A land surveying and mapping method and system for dynamic remote sensing monitoring

Abstract

The invention provides a land surveying and mapping method for dynamic remote sensing monitoring, which comprises the following steps: obtaining splicing targets in a region to be measured, sorting the splicing targets by priority, and forming a sorting table; using remote sensing acquisition equipment to acquire remote sensing images containing the splicing targets, and the acquired adjacent remote sensing images all contain the same splicing target with a higher priority; cutting the remote sensing images based on the splicing targets; performing radiation correction on the cut remote sensing images; and splicing the corrected remote sensing images to form a complete regional remote sensing image. Before the remote sensing image is collected, the splicing targets contained in the region to be measured are firstly obtained, and then sorting the priorities according to the dispersion degree of the splicing targets. Then, when the remote sensing images are collected, the splicing targets are included in the remote sensing images, so that the splicing targets are used as references during the later cutting, and it is ensured that the spliced ​​regional remote sensing images can accurately reflect the topography of the region to be measured.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing survey technology, and in particular to a land surveying and mapping method and system for dynamic remote sensing monitoring. Background Art

[0002] Land surveying and mapping refers to the work of measuring and mapping the national territory. Its purpose is to understand and grasp the land features and its changes, provide a scientific basis for national planning, decision-making, and management, and ensure national security and sustainable development. At present, land surveying and mapping mainly adopts remote sensing mapping. The remote sensing acquisition equipment is set up to transmit electromagnetic waves to the underground in the air, and the terrain is mapped after receiving the returned electromagnetic waves. In order to identify and quantify the changes in surface types, spatial distribution and changes, it is necessary to obtain remote sensing images of the same terrain at different times or under different conditions. This process is dynamic remote sensing monitoring. Through dynamic remote sensing monitoring, land changes can be investigated, urban construction land can be controlled, and illegal land use can be checked.

[0003] In the process of land surveying and mapping, it is necessary to obtain a large number of remote sensing images, and then stitch the remote sensing images together to form a complete remote sensing image of the area to be surveyed. In the process of remote sensing image acquisition, there is no detailed division of the acquired area. It is just that after acquiring multiple remote sensing images, the remote sensing images are simply screened, and finally the overlapping parts of different remote sensing images are cut off and stitched. However, due to the large number of remote sensing images, it takes a lot of time just to screen, and when stitching in the later stage, it is necessary to additionally identify the overlapping parts of each remote sensing image for cutting. The workload is huge and the efficiency is low. There is no guarantee that the overlapping area can be completely cut off, resulting in inaccurate surveying and mapping results. Summary of the invention

[0004] In view of this, the present invention proposes a land surveying and mapping method and system for dynamic remote sensing monitoring, which can collect and crop remote sensing images based on stitching targets to ensure that the stitched regional remote sensing images can accurately reflect the actual terrain of the area to be measured.

[0005] The technical solution of the present invention is achieved in this way:

[0006] A land surveying and mapping method for dynamic remote sensing monitoring comprises the following steps:

[0007] Step S1, obtaining the stitching targets in the area to be tested, sorting the stitching targets by priority, and forming a sorting table;

[0008] Step S2, controlling the remote sensing acquisition device to acquire remote sensing images containing the stitching target, and the acquired adjacent remote sensing images all contain the same stitching target with a higher priority;

[0009] Step S3, cropping the remote sensing image based on the stitching target;

[0010] Step S4, performing radiation correction on the cropped remote sensing image;

[0011] Step S5: stitching the corrected remote sensing images to form a complete regional remote sensing image.

[0012] Preferably, the splicing targets include rivers, bridges, lakes, dams, airports and forest parks.

[0013] Preferably, the specific steps of step S1 are:

[0014] Step S11, obtaining historical map data of the area to be tested;

[0015] Step S12, extracting the position of each splicing target object in the historical map data, and outputting the map image containing a single splicing target object;

[0016] Step S13, inputting a map image containing a single type of stitching target object into a trained neural network, and having the neural network output a dispersion result of the stitching target object;

[0017] Step S14: Prioritize the splicing targets based on the dispersion results to obtain a ranking table, wherein the more dispersed the splicing targets are, the higher their priority.

[0018] Preferably, the specific steps of step S2 are:

[0019] Step S21, controlling the remote sensing acquisition device to acquire remote sensing images, obtaining stitching targets contained in the acquired remote sensing images, and selecting the stitching targets with the highest priority based on the sorting table;

[0020] Step S22, controlling the remote sensing acquisition device to acquire adjacent remote sensing images, and making the acquired remote sensing images contain the same stitching target object selected during the previous remote sensing image acquisition;

[0021] Step S23, repeatedly collecting remote sensing images until the remote sensing image collection of the entire area to be measured is completed.

[0022] Preferably, the specific steps of step S3 are: cutting at the center of the same stitching target object selected from two adjacent remote sensing images.

[0023] Preferably, the specific steps of step S4 include:

[0024] Step S41, obtaining a remote sensing image to be corrected, and searching for a multi-temporal remote sensing image corresponding to the remote sensing image to be corrected;

[0025] Step S42, determining the remote sensing image acquisition area through multi-temporal remote sensing images;

[0026] Step S43, obtaining a first acquisition time of the remote sensing image to be corrected, and obtaining a second acquisition time of the multi-temporal remote sensing image;

[0027] Step S44, evaluating the influence of seasonal changes, human activities and rainfall and wind on the remote sensing image acquisition area, and obtaining an invariant target set, and selecting an invariant target for correction through the invariant target set;

[0028] Step S45, performing radiation correction using the grayscale value of the same correction invariant target in the multi-temporal remote sensing image and the remote sensing image to be corrected.

[0029] Preferably, the invariant targets include clear and deep oligotrophic lakes, dense forests, dark water bodies, flat asphalt roofs, concrete roads or large parking lots, concrete aprons, gravel-covered areas without impurities, gravel surfaces, bright sand, and bare sand soil.

[0030] Preferably, the specific steps of step S44 include:

[0031] Step S441, obtaining the climate type of the remote sensing image acquisition area, and obtaining temperature data at the first acquisition time and the second acquisition time based on the climate type;

[0032] Step S442: if the temperature data difference is greater than the set threshold, the clear and deep oligotrophic lakes, dense forests, and dark water bodies are removed from the invariant targets, and the remaining invariant targets are combined into an invariant target set;

[0033] Step S443, obtaining the vehicle flow and pedestrian flow in the remote sensing image acquisition area between the first acquisition time and the second acquisition time based on the high-precision map software;

[0034] Step S444: if the vehicle flow and the pedestrian flow are greater than a preset threshold, the flat asphalt roof, concrete road surface or large parking lot, concrete apron are removed from the invariant targets, and the remaining invariant targets are combined into an invariant target set;

[0035] Step S445, collecting data on rainfall and wind speed in the remote sensing image collection area between the first collection time and the second collection time;

[0036] Step S446: if the collected rainfall and wind data are greater than a preset threshold, the gravel-covered area, gravel surface, bright sandy land and bare sandy soil without impurities are removed from the invariant targets, and the remaining invariant targets are combined into an invariant target set;

[0037] Step S447, performing image recognition on the multi-temporal remote sensing images to obtain the invariant targets to be selected contained therein;

[0038] Step S448: output the invariant targets to be selected contained in the invariant target set as invariant targets for correction.

[0039] Preferably, the specific steps of step S45 are: according to the grayscale values ​​of the same correction invariant target in the multi-temporal remote sensing image and the remote sensing image to be corrected, the gain and offset values ​​are calculated using a linear regression method, and the grayscale values ​​of each band of the remote sensing image to be corrected are linearly transformed to complete the relative radiation correction.

[0040] A land surveying and mapping system for dynamic remote sensing monitoring, comprising:

[0041] A sorting unit, used for obtaining the stitching targets in the area to be tested, sorting the stitching targets by priority, and forming a sorting table;

[0042] The acquisition unit is used to control the remote sensing acquisition device to acquire remote sensing images containing the stitching target, and the acquired adjacent remote sensing images all contain the same stitching target with a higher priority;

[0043] A cropping unit, used for cropping the remote sensing image based on the target object for stitching;

[0044] A correction unit, used for performing radiation correction on the cropped remote sensing image;

[0045] A stitching unit, used for stitching the corrected remote sensing images to form a complete regional remote sensing image;

[0046] The sorting unit, the collecting unit, the cutting unit, the correcting unit and the splicing unit are sequentially data-connected.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] ① Before collecting remote sensing images, the stitching targets contained in the area to be measured are first obtained and prioritized. Then, when collecting remote sensing images, the stitching targets are included in the stitched remote sensing images, so that the stitching targets can be used as a reference during stitching to ensure that the stitched regional remote sensing images can accurately reflect the actual terrain of the area to be measured;

[0049] ② Before stitching, the remote sensing images will be subjected to radiation correction to eliminate the influence of atmospheric transmittance and solar altitude angle, improve the quality and availability of remote sensing images, and facilitate more accurate analysis of changes and characteristics of the earth's surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 A flow chart of a land surveying and mapping method for dynamic remote sensing monitoring according to the present invention;

[0052] Figure 2 A flowchart of step S1 of a land surveying and mapping method for dynamic remote sensing monitoring according to the present invention;

[0053] Figure 3 A flowchart of step S2 of a land surveying and mapping method for dynamic remote sensing monitoring according to the present invention;

[0054] Figure 4 A flowchart of step S4 of a land surveying and mapping method for dynamic remote sensing monitoring according to the present invention;

[0055] Figure 5 A flowchart of step S44 of a land surveying and mapping method for dynamic remote sensing monitoring according to the present invention;

[0056] Figure 6 A schematic diagram of a land surveying and mapping system for dynamic remote sensing monitoring according to the present invention;

[0057] In the figure, 1 is a sorting unit, 2 is a collection unit, 3 is a cutting unit, 4 is a correction unit, and 5 is a stitching unit. DETAILED DESCRIPTION

[0058] In order to better understand the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described in conjunction with the accompanying drawings.

[0059] See also Figures 1 to 5 The present invention provides a land surveying and mapping method for dynamic remote sensing monitoring, comprising the following steps:

[0060] Step S1: obtaining the stitching targets in the area to be tested, the stitching targets include rivers, bridges, lakes, dams, airports and forest parks, sorting the stitching targets by priority, and forming a sorting table. The specific steps are as follows:

[0061] Step S11, obtaining historical map data of the area to be tested;

[0062] Step S12, extracting the position of each splicing target object in the historical map data, and outputting the map image containing a single splicing target object;

[0063] Step S13, inputting a map image containing a single type of stitching target object into a trained neural network, and having the neural network output a dispersion result of the stitching target object;

[0064] Step S14: Prioritize the splicing targets based on the dispersion results to obtain a ranking table, wherein the more dispersed the splicing targets are, the higher their priority.

[0065] When conducting land surveying and mapping, it is necessary to collect multiple remote sensing images, and then stitch the collected multiple remote sensing images together to form a complete regional remote sensing image. Before stitching, it is often necessary to cut off the redundant overlapping parts in the remote sensing image. The traditional method requires determining the cropping range of the remote sensing image, which takes a lot of time and is prone to errors. For this reason, the present invention first obtains stitching targets in the area to be measured before collecting remote sensing images, wherein the stitching targets include some large fixed objects such as rivers, bridges, lakes, dams, airports and forest parks. These large fixed objects are large in size and will not move. They are displayed more clearly in the remote sensing image. The stitching targets can be used as the boundaries of the collected remote sensing images for collection, and then when cutting, the stitching targets can be used as reference for cutting.

[0066] When obtaining stitching targets, first obtain the historical map data of the area to be tested, then locate the position of each stitching target in the historical map data, then display the same stitching target on the map, and output the corresponding map image. The degree of dispersion of the stitching targets in the map is identified through the trained neural network, and the dispersion result of each stitching target is obtained. Finally, the stitching targets can be sorted according to the dispersion results. The more dispersed the stitching targets are, the higher their priority is. The reason is that the collected remote sensing images can be closer to the collected image area of ​​the remote sensing collection equipment. If the number of stitching targets is small, remote sensing images with overlapping parts cannot be obtained.

[0067] Step S2: Control the remote sensing acquisition device to acquire remote sensing images containing the stitching target, and the acquired adjacent remote sensing images all contain the same stitching target with a higher priority. The specific steps are:

[0068] Step S21, controlling the remote sensing acquisition device to acquire remote sensing images, obtaining stitching targets contained in the acquired remote sensing images, and selecting the stitching targets with the highest priority based on the sorting table;

[0069] Step S22, controlling the remote sensing acquisition device to acquire adjacent remote sensing images, and making the acquired remote sensing images contain the same stitching target object selected during the previous remote sensing image acquisition;

[0070] Step S23, repeatedly collecting remote sensing images until the remote sensing image collection of the entire area to be measured is completed.

[0071] When collecting remote sensing images, they are collected in a certain order. There is an overlapping area between the remote sensing image collected later and the remote sensing image collected earlier. The overlapping area will contain the stitching target determined by the remote sensing image collected earlier. Therefore, the two remote sensing images before and after contain the same stitching target, which is convenient for subsequent cutting.

[0072] When collecting the first remote sensing image, the stitching targets contained therein are extracted and classified, the stitching targets are compared with the sorting table, and the stitching targets with the highest priority are selected based on the sorting table. When collecting the second remote sensing image, the stitching targets selected in the first remote sensing image need to be included inside. After the acquisition is completed, the stitching targets in the second remote sensing image are extracted and judged again, and the optimal stitching targets based on the second remote sensing image are selected. By analogy, all remote sensing images finally obtained will have the same stitching targets as other remote sensing images. Then, in step S3, the remote sensing image can be cropped based on the stitching targets. The specific steps are to crop from the center of the same stitching target selected from two adjacent remote sensing images, that is, to crop the overlapping part containing the stitching target, and there will be no overlapping part in the remaining remote sensing image. Since the cropping is performed by the stitching target, the accuracy of the cropping can be guaranteed to avoid excessive or insufficient cropping.

[0073] Since the collected remote sensing images may cause radiation distortion or aberration due to atmospheric transmittance and solar altitude angle, in step S4, radiation correction is performed on the cropped remote sensing images. The specific steps include:

[0074] Step S41, obtaining a remote sensing image to be corrected, and searching for a multi-temporal remote sensing image corresponding to the remote sensing image to be corrected;

[0075] When remote sensing acquisition equipment collects remote sensing images in the air, due to the influence of atmospheric transmittance and solar altitude angle, etc., the collected image data may have radiation distortion or distortion problems. After the remote sensing acquisition equipment collects the remote sensing image, it is judged whether the remote sensing image has radiation distortion or distortion problems through artificial or deep learning methods, and the remote sensing image with problems is output as the remote sensing image to be corrected. Then, according to the positioning information and time information when the remote sensing equipment that collects the remote sensing image to be corrected collects the image information, multi-phase remote sensing images with the same position as the current positioning information but different collection time can be found in the remote sensing database. For the multi-phase remote sensing images stored in the remote sensing database, they are all standard remote sensing images without radiation distortion or distortion. The purpose is to achieve relative radiation correction of the remote sensing image to be corrected through standard remote sensing images.

[0076] Step S42, determining the remote sensing image acquisition area through multi-temporal remote sensing images;

[0077] Step S43, obtaining a first acquisition time of the remote sensing image to be corrected, and obtaining a second acquisition time of the multi-temporal remote sensing image;

[0078] For the invariant targets, they include clear and deep oligotrophic lakes, dense woods, dark water bodies, flat asphalt roofs, concrete roads or large parking lots, concrete aprons, gravel-covered areas without impurities, gravel surfaces, bright sand and bare sand soil. In multi-temporal remote sensing images, there will be multiple invariant targets. The standard state of the invariant targets in the multi-temporal remote sensing images can be used to adjust the invariant targets in the remote sensing images to be corrected, thereby achieving radiation correction. Among the above-mentioned multiple invariant targets, they will be affected by external factors over time. Therefore, it is necessary to judge the changes of the invariant targets between the first acquisition moment and the second acquisition moment.

[0079] To this end, in step S44, the remote sensing image acquisition area is evaluated for the degree of influence of seasonal changes, human activities, and rainfall and wind, and an invariant target set is obtained. The invariant target for correction is selected through the invariant target set. The specific steps include:

[0080] Step S441, obtaining the climate type of the remote sensing image acquisition area, and obtaining temperature data at the first acquisition time and the second acquisition time based on the climate type;

[0081] Step S442: if the temperature data difference is greater than the set threshold, clear and deep oligotrophic lakes, dense forests, and dark water bodies are removed from the invariant targets, and the remaining invariant targets are combined into an invariant target set;

[0082] For the three invariant targets of clear and deep oligotrophic lakes, dense forests, and dark water bodies, they are greatly affected by seasonal climate. In some climates, when the temperature is low in autumn and winter, lakes and water bodies will freeze, and leaves will fall in the woods. If the remote sensing image acquisition area includes these three invariant targets, it is necessary to consider whether the climate change in the remote sensing image acquisition area has an impact on the three invariant targets. Therefore, it is first necessary to judge the climate type of the remote sensing image acquisition area. After obtaining the climate type, the temperature data of the invariant targets at the first acquisition time and the second acquisition time can be collected. If the difference between the temperature data at the first acquisition time and the second acquisition time is large, it means that there will be autumn leaves and winter ice. Therefore, the invariant targets at this time do not need to consider the clear and deep oligotrophic lakes, dense forests, and dark water bodies. These three types are eliminated from the invariant targets to form an invariant target set.

[0083] As for the judgment of the difference in temperature data, there is a constraint condition, that is, the signs of the temperature data at the first collection moment and the second collection moment must be opposite, that is, one is above zero degrees and the other is below zero degrees, to avoid the situation where both temperature data are below zero degrees and the difference is greater than the preset threshold and is judged to have a great impact on the invariant target. For example, when the two temperature data are -5 degrees and -35 degrees respectively, the water body and the lake are both in a frozen state, that is, the invariant target will not be affected by the external temperature changes. At this time, the water body and the lake can be selected as the invariant target for correction.

[0084] Step S443, obtaining the vehicle flow and pedestrian flow in the remote sensing image acquisition area between the first acquisition time and the second acquisition time based on the high-precision map software;

[0085] Step S444: if the vehicle flow and the pedestrian flow are greater than a preset threshold, the flat asphalt roof, concrete road surface or large parking lot, concrete apron are removed from the invariant targets, and the remaining invariant targets are combined into an invariant target set;

[0086] For the invariant targets such as flat asphalt roofs, concrete pavements, large parking lots, and concrete aprons, traces of human activities may affect the emission and reception of electromagnetic wave signals. Therefore, it is necessary to determine whether there are too many people or vehicles in the remote sensing image acquisition area. The vehicle flow and pedestrian flow between the first acquisition moment and the second acquisition moment are measured using high-precision map software. If the flow is greater than the set threshold, it means that there are many human activity traces and some invariant targets will be greatly affected. Therefore, the flat asphalt roofs, concrete pavements, large parking lots, and concrete aprons are eliminated to form a collection of invariant targets.

[0087] Step S445, collecting data on rainfall and wind speed in the remote sensing image collection area between the first collection time and the second collection time;

[0088] Step S446: if the collected rainfall and wind data are greater than a preset threshold, the gravel-covered area, gravel surface, bright sandy land and bare sandy soil without impurities are removed from the invariant targets, and the remaining invariant targets are combined into an invariant target set;

[0089] For the unimportant gravel-covered areas, gravel surfaces, bright sand and bare sand soil, if it rains or the wind is strong, the gravel and soil will be lost, resulting in a change in the overall state. Therefore, it is necessary to collect data on the rainfall and wind speed in the remote sensing image area between the two collection times. If the collected rainfall and wind speed data are greater than the preset threshold, the unimportant gravel-covered areas, gravel surfaces, bright sand and bare sand soil will be eliminated to form a collection of unimportant targets.

[0090] As for the preset thresholds, they can be set by the staff according to the region where they are located.

[0091] Step S447, performing image recognition on the multi-temporal remote sensing images to obtain the invariant targets to be selected contained therein;

[0092] Step S448: output the invariant target to be selected contained in the invariant target set as the invariant target for correction.

[0093] After obtaining the invariant target set, the invariant targets to be selected contained in the multi-temporal remote sensing images are extracted, and then the invariant targets to be selected are compared with the invariant targets in the invariant target set, and the invariant targets contained in the invariant target set are selected as the invariant targets for correction.

[0094] Step S45, radiation correction is performed using the grayscale values ​​of the same calibration invariant target in the multi-temporal remote sensing images and the remote sensing images to be corrected. The specific steps are: based on the grayscale values ​​of the same calibration invariant target in the multi-temporal remote sensing images and the remote sensing images to be corrected, the gain and offset values ​​are calculated using a linear regression method, and the grayscale values ​​of each band of the remote sensing image to be corrected are linearly transformed to complete relative radiation correction.

[0095] Since the multi-temporal remote sensing images are standard remote sensing images, after selecting the invariant target in the multi-temporal remote sensing images, the grayscale value difference between the two is used for calculation, so that the gain and offset can be obtained. According to the gain and offset, the grayscale values ​​of each band of the remote sensing image to be corrected can be linearly transformed to complete the relative radiation correction.

[0096] Step S5, stitching the corrected remote sensing images to form a complete regional remote sensing image. Since the target object is used as a reference line for cutting through stitching, over- or under-cutting can be avoided. Therefore, the stitched complete regional remote sensing image can accurately reflect the actual topography, so as to provide theoretical guidance for land surveying and mapping.

[0097] Reference Figure 6 A land surveying and mapping system for dynamic remote sensing monitoring is shown, comprising:

[0098] A sorting unit 1 is used to obtain the stitching targets in the area to be tested, sort the stitching targets by priority, and form a sorting table;

[0099] The acquisition unit 2 is used to control the remote sensing acquisition device to acquire remote sensing images containing the stitching target, and the acquired adjacent remote sensing images all contain the same stitching target with a higher priority;

[0100] A cropping unit 3, used for cropping the remote sensing image based on the stitching target;

[0101] A correction unit 4, used for performing radiation correction on the cropped remote sensing image;

[0102] A stitching unit 5 is used to stitch the corrected remote sensing images to form a complete regional remote sensing image;

[0103] The sorting unit 1, the collecting unit 2, the cutting unit 3, the correcting unit 4 and the splicing unit 5 are sequentially connected in data.

[0104] The sorting unit 1 can prioritize the selected stitching targets, and then the acquisition unit 2 can control the remote sensing acquisition equipment to acquire remote sensing images. During the acquisition process, adjacent remote sensing images containing the same stitching target are acquired, and then the cropping unit 3 can perform cropping based on the stitching target to avoid over- or under-cropping. After cropping, radiation correction can be performed, and finally the corrected remote sensing images can be spliced ​​into a complete regional remote sensing image to ensure that the spliced ​​regional remote sensing images can accurately reflect the actual terrain of the area to be measured.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A land surveying and mapping method for dynamic remote sensing monitoring, characterized in that: The following steps are involved: Step S1, obtaining the stitching targets in the area to be tested, sorting the stitching targets by priority, and forming a sorting table; Step S2, controlling the remote sensing acquisition device to acquire remote sensing images containing the stitching target, and the acquired adjacent remote sensing images all contain the same stitching target with a higher priority; Step S3, cropping the remote sensing image based on the stitching target; Step S4, performing radiation correction on the cropped remote sensing image; Step S5, stitching the corrected remote sensing images to form a complete regional remote sensing image; The specific steps of step S1 are: Step S11, obtaining historical map data of the area to be tested; Step S12, extracting the position of each splicing target object in the historical map data, and outputting the map image containing a single splicing target object; Step S13, inputting a map image containing a single type of stitching target object into a trained neural network, and having the neural network output a dispersion result of the stitching target object; Step S14: Prioritize the splicing targets based on the dispersion results to obtain a ranking table, wherein the more dispersed the splicing targets are, the higher their priority.

2. A land surveying and mapping method for dynamic remote sensing monitoring according to claim 1, characterized in that: The objects for splicing include rivers, bridges, lakes, dams, airports and forest parks.

3. The method for land surveying and mapping by dynamic remote sensing monitoring according to claim 1, characterized in that: The specific steps of step S2 are: Step S21, controlling the remote sensing acquisition device to acquire remote sensing images, obtaining stitching targets contained in the acquired remote sensing images, and selecting the stitching targets with the highest priority based on the sorting table; Step S22, controlling the remote sensing acquisition device to acquire adjacent remote sensing images, and making the acquired remote sensing images contain the same stitching target object selected during the previous remote sensing image acquisition; Step S23, repeatedly collecting remote sensing images until the remote sensing image collection of the entire area to be measured is completed.

4. A land surveying and mapping method for dynamic remote sensing monitoring according to claim 3, characterized in that: The specific steps of step S3 are: cutting at the center of the same stitching target object selected from two adjacent remote sensing images.

5. The method for land surveying and mapping by dynamic remote sensing monitoring according to claim 1, characterized in that: The specific steps of step S4 include: Step S41, obtaining a remote sensing image to be corrected, and searching for a multi-temporal remote sensing image corresponding to the remote sensing image to be corrected; Step S42, determining the remote sensing image acquisition area through multi-temporal remote sensing images; Step S43, obtaining a first acquisition time of the remote sensing image to be corrected, and obtaining a second acquisition time of the multi-temporal remote sensing image; Step S44, evaluating the influence of seasonal changes, human activities and rainfall and wind on the remote sensing image acquisition area, and obtaining an invariant target set, and selecting an invariant target for correction through the invariant target set; Step S45, performing radiation correction using the grayscale value of the same correction invariant target in the multi-temporal remote sensing image and the remote sensing image to be corrected.

6. A land surveying and mapping method for dynamic remote sensing monitoring according to claim 5, characterized in that: The invariant targets include clear and deep oligotrophic lakes, dense forests, dark water bodies, flat asphalt roofs, concrete roads or large parking lots, concrete aprons, clean gravel covers, gravel surfaces, bright sand, and bare sand soils.

7. The method for land surveying and mapping by dynamic remote sensing monitoring according to claim 5, characterized in that: The specific steps of step S44 include: Step S441, obtaining the climate type of the remote sensing image acquisition area, and obtaining temperature data at the first acquisition time and the second acquisition time based on the climate type; Step S442: if the temperature data difference is greater than the set threshold, the clear and deep oligotrophic lakes, dense forests, and dark water bodies are removed from the invariant targets, and the remaining invariant targets are combined into an invariant target set; Step S443, obtaining the vehicle flow and pedestrian flow in the remote sensing image acquisition area between the first acquisition time and the second acquisition time based on the high-precision map software; Step S444: if the vehicle flow and the pedestrian flow are greater than a preset threshold, the flat asphalt roof, concrete road surface or large parking lot, concrete apron are removed from the invariant targets, and the remaining invariant targets are combined into an invariant target set; Step S445, collecting data on rainfall and wind speed in the remote sensing image collection area between the first collection time and the second collection time; Step S446: if the collected rainfall and wind data are greater than a preset threshold, the gravel-covered area, gravel surface, bright sandy land and bare sandy soil without impurities are removed from the invariant targets, and the remaining invariant targets are combined into an invariant target set; Step S447, performing image recognition on the multi-temporal remote sensing images to obtain the invariant targets to be selected contained therein; Step S448: output the invariant target to be selected contained in the invariant target set as the invariant target for correction.

8. The method for land surveying and mapping by dynamic remote sensing monitoring according to claim 5, characterized in that: The specific steps of step S45 are: according to the grayscale values ​​of the same calibration invariant target in the multi-temporal remote sensing image and the remote sensing image to be corrected, the gain and offset values ​​are calculated by linear regression method, and the grayscale values ​​of each band of the remote sensing image to be corrected are linearly transformed to complete the relative radiation correction.

9. A land surveying and mapping system for dynamic remote sensing monitoring using the land surveying and mapping method for dynamic remote sensing monitoring according to any one of claims 1 to 8, characterized in that: include: A sorting unit, used for obtaining the stitching targets in the area to be tested, sorting the stitching targets by priority, and forming a sorting table; The acquisition unit is used to control the remote sensing acquisition device to acquire remote sensing images containing the stitching target, and the acquired adjacent remote sensing images all contain the same stitching target with a higher priority; A cropping unit, used for cropping the remote sensing image based on the target object for stitching; A correction unit, used for performing radiation correction on the cropped remote sensing image; A stitching unit, used for stitching the corrected remote sensing images to form a complete regional remote sensing image; The sorting unit, the collecting unit, the cutting unit, the correcting unit and the splicing unit are sequentially data-connected.

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