CN117541514A - Geometric correction method, system and equipment for satellite thermal infrared remote sensing image - Google Patents
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Abstract
The invention discloses a geometric correction method, a system and equipment for thermal infrared remote sensing images, and relates to the field of geometric correction of thermal infrared remote sensing images, wherein the method comprises the following steps: determining the geometric center of the industrial heat source based on fire point data in the satellite thermal infrared remote sensing image; constructing a high-reliability point data set according to the industrial heat source geometric center; selecting all high reliability point data sets in the accuracy range of the reliability points in the geometric rough correction satellite remote sensing image to construct a characteristic point data set; constructing a minimum triangle for the non-homonymous feature points in the feature point data set, and determining a matching point set at the minimum graph matching cost; and matching the matching point set with the high-reliability point data set to finish the precise geometric correction of the satellite thermal infrared remote sensing image. The invention can geometrically correct a large-scale thermal infrared image, and can effectively resist noise and distortion of sweeping imaging.
Description
Technical Field
The invention relates to the technical field of geometric correction of thermal infrared remote sensing images, in particular to a geometric correction method, a geometric correction system and geometric correction equipment of thermal infrared remote sensing images.
Background
For common geometric correction methods, either based on a strict imaging model or a rational function model, a certain number of control points or DEM (digital elevation model) data-aided corrections with determined geographical locations are required. The geographical position of the data needs to be accurately measured and calibrated to ensure the calibration precision. The control point information of the visible light image is obtained by manual measurement, map labeling or registration with high-precision satellite images and DEM data, but because of the characteristics of a thermal infrared band, the thermal radiation energy of an object is larger than the reflection energy of the sun in the thermal infrared band range, compared with the satellite visible light image, the thermal infrared remote sensing mainly uses a remote sensing means to sense the difference of the thermal radiation energy emitted by a ground object, the conventional characteristics of texture, geometry and the like of the satellite thermal infrared image are fuzzy, and the texture, geometry and the like of the satellite thermal infrared image are difficult to manually identify or match with high-precision reference data so as to obtain accurate control point coordinates. The existing geometric correction method for the thermal infrared remote sensing image is usually to complete geometric correction by using the corresponding relation between homonymous points through registering with a visible light image with high resolution and high precision.
However, the following problems exist in the geometric correction of the thermal infrared remote sensing image in the conventional method:
(1) The different spatial resolutions means that the remote sensing images with high spatial resolution need to be resampled, which may cause problems of pixel displacement, local spatial information loss and the like;
(2) Compared with a thermal infrared camera, the satellite-mounted visible light sensor has smaller breadth, and in the matching process, a plurality of reference images are needed to be spliced, and a large amount of data are needed to be accumulated and manual operation is needed to be assisted;
(3) The geometric features of the thermal infrared image are fuzzy, the control point extraction is mainly manual identification and feature extraction, mismatching point pairs are required to be manually removed, and the geometric correction operation of a plurality of remote sensing images cannot be automatically completed.
Disclosure of Invention
In order to solve the problems, the invention provides a geometric correction method, a geometric correction system and geometric correction equipment for satellite thermal infrared remote sensing images.
In order to achieve the above object, the present invention provides the following solutions:
a geometric correction method for a satellite thermal infrared remote sensing image comprises the following steps:
determining the geometric center of the industrial heat source based on fire point data in the satellite thermal infrared remote sensing image;
constructing a high-reliability point data set according to the industrial heat source geometric center;
selecting all high reliability point data sets in the accuracy range of the reliability points in the geometric rough correction satellite remote sensing image to construct a characteristic point data set;
constructing a minimum triangle for the non-homonymous feature points in the feature point data set, and determining a matching point set at the minimum graph matching cost;
and matching the matching point set with the high-reliability point data set to finish the precise geometric correction of the satellite thermal infrared remote sensing image.
Optionally, determining the geometric center of the industrial heat source based on the fire point data in the satellite thermal infrared remote sensing image specifically includes:
performing cluster analysis and extraction on fire data in the satellite thermal infrared remote sensing image to determine a cluster center, and constructing a cluster center sample set according to the cluster center; the clustering center is a minimum circumscribed rectangular center formed by all the points of each cluster;
collecting remote sensing images covering an earth surface industrial heat source and establishing a training data set;
training the Faster-RCNN neural network model through the training data set to obtain an industrial heat source building geometric center recognition model;
and inputting the cluster center sample set into the industrial heat source building geometric center recognition model for iteration to obtain the industrial heat source geometric center.
Optionally, a density-based DBSCAN clustering method is used for carrying out cluster analysis on the historical satellite remote sensing fire data, and a cluster center is extracted.
Optionally, constructing a minimum triangle for the feature points with different names in the feature point data set, and determining a matching point set with minimum graph matching cost, which specifically includes:
constructing a minimum triangle for the feature points with different names in the feature point data set;
and selecting three vertexes of the smallest triangle with the smallest graph matching cost as matching points, and constructing a matching point set.
The invention also provides a satellite thermal infrared remote sensing image geometric correction system, which comprises:
the industrial heat source geometric center determining module is used for determining the industrial heat source geometric center based on fire point data in the satellite thermal infrared remote sensing image;
the high-reliability point data set construction module is used for constructing a high-reliability point data set according to the industrial heat source geometric center;
the geometric rough correction and characteristic point data set construction module is used for selecting all high-reliability point data sets within the reliability point precision range in the geometric rough correction satellite remote sensing image to construct a characteristic point data set;
the matching point set determining module is used for constructing a minimum triangle for the non-homonymous feature points in the feature point data set and determining a matching point set at the minimum graph matching cost;
and the matching module is used for matching the matching point set with the high-reliability point data set to finish the precise geometric correction of the satellite thermal infrared remote sensing image.
Optionally, the industrial heat source geometric center determining module specifically includes:
the system comprises a clustering center sample set construction unit, a clustering center analysis unit and a clustering center analysis unit, wherein the clustering center sample set construction unit is used for carrying out clustering analysis and extraction on fire data in a satellite thermal infrared remote sensing image to determine a clustering center, and constructing a clustering center sample set according to the clustering center; the clustering center is a minimum circumscribed rectangular center formed by all the points of each cluster;
the training data set establishing unit is used for collecting remote sensing images covering the surface industrial heat source and establishing a training data set;
the training unit is used for training the Faster-RCNN neural network model through the training data set to obtain an industrial heat source building geometric center recognition model;
and the industrial heat source geometric center determining unit is used for inputting the clustering center sample set into the industrial heat source building geometric center recognition model for iteration to obtain an industrial heat source geometric center.
The invention also provides electronic equipment, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic equipment to execute the satellite thermal infrared remote sensing image geometric correction method.
The invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the satellite thermal infrared remote sensing image geometric correction method.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
(1) The invention can carry out geometric correction on a large-range thermal infrared image. The high-reliability point data set established by the invention can cover the surface area of the industrial factory with heat emission in the global scope, has wide coverage area, has good identification characteristics on the thermal infrared image, does not need image registration, and can be quickly geometrically corrected by simple calculation.
(2) And the noise rejection capability is strong. After the minimum triangle is formed, the similarity of the triangle is used for matching cost, and noise and distortion of the sweeping imaging are effectively resisted.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for geometric correction of satellite thermal infrared remote sensing images according to an embodiment of the invention;
fig. 2 is a detailed flowchart of a geometric correction method for satellite thermal infrared remote sensing images according to an embodiment of the invention.
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.
The invention aims to provide a geometric correction method, a geometric correction system and geometric correction equipment for satellite thermal infrared remote sensing images.
The invention uses the high reliable point set with good identification characteristic in the thermal infrared remote sensing image to replace the control point library, eliminates the problem in the general correction method, can identify the homonymous matching points corresponding to the reliable point set in the infrared image by the pattern matching method, removes the mismatching points by RMSE, and rapidly completes the geometric correction.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in fig. 1-2, the geometric correction method for satellite thermal infrared remote sensing images provided in this embodiment includes the following steps:
s1: and determining the geometric center of the industrial heat source based on the fire point data in the satellite thermal infrared remote sensing image.
Collecting public remote sensing fire point data, performing cluster analysis on historical satellite remote sensing fire point data by using a density-based DBSCAN clustering method, extracting a clustering center, and constructing a sample set D (x 1, x2, the., x m )。
Collecting remote sensing images covering the ground surface industrial heat source, establishing a data set, constructing a Faster-RCNN neural network model, and training to obtain an industrial heat source building geometric center identification model, and finally achieving the aim of identifying the industrial heat source building geometric center in the remote sensing images.
In this embodiment, the cluster centers are defined by circumscribed rectangular centers of a single cluster category, and each cluster center corresponds to one industrial heat source, so that industrial heat source high-resolution remote sensing images corresponding to the cluster centers can be obtained in global image service products such as Google Earth or a sky map, and each industrial heat source remote sensing image is labeled by manual visual interpretation, so that an industrial heat source remote sensing data set can be established. The industrial heat source remote sensing data set is divided into a test set (80%), a verification set (20%), and the standard fast-Rcnn is taken as an example, an image is input into the VGG16 for feature extraction, an RPN layer is used for generating detection frames, ROI pooling is carried out on each detection frame to obtain feature vectors, finally a Softmax classification and frame regression model is generated by combining a full-connection layer, an industrial heat source building geometric center identification model is obtained, and finally the detection frame center is taken as an industrial heat source geometric center to achieve the aim of identifying the industrial heat source building geometric center in the remote sensing image.
Using a cluster center sample set D (x 1 ,x 2 ,...,x m ) And iterating the trained neural network model in the remote sensing image with high precision and high resolution to determine the geometric center of the industrial heat source.
S2: and constructing a high-reliability point data set according to the industrial heat source geometric center.
Taking industrial heat source geometric center as a trusted point, and constructing a high trusted point data set V (P 1 ，P 2 ，P 3 ，……，P n ) N is the number of trusted points, P i Is (x) i ，y i ) Coordinate point, P of (2) i ∈V(P 1 ，P 2 ，P 3 ，……，P n )。
S3: and selecting all high-reliability point data sets within the accuracy range of the reliability points in the geometric rough correction satellite remote sensing image to construct a characteristic point data set.
The satellite ground station generally provides a geometrically rough corrected satellite image, the high-reliability point data set is expressed as a highlight pixel point in a certain area range in the geometrically rough corrected thermal infrared remote sensing image, all high-reliability points in the reliability point precision range are selected on the roughly corrected satellite image, and the characteristic point data set N is preliminarily determined (S 1 ,S 2 ,……,S n )，S i Is P i Within a precision range of (a) determining a set of feature points S i ∈N，S ij Is S i A feature point S of ij ∈S i The selection mode is as follows:
D＝[d((S ij ),(P i (x i ，y i )))]≤λ
lambda is set as a satellite coarse calibration precision threshold value, D is a plane geometric distance, and a point composition point data set N meeting the condition in the formula is selected.
S4: and constructing a minimum triangle for the non-homonymous feature points in the feature point data set, and determining a matching point set at the minimum graph matching cost.
And constructing a minimum triangle by the feature points with different names, and selecting an error threshold value. And finally, selecting a group of data to perform pattern matching with the trusted points.
Let Δabc and Δa ' B ' C ' be two triangles that need to be judged if they are similar, where point a and point a ', point B and point B ', and points C and C ' are respectively corresponding point pairs, then the similarity of the two angles +.a (assuming that the value is a) and +.a ' (assuming that the value is x) is:
wherein the method comprises the steps ofσ＝a/6。
Averaging the three corresponding interior angle similarities of the triangle to obtain the similarity of the triangle:
I＝(I a +I b +I c )/3
pattern matching cost Algorithm:
Algorith m Cost ＝Min(I Δ1,Δ1i )
i＝(1,……，(1/N(S A ))*(1/N(S B ))*(1/N(S C )))，N(S A ),N(S B ),N(S C ) The maximum extractable feature points of the triangle corner points are respectively obtained.
And selecting three vertexes of the smallest triangle with the smallest graph matching cost as matching points.
S5: and matching the matching point set with the high-reliability point data set to finish the precise geometric correction of the satellite thermal infrared remote sensing image.
After steps S1-S5, the matching points and the corresponding homonymous trusted points are selected by the pattern matching conditions, so as to complete geometric correction, and the correction accuracy is generally represented by using Root Mean Square Error (RMSE) of verification points:
y i is true, f (x i ) For the measured value, the root mean square error reflects the precision information of the verification points, and a part of verification points are optimized through residual values, for example, a certain matching point causes overlarge residual error of the control network adjustment, the residual error is selected and removed, the Delaunay triangle network and the geometric correction operation are reconstructed, the excessive residual error matching point is removed again until a certain range condition is met, and the current matching result is regarded as the best matching condition.
The geometric correction method for the satellite thermal infrared remote sensing image provided by the embodiment has the following advantages:
1. can geometrically correct a wide range of thermal infrared images
Compared with a visible light sensor, the imaging breadth of the thermal infrared sensor is larger, and the spatial resolution is slightly lower. The geometric correction completed by the common image registration method requires the work of splicing, resampling and the like of visible light images with multiple scenes, small breadth and high resolution, so that the extracted features are matched with the thermal infrared remote sensing images. The high-reliability point data set established by the method can cover the surface area of the industrial factory with heat emission in the global scope, has wide coverage area, has good identification characteristics on the thermal infrared image, does not need image registration, and can be quickly geometrically corrected through simple calculation.
2. High noise eliminating capacity
Because of the imaging mechanism, the thermal infrared remote sensing image has good detection characteristics on the earth surface object with higher temperature, and noise points can be mixed when the same-name matching points of the reliable point set are found in the rough correction satellite image. According to the spatial pattern matching method, after the minimum triangle is formed, the similarity of the triangle is used for matching cost, and noise and distortion of the sweeping imaging are effectively resisted.
Example two
In order to execute the corresponding method of the above embodiment to achieve the corresponding functions and technical effects, a geometric correction system for satellite thermal infrared remote sensing images is provided below.
The system comprises:
and the industrial heat source geometric center determining module is used for determining the industrial heat source geometric center based on fire point data in the satellite thermal infrared remote sensing image.
And the high-reliability point data set construction module is used for constructing a high-reliability point data set according to the industrial heat source geometric center.
And the geometric rough correction and characteristic point data set construction module is used for selecting all high-reliability point data sets within the reliability point precision range in the geometric rough correction satellite remote sensing image to construct the characteristic point data set.
And the matching point set determining module is used for constructing a minimum triangle for the non-homonymous feature points in the feature point data set and determining a matching point set at the minimum graph matching cost.
And the matching module is used for matching the matching point set with the high-reliability point data set to finish the precise geometric correction of the satellite thermal infrared remote sensing image.
Further, the industrial heat source geometric center determining module specifically comprises:
the system comprises a clustering center sample set construction unit, a clustering center analysis unit and a clustering center analysis unit, wherein the clustering center sample set construction unit is used for carrying out clustering analysis and extraction on fire data in a satellite thermal infrared remote sensing image to determine a clustering center, and constructing a clustering center sample set according to the clustering center; the clustering center is the minimum circumscribed rectangular center formed by all the points of each cluster.
The training data set establishing unit is used for collecting remote sensing images covering the surface industrial heat source and establishing a training data set.
And the training unit is used for training the Faster-RCNN neural network model through the training data set to obtain an industrial heat source building geometric center recognition model.
And the industrial heat source geometric center determining unit is used for inputting the clustering center sample set into the industrial heat source building geometric center recognition model for iteration to obtain an industrial heat source geometric center.
Example III
An electronic device according to a third embodiment of the present invention includes a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to execute the computer program to cause the electronic device to execute the satellite thermal infrared remote sensing image geometric correction method according to the first embodiment.
In practical applications, the electronic device may be a server.
In practical applications, the electronic device includes: at least one processor (processor), memory (memory), bus, and communication interface (communication interface).
Wherein: the processor, communication interface, and memory communicate with each other via a communication bus.
And the communication interface is used for communicating with other devices.
And a processor, configured to execute a program, and specifically may execute the method described in the foregoing embodiment.
In particular, the program may include program code including computer-operating instructions.
The processor may be a central processing unit, CPU, or specific integrated circuit ASIC (ApplicationSpecificIntegratedCircuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the electronic device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
And the memory is used for storing programs. The memory may comprise high-speed RAM memory or may further comprise non-volatile memory (non-volatile memory), such as at least one disk memory.
Example IV
Based on the description of the third embodiment, a storage medium is provided, on which a computer program is stored, and the computer program can be executed by a processor to implement the satellite thermal infrared remote sensing image geometric correction method of the first embodiment.
The geometric correction system for satellite thermal infrared remote sensing images provided in the second embodiment of the present invention exists in various forms, including but not limited to:
(1) A mobile communication device: such devices are characterized by mobile communication capabilities and are primarily aimed at providing voice, data communications. Such terminals include: smart phones (e.g., iPhone), multimedia phones, functional phones, and low-end phones, etc.
(2) Ultra mobile personal computer device: such devices are in the category of personal computers, having computing and processing functions, and generally having mobile internet access capabilities. Such terminals include: PDA, MID, and UMPC devices, etc., such as iPad.
(3) Portable entertainment device: such devices may display and play multimedia content. The device comprises: audio, video players (e.g., iPod), palm game consoles, electronic books, and smart toys and portable car navigation devices.
(4) Other electronic devices with data interaction functions.
Thus, particular embodiments of the present subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
The system, apparatus, module or unit set forth in the above embodiments may be implemented in particular by a computer chip or entity, or by a product having a certain function. One typical implementation is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being functionally divided into various units, respectively. Of course, the functions of each element may be implemented in the same piece or pieces of software and/or hardware when implementing the present invention. It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash memory (flashRAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by the computing device. Computer readable media, as defined in the present invention, does not include transitory computer readable media (transshipment) such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer.
Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular transactions or implement particular abstract data types. The invention may also be practiced in distributed computing environments where transactions are performed by remote processing devices that are connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. A geometric correction method for a satellite thermal infrared remote sensing image is characterized by comprising the following steps:
determining the geometric center of the industrial heat source based on fire point data in the satellite thermal infrared remote sensing image;
constructing a high-reliability point data set according to the industrial heat source geometric center;
selecting all high reliability point data sets in the accuracy range of the reliability points in the geometric rough correction satellite remote sensing image to construct a characteristic point data set;
constructing a minimum triangle for the non-homonymous feature points in the feature point data set, and determining a matching point set at the minimum graph matching cost;
and matching the matching point set with the high-reliability point data set to finish the precise geometric correction of the satellite thermal infrared remote sensing image.
2. The geometric correction method for satellite thermal infrared remote sensing images according to claim 1, wherein determining the geometric center of the industrial heat source based on the fire data in the satellite thermal infrared remote sensing images comprises:
performing cluster analysis and extraction on fire data in the satellite thermal infrared remote sensing image to determine a cluster center, and constructing a cluster center sample set according to the cluster center; the clustering center is a minimum circumscribed rectangular center formed by all the points of each cluster;
collecting remote sensing images covering an earth surface industrial heat source and establishing a training data set;
training the Faster-RCNN neural network model through the training data set to obtain an industrial heat source building geometric center recognition model;
and inputting the cluster center sample set into the industrial heat source building geometric center recognition model for iteration to obtain the industrial heat source geometric center.
3. The geometric correction method for the satellite thermal infrared remote sensing image according to claim 2, wherein a density-based DBSCAN clustering method is used for carrying out clustering analysis on historical satellite remote sensing fire data, and a clustering center is extracted.
4. The geometric correction method for satellite thermal infrared remote sensing images according to claim 1, wherein constructing a minimum triangle from non-homonymous feature points in the feature point dataset and determining a matching point set with minimum pattern matching cost comprises:
constructing a minimum triangle for the feature points with different names in the feature point data set;
and selecting three vertexes of the smallest triangle with the smallest graph matching cost as matching points, and constructing a matching point set.
5. A geometric correction system for satellite thermal infrared remote sensing images, comprising:
the industrial heat source geometric center determining module is used for determining the industrial heat source geometric center based on fire point data in the satellite thermal infrared remote sensing image;
the high-reliability point data set construction module is used for constructing a high-reliability point data set according to the industrial heat source geometric center;
the geometric rough correction and characteristic point data set construction module is used for selecting all high-reliability point data sets within the reliability point precision range in the geometric rough correction satellite remote sensing image to construct a characteristic point data set;
the matching point set determining module is used for constructing a minimum triangle for the non-homonymous feature points in the feature point data set and determining a matching point set at the minimum graph matching cost;
and the matching module is used for matching the matching point set with the high-reliability point data set to finish the precise geometric correction of the satellite thermal infrared remote sensing image.
6. The geometric correction system for satellite thermal infrared remote sensing images according to claim 5, wherein said industrial heat source geometric center determination module comprises:
the system comprises a clustering center sample set construction unit, a clustering center analysis unit and a clustering center analysis unit, wherein the clustering center sample set construction unit is used for carrying out clustering analysis and extraction on fire data in a satellite thermal infrared remote sensing image to determine a clustering center, and constructing a clustering center sample set according to the clustering center; the clustering center is a minimum circumscribed rectangular center formed by all the points of each cluster;
the training data set establishing unit is used for collecting remote sensing images covering the surface industrial heat source and establishing a training data set;
the training unit is used for training the Faster-RCNN neural network model through the training data set to obtain an industrial heat source building geometric center recognition model;
and the industrial heat source geometric center determining unit is used for inputting the clustering center sample set into the industrial heat source building geometric center recognition model for iteration to obtain an industrial heat source geometric center.
7. An electronic device comprising a memory and a processor, the memory configured to store a computer program, the processor configured to execute the computer program to cause the electronic device to perform the satellite thermal infrared remote sensing image geometry correction method of any one of claims 1-4.
8. A computer readable storage medium, characterized in that it stores a computer program, which when executed by a processor implements the satellite thermal infrared remote sensing image geometry correction method according to any one of claims 1-4.
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