CN111611921A - Solar panel identification system based on remote sensing big data - Google Patents
Solar panel identification system based on remote sensing big data Download PDFInfo
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Abstract
The invention relates to a solar panel identification system based on remote sensing big data, which comprises: the sensor is used for detecting the whole area through the sensor to obtain a thermal imaging image, analyzing a target area where a target object exists according to the thermal imaging image and dividing the target area into a plurality of sub-target areas; extracting a target image of each sub-target area, and calculating a characteristic index of each sub-target area; acquiring a feature index weight value of a sub-target area according to the feature index of the sub-target area, analyzing parameter information of the target area, extracting a target image, and superposing the target image and a thermal imaging image; through information transmission and processing analysis, the attribute and the distribution characteristic of the target object in the sub-target area are identified to form sub-target area distribution information, and the whole-area remote sensing image is analyzed to obtain the total distribution characteristic of the target object and result information; and transmitting the result information to the cloud server in a preset mode.
Description
Technical Field
The invention relates to the field of solar panel identification or solar panel distribution detection or big data processing, in particular to a solar panel identification system based on remote sensing big data.
Background
At present, remote sensing technology is widely applied to the fields of agriculture, forestry, surveying and mapping, meteorology, traffic, homeland resource exploration and the like, and the remote sensing technology refers to a technology for detecting and sensing objects or things from a distance in a broad sense, does not directly contact the objects, detects and receives information from target objects from the distance through instruments or sensors, and identifies characteristics such as object attributes and distribution through information transmission and analysis processing.
A block chain (Blockchain) is an important concept of a bit coin, is essentially a decentralized database, is simultaneously used as a bottom layer technology of the bit coin, is a series of data blocks which are produced by correlating by using a cryptography method, each data block comprises information of a batch of bit coin network transactions and is used for verifying the validity (anti-counterfeiting) of the information and generating a next block, the block chain technology and remote sensing imaging are combined for carrying out regional solar panel identification monitoring, the high efficiency of detection and the safety of monitoring data can be realized, the transmission of the data in the monitoring process is automatically identified and transmitted through the Internet of things, the rapidity of data transmission is ensured, most of the distribution of the solar panels in the existing detection area is aerial photographed by an unmanned aerial vehicle, then the photographed image is analyzed, and in the aerial photographing process, due to the influence of factors such as pixels or weather, cause to shoot picture quality great, the error is great, in order to can accurate analysis solar panel's distribution region and distribution characteristic, carry out regional detection, so that better carry out regional planning, need develop a section and survey rather than assorted system, accurate acquisition solar panel's distribution, but in surveying the in-process, how to realize accurate detection, and how to handle the probing result at surveying the in-process, all be the problem that can not wait to solve urgently.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a solar panel identification system based on remote sensing big data.
In order to achieve the purpose, the invention adopts the technical scheme that: a solar panel identification method based on remote sensing big data comprises the following steps:
carrying out full-area detection through a sensor, obtaining a thermal imaging diagram, analyzing a target area where a target object exists according to the thermal imaging diagram, and dividing the target area into a plurality of sub-target areas;
extracting a target image of each sub-target area, and calculating a characteristic index of each sub-target area;
acquiring a feature index weight value of a sub-target area according to the feature index of the sub-target area, analyzing parameter information of the target area, extracting a target image, and superposing the target image and a thermal imaging image;
through information transmission and processing analysis, the attributes and distribution characteristics of the target objects in the sub-target areas are identified, and sub-target area distribution information is formed;
extracting the distribution information of the sub-target areas, forming original images of the sub-target areas in a remote sensing imaging processing mode, and acquiring the information of the original images of the sub-target areas;
processing the original image of the sub-target area through an image processing module to obtain a sub-target enhanced image and obtain sub-target enhanced image information;
extracting remote sensing information of the sub-target enhanced image to obtain a sub-target thermal remote sensing image, and aggregating a plurality of sub-target thermal remote sensing images to obtain a full-area remote sensing image;
analyzing the whole-region remote sensing image acquisition to obtain the overall distribution characteristics of the target object and obtain result information;
and transmitting the result information to the cloud server in a preset mode.
Preferably, the target area is divided into a plurality of sub-target areas in equal area according to different geographic locations, and the characteristic index of each sub-target area is stored in the corresponding sub-database.
Preferably, the characteristic index includes one or a combination of two or more of a location index, a building aggregation index, a mountain distribution index, a jungle distribution index, a mountain height or a slope index.
Preferably, according to the sub-target area characteristic index weight value, establishing a target object distribution gradient in the sub-target area, and establishing a target object distribution curve in the sub-target area;
preprocessing the target object distribution curve in each sub-target area, and removing points with deviation larger than a preset threshold value on the target object distribution curve in the sub-target area;
and carrying out aggregation processing on the target object distribution curves in the plurality of sub-target areas to obtain a full-area target object distribution curve.
Preferably, the forming of the original image of the sub-target area by the processing mode of remote sensing imaging specifically includes:
transmitting electromagnetic waves to the whole area through an aviation multispectral scanner, receiving reflected electromagnetic wave signals and generating electromagnetic wave information;
analyzing the electromagnetic wave information, extracting object information corresponding to the electromagnetic wave information, and forming an image;
and acquiring the original image of the sub-target area according to the electromagnetic wave characteristic of the target object.
Preferably, the thermal infrared band electromagnetic wave of 8-14 μm is emitted by an aviation multispectral scanner;
detecting the heat radiation energy of an object and displaying the radiation temperature or the thermal field image of the object;
screening out a thermal field image corresponding to the target object according to the thermal radiation characteristic of the target object;
and processing the thermal field image to obtain a target object distribution area.
The invention provides a solar panel identification system based on remote sensing big data in a second aspect, which comprises: the solar panel identification method based on the remote sensing big data comprises a memory and a processor, wherein the memory comprises a solar panel identification method program based on the remote sensing big data, and the following steps are realized when the solar panel identification method program based on the remote sensing big data is executed by the processor:
carrying out full-area detection based on an infrared thermal imaging principle, obtaining a thermal imaging diagram, analyzing a target area where a target object exists according to the thermal imaging diagram, and dividing the target area into a plurality of sub-target areas;
extracting a target image of each sub-target area, and calculating a characteristic index of each sub-target area;
acquiring a feature index weight value of a sub-target area according to the feature index of the sub-target area, analyzing parameter information of the target area, extracting a target image, and superposing the target image and a thermal imaging image;
through information transmission and processing analysis, the attributes and distribution characteristics of the target objects in the sub-target areas are identified, and sub-target area distribution information is formed;
extracting the distribution information of the sub-target areas, forming original images of the sub-target areas in a remote sensing imaging processing mode, and acquiring the information of the original images of the sub-target areas;
processing the original image of the sub-target area through an image processing module to obtain a sub-target enhanced image and obtain sub-target enhanced image information;
extracting remote sensing information of the sub-target enhanced image to obtain a sub-target thermal remote sensing image, and aggregating a plurality of sub-target thermal remote sensing images to obtain a full-area remote sensing image;
analyzing the whole-region remote sensing image acquisition to obtain the overall distribution characteristics of the target object and obtain result information;
and transmitting the result information to the cloud server in a preset mode.
Preferably, the characteristic index includes one or a combination of two or more of a location index, a building aggregation index, a mountain distribution index, a jungle distribution index, a mountain height or a slope index.
Preferably, the thermal infrared band electromagnetic wave of 8-14 μm is emitted by an aviation multispectral scanner;
detecting the heat radiation energy of an object and displaying the radiation temperature or the thermal field image of the object;
screening out a thermal field image corresponding to the target object according to the thermal radiation characteristic of the target object;
and processing the thermal field image to obtain a target object distribution area.
Preferably, an image model is established, and the image model is trained to obtain a two-classification model;
predicting a target object through a binary classification model to obtain a plurality of prediction images, overlapping the prediction images, and synthesizing a finished prediction image;
cutting the full-area remote sensing image through a U-Net model, analyzing and processing the cut image, and synthesizing the cut image;
and comparing the analyzed and processed image with the prediction graph, and when the deviation threshold value is smaller than a preset threshold value, analyzing and identifying the synthesized prediction graph as a solar panel distribution graph.
The invention solves the defects in the background technology, and has the following beneficial effects:
(1) the invention is based on a block chain technology, decentralized processing is carried out, data are verified and stored by using a block chain type data structure, data are generated and updated by using a distributed node consensus algorithm, the safety of data transmission and access is ensured by using a cryptology mode, a target area is detected by using an image processing module, an initial block is generated by detecting data, then authentication is carried out by using an authentication node, after the authentication is completed, a new block is generated by using the position characteristics of a solar panel, a block chain is formed, and the block chain is permanently stored.
(2) The whole area is divided into a plurality of sub-target areas, each sub-target area is independently identified, an independent sub-target remote sensing image is obtained for analysis and comparison, the sub-target remote sensing images are aggregated, the whole area remote sensing image is obtained, the processing mode reduces the deviation in the process of processing the remote sensing image, and the obtained whole area remote sensing image is more accurate.
(3) The aerial multi-spectrometer is used for emitting electromagnetic waves, thermal field images of the target object are screened by using the difference of the thermal radiation characteristics of the objects, the distribution area of the target object is obtained, and the obtained target object is accurately distributed.
(4) The full-region remote sensing image is cut through the U-Net model, the cut image is analyzed and processed, the cut image is synthesized, the number of splicing traces of the image trained through the U-Net model is small, the image overlapping region is reduced, the processed image is closer to the original image, and therefore errors in the solar panel identification process are reduced.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 shows a block diagram of a solar panel identification system based on remote sensing big data according to the invention;
FIG. 2 illustrates a target object profile method flow diagram;
FIG. 3 shows a flow chart of a method of obtaining an original image of a target area;
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
FIG. 1 shows a block diagram of a solar panel identification system based on remote sensing big data.
The invention provides a solar panel identification method based on remote sensing big data, which comprises the following steps:
s102, detecting the whole area through a sensor, acquiring a thermal imaging diagram, analyzing a target area where a target object exists according to the thermal imaging diagram, and dividing the target area into a plurality of sub-target areas;
s104, extracting a target image of each sub-target area, and calculating a characteristic index of each sub-target area;
s106, acquiring a sub-target area characteristic index weight value according to the characteristic index of the sub-target area, analyzing parameter information of the target area, extracting a target image, and superposing the target image and a thermal imaging image;
s108, identifying the attribute and the distribution characteristic of a target object in the sub-target area through information transmission, processing and analysis, forming sub-target area distribution information, extracting the sub-target area distribution information, forming a sub-target area original image through a remote sensing imaging processing mode, and acquiring the sub-target area original image information;
s110, processing the original image of the sub-target area through an image processing module to obtain a sub-target enhanced image, acquiring sub-target enhanced image information, extracting remote sensing information of the sub-target enhanced image to obtain a sub-target thermal remote sensing image, and aggregating a plurality of sub-target thermal remote sensing images to obtain a whole-area remote sensing image;
s112, analyzing the whole-region remote sensing image to obtain the overall distribution characteristics of the target object, and obtaining result information;
and S114, transmitting the result information to the cloud server in a preset mode.
The method includes dividing a whole region into a plurality of sub-target regions, performing individual identification on each sub-target region, obtaining individual sub-target remote sensing images for analysis and comparison, and aggregating the sub-target remote sensing images to obtain a whole region remote sensing image.
According to the embodiment of the invention, the target area is divided into a plurality of sub-target areas in equal area according to different geographic positions, and the characteristic index of each sub-target area is respectively stored in the corresponding sub-database.
According to an embodiment of the present invention, the characteristic index includes one or a combination of two or more of a location index, a building aggregation index, a mountain distribution index, a jungle distribution index, a mountain height or a slope index.
FIG. 2 discloses a flow chart of a target object distribution curve method of the present invention;
according to the embodiment of the invention, S202, according to the weight value of the characteristic index of the sub-target area, the distribution gradient of the target object in the sub-target area is established, and the distribution curve of the target object in the sub-target area is established;
s204, preprocessing the target object distribution curve in each sub-target area, and removing points with deviation larger than a preset threshold value on the target object distribution curve in the sub-target area;
and S206, carrying out aggregation processing on the target object distribution curves in the plurality of sub-target areas to obtain a full-area target object distribution curve.
FIG. 3 discloses a flowchart of a method for obtaining an original image of a target area according to the present invention;
according to the embodiment of the invention, the forming of the original image of the sub-target area by the processing mode of remote sensing imaging specifically comprises the following steps:
s302, transmitting electromagnetic waves to the whole area through an aviation multispectral scanner, receiving reflected electromagnetic wave signals and generating electromagnetic wave information;
s304, analyzing the electromagnetic wave information, extracting object information corresponding to the electromagnetic wave information, and forming an image;
s306, acquiring the original image of the sub-target area according to the electromagnetic wave characteristic of the target object.
According to the embodiment of the invention, the aviation multispectral scanner is used for emitting the electromagnetic waves in the thermal infrared band of 8-14 microns;
detecting the heat radiation energy of an object and displaying the radiation temperature or the thermal field image of the object;
screening out a thermal field image corresponding to the target object according to the thermal radiation characteristic of the target object;
and processing the thermal field image to obtain a target object distribution area.
It should be noted that the aviation multi-spectrometer is used for emitting electromagnetic waves, and the thermal field image of the target object is screened by using the difference of the thermal radiation characteristics of the objects, so as to obtain the distribution area of the target object, and the obtained target object is accurately distributed.
The invention provides a solar panel identification system based on remote sensing big data in a second aspect, which comprises: the solar panel identification method based on the remote sensing big data comprises a memory and a processor, wherein the memory comprises a solar panel identification method program based on the remote sensing big data, and the following steps are realized when the solar panel identification method program based on the remote sensing big data is executed by the processor:
carrying out full-area detection based on an infrared thermal imaging principle, obtaining a thermal imaging diagram, analyzing a target area where a target object exists according to the thermal imaging diagram, and dividing the target area into a plurality of sub-target areas;
extracting a target image of each sub-target area, and calculating a characteristic index of each sub-target area;
acquiring a feature index weight value of a sub-target area according to the feature index of the sub-target area, analyzing parameter information of the target area, extracting a target image, and superposing the target image and a thermal imaging image;
through information transmission and processing analysis, the attributes and distribution characteristics of the target objects in the sub-target areas are identified, and sub-target area distribution information is formed;
extracting the distribution information of the sub-target areas, forming original images of the sub-target areas in a remote sensing imaging processing mode, and acquiring the information of the original images of the sub-target areas;
processing the original image of the sub-target area through an image processing module to obtain a sub-target enhanced image and obtain sub-target enhanced image information;
extracting remote sensing information of the sub-target enhanced image to obtain a sub-target thermal remote sensing image, and aggregating a plurality of sub-target thermal remote sensing images to obtain a full-area remote sensing image;
analyzing the whole-region remote sensing image acquisition to obtain the overall distribution characteristics of the target object and obtain result information;
and transmitting the result information to the cloud server in a preset mode.
According to an embodiment of the present invention, the characteristic index includes one or a combination of two or more of a location index, a building aggregation index, a mountain distribution index, a jungle distribution index, a mountain height or a slope index.
According to the embodiment of the invention, the aviation multispectral scanner is used for emitting the electromagnetic waves in the thermal infrared band of 8-14 microns;
detecting the heat radiation energy of an object and displaying the radiation temperature or the thermal field image of the object;
screening out a thermal field image corresponding to the target object according to the thermal radiation characteristic of the target object;
and processing the thermal field image to obtain a target object distribution area.
According to the embodiment of the invention, an image model is established, and the image model is trained to obtain a two-classification model;
predicting a target object through a binary classification model to obtain a plurality of prediction images, overlapping the prediction images, and synthesizing a finished prediction image;
cutting the full-area remote sensing image through a U-Net model, analyzing and processing the cut image, and synthesizing the cut image;
and comparing the analyzed and processed image with the prediction graph, and when the deviation threshold value is smaller than a preset threshold value, analyzing and identifying the synthesized prediction graph as a solar panel distribution graph.
The U-Net model is used for training the images, the images are cut, the images are analyzed and processed, the cut images are synthesized, the number of splicing traces of the images trained through the U-Net model is small, the image overlapping area is reduced, the processed images are closer to the original images, and therefore errors in the solar panel identification process are reduced.
The invention is based on block chain technology, removes centralized processing, utilizes a block chain type data structure to verify and store data, utilizes a distributed node consensus algorithm to generate and update data, utilizes a cryptology mode to ensure the safety of data transmission and access, detects a target area through an image processing module, generates an initial block by detecting data, then carries out authentication through an authentication node, generates a new block by the position characteristics of a solar panel after the authentication is finished, forms a block chain, and permanently stores the block chain, wherein in the identification process, different solar panels in the same area have different included angles with the sun, in the remote sensing image acquisition process, the different solar panels have different thermal values, meanwhile, the remote sensing image has different color weighted values, and the position with larger color weighted value shows that the angle between the solar panel and the sun is different, the method can accurately detect the angle information and the spatial position information of the solar panel in the area according to the position of the sun.
The collected data are intelligently identified and transmitted through the Internet of things, and the Internet of things is the Internet with the objects connected. The method has two layers, namely, the core and the foundation of the Internet of things are still the Internet, and the Internet is an extended and expanded network on the basis of the Internet; and secondly, the user side extends and expands to any article to perform information exchange and communication, namely, the article information. The internet of things is defined as a huge network formed by combining various information sensing devices, such as sensors, Radio Frequency Identification (RFID) technologies, global positioning systems, infrared sensors, laser scanners, gas sensors and other various devices and technologies, to collect any object or process needing monitoring, connection and interaction in real time, collect various required information of sound, light, heat, electricity, mechanics, chemistry, biology, position and the like of the object through intelligent sensing, identification technology, pervasive computing and other communication sensing technologies. The purpose is to realize the connection of objects, objects and people, and all objects and networks, and facilitate the identification, management and control.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of a unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A solar panel identification method based on remote sensing big data is characterized by comprising the following steps:
carrying out full-area detection through a sensor, obtaining a thermal imaging diagram, analyzing a target area where a target object exists according to the thermal imaging diagram, and dividing the target area into a plurality of sub-target areas;
extracting a target image of each sub-target area, and calculating a characteristic index of each sub-target area;
acquiring a feature index weight value of a sub-target area according to the feature index of the sub-target area, analyzing parameter information of the target area, extracting a target image, and superposing the target image and a thermal imaging image;
through information transmission and processing analysis, the attributes and distribution characteristics of the target objects in the sub-target areas are identified, and sub-target area distribution information is formed;
extracting the distribution information of the sub-target areas, forming original images of the sub-target areas in a remote sensing imaging processing mode, and acquiring the information of the original images of the sub-target areas;
processing the original image of the sub-target area through an image processing module to obtain a sub-target enhanced image and obtain sub-target enhanced image information;
extracting remote sensing information of the sub-target enhanced image to obtain a sub-target thermal remote sensing image, and aggregating a plurality of sub-target thermal remote sensing images to obtain a full-area remote sensing image;
analyzing the whole-region remote sensing image acquisition to obtain the overall distribution characteristics of the target object and obtain result information;
and transmitting the result information to the cloud server in a preset mode.
2. The solar panel identification method based on the remote sensing big data as claimed in claim 1, wherein: and dividing the target area into a plurality of sub-target areas in equal area according to different geographic positions, and respectively storing the characteristic index of each sub-target area in a corresponding sub-database.
3. The solar panel identification method based on the remote sensing big data as claimed in claim 1, wherein: the characteristic index comprises one or more than two of a location index, a building aggregation index, a mountain distribution index, a jungle distribution index, a mountain height or slope index.
4. The solar panel identification method based on the remote sensing big data as claimed in claim 1, wherein:
establishing a target object distribution gradient in the sub-target area according to the sub-target area characteristic index weight value, and establishing a target object distribution curve in the sub-target area;
preprocessing the target object distribution curve in each sub-target area, and removing points with deviation larger than a preset threshold value on the target object distribution curve in the sub-target area;
and carrying out aggregation processing on the target object distribution curves in the plurality of sub-target areas to obtain a full-area target object distribution curve.
5. The solar panel identification method based on the remote sensing big data as claimed in claim 1, wherein: forming a sub-target area original image through a remote sensing imaging processing mode, which specifically comprises the following steps:
transmitting electromagnetic waves to the whole area through an aviation multispectral scanner, receiving reflected electromagnetic wave signals and generating electromagnetic wave information;
analyzing the electromagnetic wave information, extracting object information corresponding to the electromagnetic wave information, and forming an image;
and acquiring the original image of the sub-target area according to the electromagnetic wave characteristic of the target object.
6. The solar panel identification method based on the remote sensing big data as claimed in claim 1, wherein: emitting 8-14 μm thermal infrared band electromagnetic waves by an aviation multispectral scanner;
detecting the heat radiation energy of an object and displaying the radiation temperature or the thermal field image of the object;
screening out a thermal field image corresponding to the target object according to the thermal radiation characteristic of the target object;
and processing the thermal field image to obtain a target object distribution area.
7. A solar panel identification system based on remote sensing big data is characterized in that the system comprises: the solar panel identification method based on the remote sensing big data comprises a memory and a processor, wherein the memory comprises a solar panel identification method program based on the remote sensing big data, and the following steps are realized when the solar panel identification method program based on the remote sensing big data is executed by the processor:
carrying out full-area detection based on an infrared thermal imaging principle, obtaining a thermal imaging diagram, analyzing a target area where a target object exists according to the thermal imaging diagram, and dividing the target area into a plurality of sub-target areas;
extracting a target image of each sub-target area, and calculating a characteristic index of each sub-target area;
acquiring a feature index weight value of a sub-target area according to the feature index of the sub-target area, analyzing parameter information of the target area, extracting a target image, and superposing the target image and a thermal imaging image;
through information transmission and processing analysis, the attributes and distribution characteristics of the target objects in the sub-target areas are identified, and sub-target area distribution information is formed;
extracting the distribution information of the sub-target areas, forming original images of the sub-target areas in a remote sensing imaging processing mode, and acquiring the information of the original images of the sub-target areas;
processing the original image of the sub-target area through an image processing module to obtain a sub-target enhanced image and obtain sub-target enhanced image information;
extracting remote sensing information of the sub-target enhanced image to obtain a sub-target thermal remote sensing image, and aggregating a plurality of sub-target thermal remote sensing images to obtain a full-area remote sensing image;
analyzing the whole-region remote sensing image acquisition to obtain the overall distribution characteristics of the target object and obtain result information;
and transmitting the result information to the cloud server in a preset mode.
8. The solar panel identification system based on remote sensing big data according to claim 7, characterized in that: the characteristic index comprises one or more than two of a location index, a building aggregation index, a mountain distribution index, a jungle distribution index, a mountain height or slope index.
9. The solar panel identification system based on remote sensing big data according to claim 7, characterized in that: emitting 8-14 μm thermal infrared band electromagnetic waves by an aviation multispectral scanner;
detecting the heat radiation energy of an object and displaying the radiation temperature or the thermal field image of the object;
screening out a thermal field image corresponding to the target object according to the thermal radiation characteristic of the target object;
and processing the thermal field image to obtain a target object distribution area.
10. The solar panel identification system based on remote sensing big data according to claim 7, characterized in that: establishing an image model, and training the image model to obtain a two-classification model;
predicting a target object through a binary classification model to obtain a plurality of prediction images, overlapping the prediction images, and synthesizing a finished prediction image;
cutting the full-area remote sensing image through a U-Net model, and analyzing and processing the cut image;
and comparing the analyzed and processed image with the prediction graph, and when the deviation threshold value is smaller than a preset threshold value, analyzing and identifying the synthesized prediction graph as a solar panel distribution graph.
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