CN112445497B - Remote sensing image processing system based on plug-in extensible architecture - Google Patents
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Abstract
The invention discloses a remote sensing image processing system based on a plug-in type extensible architecture, and belongs to the field of remote sensing image processing. The method adopts a platform-plug-in architecture of a microkernel, divides a function system into three layers of a platform function, a basic plug-in and a processing plug-in, and loads the plug-ins meeting standard specifications on the platform to perform the full life cycle management of the plug-ins; the method comprises the steps that a plug-in management function is used for identifying, loading, running and unloading basic plug-ins, processing plug-ins and other plug-ins, a communication management function is used for completing communication between a platform and the plug-ins and between the plug-ins and the communication management function, the basic plug-in function comprises a user interaction interface, remote sensing data analysis, coordinate projection conversion and log management, and the processing plug-ins comprise image pyramid generation, image denoising, geometric correction, image fusion, splicing and embedding, image enhancement, target detection and image classification. The invention adopts the structure of the microkernel, the system occupies less resources, the starting speed is high, and the hot plug-in can be realized.
Description
Technical Field
The invention belongs to the technical field of remote sensing image processing, and particularly relates to a remote sensing image processing system based on a plug-in extensible architecture.
Background
With the continuous development of aerospace technologies, computer technologies, communication technologies and sensor technologies, the development progress of satellites and aviation remote sensing technologies is greatly promoted, and all-weather, multi-angle, multi-time-phase, multi-source and multi-resolution earth observation is realized. The data volume of the remote sensing data acquired by the remote sensing means is also increased rapidly and exponentially, and the method is widely popularized and applied in multiple industrial fields of military affairs, environment, national soil, national defense, agriculture, forestry, water conservancy, traffic, disaster reduction and the like, and promotes the rapid development of national economy.
The full play of the earth observation system depends on not only the advancement of the carrier and the improvement of the performance of various sensors, but also the improvement of the ground image information processing system and the related processing application technology such as image analysis and recognition. The rapid development of the application of the remote sensing technology in various industries leads the remote sensing information processing software to be gradually developed from a professional strong software technology to popular geographic information service platform software, and plays an important role in more and more fields.
At present, a plurality of remote sensing image processing software exist at home and abroad, but the problems of authorization permission, usability, expandability and the like exist. Generally speaking, domestic remote sensing image processing software is low in price, an operation method, a using process and a user interface are easily accepted by domestic users, but the software is relatively late to start, the software design and the function are not mature, and the requirement of large remote sensing data processing application cannot be met at present; and foreign software is developed earlier, functions are relatively powerful, the system is relatively mature, and the method has the advantages of advanced image processing technology, powerful functions, rich application fields, high integration of remote sensing and geographic information systems and the like, but the purchase price is expensive.
In summary, the remote sensing data processing platform in China currently faces the problems of insufficient system expansibility, low system flexibility, low data processing efficiency and the like.
Disclosure of Invention
In view of the above, the invention provides a remote sensing image processing system based on a plug-in type extensible architecture, which adopts the plug-in type extensible architecture, can reduce the complexity of the system through modularization, simplify the deployment and update of software, improve the starting efficiency, and effectively reduce the occupation of system resources, thereby solving the problems of system extensibility and dynamics.
In order to achieve the purpose, the invention adopts the technical scheme that:
a remote sensing image processing system based on a plug-in type extensible architecture comprises the following program modules running on a server:
remote sensing plug-in loading module: searching a specified directory for a kernel module of the system according to a configuration file of software, analyzing DLL files in the directory according to a system definition plug-in rule, searching plug-in files meeting conditions, analyzing the starting priority and the dependency of the plug-ins, constructing a plug-in dependency tree, and starting the plug-ins in sequence according to the dependency of the plug-ins;
an interface support module: providing a human-computer interface supporting service required by system operation, wherein the human-computer interface supporting service comprises a main program interface, a button space, a map control, a floating window control, a remote sensing plotting control and vector elements of the system, providing service in an API mode and providing a corresponding interface for plug-in units;
a communication service module: providing communication services between a platform and a plug-in as well as between the plug-ins in a message flow and data flow mode, wherein the message flow is a data set containing single or multiple execution commands, and the data flow is a to-be-processed data set containing remote sensing image data, vector data and text parameters; the system separately transmits the message flow and the data flow, and has higher stability and expandability.
The remote sensing plug-in life cycle management module: receiving plugin information sent by a remote sensing plugin loading module, and completing management of a lifecycle of a plugin, wherein the managed plugin states include six states of an identification state, an analysis state, a starting state, an activation state, a stopping state and an unloading state, and the six states can be mutually converted;
the log management module: providing log management service, dividing the log into general information, debugging information, warning information, error information and fatal error 5 categories, providing service in an API form, and calling corresponding service by a plug-in to complete the formatted record of the log information;
remote sensing data analysis module: the method comprises the steps of completing the analysis of various remote sensing data, including raster image data and vector data, used for analyzing the formats of various data, acquiring metadata information of the data, reading data contents, and providing services to the outside in an API (application program interface) form; the module can perform plug-in extension on various elements such as satellite types, data formats, data bit depths and the like, and has strong expandability.
The remote sensing algorithm support module: providing management functions of a remote sensing processing algorithm, including functions of algorithm registration, algorithm unloading, algorithm monitoring and algorithm maintenance, and performing classification management according to a human-computer interaction algorithm and automatic processing;
coordinate and projection conversion module: the conversion function of different coordinate systems and different projection modes is completed, so that the data of different coordinates and projection systems can be processed and displayed in the same coordinate and projection mode;
an image pyramid generation module: in order to realize a smooth image browsing effect, providing an image pyramid generation service for wide remote sensing images and high-resolution remote sensing images, automatically determining the grade number of an image pyramid according to the size of an image, generating a corresponding pyramid, and automatically selecting corresponding pyramid data to display according to a browsing area of the image when the image is browsed;
an image denoising module: the method has the advantages that the noise removal of the optical image and the SAR image is completed, the application effect of the image is enhanced, and the processing capability of Gaussian noise, salt and pepper noise and stripe noise is realized;
a geometric correction module: finishing image correction based on RPC information, and resampling image data according to a selected coordinate system and a projection mode to generate a secondary remote sensing image product;
the image fusion module: performing fusion processing on the input panchromatic image and the multispectral image by using a Panshipen processing algorithm to generate a fused remote sensing image, wherein the spatial resolution of the generated fused remote sensing image is consistent with that of the panchromatic image, and the spectral resolution of the generated fused remote sensing image is consistent with that of the multispectral image;
splicing and inlaying modules: splicing a plurality of images adjacent or overlapped in a geographical position, roughly determining the overlapped position of the images according to the geographical position information of the images, respectively extracting SIFT feature points from the images, calculating homonymy feature point pairs among the images, calculating mosaic lines on the basis, calculating pixel values of the images in the overlapped area by adopting a bilinear difference algorithm, and then performing feathering processing on a splicing area;
the image enhancement module: the image visualization effect is improved by stretching the gray scale, and supported enhancement modes comprise linear 2% stretching, gamma parameter stretching, histogram equalization and histogram matching;
a target detection module: carrying out target detection on input panchromatic and multispectral images or fused images generated by an image fusion module based on a trained target detection deep learning model, and outputting position coordinates of a target circumscribed rectangular frame to support the detected target types including ships and airplanes;
an image classification module: carrying out image classification on the input panchromatic and multispectral images or fused images generated by an image fusion module based on an image classification deep learning model which is trained, and outputting an image classification result; the target types supporting classification comprise water, vegetation, soil, buildings and roads.
Furthermore, each plug-in describes the plug-in specification through an XML document, and describes the name, version, initialization state, starting level, dependency information, activation strategy and extension point information of the plug-in; the plug-in specification comprises four aspects of plug-in interface specification, plug-in communication specification, plug-in management specification and plug-in description specification, wherein the plug-in interface comprises an interface, a service interface and a management interface, and the three types of the interface are as follows: the interface provides interface element generation and response functions, each functional plug-in can add buttons and tool bars to the platform, the service interface is used for providing communication service functions and meeting communication requirements between the platform and the plug-in as well as between the plug-in and the plug-in, and the management interface has openness and universality, so that an application framework can adopt various development kits and support various data sources; the plug-in communication specification adopts message flow to carry out communication between plug-ins, separately transmits instructions and data, clearly divides the processing flow of the whole system into two lines of messages and data, and realizes parallel computation of a plurality of computation modules by a message transmission mechanism; the plug-in management specification includes seven basic steps: the method comprises the steps of searching plug-in files in a specified format at a specified position, preloading the plug-ins, checking validity of the plug-ins, identifying and searching plug-in dependence items, preloading, loading the plug-in files, generating interface elements customized for the plug-ins, and using and setting the plug-ins.
Further, the lifecycle of the plugin includes six states of an identification state, an analysis state, a starting state, an activation state, a stopping state and an unloading state, and the conversion rules of the six states are as follows: the plug-in framework firstly verifies the plug-in before installing the plug-in, and the state of the plug-in is changed into an identification state after the verification is passed; after the plug-in is successfully installed, the plug-in framework analyzes the plug-in resources and the dependence, the analysis is successful, the state of the plug-in is changed into an analysis state, and the plug-in is still in an identification state if the plug-in fails; after the plug-in is successfully analyzed, the plug-in framework can start the plug-in, the plug-in starting operation starts the plug-in by calling a plug-in activator and according to an activation strategy, and the plug-in becomes an activated state after the plug-in is successfully started; starting the plug-in to the process of finishing the starting, wherein the plug-in is in a starting state; stopping the plug-in by calling a Stop method of the plug-in activator, and returning the plug-in to a resolution state after the plug-in stops; in the process of stopping the plug-in, the plug-in is in a stopping state; the plug-in becomes the analytic state after stopping finishing; unloading of the plug-in is realized by a Uninstall method, and the plug-in becomes an unloaded state after unloading is finished.
The invention has the following advantages:
(1) By adopting a micro-kernel architecture, the system occupies less resources and has high starting speed;
(2) Based on the plug-in architecture, the platform functions can be dynamically loaded or unloaded, namely hot plugging, without restarting the system.
Drawings
Fig. 1 is an overall flowchart of system construction in the embodiment of the present invention.
FIG. 2 is a schematic diagram of the logical relationship between the system platform functions, the base plug-ins, and the processing plug-ins.
FIG. 3 shows the conditions for the interconversion of six states in the lifecycle of the plugin.
Detailed Description
The invention is further described in detail below with reference to the drawings and specific embodiments.
As shown in fig. 2, a remote sensing image processing system based on a plug-in extensible architecture comprises the following program modules running on a server:
remote sensing plug-in loading module: searching a specified directory for a kernel module of the system according to a configuration file of software, analyzing a DLL file in the directory according to a system definition plug-in rule, searching plug-in files meeting conditions, analyzing the starting priority and the dependency relationship of the plug-ins, constructing a plug-in dependency tree, and starting each plug-in sequence according to the dependency relationship of the plug-ins;
an interface support module: providing a human-computer interface supporting service required by system operation, wherein the human-computer interface supporting service comprises a main program interface, a button space, a map control, a floating window control, a remote sensing plotting control and vector elements of the system, providing service in an API mode and providing a corresponding interface for plug-in units;
a communication service module: providing communication services between a platform and a plug-in as well as between the plug-ins in a message flow and data flow mode, wherein the message flow is a data set containing single or multiple execution commands, and the data flow is a to-be-processed data set containing remote sensing image data, vector data and text parameters; the system separately transmits the message flow and the data flow, and has higher stability and expandability.
The remote sensing plug-in life cycle management module: receiving plugin information sent by a remote sensing plugin loading module, and completing management of a lifecycle of a plugin, wherein the managed plugin states include six states of an identification state, an analysis state, a starting state, an activation state, a stopping state and an unloading state, and the six states can be mutually converted;
a log management module: providing log management service, dividing the log into general information, debugging information, warning information, error information and fatal error 5 categories, providing service in an API form, and calling corresponding service by a plug-in to complete the formatted record of the log information;
remote sensing data analysis module: the method comprises the steps of completing the analysis of various remote sensing data, including raster image data and vector data, used for analyzing the formats of various data, acquiring metadata information of the data, reading data contents, and providing services to the outside in an API (application program interface) form; the module can perform plug-in extension on various elements such as satellite types, data formats, data bit depths and the like, and has strong expandability.
The remote sensing algorithm support module: providing management functions of a remote sensing processing algorithm, including functions of algorithm registration, algorithm unloading, algorithm monitoring and algorithm maintenance, and performing classification management according to a human-computer interaction algorithm and automatic processing;
coordinate and projection conversion module: the conversion function of different coordinate systems and different projection modes is completed, so that the data of different coordinates and projection systems can be processed and displayed in the same coordinate and projection mode;
an image pyramid generation module: in order to realize a smooth image browsing effect, providing an image pyramid generation service for wide remote sensing images and high-resolution remote sensing images, automatically determining the grade number of an image pyramid according to the size of an image, generating a corresponding pyramid, and automatically selecting corresponding pyramid data to display according to a browsing area of the image when the image is browsed;
an image denoising module: the method has the advantages that the noise removal of the optical image and the SAR image is completed, the application effect of the image is enhanced, and the processing capability of Gaussian noise, salt and pepper noise and stripe noise is realized;
a geometric correction module: finishing image correction based on RPC information, and resampling image data according to a selected coordinate system and a projection mode to generate a secondary remote sensing image product;
an image fusion module: performing fusion processing on the input panchromatic image and the multispectral image by using a Panshipen processing algorithm to generate a fused remote sensing image, wherein the spatial resolution of the generated fused remote sensing image is consistent with that of the panchromatic image, and the spectral resolution of the generated fused remote sensing image is consistent with that of the multispectral image;
splicing and inlaying modules: splicing a plurality of images which are adjacent or overlapped in geographic positions, roughly determining the overlapping positions of the images according to the geographic position information of the images, then respectively extracting SIFT feature points from the images, calculating homonymy feature point pairs among the plurality of images, calculating inlaid lines on the basis, calculating pixel values of the images in the overlapping area by adopting a bilinear difference algorithm, and then performing eclosion processing on a splicing area;
the image enhancement module: the image visualization effect is improved by stretching the gray scale, and supported enhancement modes comprise linear 2% stretching, gamma parameter stretching, histogram equalization and histogram matching;
a target detection module: carrying out target detection on input panchromatic and multispectral images or fused images generated by an image fusion module based on a trained target detection deep learning model, and outputting position coordinates of a target circumscribed rectangular frame to support the detected target types including ships and airplanes;
an image classification module: carrying out image classification on the input panchromatic and multispectral images or fused images generated by an image fusion module based on an image classification deep learning model which is trained, and outputting an image classification result; the target types supporting classification comprise water, vegetation, soil, buildings and roads.
Each plug-in describes the plug-in specification through an XML document, describes the name, the version, the initialization state, the starting level, the dependency information, the activation strategy and the extension point information of the plug-in; the plug-in specification comprises four aspects of plug-in interface specification, plug-in communication specification, plug-in management specification and plug-in description specification, wherein the plug-in interface comprises an interface, a service interface and a management interface, and the three types of the interface are as follows: the interface provides interface element generation and response functions, each functional plug-in can add buttons and tool bars to the platform, the service interface is used for providing communication service functions and meeting communication requirements between the platform and the plug-in as well as between the plug-in and the plug-in, and the management interface has openness and universality, so that an application framework can adopt various development kits and support various data sources; the plug-in communication specification adopts message flow to carry out communication between plug-ins, separately transmits instructions and data, clearly divides the processing flow of the whole system into two lines of messages and data, and realizes parallel computation of a plurality of computation modules by a message transmission mechanism; the plug-in management specification includes seven basic steps: the method comprises the steps of searching plug-in files in a specified format at a specified position, preloading the plug-ins, checking validity of the plug-ins, identifying and searching plug-in dependence items, preloading, loading the plug-in files, generating interface elements customized for the plug-ins, and using and setting the plug-ins.
As shown in fig. 3, the lifecycle of the plug-in includes six states, i.e. an identification state, an analysis state, a start state, an activation state, a stop state, and an uninstall state, and the conversion rules of the six states are as follows: the plug-in framework firstly verifies the plug-in before the plug-in is installed, and the state of the plug-in is changed into an identification state after the verification is passed; after the plug-in is successfully installed, the plug-in framework analyzes the plug-in resources and the dependence, the analysis is successful, the state of the plug-in is changed into an analysis state, and the plug-in is still in an identification state if the plug-in fails; after the plug-in is successfully analyzed, the plug-in framework can start the plug-in, the plug-in starting operation starts the plug-in by calling a plug-in activator and according to an activation strategy, and the plug-in becomes an activated state after the plug-in is successfully started; starting the plug-in to the process of finishing the starting, wherein the plug-in is in a starting state; stopping the plug-in by calling a Stop method of the plug-in activator, and returning the plug-in to a resolution state after the plug-in stops; in the process of stopping the plug-in, the plug-in is in a stopping state; the plug-in becomes the analytic state after stopping finishing; unloading of the plug-in is realized by a Uninstall method, and the plug-in becomes an unloaded state after unloading is finished.
As shown in fig. 1, the module is constructed in the following manner:
a) A platform-plug-in architecture employing microkernels;
b) The functional system is divided into three layers: platform function, basic plug-in, processing plug-in;
c) The platform can load plug-ins meeting the standard specification;
d) The platform carries out full life cycle management on the plug-in;
e) The platform can perform hot plug of the plug-in.
In the system, a platform of a microkernel performs plug-in management and communication management functions, wherein the plug-in management function completes the management of the whole life cycle of identification, loading, operation, unloading and the like of basic plug-ins, processing plug-ins and other plug-ins, and the communication management function completes the communication between the platform and the plug-ins and between the plug-ins; the basic plug-in function comprises 4 functions of a user interaction interface, remote sensing data analysis, coordinate projection conversion and log management, and has plug-in expansion capability; the processing plug-in comprises 8 processing functions of image pyramid generation, image denoising, geometric correction, image fusion, splicing and embedding, image enhancement, target detection and image classification, and has plug-in expansion capability. The platform function can be called by the basic plug-in and the processing plug-in, the basic plug-in function can be called by the processing plug-in, and the processing plug-in functions can be mutually called.
In a word, aiming at the problems of backward development mode, poor universality, weak expansion capability and the like of a remote sensing information processing information system, a platform-plug-in system architecture model is adopted to comprehensively meet the processing application requirements of remote sensing information processing, the current situation and the development trend of a remote sensing information processing platform are combined, a series of advanced technologies such as platforms/plug-ins and the like are adopted to construct an information system entity with a general technical architecture and good expandability, the dynamic loading operation capability of flexible assembly and dynamic loading is realized, the concept of sharing remote sensing information and sharing resources is shown, and the technical support and the achievement basis are provided for promoting the application of the remote sensing information.
Claims (3)
1. A remote sensing image processing system based on a plug-in type extensible architecture is characterized in that a platform-plug-in architecture of a microkernel is adopted, a function system is divided into three levels of a platform function, a basic plug-in and a processing plug-in, wherein the platform is used for loading the plug-ins meeting standard specifications, carrying out full life cycle management on the plug-ins and supporting hot plug of the plug-ins; the system comprises the following program modules running on the server:
remote sensing plug-in loading module: searching a specified directory for a kernel module of the system according to a configuration file of software, analyzing a DLL file in the directory according to a system definition plug-in rule, searching plug-in files meeting conditions, analyzing the starting priority and the dependency relationship of the plug-ins, constructing a plug-in dependency tree, and starting each plug-in sequence according to the dependency relationship of the plug-ins;
an interface support module: providing a human-computer interface supporting service required by system operation, wherein the human-computer interface supporting service comprises a main program interface, a button space, a map control, a floating window control, a remote sensing plotting control and vector elements of the system, providing service in an API mode and providing a corresponding interface for plug-in units;
a communication service module: providing communication services between a platform and a plug-in as well as between the plug-ins in a message flow and data flow mode, wherein the message flow is a data set containing single or multiple execution commands, and the data flow is a to-be-processed data set containing remote sensing image data, vector data and text parameters;
the remote sensing plug-in life cycle management module: receiving plugin information sent by a remote sensing plugin loading module, and completing management of a lifecycle of a plugin, wherein the managed plugin states comprise six states of an identification state, an analysis state, a starting state, an activation state, a stopping state and an unloading state, and the six states can be mutually converted;
the log management module: providing log management service, dividing the log into general information, debugging information, warning information, error information and fatal error 5 categories, providing service in an API form, and calling corresponding service by a plug-in to complete the formatted record of the log information;
remote sensing data analysis module: completing the analysis of various remote sensing data, including raster image data and vector data, for analyzing the formats of various data, acquiring metadata information of the data, reading the data content, and providing service to the outside in the form of API;
the remote sensing algorithm support module: providing management functions of a remote sensing processing algorithm, including functions of algorithm registration, algorithm unloading, algorithm monitoring and algorithm maintenance, and performing classification management according to a human-computer interaction algorithm and automatic processing;
coordinate and projection conversion module: the conversion function of different coordinate systems and different projection modes is completed, so that the data of different coordinate systems and projection systems can be processed and displayed in the same coordinate and projection mode;
an image pyramid generation module: in order to realize a smooth image browsing effect, providing an image pyramid generation service for wide remote sensing images and high-resolution remote sensing images, automatically determining the grade of an image pyramid according to the size of an image, generating a corresponding pyramid, and automatically selecting corresponding pyramid data to display according to a browsing area of the image when the image is browsed;
an image denoising module: the method has the advantages that the noise removal of the optical image and the SAR image is completed, the application effect of the image is enhanced, and the method has the processing capacity of Gaussian noise, salt and pepper noise and stripe noise;
a geometric correction module: finishing image correction based on RPC information, resampling image data according to a selected coordinate system and a projection mode, and generating a secondary remote sensing image product;
the image fusion module: performing fusion processing on the input panchromatic image and the multispectral image by using a Panshipen processing algorithm to generate a fused remote sensing image, wherein the spatial resolution of the generated fused remote sensing image is consistent with that of the panchromatic image, and the spectral resolution of the generated fused remote sensing image is consistent with that of the multispectral image;
splicing and inlaying modules: splicing a plurality of images which are adjacent or overlapped in geographic positions, roughly determining the overlapping positions of the images according to the geographic position information of the images, then respectively extracting SIFT feature points from the images, calculating homonymy feature point pairs among the plurality of images, calculating inlaid lines on the basis, calculating pixel values of the images in the overlapping area by adopting a bilinear difference algorithm, and then performing eclosion processing on a splicing area;
the image enhancement module: the image visualization effect is improved by stretching the gray scale, and supported enhancement modes comprise linear 2% stretching, gamma parameter stretching, histogram equalization and histogram matching;
a target detection module: carrying out target detection on the input panchromatic and multispectral images or fused images generated by an image fusion module based on a trained target detection deep learning model, and outputting position coordinates of a target circumscribed rectangular frame, wherein the types of the targets supported for detection comprise ships and airplanes;
an image classification module: carrying out image classification on the input panchromatic and multispectral images or fused images generated by an image fusion module based on an image classification deep learning model which is trained, and outputting an image classification result; the target types supporting classification comprise water bodies, vegetation, soil, buildings and roads;
in the system, a platform of a microkernel performs plug-in management and communication management functions, wherein the plug-in management function completes the management of the whole life cycle of identification, loading, operation and unloading of a basic plug-in and a processing plug-in, and the communication management function completes the communication between the platform and the plug-in as well as between the plug-ins; the basic plug-in function comprises 4 functions of user interaction interface, remote sensing data analysis, coordinate projection conversion and log management, and has plug-in expansion capability; the processing plug-in comprises 8 processing functions of image pyramid generation, image denoising, geometric correction, image fusion, splicing and embedding, image enhancement, target detection and image classification, and has plug-in expansion capability; the platform function can be called by the basic plug-in and the processing plug-in, the basic plug-in function can be called by the processing plug-in, and the processing plug-in functions can be mutually called.
2. The remote sensing image processing system based on the plug-in extensible architecture of claim 1, wherein each plug-in describes the plug-in specification through an XML document, describes the name, version, initialization state, starting level, dependency information, activation strategy and extension point information of the plug-in; the plug-in components standard includes four aspects of plug-in components interface standard, plug-in components communication standard, plug-in components management standard, plug-in components description standard, wherein, the plug-in components interface includes interface, service interface, three kinds of types of management interface: the interface provides interface element generation and response functions, each functional plug-in can add a button and a tool bar to the platform, the service interface is used for providing communication service functions and meeting the communication requirements between the platform and the plug-in as well as between the plug-in and the plug-in, and the management interface has openness and universality, so that an application framework can adopt various development kits and support various data sources; the plug-in communication specification adopts message flow to carry out communication between plug-ins, separately transmits instructions and data, clearly divides the processing flow of the whole system into two lines of messages and data, and realizes parallel computation of a plurality of computation modules by a message transmission mechanism; the plug-in management specification includes seven basic steps: the method comprises the steps of searching plug-in files in a specified format at a specified position, preloading the plug-ins, checking validity of the plug-ins, identifying and searching plug-in dependence items, preloading, loading the plug-in files, generating interface elements customized for the plug-ins, and using and setting the plug-ins.
3. The remote sensing image processing system based on the plug-in extensible architecture as claimed in claim 1, wherein the lifecycle of the plug-in includes six states of an identification state, an analysis state, a start state, an activation state, a stop state and an uninstall state, and the conversion rules of the six states are as follows: the plug-in framework firstly verifies the plug-in before installing the plug-in, and the state of the plug-in is changed into an identification state after the verification is passed; after the plug-in is successfully installed, the plug-in framework analyzes the plug-in resources and the dependence, the analysis is successful, the state of the plug-in is changed into an analysis state, and the plug-in is still in an identification state if the plug-in fails; after the plug-in is successfully analyzed, the plug-in framework can start the plug-in, the plug-in starting operation starts the plug-in by calling a plug-in activator and according to an activation strategy, and the plug-in becomes an activated state after the plug-in is successfully started; starting the plug-in to the process of finishing the starting, wherein the plug-in is in a starting state; stopping the plug-in by calling a Stop method of the plug-in activator, and returning the plug-in to a resolution state after the plug-in stops; in the process of stopping the plug-in, the plug-in is in a stopping state; the plug-in becomes the analytic state after stopping finishing; unloading of the plug-in is realized by a Uninstall method, and the plug-in becomes an unloaded state after unloading is finished.
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