies and bare land in the dominant response map, identify the distortion caused by the water body edge and bare area in the remote sensing image and perform geometric position registration to generate a standardized boundary structure; S3: Based on the standardized boundary structure, obtain the center frequency and incident angle information of the sensor band, combine it with the semi-arid climate, correct the image through the atmospheric radiative transfer model, restore the real reflectivity of the ground object, and generate a reflectivity layer. S4: Obtain the bandwidth and delay parameters of the reflectivity layer in the transmission link. When the bandwidth is less than the bandwidth required for the task data or the delay exceeds the specified threshold, generate a layer transfer instruction. S5: Based on the layer transfer instruction, reconstruct the layer processing queue using the shortest job first algorithm, and perform radiometric calibration, atmospheric correction, and geometric registration according to the layer processing queue to generate a sorted and preprocessed layer set.

2. The satellite remote sensing data collection and preprocessing method according to claim 1, characterized in that, The dominant response map includes water body boundary features, bare land boundary features, and brightness gradient distribution. The standardized boundary structure includes registration edge lines, distortion information, and boundary geometric labels. The reflectance layer results include reflectance distribution, pixel spectral response, and atmospheric correction parameters. The layer transfer instructions are task transmission node identifier and transmission priority parameters. The sorted preprocessed layer set consists of a radiometric calibration layer, an atmospheric correction layer, and a spatial consistency layer.

3. The satellite remote sensing data collection and preprocessing method according to claim 1, characterized in that, The specific steps for acquiring remote sensing images of a semi-arid watershed using satellite multi-source sensors, dividing the images into multiple channels according to image bands, calculating the radiant intensity and spatial brightness gradient of corresponding pixels, identifying the gradient characteristics of bare land and water bodies under arid conditions, and extracting response regions to construct dominant response maps are as follows: S101: Acquire remote sensing image data collected by satellite multi-source sensors, classify them according to band channels and extract spectral radiance values, call the spectral radiance values ​​in the image as the dominant parameter and group the pixels, calculate the normalized ratio of spectral reflectance to solar zenith angle of each group of pixels, and generate multi-band spectral intensity values. S102: Based on the multi-band spectral intensity, perform gray-level difference on pixels of the same geographical location under multiple channels according to the column direction and row direction respectively, extract the horizontal gradient and vertical gradient and calculate the spatial gradient of each pixel to obtain the brightness gradient of the whole image. S103: Based on the brightness gradient of the entire image, compare the gradient distribution of water bodies and bare land areas to determine the frequency gradient interval. Based on the pixel ratio within the interval, extract areas with similar gradient characteristics to water bodies or bare land and overlay band response layers to generate a dominant response map.

4. The satellite remote sensing data collection and preprocessing method according to claim 1, characterized in that, The specific steps for extracting the boundary structures of water bodies and bare land in the dominant response map, identifying distortions caused by water body edges and exposed areas in remote sensing imaging, performing geometric registration, and generating standardized boundary structures are as follows: S201: Obtain the exposed area and water body area layers in the dominant response map, calculate the contour change of the corresponding area boundary and extract the edge coordinate lines to generate a boundary coordinate line sequence; S202: Obtain the curve direction of the exposed area and the edge of the water body in the boundary coordinate line sequence, calculate the coordinate offset value between the curve and the surrounding pixel position and perform spatial offset analysis to generate a position offset value interval table. S203: Based on the coordinate offset values ​​in the position offset value interval table, correct the boundary direction and pixel pattern of the corresponding region, reconstruct the spatial position distribution of the edge contour lines on the raster image and perform alignment processing to obtain a standardized boundary structure layer.

5. The satellite remote sensing data collection and preprocessing method according to claim 1, characterized in that, The steps for obtaining the center frequency and incident angle information of the sensor based on the standardized boundary structure, combining it with the semi-arid climate, correcting the image using an atmospheric radiative transfer model, restoring the true reflectivity of ground objects, and generating a reflectivity layer are as follows: S301: Extract the band identifier corresponding to the layer based on the spatial position index in the standardized boundary structure layer, obtain the center frequency of the corresponding band through the band identifier index sensor, and extract the scanning angle recorded during image imaging to generate a dataset of band center frequency and incident angle. S302: Based on the data set of center frequency and incident angle of the band, combined with the surface temperature and water vapor content of the semi-arid climate zone where the image acquisition area is located, calculate the atmospheric attenuation value of the image signal and generate the radiation attenuation correction coefficient. The structure of the atmospheric radiative transfer model includes atmospheric transmittance, atmospheric upward radiative transfer and downward radiative transfer, and the original pixel radiative values ​​are corrected by substituting them into the radiative transfer equation. S303: Based on the radiation attenuation correction coefficient and the band pixels of the original remote sensing image, the reflectance value of each pixel after atmospheric influence is calculated pixel by pixel, and the reflectance layer result is generated.

6. The satellite remote sensing data collection and preprocessing method according to claim 5, characterized in that, The calculation of atmospheric attenuation of image signals The formula used is: ； in, Represents standardized surface temperature. The index represents the standardized atmospheric water vapor content, and is set based on the nonlinear decay law of water vapor absorption characteristics in semi-arid climate zones. Represents the upward radiation intensity, a normalized dimensionless value. Represents the downward radiation intensity, a normalized dimensionless value. Represents standardized atmospheric pressure, dimensionless. Represents standardized atmospheric temperature, dimensionless. Represents the thermal radiation absorption coefficient, which is dimensionless.

7. The satellite remote sensing data collection and preprocessing method according to claim 1, characterized in that, The steps for obtaining the bandwidth and latency parameters of the reflectivity layer in the transmission link, and generating a layer transfer instruction when the bandwidth is less than the bandwidth required for the task data volume or the latency exceeds the specified threshold in the response are as follows: S401: Based on the reflectance layer results, obtain the bandwidth and delay information required for the image data in the transmission link, and obtain a bandwidth and delay parameter dataset; S402: Based on the bandwidth and latency parameter dataset, compare the bandwidth required for image transmission with the current link bandwidth value to determine whether the image transmission requirements are met. For the latency parameter, obtain the transmission latency of the image data and compare it with the specified threshold of the response to generate a performance evaluation result. The response time threshold is set based on expected latency requirements, quality assurance requirements, and network capacity. S403: Based on the performance evaluation results, if the bandwidth is insufficient or the latency exceeds the standard, initiate a link reselection that meets the bandwidth and latency requirements and perform a transfer, generating a layer transfer instruction.

8. The satellite remote sensing data collection and preprocessing method according to claim 1, characterized in that, Based on the layer transfer instructions, the specific steps for reconstructing the layer processing queue using the shortest job first algorithm, and then performing radiometric calibration, atmospheric correction, and geometric registration according to the layer processing queue to generate a sorted preprocessed layer set are as follows: S501: Based on the layer transfer instruction, extract the processing duration and start timestamp of all layer tasks, calculate the task scheduling priority value of the layer using the shortest job first algorithm, reconstruct the scheduling list and adjust the execution order to obtain the layer update scheduling sequence; S502: Call the layer update scheduling sequence, read the layer image data and sensor response parameters item by item and calculate the layer radiation intensity, calculate the deviation between the radiation intensity and the ideal response value set by the sensor and statistically analyze the numerical distribution range to obtain the radiation calibration deviation distribution statistical results. The ideal response value is set by calibration using a standard radiation source integrating sphere and a standard lamp under controlled conditions. S503: Based on the statistical results of the radiation calibration deviation distribution, extract the ground control point coordinates, attitude records and imaging time of the layer image, adjust the layer radiation intensity, spatial position and coordinate position, and generate a sorted preprocessed layer set.

9. The satellite remote sensing data collection and preprocessing method according to claim 8, characterized in that, The task scheduling priority value of the layer is calculated using the shortest job first algorithm. The formula used is: ； in, This represents the processing duration of the j-th layer task, in seconds. This represents the arithmetic mean of the duration of all layer task processing, in seconds. This represents the waiting time for the j-th layer task, in seconds. This represents the resource urgency coefficient of the j-th layer task, with a value ranging from 0.1 to 1.

0. This represents the data dependency weight factor of the j-th layer. This represents the real-time memory usage of the j-th layer task, which is normalized by dividing by 100.

10. A satellite remote sensing data collection and preprocessing system, characterized in that, The system is used to implement the satellite remote sensing data collection and preprocessing method according to any one of claims 1-9, the system comprising: The image acquisition module is used to acquire remote sensing images of watersheds in semi-arid regions through satellite multi-source sensors, divide multiple channels according to image bands and calculate the radiation intensity and spatial brightness gradient of corresponding pixels, identify the gradient characteristics of bare land and water bodies in arid environments, extract response areas to construct dominant response maps and transfer them to the boundary extraction module. The boundary extraction module is used to extract the boundary structure of water bodies and bare land in the dominant response map, identify the distortion caused by the water body edge and bare area in the remote sensing image and perform geometric position registration, generate a standardized boundary structure and transfer it to the image correction module. The image correction module is used to obtain the center frequency and incident angle information of the sensor through the standardized boundary structure, combine it with the semi-arid climate, correct the image through the atmospheric radiative transfer model, restore the real reflectivity of the ground objects, generate a reflectivity layer and transfer it to the bandwidth analysis module. The bandwidth analysis module is used to obtain the bandwidth and delay parameters of the reflectivity layer in the transmission link. When the bandwidth is less than the bandwidth required for the task data or the delay exceeds the specified threshold, a layer transfer instruction is generated and transmitted to the queue reconstruction module. The queue reconstruction module is used to reconstruct the layer processing queue using the shortest job first algorithm based on the layer transfer instruction, and to perform radiometric calibration, atmospheric correction, and geometric registration based on the layer processing queue to generate a sorted and preprocessed layer set.

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