CN115830806B - Natural disaster condition analysis system based on high-resolution remote sensing - Google Patents
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Abstract
The invention discloses a natural disaster condition analysis system based on high-resolution remote sensing, which comprises a shooting point comparison unit, a comparison abnormal point extraction unit and an abnormal image definition processing unit, wherein the shooting point comparison unit is used for comparing the shooting points of the natural disaster condition; the normal image of the same regional point and the image information of the regional point which is currently shot are combined through the shooting point comparison unit, the characteristic points of the normal image and the regional point are compared, the abnormal regional point is determined through the abnormal point comparison extraction unit, then the image of the abnormal regional point is subjected to image noise reduction through the abnormal image sharpness processing unit, so that a sharpness image is obtained, the abnormal image is analyzed before the monitoring image is not subjected to noise reduction, at the moment, the normal image is not required to be subjected to noise reduction, and only the image of the abnormal regional point is required to be subjected to noise reduction, so that noise reduction cost is saved, and the abnormal image processing efficiency is improved.
Description
Technical Field
The invention relates to the technical field of natural disaster monitoring, in particular to a natural disaster condition analysis system based on high-resolution remote sensing.
Background
The remote sensing technology is applied to the disaster prevention and treatment field, the advantages of the remote sensing technology can be fully exerted, and the disaster assessment is more accurate by researching the remote sensing image data based on high-resolution, so that help is provided for rapid post-disaster rescue, and the loss is reduced to the greatest extent.
The invention provides a high-efficiency natural disaster automatic early warning system, which comprises an information acquisition device, a communication device, an information processing device and an early warning device, wherein the information acquisition device is a vehicle-mounted mobile measurement system and is used for acquiring measurement information of an early warning area, the communication device is used for transmitting the measurement information to the information processing device, the information processing device is used for processing the measurement information and generating an early warning signal according to a processing result, the early warning device is used for receiving the early warning signal and sending out an alarm, the system needs to analyze and process each received image, and the number of images to be processed every day is excessive no matter whether the received image is a normal image when no natural disaster occurs or not.
Disclosure of Invention
The invention aims to provide a natural disaster situation analysis system based on high-resolution remote sensing so as to solve the problems in the background technology.
In order to achieve the above purpose, a natural disaster condition analysis system based on high-resolution remote sensing is provided, the system comprises a high-resolution remote sensing monitoring unit, the high-resolution remote sensing monitoring unit adopts a high-resolution remote sensing technology to monitor and process a monitored area in real time, the output end of the high-resolution remote sensing monitoring unit is connected with a point region shooting unit, the point region shooting unit is used for determining monitoring points at different positions of the monitored area and shooting the determined monitoring points in a point manner, regional point image information is generated, the output end of the point region shooting unit is connected with a shooting point comparison unit, the input end of the shooting point comparison unit is also connected with a shooting point normal feature extraction unit, the shooting point normal feature extraction unit records normal images when natural disasters do not occur according to the determined monitoring points, and transmits the normal images to the shooting point comparison unit, the shooting point comparison unit combines the normal images of the same regional point and the regional point image information which are shot at present, the output end of the shooting point comparison unit is connected with a comparison abnormal point extraction unit, the comparison abnormal point extraction unit is preset with a comparison threshold value, the comparison point comparison unit is used for comparing the abnormal point image, the abnormal image is processed with the abnormal image, the abnormal image is processed, and the abnormal image is clear, and the abnormal image is processed, and the abnormal image is obtained.
As a further improvement of the technical scheme, the shooting point normal feature extraction unit comprises a regional point geographical environment analysis module, the regional point geographical environment analysis module analyzes and processes the monitored point geographical environment according to real-time monitoring information, an obvious feature point marking module is connected to the output end of the regional point geographical environment analysis module, and the obvious feature point marking module marks obvious feature points of the monitored regional point according to the analysis and processing result of the monitored point geographical environment.
As a further improvement of the technical scheme, the output end of the obvious characteristic point marking module is connected with a characteristic point replacement module, and the characteristic point replacement module is used for replacing the position of the characteristic point in real time according to the environmental change rule of the monitoring point.
As a further improvement of the technical scheme, the shooting point comparison unit adopts an image analysis algorithm, and the algorithm formula is as follows:
U＝[μ1,μ2,…,μm]；
Wherein mu is the image mean value of each image, i is the label of different images, n is the number of pixels, p i is the pixel value, U is the image mean value set shot in the normal state of each monitoring point, mu 1 to mu m are the image mean values shot in the normal state of each monitoring point, f (mu) is the mean value contrast function, Is the contrast value of the average value of the currently shot image and the average value of the image in the normal state of the image,/>For comparison threshold, when the comparison value/>When the average value comparison function f (mu) is smaller than the comparison threshold value, the output of the average value comparison function f (mu) is 0, which indicates that the currently shot image is abnormal, and when the comparison value/>And when the average value comparison function f (mu) is not smaller than the comparison threshold value, outputting the average value comparison function f (mu) to be 1, and indicating that the currently shot image is in a normal state.
As a further improvement of the technical scheme, the abnormal image sharpening processing unit comprises a block point ambiguity determining module, wherein the block point ambiguity determining module is used for dividing the abnormal image, dividing each abnormal image into blocks with the same number according to the same separation mode, and determining the ambiguity degree of each block point, the output end of the block point ambiguity determining module is connected with a characteristic point determining module, the characteristic point determining module is used for determining the block position of the characteristic point of each abnormal image, the output end of the characteristic point determining module is connected with a characteristic block point sharpening module, and the characteristic block point sharpening module is used for sharpening the block with the characteristic point according to the block position of the characteristic of each abnormal image.
As a further improvement of the technical scheme, the output end of the abnormal image sharpening processing unit is connected with a data storage unit, and the data storage unit is used for distributing a database and storing different types of data in a classified mode.
As a further improvement of the technical scheme, the output end of the high-resolution remote sensing monitoring unit is connected with a shooting area position determining unit, the output of the shooting area position determining unit is connected with the input end of the point-division area shooting unit, and the shooting area position determining unit is used for determining the geographic position of the monitoring point shot each time.
As a further improvement of the technical scheme, the output end of the shooting area position determining unit is connected with the input end of the shooting point normal feature extracting unit, the output end of the shooting point normal feature extracting unit is connected with the input end of the data storage unit, and the shooting area position determining unit provides the monitoring point geographic position for the shooting point normal feature extracting unit so as to determine the image information of the monitoring point geographic position in a normal state.
As a further improvement of the technical scheme, the input end of the shooting unit of the dotted area is connected with a shooting interval time determining unit, the shooting interval time determining unit is used for determining the interval time of each shooting, and the output end of the shooting interval time determining unit is connected with the input end of the data storage unit.
Compared with the prior art, the invention has the beneficial effects that:
According to the invention, the normal image of the same regional point and the image information of the regional point which is shot currently are combined by the shooting point comparison unit, the characteristic points of the normal image and the regional point are compared, the abnormal regional point is determined by the abnormal point comparison extraction unit, then the image of the abnormal regional point is subjected to image noise reduction by the abnormal image sharpness processing unit, so that a sharpness image is obtained, the abnormal image is analyzed before the monitoring image is not subjected to noise reduction, at the moment, the normal image is not required to be subjected to noise reduction, and only the image of the abnormal regional point is required to be subjected to noise reduction, so that the noise reduction cost is saved, and the abnormal image processing efficiency is improved.
The invention divides the abnormal image by the block point ambiguity determining module, divides each abnormal image into the same number of blocks according to the same dividing mode, determines the ambiguity of each block point, determines the block position of the characteristic of each abnormal image by the characteristic point determining module, and performs the sharpening process of the block with the abnormal characteristic point by the characteristic block point sharpening module without comprehensively reducing the noise of each abnormal image, and only performs the sharpening process of the block with the abnormal characteristic point, thereby improving the processing efficiency and reducing the processing flow of the abnormal image.
According to the invention, the geographical position of the monitoring point shot each time is determined by the shooting area position determining unit, shooting memory is formed, and when the monitoring is carried out to the determined geographical position of the monitoring point, the corresponding geographical position image is shot for accurate comparison in the later period, so that the generation of comparison errors is reduced.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a flowchart of a normal feature extraction unit of a shooting point according to the present invention;
FIG. 3 is a flowchart of an outlier image sharpening process unit according to the present invention.
The meaning of each reference sign in the figure is:
10. A high-resolution remote sensing monitoring unit;
20. a split-point region shooting unit;
30. a shooting point comparison unit;
40. A shooting point normal feature extraction unit; 410. the regional point geographic environment analysis module; 420. an obvious feature point marking module; 430. a feature point replacement module;
50. comparing the abnormal point extraction unit;
60. An abnormal image sharpening processing unit; 610. a block point ambiguity determining module; 620. a feature point determining module; 630. the characteristic block point sharpening module;
70. A data storage unit;
80. A shooting region position determining unit;
90. And a shooting interval time determining unit.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1-3, the present embodiment provides a natural disaster situation analysis system based on high-resolution remote sensing, which includes a high-resolution remote sensing monitoring unit 10, the high-resolution remote sensing monitoring unit 10 monitors a monitored area in real time by adopting a high-resolution remote sensing technology, a point-division area shooting unit 20 is connected to an output end of the high-resolution remote sensing monitoring unit 10, the point-division area shooting unit 20 is used for determining monitoring points at different positions of the monitored area, and shooting the determined monitoring points in different points to generate area point image information, an output end of the point-division area shooting unit 20 is connected with a shooting point comparison unit 30, an input end of the shooting point comparison unit 30 is also connected with a shooting point normal feature extraction unit 40, the shooting point normal feature extraction unit 40 records normal images when no natural disasters occur according to the determined monitoring points, and transmits the normal images to the shooting point comparison unit 30, the shooting point comparison unit 30 combines the normal images of the same area point and the image information of the area point images currently shot, the contrast ratio is obtained by comparing images at the same positions of two different time points, the same points, and the contrast ratio is also based on different points of images, and a small amount of desert or a small amount of desert characteristic is required, and the desert characteristic is formulated as a small amount of a point, and the green characteristic is required. The output end of the shooting point comparison unit 30 is connected with a comparison abnormal point extraction unit 50, the comparison abnormal point extraction unit 50 presets a comparison threshold value, the regional point image information of the comparison threshold value exceeding the comparison value is marked as an abnormal regional point, the output end of the comparison abnormal point extraction unit 50 is connected with an abnormal image sharpening processing unit 60, and the abnormal image sharpening processing unit 60 carries out image noise reduction on the image of the abnormal regional point so as to obtain a sharpened image.
When the system is particularly used, the high-resolution remote sensing monitoring unit 10 monitors a monitored area in real time by adopting a high-resolution remote sensing technology, the high-resolution remote sensing technology adopts a high-resolution remote sensing image semantic segmentation network (GBFNet) based on a global-boundary fusion network, the semantic consistency among distinguishing features is enhanced by introducing image context, and meanwhile, boundary information is introduced by using a boundary network so as to increase the feature distance between adjacent ground objects and relieve the phenomenon of indiscriminate between the classes. The point area shooting unit 20 is configured to determine monitoring points at different positions of a monitoring area, when the monitoring point area shooting unit 20 monitors the determined monitoring points, perform point shooting on the determined monitoring points, generate area point image information, and transmit the area point image information to the shooting point contrast unit 30, the shooting point normal feature extraction unit 40 records normal images when no natural disaster occurs according to the determined monitoring points, and transmits the normal images to the shooting point contrast unit 30, the shooting point contrast unit 30 combines the normal images of the same area point with the area point image information currently shot, compares the feature points of the two to obtain a contrast value, the comparison anomaly point extraction unit 50 presets a comparison threshold, marks the area point image information with the contrast value exceeding the comparison threshold as an anomaly area point, and the anomaly image sharpening unit 60 performs image noise reduction on the images of the anomaly area point to obtain a sharpened image.
According to the invention, the shot point comparison unit 30 combines the normal image of the same regional point and the image information of the regional point shot currently, the characteristic points of the shot point comparison unit and the shot point comparison unit are used for comparing the characteristic points of the shot point comparison unit and the shot point comparison unit, the abnormal regional point extraction unit 50 is used for determining the abnormal regional point, and the abnormal image sharpening processing unit 60 is used for carrying out image denoising on the image of the abnormal regional point so as to obtain a sharpened image, so that the abnormal image is analyzed before the monitoring image is not denoised, and at the moment, the normal image is not required to be denoised, and only the image of the abnormal regional point is required to be denoised, so that the denoising cost is saved, and the abnormal image processing efficiency is improved.
In addition, the shooting point normal feature extraction unit 40 includes a regional point geographical environment analysis module 410, the regional point geographical environment analysis module 410 analyzes and processes the monitored point geographical environment according to the real-time monitoring information, the output end of the regional point geographical environment analysis module 410 is connected with an obvious feature point marking module 420, and the obvious feature point marking module 420 marks the obvious feature point of the monitored regional point according to the analysis and processing result of the monitored point geographical environment. When the method is specifically used, in the process of monitoring and shooting normal regional points, because of the difference of geographic environments of the monitoring points, geographic patterns are uneven, if the monitoring points are shot immediately, the comparison value is difficult to determine in the later period, at this time, the regional point geographic environment analysis module 410 is used for analyzing and processing the geographic environments of the monitoring points according to real-time monitoring information, the analysis processing result is transmitted to the obvious characteristic point marking module 420, the obvious characteristic point marking module 420 is used for marking the obvious characteristic points of the monitoring regional points according to the analysis processing result of the geographic environments of the monitoring points, for example, in the process of monitoring and predicting natural disasters of the debris flow, the general monitoring points are cliffs or faults, at this time, the regional point of the monitoring points is analyzed to be the cliffs or faults by the regional point geographic environment analysis module 410, after the position is determined, the characteristic points of the cliffs or faults are observed, for example, the cliffs or rocks with the edges protruding are detected, in the later period, whether the images of the regional point of the current monitoring are abnormal can be determined in advance by judging the change conditions of the characteristic points of two images in the comparison process, the comparison efficiency is improved, and the comparison error is reduced.
Further, the output end of the obvious feature point marking module 420 is connected with a feature point replacing module 430, and the feature point replacing module 430 replaces the feature point positions in real time according to the change rule of the environment of the monitoring points. When the system is specifically used, the monitoring points are not only affected by natural disasters, but also have artificial influence, for example, when fire monitoring is performed on areas with dense trees such as forests, the characteristic points of the monitoring areas generally select higher trees as characteristic points, but after the trees are cut down manually, the characteristic points disappear at the moment, the characteristic points are reselected through the characteristic point replacing module 430, and then the characteristic points determined previously are replaced.
Still further, the shooting point comparing unit 30 adopts an image analysis algorithm, and the algorithm formula is as follows:
U＝[μ1,μ2,…,μm]；
Wherein mu is the image mean value of each image, i is the label of different images, n is the number of pixels, p i is the pixel value, U is the image mean value set shot in the normal state of each monitoring point, mu 1 to mu m are the image mean values shot in the normal state of each monitoring point, f (mu) is the mean value contrast function, Is the contrast value of the average value of the currently shot image and the average value of the image in the normal state of the image,/>For comparison threshold, when the comparison value/>When the average value comparison function f (mu) is smaller than the comparison threshold value, the output of the average value comparison function f (mu) is 0, which indicates that the currently shot image is abnormal, and when the comparison value/>And when the average value comparison function f (mu) is not smaller than the comparison threshold value, outputting the average value comparison function f (mu) to be 1, and indicating that the currently shot image is in a normal state.
Specifically, the abnormal image sharpening processing unit 60 includes a block point ambiguity determining module 610, where the block point ambiguity determining module 610 is configured to divide the abnormal image, divide each abnormal image into the same number of blocks according to the same division manner, and determine the degree of blurring of each block point, the output end of the block point ambiguity determining module 610 is connected with a feature point determining module 620, the feature point determining module 620 is configured to determine the block position where the feature point of each abnormal image is located, the output end of the feature point determining module 620 is connected with a feature block point sharpening module 630, and the feature block point sharpening module 630 performs sharpening processing on the block with the feature point according to the block position where the feature of each abnormal image is located. When the method is specifically used, the block point ambiguity determining module 610 is used for dividing the abnormal images, dividing each abnormal image into the same number of blocks according to the same separation mode, determining the ambiguity degree of each block point, determining the block position of the feature of each abnormal image through the feature point determining module 620, and performing sharpening processing on the block with the feature point through the feature block point sharpening module 630, so that the method does not need to comprehensively reduce noise of each abnormal image, only needs to perform sharpening processing on the block with the feature point, improves processing efficiency, and reduces the processing flow of the abnormal image.
In addition, the output end of the abnormal image sharpening processing unit 60 is connected with a data storage unit 70, and the data storage unit 70 is used for distributing a database and classifying and storing different types of data. When the feature point determining module 620 determines the block position of the feature point of each abnormal image, generates feature point block information, and transmits the feature point block information to the data storage unit 70, the data storage unit 70 sets a feature point block database, and classifies and stores the feature point block information of each abnormal image, and when the secondary sharpness processing is performed in the later stage, the corresponding feature point block information can be directly called from the data storage unit 70 without reconfirming the block position of the feature point of the abnormal image, so that the sharpness processing efficiency of the abnormal image is further improved.
Further, the output end of the high-resolution remote sensing monitoring unit 10 is connected with a shooting area position determining unit 80, the output end of the shooting area position determining unit 80 is connected with the input end of the split area shooting unit 20, and the shooting area position determining unit 80 is used for determining the geographic position of the monitoring point of each shooting. In particular, during the process of shooting the monitoring points, the shooting area position determining unit 80 is required to determine the geographic position of the monitoring point shot each time to form shooting memory, and when the monitoring is performed to the determined geographic position of the monitoring point, the corresponding geographic position image is shot for accurate comparison in the later period, so that the generation of comparison errors is reduced.
Still further, the output end of the shooting area position determining unit 80 is connected to the input end of the shooting point normal feature extracting unit 40, the output end of the shooting point normal feature extracting unit 40 is connected to the input end of the data storage unit 70, the shooting area position determining unit 80 provides the shooting point normal feature extracting unit 40 with the geographical position of the monitoring point, so that the image information of the geographical position of the monitoring point in the normal state is determined, and the geographical position of the monitoring point and the corresponding image information of the geographical position of the monitoring point in the normal state are transmitted to the data storage unit 70 for storage.
Further, an imaging interval time determining unit 90 is connected to the input of the split area imaging unit 20, the imaging interval time determining unit 90 is used for determining an interval time of each imaging, and an output of the imaging interval time determining unit 90 is connected to the input of the data storage unit 70. In specific use, the shooting interval time determining unit 90 determines the interval time of each shooting, generates shooting interval time information, and transmits the shooting interval time information to the sub-point region shooting unit 20, and the sub-point region shooting unit 20 performs interval shooting on the monitoring region according to the shooting interval time information, and simultaneously transmits the shooting interval time information to the data storage unit 70 for storage.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. The utility model provides a natural disaster condition analysis system based on high-resolution remote sensing, includes high-resolution remote sensing monitor cell (10), high-resolution remote sensing monitor cell (10) adopt high-resolution remote sensing technique to carry out real-time monitoring to the monitored area and handle, high-resolution remote sensing monitor cell (10) output is connected with minute point regional shooting unit (20), minute point regional shooting unit (20) are used for confirming the monitored point in the different positions of monitored area to carry out minute point shooting to the monitored point of confirming, generate regional point image information, its characterized in that: the method comprises the steps that an imaging point comparison unit (30) is connected to the output end of a point area imaging unit (20), an imaging point normal feature extraction unit (40) is further connected to the input end of the imaging point comparison unit (30), normal images of the imaging point normal feature extraction unit (40) when natural disasters do not occur are recorded according to determined monitoring points, the normal images are transmitted to the imaging point comparison unit (30), the imaging point comparison unit (30) combines normal images of the same area points and image information of the area points imaged currently, feature points of the imaging point comparison unit (30) are compared to obtain comparison values, a comparison abnormal point extraction unit (50) is connected to the output end of the imaging point comparison unit (30), the comparison abnormal point extraction unit (50) is preset with a comparison threshold, the image information of the area points with the comparison values exceeding the comparison threshold is marked as abnormal area points, the output end of the comparison abnormal point extraction unit (50) is connected with an abnormal image sharpening processing unit (60), and the abnormal image sharpening processing unit (60) carries out image noise reduction on the images of the abnormal area points to obtain sharpening images.
2. The natural disaster situation analysis system based on high-resolution remote sensing as set forth in claim 1, wherein: the shooting point normal feature extraction unit (40) comprises a regional point geographical environment analysis module (410), the regional point geographical environment analysis module (410) analyzes and processes the geographical environment of the monitored point according to real-time monitoring information, the output end of the regional point geographical environment analysis module (410) is connected with an obvious feature point marking module (420), and the obvious feature point marking module (420) marks obvious feature points of the monitored regional point according to the analysis and processing result of the geographical environment of the monitored point.
3. The natural disaster situation analysis system based on high-resolution remote sensing as set forth in claim 2, wherein: the output end of the obvious characteristic point marking module (420) is connected with a characteristic point replacing module (430), and the characteristic point replacing module (430) replaces the position of the characteristic point in real time according to the environmental change rule of the monitoring point.
4. The natural disaster situation analysis system based on high-resolution remote sensing as set forth in claim 1, wherein: the shooting point comparison unit (30) adopts an image analysis algorithm, and the algorithm formula is as follows:
U＝[μ1,μ2,…,μm]；
Wherein mu is the image mean value of each image, i is the label of different images, n is the number of pixels, p i is the pixel value, U is the image mean value set shot in the normal state of each monitoring point, mu 1 to mu m are the image mean values shot in the normal state of each monitoring point, f (mu) is the mean value contrast function, Is the contrast value of the average value of the currently shot image and the average value of the image in the normal state of the image,/>For comparison threshold, when the comparison value/>When the average value comparison function f (mu) is smaller than the comparison threshold value, the output of the average value comparison function f (mu) is 0, which indicates that the currently shot image is abnormal, and when the comparison value/>And when the average value comparison function f (mu) is not smaller than the comparison threshold value, outputting the average value comparison function f (mu) to be 1, and indicating that the currently shot image is in a normal state.
5. The natural disaster situation analysis system based on high-resolution remote sensing as set forth in claim 1, wherein: the abnormal image sharpening processing unit (60) comprises a block point ambiguity determining module (610), wherein the block point ambiguity determining module (610) is used for dividing abnormal images, dividing each abnormal image into blocks with the same number according to the same separation mode, determining the ambiguity degree of each block point, the output end of the block point ambiguity determining module (610) is connected with a characteristic point determining module (620), the characteristic point determining module (620) is used for determining the block position of the characteristic point of each abnormal image, the output end of the characteristic point determining module (620) is connected with a characteristic block point sharpening module (630), and the characteristic block point sharpening module (630) is used for sharpening the block with the characteristic point according to the block position of the characteristic of each abnormal image.
6. The natural disaster situation analysis system based on high-resolution remote sensing according to claim 5, wherein: the output end of the abnormal image sharpening processing unit (60) is connected with a data storage unit (70), and the data storage unit (70) is used for distributing a database and storing different types of data in a classified mode.
7. The natural disaster situation analysis system based on high-resolution remote sensing as set forth in claim 6, wherein: the high-resolution remote sensing monitoring system is characterized in that the output end of the high-resolution remote sensing monitoring unit (10) is connected with a shooting area position determining unit (80), the output end of the shooting area position determining unit (80) is connected with the input end of the point-division area shooting unit (20), and the shooting area position determining unit (80) is used for determining the geographic position of a monitoring point shot each time.
8. The natural disaster situation analysis system based on high-resolution remote sensing as set forth in claim 7, wherein: the output end of the shooting area position determining unit (80) is connected with the input end of the shooting point normal feature extracting unit (40), the output end of the shooting point normal feature extracting unit (40) is connected with the input end of the data storage unit (70), and the shooting area position determining unit (80) provides a monitoring point geographic position for the shooting point normal feature extracting unit (40) so as to determine image information of the monitoring point geographic position in a normal state.
9. The natural disaster situation analysis system based on high-resolution remote sensing as set forth in claim 8, wherein: the input end of the sub-point region shooting unit (20) is connected with a shooting interval time determining unit (90), the shooting interval time determining unit (90) is used for determining interval time of shooting each time, and the output end of the shooting interval time determining unit (90) is connected with the input end of the data storage unit (70).
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