CN115861798A - Remote sensing big data interpretation system and method applied to homeland space monitoring - Google Patents
Remote sensing big data interpretation system and method applied to homeland space monitoring Download PDFInfo
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Abstract
The invention provides a remote sensing big data interpretation system and method applied to homeland space monitoring, wherein the system comprises: the acquisition module is used for distributing acquisition equipment to acquire an original remote sensing image of the area to be detected according to the flight path; the human-computer cooperation intelligent interpretation module is used for automatically interpreting the original remote sensing image according to the multi-stage coupling intelligent interpretation model, automatically extracting an interpretation result, verifying the interpretation result based on a semi-automatic extraction method, and outputting a verified interpretation result; and the homeland monitoring module is used for acquiring a classification instruction, classifying the verified interpretation results of each time phase of each partition in each region to be detected based on the image intelligent classification model and the classification instruction, and forming a required homeland space interpretation result according to the classification result.
Description
Technical Field
The invention relates to the technical field of homeland surveying and mapping, in particular to a remote sensing big data interpretation system and method applied to homeland space monitoring.
Background
The national resources are a place and a production base for the life of the people in the country, and are the basis for the survival and development of the countries and people, so that the national resources can be regarded as a general term for all natural resources in a national dominance management region in the sense, wherein the most important resources are land, water, climate, biology and mineral resources. The total amount of Chinese land resources is rich, land use information is complete, but the occupied land per capita is small, the occupied proportion of various lands is unreasonable, and the land resources are relatively insufficient.
The land surveying and mapping technology is the basis for reasonably applying the land, and along with the development of economy and scientific technology, the land surveying and mapping technology is gradually changed from the traditional measurement to the modern surveying and mapping technology which is characterized by digitalization and informatization; the land surveying and mapping requirements are strong in speciality, the requirements for data measuring accuracy are higher and higher, people are required to master more advanced measuring means, and the existing surveying and mapping technology cannot meet the requirements of modern land surveying work.
Therefore, a remote sensing big data interpretation system and method applied to homeland space monitoring are needed, and the remote sensing mapping information of the homeland space is more accurately positioned by performing remote sensing monitoring on the region to be detected and accurately interpreting the remote sensing data.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a remote sensing big data interpretation system and method applied to the monitoring of the homeland space, which can more accurately position the remote sensing mapping information of the homeland space by carrying out remote sensing monitoring on the region to be detected and carrying out accurate interpretation on the remote sensing data.
A remote sensing big data interpretation system applied to homeland space monitoring comprises: the acquisition module is used for distributing acquisition equipment to acquire an original remote sensing image of the area to be detected according to the flight path; the human-computer cooperation intelligent interpretation module is used for automatically interpreting the original remote sensing image according to the multi-level coupling intelligent interpretation model, automatically extracting an interpretation result, verifying the interpretation result based on a semi-automatic extraction method and outputting the verified interpretation result; and the homeland monitoring module is used for acquiring a classification instruction, classifying the verified interpretation results of each time phase of each partition in each region to be detected based on the image intelligent classification model and the classification instruction, and forming a required homeland space interpretation result according to the classification result.
As an embodiment of the present invention, the acquisition module includes: the area determining unit is used for determining the starting position of the acquisition equipment and the area to be detected; the environment state information determining unit is used for determining the environment information of the area to be detected and the state parameter information of the acquisition equipment; the flight path generating unit is used for generating an optimal remote sensing flight path instruction and an acquisition start and end instruction based on a big data technology according to the starting position of the acquisition equipment, the area to be detected, the environmental information and the state parameter information; and the acquisition equipment control unit is used for controlling the acquisition equipment to carry out corresponding flight operation according to the flight path instruction and controlling the acquisition equipment to carry out corresponding original remote sensing image acquisition operation according to the acquisition starting and ending instruction.
As an embodiment of the invention, the man-machine cooperation intelligent interpretation module comprises: the intelligent computing background unit is used for automatically interpreting the acquired original remote sensing image based on the multi-level coupling intelligent interpretation model constructed based on the deep learning to generate an interpretation result; the intelligent engine unit is used for automatically extracting an interpretation result and pushing the interpretation result to a human-computer interaction interpretation foreground; and the human-computer interaction interpretation foreground is used for verifying the interpretation result by utilizing a semi-automatic extraction method and outputting the verified interpretation result.
As an embodiment of the invention, the intelligent computing background unit executes the following operations: constructing an interpretation model based on a deep learning network, and classifying preset remote sensing image sample information according to a multi-level knowledge coupling technology, an object intelligent computing technology and a layered partition extraction technology to obtain a scene sample set, an object sample set and a pixel sample set; inputting a scene sample set, an object sample set and a pixel sample set in sequence to train an interpretation model to obtain a scene-object-pixel-based multi-level coupling intelligent interpretation model; and automatically interpreting the obtained original remote sensing image according to the multi-stage coupling intelligent interpretation model to generate an interpretation result.
As an embodiment of the present invention, the homeland monitoring module includes: the automatic generation unit of the time phase sample of the partition is used for automatically extracting the verified interpretation result of each time phase of the partition in each area to be detected to generate a time phase sample of the partition; the human-computer interaction editing unit is used for acquiring a user classification instruction; the image intelligent classification unit is used for selecting a corresponding image intelligent classification model according to the user classification instruction; and the classified sample automatic feedback unit is used for classifying each partitioned time-sharing phase sample based on the corresponding image intelligent classification model and forming a required territorial space interpretation result according to the classification result for automatic feedback.
As an embodiment of the present invention, the homeland monitoring module further includes a classification model iterative optimization unit, configured to form a partition time-sharing phase sample automatic update, and complete a closed loop of iterative autonomous learning of the corresponding image intelligent classification model according to the update sample.
As an embodiment of the invention, the remote sensing big data interpretation system applied to the territorial space monitoring further comprises a change information generation module, and the change information generation module executes the following operations: based on a big data technology, acquiring image characteristics of land use of the homeland space of each region to be detected as characteristic identification; comparing the verified interpretation results according to the characteristic identification to generate land use information of the territorial space; and comparing the historical land use information of the land space with the corresponding land use information of the land space to generate land space change information for automatic feedback.
As an embodiment of the present invention, a remote sensing big data interpretation system applied to homeland space monitoring further includes a homeland space change rational recommendation report generation module, and the homeland space change rational recommendation report generation module performs operations including: acquiring homeland space information with change in the homeland space change information as change homeland space information; the land use information and the land range information are used for changing the land space information; determining the influence radiation range of the changed territorial earth space according to the information of the changed territorial earth space based on a big data technology; acquiring other homeland space information contained in the radiation range of the influence of the changed homeland space; determining a correlation strength value of land use information for changing the land space information and other land space information according to the Wei-Tomas index; judging that the other territorial space information with the correlation strength value lower than the preset value is low economic correlation territorial; and if no low-economic associated country soil exceeding a preset number exists in other country soil space information, judging that the country soil space information is reasonably changed, and generating a country soil space change reasonable recommendation report of the low-economic associated country soil according to the strength value of the association with the changed country soil space information and the existing country soil space land use information of other non-low-economic associated country soil based on a big data technology.
A remote sensing big data interpretation method applied to homeland space monitoring comprises the following steps: arranging acquisition equipment to acquire an original remote sensing image of a region to be detected according to a flight path; automatically interpreting the original remote sensing image according to the multi-stage coupling intelligent interpretation model, automatically extracting an interpretation result, verifying the interpretation result based on a semi-automatic extraction method, and outputting the verified interpretation result; and acquiring a classification instruction, classifying verified interpretation results of each time phase of each sub-region in each region to be detected based on the image intelligent classification model and the classification instruction, and forming a required territorial space interpretation result according to the classification result.
As an embodiment of the present invention, a remote sensing big data interpretation method applied to homeland space monitoring further includes: based on a big data technology, acquiring image characteristics of land use of the homeland space of each region to be detected as characteristic identification; comparing the verified and interpreted results according to the characteristic identification to generate land use information of the homeland space; and comparing the historical land use information of the land space with the corresponding land use information of the land space to generate land space change information for automatic feedback.
The invention has the beneficial effects that:
the invention provides a remote sensing big data interpretation system and method applied to monitoring of a national soil space.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a system module of a remote sensing big data interpretation system applied to homeland space monitoring in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a collecting module in a remote sensing big data interpretation system applied to homeland space monitoring in the embodiment of the present invention;
FIG. 3 is a schematic diagram of a human-computer cooperative intelligent interpretation module in a remote sensing big data interpretation system applied to homeland space monitoring in the embodiment of the present invention;
FIG. 4 is a schematic diagram of a unit of a China soil monitoring module of a remote sensing big data interpretation system applied to China soil space monitoring in the embodiment of the present invention;
fig. 5 is a flowchart of a remote sensing big data interpretation method applied to homeland space monitoring in the embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to fig. 1, an embodiment of the present invention provides a remote sensing big data interpretation system applied to homeland space monitoring, including: the acquisition module 1 is used for distributing acquisition equipment to acquire an original remote sensing image of a region to be detected according to a flight path; the man-machine cooperation intelligent interpretation module 2 is used for automatically interpreting the original remote sensing image according to the multi-level coupling intelligent interpretation model, automatically extracting an interpretation result, verifying the interpretation result based on a semi-automatic extraction method, and outputting a verified interpretation result; the homeland monitoring module 3 is used for acquiring a classification instruction, classifying verified interpretation results of each time phase of each partition in each region to be detected based on the image intelligent classification model and the classification instruction, and forming a required homeland space interpretation result according to the classification result;
the working principle and the beneficial effects of the technical scheme are as follows: the system comprises an acquisition module 1, a man-machine cooperative intelligent interpretation module 2 and a control module, wherein the acquisition module 1 is used for arranging acquisition equipment according to an area which a user wants to survey and drawing, acquiring an original remote sensing image of an area to be measured according to a set flight path, simultaneously transmitting the original remote sensing image back to a background in real time to be synchronized and interpreted by the man-machine cooperative intelligent interpretation module 2, and improving interpretation efficiency, the acquisition equipment comprises but is not limited to unmanned aerial vehicles, people and machines or satellites and other equipment, when the satellite and other equipment are involved, the flight path is determined in advance according to a satellite track, and then the area to be measured is selected according to the track; meanwhile, if the interpretation has problems, the flight route can be re-planned to select to shoot the area to be measured with interpretation problems again; the man-machine cooperative intelligent interpretation module 2 adopts a mode of an intelligent computing background, an intelligent engine and a man-machine interactive interpretation platform, after an original remote sensing image is received, the intelligent computing background performs intelligent computing by using methods such as deep learning, automatic extraction results are pushed to a man-machine interactive interpretation foreground through the intelligent engine when the results exist, the man-machine interactive interpretation foreground judges and verifies the computing results through a preset semi-automatic extraction method, and through the steps, the overall operation efficiency can be improved by 20%; after a required interpretation result is obtained, the territorial monitoring module 3 obtains a classification instruction input by a user, the classification instruction is selected autonomously according to which territorial of the type the user wants to check, then the verified interpretation results of each sub-division time phase in each region to be detected are classified based on an image intelligent classification model and the classification instruction, and the required territorial space interpretation result is formed according to the classification result, the territorial monitoring module 3 breaks through a remote sensing image sample generation and intelligent classification integrated iteration method considering space-time characteristics, the recall ratio is superior to 85%, and by means of the technical scheme, the method is beneficial to accurately interpreting remote sensing data and further accurately positioning remote sensing mapping information of the territorial space.
Referring to fig. 2, in one embodiment, the acquisition module 1 includes: the area determining unit 10 is used for determining the starting position of the acquisition equipment and the area to be measured; the environment state information determining unit 11 is configured to determine environment information of the area to be measured and state parameter information of the acquisition device; the flight path generation unit 12 is used for generating an optimal remote sensing flight path instruction and an acquisition start and end instruction based on a big data technology according to the starting position of the acquisition equipment, the area to be detected, the environmental information and the state parameter information; the acquisition equipment control unit 13 is used for controlling the acquisition equipment to perform corresponding flight operation according to the flight path instruction and controlling the acquisition equipment to perform corresponding original remote sensing image acquisition operation according to the acquisition start and end instruction;
the working principle and the beneficial effects of the technical scheme are as follows: the acquisition module 1 is used for acquiring an original remote sensing image of an area to be detected, and when the acquisition equipment carries a radar for a flying machine such as an unmanned aerial vehicle or a man-machine, a flying route needs to be set according to environmental state information in the area to be detected; before collection, according to a to-be-detected area input by a user, acquiring current environment information of the to-be-detected area based on a network, wherein the current environment information comprises weather information, illumination information and the like, and simultaneously acquiring information of longitude, latitude and the like of the to-be-detected area, selecting collection equipment capable of taking off, and acquiring the taking-off position of the collection equipment and state parameters of the collection equipment, such as state parameter information of electric quantity, endurance time, minimum safe flight altitude, minimum turning radius and the like; after the parameters are determined, based on a big data technology, the optimal remote sensing flight path and the collection position of the collection equipment are calculated, a corresponding instruction is generated, and finally the collection equipment is controlled to start according to the corresponding instruction.
Referring to fig. 3, in one embodiment, the human-computer collaboration intelligent interpretation module 2 includes: the intelligent computing background unit 20 is used for automatically interpreting the acquired original remote sensing image based on a multistage coupling intelligent interpretation model constructed by deep learning to generate an interpretation result; the intelligent engine unit 21 is used for automatically extracting an interpretation result and pushing the interpretation result to a human-computer interaction interpretation foreground; the human-computer interaction interpretation foreground 22 is used for verifying the interpretation result by using a semi-automatic extraction method and outputting the verified interpretation result;
the intelligent computing background unit 21 performs operations including: constructing an interpretation model based on a deep learning network, and classifying preset remote sensing image sample information according to a multi-level knowledge coupling technology, an object intelligent computing technology and a layered partition extraction technology to obtain a scene sample set, an object sample set and a pixel sample set; inputting a scene sample set, an object sample set and a pixel sample set in sequence to train an interpretation model to obtain a scene-object-pixel-based multi-level coupling intelligent interpretation model; automatically interpreting the obtained original remote sensing image according to the multi-stage coupling intelligent interpretation model to generate an interpretation result;
the working principle and the beneficial effects of the technical scheme are as follows: the man-machine cooperation intelligent interpretation module 2 is arranged at the background and used for receiving the information sent by the acquisition module to interpret in real time, wherein the intelligent calculation background unit 21 constructs a scene-object-pixel multi-level coupling intelligent interpretation model, the technologies of multi-level knowledge coupling, layered partition extraction, object intelligent calculation and the like are broken through, scene level layered extraction, object level anomaly detection and pixel level intelligent calculation are realized, the compression operation range exceeds 80%, and the whole interpretation efficiency is greatly improved.
Referring to fig. 4, in one embodiment, the homeland monitoring module 3 includes: the automatic generation unit 30 of the time phase sample of the partition, is used for extracting the already verified interpretation result of every time phase of partition in every area to be measured automatically and generating the time phase sample of the partition; a human-computer interaction editing unit 31, configured to obtain a user classification instruction; the image intelligent classification unit 32 is used for selecting a corresponding image intelligent classification model according to the user classification instruction; the classified sample automatic feedback unit 33 is used for classifying each partitioned time-sharing phase sample based on the corresponding image intelligent classification model and forming a required territorial space interpretation result according to the classification result for automatic feedback;
the homeland monitoring module also comprises a classification model iterative optimization unit which is used for forming partition time-sharing phase sample automatic updating and completing a closed loop of iterative autonomous learning of a corresponding image intelligent classification model according to the updated sample;
the working principle and the beneficial effects of the technical scheme are as follows: when the original remote sensing images of a plurality of regions to be tested are collected simultaneously or a single region to be tested is collected, the homeland monitoring module 3 is used for classifying the interpretation contents according to the emphasis of a user, and certainly, if the user does not select the emphasis, the homeland monitoring module 3 can directly output the collected interpretation results for the reference of the user, wherein the classification mode in the homeland monitoring module 3 breaks through the remote sensing image sample generation and intelligent classification integrated iteration method considering the space-time characteristics, the automatic generation of the partitioned time-sharing phase samples, the intelligent classification of the images, the human-computer interaction editing, the automatic feedback of the classified samples and the iterative optimization of the classification module are realized, a closed loop of automatic sample updating and the autonomous model learning is formed, and the recall ratio is better than 85%.
In one embodiment, the remote sensing big data interpretation system applied to homeland space monitoring further comprises a change information generation module, and the change information generation module executes the following operations: based on a big data technology, acquiring image characteristics of land use of the homeland space of each region to be detected as characteristic identification; comparing the verified and interpreted results according to the characteristic identification to generate land use information of the homeland space; comparing the historical land use information of the land space with the corresponding land use information of the land space to generate land space change information for automatic feedback;
the working principle and the beneficial effects of the technical scheme are as follows: the change information generation module is used for facilitating a user to check the change of the land use information of the historical past Chinese soil space more conveniently and facilitating more accurate drawing of a soil space map, wherein the change information generation module acquires the image characteristics of the land use of the soil space of each region to be detected based on a big data technology and takes the image characteristics as feature marks, and the acquired image characteristics are image difference characteristics of remote sensing images of the soil space of each region to be detected when the remote sensing images face different land uses; and comparing the verified interpretation results according to the characteristic identifiers to generate land use information of the territorial soil space, namely performing land use identification on the interpreted territorial soil space according to the corresponding characteristic identifiers, and finally comparing the land use information of the historical territorial soil space with the land use information of the corresponding territorial soil space to judge whether the land use on the historical territorial soil space is changed or not and whether the size of the occupied territorial soil space is changed or not and the like, and generating land space change information to automatically feed back to a user.
In one embodiment, the remote sensing big data interpretation system applied to the territorial space monitoring further comprises an territorial space change reasonable recommendation report generation module, and the territorial space change reasonable recommendation report generation module executes the following operations: acquiring homeland space information with change in the homeland space change information as change homeland space information; the land use information and the land range information are used for changing the land space information; determining the influence radiation range of the changed territorial earth space according to the information of the changed territorial earth space based on a big data technology; acquiring other territorial space information contained in the influence radiation range of the changed territorial space; determining a correlation strength value of land use information for changing the land space information and other land space information according to the Wei-Tomas index; judging that the other territorial space information with the correlation strength value lower than the preset value is low economic correlation territorial; if no low-economic associated country soil exceeding a preset number exists in other country soil space information, the country soil space information is judged to be reasonably changed, and meanwhile, based on a big data technology, a country soil space change reasonable recommendation report of the low-economic associated country soil is generated according to the strength value of the association with the changed country soil space information and the existing country soil space land use information of other non-low-economic associated country soil;
the working principle of the technical scheme is as follows: the land space change reasonable recommendation report generating module is used for assisting a user in judging the rationality of a changed land space, providing a more reasonable recommendation report for land use of other land spaces around the changed land space according to an economic development index, and further providing certain auxiliary data support for reasonable use of the land space, wherein the land space change reasonable recommendation report generating module executes the following operations: firstly, acquiring the territorial space information with change in the territorial space change information as the change territorial space information; wherein, the change of the homeland space information comprisesBut not limited to, spatial information such as land use information and changed land range information, where the land use information may be referred to as factories, houses, malls, farming lands, and the like; then based on big data technology, determining the influence radiation range of the changed homeland space according to the changed homeland space information, wherein relevant data for determining the influence radiation range comprises but is not limited to land use information of the changed homeland space information and changed land range information; then acquiring other territorial space information contained in the radiation range of the influence of the changed territorial space; determining the correlation strength value of land use information for changing the land space information and other land space information according to the Wei-Tomas index, wherein the calculation formula is preferably
Wherein, translate n,m Defining alpha (n, u) as a consumption coefficient of a u index corresponding to the n industry, wherein n =1,2,3.. P, and arranging alpha (1,m), alpha (2,m),. The alpha (p, m) in a descending order, and m =1,2,3.. U, so as to obtain a new sample sequence of alpha (1,m), alpha (2,m),. The alpha (p, m), and the f are any one of m indexes; for example, if the land use of the territorial space is changed to a factory which emits certain harmful substances, the dominating index class is preferentially not suitable for living, and the consumption coefficient of the index which is suitable for living of the land use information of each other territorial space information is correspondingly changed, so that the relevance strength value of the index which is suitable for living is reduced, and when the other territorial space information of which the relevance strength value is lower than the preset value is judged to be low-economic relevance territorial; if no low economic associated country soil exceeding a preset number exists in other country soil space information, the country soil space information is judged to be reasonably changed, and meanwhile, based on a big data technology, according to the strength value of the association with the changed country soil space information and other non-low country soil space informationThe land use information of the existing land space of the economy associated with the state is generated to generate a reasonable recommendation report of the state space change of the low economy associated with the state, namely, a land use distribution recommendation report which is more adaptive to the land use of the changed state space information is generated;
the beneficial effects of the above technical scheme are: through above-mentioned technical scheme, use on the survey and drawing data for the homeland space and provide certain reasonable auxiliary data and support, improve survey and drawing data's variety, realize land resource utilization's maximize benefit.
Referring to fig. 5, a remote sensing big data interpretation method applied to homeland space monitoring includes: arranging acquisition equipment to acquire an original remote sensing image of a region to be detected according to a flight path; automatically interpreting the original remote sensing image according to the multi-stage coupling intelligent interpretation model, automatically extracting an interpretation result, verifying the interpretation result based on a semi-automatic extraction method, and outputting the verified interpretation result; and acquiring a classification instruction, classifying the verified interpretation results of each classified time phase in each region to be detected based on the image intelligent classification model and the classification instruction, and forming a required territorial space interpretation result according to the classification result.
In one embodiment, the remote sensing big data interpretation method applied to homeland space monitoring further comprises the following steps: based on a big data technology, acquiring image characteristics of land use of the homeland space of each region to be detected as characteristic identification; comparing the verified interpretation results according to the characteristic identification to generate land use information of the territorial space; and comparing the historical land use information of the land space with the corresponding land use information of the land space to generate land space change information for automatic feedback.
The working principle and the beneficial effect of the remote sensing big data interpretation method applied to the homeland space monitoring can refer to the working principle and the beneficial effect correspondingly mentioned in each functional module of the remote sensing big data interpretation system applied to the homeland space monitoring, and repeated description is not repeated here.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A remote sensing big data interpretation system applied to homeland space monitoring is characterized by comprising: the acquisition module is used for distributing acquisition equipment to acquire an original remote sensing image of the area to be detected according to the flight path; the human-computer cooperation intelligent interpretation module is used for automatically interpreting the original remote sensing image according to the multi-level coupling intelligent interpretation model, automatically extracting an interpretation result, verifying the interpretation result based on a semi-automatic extraction method and outputting the verified interpretation result; and the territory monitoring module is used for acquiring a classification instruction, classifying verified interpretation results of each sub-time phase in each region to be detected based on the image intelligent classification model and the classification instruction, and forming a required territory space interpretation result according to the classification result.
2. The remote sensing big data interpretation system applied to homeland space monitoring as claimed in claim 1, wherein the collection module comprises: the area determining unit is used for determining the starting position of the acquisition equipment and the area to be detected; the environment state information determining unit is used for determining the environment information of the area to be detected and the state parameter information of the acquisition equipment; the flight path generation unit is used for generating an optimal remote sensing flight path instruction and a collection start and end instruction based on a big data technology according to the starting position of the collection equipment, the area to be detected, the environmental information and the state parameter information; and the acquisition equipment control unit is used for controlling the acquisition equipment to carry out corresponding flight operation according to the flight path instruction and controlling the acquisition equipment to carry out corresponding original remote sensing image acquisition operation according to the acquisition start and end instruction.
3. The remote sensing big data interpretation system applied to homeland space monitoring as claimed in claim 1, wherein the human-computer cooperation intelligent interpretation module comprises: the intelligent computing background unit is used for automatically interpreting the acquired original remote sensing image based on the multi-level coupling intelligent interpretation model constructed based on the deep learning to generate an interpretation result; the intelligent engine unit is used for automatically extracting the interpretation result and pushing the interpretation result to the human-computer interaction interpretation foreground; and the human-computer interaction interpretation foreground is used for verifying the interpretation result by using a semi-automatic extraction method and outputting the verified interpretation result.
4. The remote sensing big data interpretation system applied to homeland space monitoring as claimed in claim 3, wherein the intelligent computing background unit performs operations comprising: constructing an interpretation model based on a deep learning network, and classifying preset remote sensing image sample information according to a multi-level knowledge coupling technology, an object intelligent computing technology and a layered partition extraction technology to obtain a scene sample set, an object sample set and a pixel sample set; inputting a scene sample set, an object sample set and a pixel sample set in sequence to train an interpretation model to obtain a scene-object-pixel-based multi-level coupling intelligent interpretation model; and automatically interpreting the obtained original remote sensing image according to the multi-stage coupling intelligent interpretation model to generate an interpretation result.
5. The remote sensing big data interpretation system applied to homeland space monitoring as claimed in claim 1, wherein the homeland monitoring module comprises: the automatic generation unit of the time phase sample of the partition is used for automatically extracting the verified interpretation result of each time phase of the partition in each area to be detected to generate a time phase sample of the partition; the human-computer interaction editing unit is used for acquiring a user classification instruction; the image intelligent classification unit is used for selecting a corresponding image intelligent classification model according to the user classification instruction; and the classified sample automatic feedback unit is used for classifying each partitioned time-sharing phase sample based on the corresponding image intelligent classification model and forming a required territorial space interpretation result according to the classification result for automatic feedback.
6. The remote sensing big data interpretation system applied to homeland space monitoring as claimed in claim 5, wherein the homeland monitoring module further comprises a classification model iterative optimization unit for forming partition time-sharing phase sample automatic updating and completing a closed loop of iterative autonomous learning of a corresponding image intelligent classification model according to the updated sample.
7. The remote sensing big data interpretation system applied to homeland space monitoring as claimed in claim 1, further comprising a change information generation module, wherein the change information generation module performs operations comprising: based on a big data technology, acquiring image characteristics of land use of the homeland space of each region to be detected as characteristic identification; comparing the verified interpretation results according to the characteristic identification to generate land use information of the territorial space; and comparing the historical land use information of the land space with the corresponding land use information of the land space to generate land space change information for automatic feedback.
8. The remote sensing big data interpretation system applied to homeland space monitoring as claimed in claim 7, further comprising a homeland space change equitable recommendation report generation module, wherein the homeland space change equitable recommendation report generation module performs operations comprising: acquiring homeland space information with change in the homeland space change information as change homeland space information; the land use information and the land range information are used for changing the land space information; determining the influence radiation range of the changed territorial earth space according to the information of the changed territorial earth space based on a big data technology; acquiring other territorial space information contained in the influence radiation range of the changed territorial space; determining a correlation strength value of land use information for changing the land space information and other land space information according to the Wei-Tomas index; judging that the other territorial space information with the correlation strength value lower than the preset value is low economic correlation territorial; and if no low-economic associated country soil exceeding a preset number exists in other country soil space information, judging that the country soil space information is reasonably changed, and generating a country soil space change reasonable recommendation report of the low-economic associated country soil according to the strength value of the association with the changed country soil space information and the existing country soil space land use information of other non-low-economic associated country soil based on a big data technology.
9. A remote sensing big data interpretation method applied to homeland space monitoring is characterized by comprising the following steps: arranging acquisition equipment to acquire an original remote sensing image of a region to be detected according to a flight path; automatically interpreting the original remote sensing image according to the multi-stage coupling intelligent interpretation model, automatically extracting an interpretation result, verifying the interpretation result based on a semi-automatic extraction method, and outputting the verified interpretation result; and acquiring a classification instruction, classifying the verified interpretation results of each classified time phase in each region to be detected based on the image intelligent classification model and the classification instruction, and forming a required territorial space interpretation result according to the classification result.
10. The remote sensing big data interpretation method applied to homeland space monitoring according to claim 9, further comprising: based on a big data technology, acquiring image characteristics of land use of the homeland space of each region to be detected as characteristic identification; comparing the verified interpretation results according to the characteristic identification to generate land use information of the territorial space; and comparing the historical land use information of the land space with the corresponding land use information of the land space to generate land space change information for automatic feedback.
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