CN115795075B - Method for constructing universal model of remote sensing image product - Google Patents
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Abstract
The invention discloses a method for constructing a universal model of a remote sensing image product, which comprises the steps of constructing a remote sensing image product definition model according to the file composition, the attribute and the quality inspection index of the remote sensing image product; labeling the remote sensing image product file attribute, the semantic attribute and the quality inspection index semantic attribute; instantiating and creating a remote sensing image product model object; updating the product file composition model and the semantic attribute of the product file through different algorithm modules; based on the semantic structure model of the remote sensing image product and the latest entity data value, the automatic collection of the remote sensing image processing interface is realized. According to the invention, general models are adopted for describing remote sensing image products and quality inspection semantics of different types and different sources, so that the update of the semantics of each functional module product in the construction of a remote sensing image system is realized, the construction progress of the quality inspection system of the remote sensing image products is improved, the construction cost of the quality inspection system of the remote sensing image products is greatly reduced, and the consistency of interoperation of the remote sensing image products among different modules of the system is improved.
Description
Technical Field
The invention relates to the technical field of remote sensing images and quality inspection of remote sensing images, in particular to a general expression method for remote sensing image product file composition and quality inspection semantic composition and state produced and processed by different satellite manufacturers.
Background
The satellite data products are multiple in sources, each satellite product is produced by different manufacturers and specified naming standards, when the remote sensing image quality inspection platform design is carried out, data produced by different manufacturers and specified naming production are required to be compatible, consistent quality inspection flow design and system development are required to be provided for data products of the same type from different sources, only one set of quality inspection system is built for one type of data product, and low-level invalid repetition of the construction of the quality inspection system of the multi-source satellite data products is avoided.
The quality inspection operation support management platform needs to realize functional plug-ins, flow design and task management of quality inspection of satellite data products of different types, also needs to operate different streams Cheng Fenzhi on certain index data values of each type of product, and collects and stores quality inspection results.
The remote sensing satellite products comprise optical satellites, radars and laser sources, each sensor comprises a sensor correction product, a digital orthographic product, an elevation product, an index product, a reflectivity product and the like, each product file is different in composition, a set of unified remote sensing satellite product file management model is constructed, and a unified access model is provided for different remote sensing satellite products.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a general model construction method for semantic definition of image sensing product composition and quality inspection, which can meet the generalized structural definition of different types of remote sensing image product composition, realize uniform semantic model provided by heterogeneous platforms for semantic attribute disclosure of multi-source remote sensing image product file matching, file access, product and quality inspection, and provide a method for accessing, instantiating, updating and collecting product semantics for different links of product operation by management platforms and algorithm plug-ins in the remote sensing image processing and quality inspection process.
The aim of the invention is achieved by the following technical scheme:
a method for constructing a universal model of a remote sensing image product comprises the following steps:
step A, constructing a remote sensing image product definition model according to remote sensing image product file composition, attributes and quality inspection indexes;
marking the file attribute, the semantic attribute and the quality inspection index semantic attribute of the remote sensing image product;
step C, instantiating and creating a remote sensing image product model object;
step D, updating the product file composition model and the semantic attribute of the product file through different algorithm modules;
and E, automatically collecting a remote sensing image processing interface based on a semantic structure model of the remote sensing image product and the latest entity data value.
One or more embodiments of the present invention may have the following advantages over the prior art:
the invention provides a general modeling method for a remote sensing image product based on product file definition and attribute and quality inspection index definition, provides a specific modeling method for the remote sensing image product, solves interface definition between specific product file types and semantic definition interfaces, and provides specific processes how specific algorithm modules such as remote sensing image standardized product production, cloud detection and radiation detection update and collection are used for creating and updating a product definition model. The method can be widely applied to the construction of a multi-source heterogeneous remote sensing image processing and quality inspection system platform based on flow modeling, and solves the problems that a remote sensing image processing scheduling module acquires and manages the latest state of a product, a product algorithm plug-in pair is a software management platform due to the characteristics of multi-source property, format difference and the like of data, and a set of public data model is provided for product operation by the algorithm plug-in pair.
Drawings
FIG. 1 is a flow chart of a method for constructing a universal model of a remote sensing image product based on product file definition and attribute and quality inspection index definition;
FIGS. 2a and 2b are examples of remote sensing image product definition model structures and optical satellite product structure formation based on the product definition structures according to embodiments of the present invention;
3a, 3b and 3c are XML files of an initialization example of a remote sensing image product in the embodiment, and the XML files are standardized and then processed by an algorithm module; and (5) instance XML after passing through a cloud cover checking algorithm module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the following examples and the accompanying drawings.
As shown in FIG. 1, in order to construct a general model of a remote sensing image product based on product file definition and attribute and quality inspection index definition, the method describes remote sensing image products of different types and sources and quality inspection semantics by adopting a general model, realizes updating of the product semantics of each functional module of remote sensing image system construction, improves the construction progress of the remote sensing image product quality inspection system, greatly reduces the construction cost of the remote sensing image product quality inspection system, and improves the consistency of interoperation of the remote sensing image products among different modules of the system. The method specifically comprises the following steps:
step 1, constructing a remote sensing image product definition model according to remote sensing image product file composition, attributes and quality inspection indexes;
marking the file attribute, the semantic attribute and the quality inspection index semantic attribute of the remote sensing image product;
step 3, instantiating and creating a remote sensing image product model object;
step 4, updating the product file composition model and the semantic attribute of the product file through different algorithm modules;
and step 5, based on a semantic structure model of the remote sensing image product and the latest entity data value, realizing automatic collection of a remote sensing image processing interface.
The step 1 specifically includes:
1.1, constructing a structural model of remote sensing image product file composition;
as shown in fig. 2a, the semantic model of the remote sensing image product is a general semantic model for structurally expressing entity files and combined files contained in the remote sensing image product by adopting a hierarchical structure model, and organizing semantic attributes and quality inspection index attributes of the remote sensing image product, the contained entity files and the combined files as nodes contained in the remote sensing image product. The entity file comprises a file name, a file type, a file matching template definition and an instance data value definition; the combined file includes a file name, a file type, an instance data value definition, wherein at least one entity file. The file matching template definition is described by adopting a grammar of "$ (dynamic parameter key) +static character string combination", wherein the value of "$ (dynamic parameter key)" contained in the file matching template definition has uniqueness after a specific product is instantiated, and the file name contained in the product is expressed by combining "$ (dynamic parameter key)" with the static character string.
1.2, marking semantic attributes of remote sensing image products and semantic attributes of quality inspection indexes;
as shown in fig. 2b, the semantic attribute and the quality inspection index attribute of the remote sensing image product are part of the entity file and the combined file of each level included in the remote sensing image product, including attribute name, attribute type and whether the attribute is available, the attribute is divided into two types of basic attribute and expression attribute, the basic attribute is the attribute of the file type, and the expression attribute is generated by the operation expression including the basic attribute.
1.3, marking the types of the files formed by the remote sensing image products;
the Image file data format types comprise TIF, erdas Image, JP2 and TIL file types; the rational function model file data comprises TXT and a plurality of custom XML formats; the projection parameter file data type comprises prj and Proj4 data formats; the metadata file and the quality inspection metadata file are in XML format, and the micro-image file and the thumb-image file belong to the type of digital positive photographic image file.
1.4 defining and describing a remote sensing image entity file template; when a remote sensing image product definition model is carried out, firstly, files contained in the product are analyzed for classification: one is an entity file (SingleFile) and the other is a combined file (GroupFile). A remote sensing image file at least comprises an entity file, and can also be a combined file (GroupFile) formed by the entity file and the combined file (GroupFile). Based on the above, a hierarchy containing structure of the remote sensing image product is established. For the fact that the combined file in the product contains sub-members, file access and semantic name marking are facilitated, and the semantic names of the contained files cannot be repeated.
The step 2 specifically includes the following steps:
2.1, analyzing the composition of remote sensing image product files;
as shown in fig. 3a, the remote sensing image products are classified according to product hierarchy: original image products, sensor correction products, geometric correction products, digital surface models, index products and other thematic products; the sensor correction product files comprise an original image file, a rational function model file, a micro graph file, a thumb graph file, a coordinate range file, a projection parameter file, a metadata file and a quality inspection metadata file. Different types of products including optics, radars, lasers, hyperspectrum and the like are divided according to different sensors; the same type of product has the same basic semantics, but different data manufacturers have certain differences in file content and data formats, especially metadata.
2.2, labeling the entity file attribute of the remote sensing image product;
as shown in fig. 3a, the remote sensing image product entity file attribute description includes five attributes of file name (name), file type (type), file template matching expression (template), and whether the file is available (use) in the initialized state, and the optional values are available|inhibited and data value (value). These attribute descriptions are all stored in the form of strings. The file names are semantic names, file template parameter definition is defined, all entity file directory file names contained in the product are analyzed, the composition semantics of the entity file directory file names are decomposed into one or more dynamic keywords and one or more static character strings, and the matching of file paths and file names of all files in the product can be achieved, wherein the dynamic keywords contained in all files are identical, and the corresponding character strings are identical when the dynamic keywords are matched. The data value is the file name matched with the current file after instantiation.
2.3, labeling the attribute of the remote sensing image product combined file;
as shown in fig. 3b, the definition of the semantic attribute and the semantic attribute of the quality inspection index of the remote sensing image product is the definition of the semantic model of the remote sensing image product and the file containing the remote sensing image product, and the product entity combination file attribute comprises a semantic name (name), a file data type (type), whether the file is available (use) in an initialized state, an optional value available|prohibited and a data value (value); these attribute descriptions are all stored in the form of strings. The file names are semantic names, file template parameter definition is defined, all entity file directory file names contained in the product are analyzed, the composition semantics of the entity file directory file names are decomposed into one or more dynamic keywords and one or more static character strings, and the matching of file paths and file names of all files in the product can be achieved, wherein the dynamic keywords contained in all files are identical, and the corresponding character strings are identical when the dynamic keywords are matched. The data value is the file name matched with the current file after instantiation. The product semantic attributes comprise product basic semantic attributes and operation expression semantic attributes; the semantic definition of the product quality inspection index is the same as the semantic attribute definition of the product.
Marking semantic attributes of remote sensing image products: the semantic attribute of the remote sensing image product is closely related to the file type, the file attribute is a representation form of the file content metadata, and the semantic attribute is a part of the file or the product attribute which needs to be accessed and modified by a platform or other modules. The file semantic attributes are densely related to the file metadata, the basic attributes are a subset of the metadata, and the extended attributes are calculated based on the operation expressions of the basic attributes.
Labeling quality inspection indexes of the remote sensing image products: as shown in fig. 3b, the quality inspection index of the remote sensing image product is the statistical feature and evaluation characteristic of the remote sensing quality description. The remote sensing quality inspection index is related to the statistical characteristics of the remote sensing image products and the files containing the remote sensing image products or the attributes related to the image application targets, and comprises data integrity, data validity, image quality indexes, radiation quality indexes and the like. The quality evaluation comprises evaluation of product integrity effectiveness, geometric quality, image radiation quality and product comprehensive quality. Since the quality evaluation index needs to be performed by the corresponding quality inspection index service module, the state of whether the model can be used or not is set to be a forbidden state (forbidden) when the model is initialized and constructed, and the state of the model can be set to be an available state only after the model is processed by the corresponding index algorithm module.
The step 3 specifically includes:
3.1 constructing remote sensing file types of remote sensing products;
because the remote sensing product is composed of two types of files, namely a single file (SingleFile) and a combined file (GroupFile), according to the model definition, whether the entity file or the combined file has file attributes (semantic names and file types), product semantic attributes and quality check index attributes related to the file are also included, and in order to realize unified management of different types of objects, a common base class (CFileObjectBase) needs to be defined, and creation of the file and the associated product semantic attributes and quality check index attributes, a calculation interface for updating whether the file is available or not, and the like are realized. When the specific file type object is defined, selecting the realization class according to different file composition structures, selecting the realization class according to the type file composition of the realization class to derive from the entity file or the combined file, identifying the file type of the realization class, and carrying out instantiation and input of the semantic name of the realization class through the constructor interface.
3.2, instantiating and constructing remote sensing file types of remote sensing products;
3.2.1 File class registration based on a plug-in mechanism. In order to solve the problem of creating an instantiation file class instance based on a file type and to realize registration and instantiation of a plurality of different types of files, a file plug-in implementation method based on a dynamic library module is provided, wherein the dynamic library module provides a fixed function interface, and the fixed function interface comprises: 1 whether a plug-in is defined for a product (isprodefplug in), 2 whether the number of plug-ins is obtained (getPluginCount), 3 whether the plug-in type is obtained (getPluginType), 4 whether a create plug-in instance (creatPluginInstance) is a plug-in. The plug-in management library judges whether to register the plug-in for the product type by loading a dynamic link library under the file directory, dynamically searching whether the plug-in contains an isprodefPlugin function pointer and calling the function pointer. And searching the file types registered in the product definition plug-in, and registering in the currently registered plug-in environment.
3.2.2 creation of product definition model class structure;
based on the product definition model file, according to the product definition hierarchical structure model, searching the corresponding realization class and the corresponding plugin from the registration class according to the product definition type, completing the creation of the class instance object by creating the plugin instance (creatPlugin instance) object, and on the basis, setting the product definition model as a parameter, instantiating and creating the file contained in the combined file and adding the file into a class instance list for management.
3.2.3 instantiation of product definition model class product files;
according to the product file catalogue to be instantiated, searching all files under the current catalogue and the sub-catalogue, selecting one initializing available file in the product files as a file template parameter definition of a main file to match, and synchronizing an actual character string corresponding to the matched dynamic keyword into other entity files in the current product once the matching is successful to form a complete expression of the entity files in the product. After the matching is completed, whether the actual product instantiation operation is realized is judged by checking whether all the current available files exist.
The step 4 includes:
4.1 method for creating and updating product definition model by remote sensing image algorithm module
Product file model instance creation: instantiating the appointed product according to the remote sensing image product definition model to form initialized product definition instantiation model data; wherein, the basic attribute of each type file in the product definition is dependent on internal implementation, and the corresponding basic type is obtained by a function interface provided by the class. For a remote sensing image product, the type of the remote sensing image product is a combined file class, if the combined file class has no special self-defined file attribute, the remote sensing image product can be realized by only serving as a file management class, the basic combined file class only realizes the management of the included entity file and the combined file and the management of the definition attribute of the derived attribute and the quality check index, and for the basic attribute, the included value is required to be realized by the synchronous exchange of the file class, and the quality check index attribute and the derived attribute are calculated by an external plug-in and a calculation formula. This property creation process can only be defined by modifying the product definition file or by the product definition visualization software through the definition of the product definition.
Product file attribute value status and value update: for the attribute of the product file, the definition is realized through an internally defined function, the internal attribute is expressed by providing read-only and read-write states, the internal state value of the file can be synchronized into the attribute value through an interface defined by a class, and the writable attribute value can be written into the file through a public interface.
The algorithm module is designed, and the product quality inspection semantic attribute is updated: as shown in fig. 3c, for the defined quality inspection semantic attribute, the quality inspection attribute is set to be unavailable (inhibited) when the product definition is initialized, and the quality inspection algorithm module is designed and implemented according to the quality inspection function to complete corresponding quality inspection index calculation or labeling. The quality check index can be set to available only after the corresponding quality check process is completed, and the quality check index data value is written into the quality check metadata file, and the quality check result is synchronized to the product semantic model file.
4.2 updating the product semantics by the remote sensing image product standardization processing algorithm
The remote sensing image product standardization processing algorithm realizes that initial sensor correction products produced by different satellite manufacturers are produced into unified standard optical satellite sensor correction products, and updates a product definition model of the optical satellite sensor correction products. The algorithm steps are divided into the following steps: 1) Reading a product definition model which needs remote sensing image product standardization, and instantiating a product definition file structure of the product definition model; 2) Obtaining basic image data, a rational function model and basic metadata according to the semantic name of the current file, and generating derivative data such as a standard metadata file, a quality inspection metadata file, a micro-graph and thumb graph file; 3) And setting the usability (available) of the generated standard metadata file, quality inspection metadata file, thumbnail and thumb file from the usability (inhibited) to the usability (available).
4.3 updating product semantics by remote sensing image product cloud amount detection algorithm
The cloud quantity detection algorithm of the remote sensing image product is realized based on a current product definition model, a cloud identification model algorithm trained based on a deep learning network model is called to calculate a cloud quantity range (cloudsurrounding) and a cloud quantity percentage (CloudPercent), the cloud quantity range (CloudPercent) and the cloud quantity percentage (CloudPercent) are stored in corresponding semantic files, and the attribute state and the attribute value of the quality inspection semantic attribute cloud quantity percentage (CloudPercent) in the product definition model are updated. The algorithm steps are divided into the following steps: 1) Reading a product definition model which needs remote sensing image product standardization, and instantiating a product definition file structure of the product definition model; 2) Acquiring a file thumbnail name (preview) file object contained in a current remote sensing image product, and obtaining a corresponding instance file path name as input image data; acquiring a semantic file name file definition cloud amount range (closed) and quality inspection metadata (metadata) in a product definition model to obtain corresponding example file path names which are respectively used as files for storing the cloud amount range and the cloud amount percentage; and calling a cloud recognition model algorithm trained based on the deep learning network model, calculating an image cloud coverage area and cloud percentage (CloudPercent), and writing corresponding files. 3) Whether the quality inspection attribute definition name (CloudPercent) in the product definition model is available or not is set from prohibited use (prohibited) to available (available), and the cloud percentage (CloudPercent) value is written to the corresponding semantic attribute value item (value).
4.4 updating the semantics of the product by the remote sensing image product radiation anomaly detection algorithm
The remote sensing image product radiation anomaly detection algorithm is used for realizing detection of radiation anomaly types based on a current product definition model, calling the remote sensing image product radiation anomaly detection algorithm, storing the radiation anomaly types into corresponding semantic files in a classified mode, and updating corresponding attribute states and attribute values of quality inspection semantic attribute radiation anomalies in the product definition model. The algorithm steps are divided into the following steps: 1) Reading a product definition model which needs remote sensing image product standardization, and instantiating a product definition file structure of the product definition model; 2) Acquiring a file thumbnail name (preview) file object contained in a current remote sensing image product, and obtaining a corresponding instance file path name as input image data; acquiring a semantic file name quality inspection metadata file (qualitydata) in a product definition model to obtain a corresponding instance file path name which is used as a file for storing radiation anomaly classification attributes; and calling a remote sensing image product radiation anomaly detection algorithm based on deep learning network model training, calculating whether an image contains radiation problems such as CCD splicing, color cast, tapping, missing, messy codes and the like, and writing the radiation problems into corresponding files. 3) The quality inspection attribute definition names in the product definition model are CCD splice (CCDedgeJoiningcheck), color cast (ColorOffsetcheck), tap (TapCheck), miss (ImageMissingcheck), and random code (random Codecheck) are set from prohibited use (prohibited) to available (available) and each type of radiation problem value is written to the corresponding semantic attribute value item (value).
The product data collection in step 5 includes:
5.1 collection of remote sensing image products
And after the product structure collection, product definition model layers, product definition types and attribute collection can be data collection of multiple layers after the product data processing and quality inspection results of the remote sensing image are finished, wherein the attribute collection can be used as a hierarchical structure model, in order to map with a common relational table database, attribute semantics and quality inspection index semantics contained in a certain layer in the product definition model are required to be selected, part or all of attribute items are selected from the attribute semantics and the quality inspection index semantics, a mapping relation is established through an XPath grammar structure to be used as a collected product model structure, and a collected data table structure is established. Since the product definition file contains a hierarchical file, collection can be performed based on file types when creating the relational data table structure, and the collection object includes file own attributes (file name, file type, instantiated file data value), and also includes semantic attributes of the product and semantic attributes of quality inspection indexes. And generating a semantic quality inspection mapping relation among the names of the data table, the names of the table fields and the types, and realizing data collection based on file types.
5.2 remote sensing image product instance data collection
And collecting product instance data, searching an established product type structure collection model based on the updated product file, loading the model based on the instantiated product file, and collecting a data type corresponding table structure and a product definition attribute corresponding model according to the requirement to collect corresponding type data and record the data table.
Although the embodiments of the present invention are described above, the embodiments are only used for facilitating understanding of the present invention, and are not intended to limit the present invention. Any person skilled in the art can make any modification and variation in form and detail without departing from the spirit and scope of the present disclosure, but the scope of the present disclosure is still subject to the scope of the appended claims.
Claims (7)
1. The method for constructing the universal model of the remote sensing image product is characterized by comprising the following steps of:
step A, constructing a remote sensing image product definition model according to remote sensing image product file composition, attributes and quality inspection indexes;
marking the file attribute, the semantic attribute and the quality inspection index semantic attribute of the remote sensing image product;
step C, instantiating and creating a remote sensing image product model object;
step D, updating the product file composition model and the semantic attribute of the product file through different algorithm modules;
e, based on a semantic structure model of the remote sensing image product and the latest entity data value, realizing automatic collection of a remote sensing image processing interface;
the step C specifically comprises the following steps:
c1, constructing a remote sensing file type class of a remote sensing product; realizing corresponding instantiation class according to the file type, and registering the type and the type instance object;
c2, instantiating and constructing remote sensing file types of remote sensing products; retrieving the contained files according to the specified product file root directory, carrying out instantiation matching on the file templates according to the defined main files, and carrying out instantiation on the template field values contained in the file templates and the instantiation of the associated files;
the step C2 specifically includes:
c2.1, registering file classes of file plug-ins based on a dynamic library module;
c2.2, creating a product definition model class structure;
c2.3 instantiating a product definition model class product file;
said step D comprises
The method comprises the steps that D1 a remote sensing image algorithm module creates a product definition model instance, updates the attribute value state and value of a product file, and completes the update of quality inspection semantic attributes of the product through a design algorithm module;
the product semantics are updated by a standardized processing algorithm of the remote sensing image product;
d3, updating the product semantics by a cloud amount detection algorithm of the remote sensing image product;
and D4, updating the product semantics by a remote sensing image product radiation anomaly detection algorithm.
2. The method for constructing a universal model of a remote sensing image product according to claim 1, wherein the step a specifically comprises:
a1, defining a remote sensing image product file composition model;
a2, defining a semantic attribute item and a quality inspection index item structure of the remote sensing image product;
a3, marking the types of the file formed by the remote sensing image product;
and A4, defining and describing a remote sensing image entity file template.
3. The method for constructing a universal model of a remote sensing image product according to claim 2, wherein the remote sensing image product is composed of files, the files are composed of an entity file and a combined file according to the files, and the entity file is composed of a main file and a plurality of dependent files; the combined file is composed of one or more entity files and a combined file; the entity file comprises a semantic name, a file type, a file template matching expression and a data value; the combined file includes a semantic name, a file type, and a data value.
4. The method for constructing a universal model of a remote sensing Image product according to claim 3, wherein the file data format types of the remote sensing Image product comprise TIF, erdas Image, JP2 and TIL file types; the rational function model file data comprises TXT and a plurality of custom XML formats; the projection parameter file data type comprises prj and Proj4 data formats; the metadata file and the quality inspection metadata file are in XML format, and the micro-image file and the thumb-image file belong to the type of digital positive photographic image file.
5. The method of claim 1, wherein the labeling of the remote sensing image product file attribute in the step B comprises: analyzing the remote sensing image product file composition; labeling the entity file attribute of the remote sensing image product; and labeling the attribute of the remote sensing image product combined file.
6. The method for constructing a universal model of a remote sensing image product according to claim 5,
the remote sensing image product comprises the following components according to the product hierarchy: the method comprises the steps of an original image product, a sensor correction product, a geometric correction product, a digital surface model and an index product, wherein the original image product, the sensor correction product, the geometric correction product, the digital surface model and the index product comprise optics, radars, lasers and hyperspectrum according to different sensor divisions;
the remote sensing image product entity file attributes comprise file names, file types, file template matching expressions, whether files are available in an initialized state, selectable values and data values;
the remote sensing image product entity combined file attribute comprises a file name, a file type, whether the file is available in an initialized state, an optional value and a data value;
the quality inspection indexes of the remote sensing image product comprise data integrity, data validity, image quality indexes and radiation quality indexes.
7. The method for constructing a universal model of a remote sensing image product according to claim 1, wherein the collecting product data in step E comprises: and collecting the remote sensing image product type structure and collecting the remote sensing image product instance data.
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