Multi-source remote sensing image metadata traceability information organization method and management system

Abstract

The application discloses a kind of multi-source remote sensing image metadata traceability information organization method and management system, the method includes: multi-source remote sensing image traceability information is abstracted as event, entity, relationship and attribute four kinds of elements, to graphically establish the concept model of multi-source remote sensing image traceability information;Establish the mapping framework of the concept model and PROV model, using PROV-O to express remote sensing image traceability information into RDF data;The metadata information of remote sensing image metadata model UMM is dimensionally induced, and traceability information is embedded into remote sensing image metadata model UMM, to obtain the metadata organization model of traceability expression enhancement;Remote sensing image traceability information acquisition method is proposed, and remote sensing image traceability information organization management system is built.The application embeds traceability model into metadata model, designs the metadata organization model of traceability expression enhancement, enriches metadata content, can be conveniently traced back to source and carries out metadata search and retrieval, meets complex traceability demand.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing image data organization and management, specifically relating to a method and management system for organizing and managing traceability information of multi-source remote sensing image metadata. Background Technology

[0002] With the continuous development of Earth observation technology, numerous remote sensing satellites have been launched internationally, enabling multi-level, multi-angle, all-round, and all-weather Earth observation. This has greatly enriched remote sensing imagery data, which has been widely applied in fields such as natural resource monitoring, resulting in a wealth of advanced data products, including those on land cover and water resource distribution. However, remote sensing imagery data exhibits varying spatiotemporal resolution and quality. The diverse processing methods from raw remote sensing data to finished data products also contribute to the inconsistent quality of these products. Source information, which records the data production process, is a crucial basis for evaluating the usability, reliability, and other data quality aspects of remote sensing data products.

[0003] Based on processing levels (radiometric correction, geometric correction, etc.), remote sensing data can be divided into different levels, with data at the same level undergoing the same processing steps. For example, L0 level represents the raw data received by ground stations; L1 level data is L0 level data after radiometric correction. Currently, official remote sensing data platforms such as Landsat, Sentinel, MODIS, and Gaofen provide data downloads by granule, with multiple images at the same level grouped into a collection. As a reusable resource, remote sensing data is frequently distributed in a network environment. After users download images by granule, they perform analysis and processing on the data according to different business needs, such as atmospheric correction, image fusion, and ground feature extraction. This process is characterized by long processing chains and diverse algorithms, and the spatial extent, spatial resolution, and spectral data of remote sensing images may change during the analysis and processing. Therefore, tracing the origin of remote sensing data is very complex, and modeling and representing its origin information is a significant technical challenge.

[0004] Patent CN111147384 A discloses a remote sensing image data transmission path encoding method for traceability. This method acquires path node information during remote sensing image data transmission, encodes and updates this information, and enables traceability of path node information. However, it primarily addresses the data transmission problem for traceability. Information from data processing and metadata are also crucial aspects of remote sensing image traceability. Currently, the traceability information recorded in remote sensing image metadata is limited and cannot meet complex traceability needs. Therefore, embedding traceability information into metadata is a critical problem to be solved. Summary of the Invention

[0005] In view of this, the present invention proposes a method and management system for organizing and managing traceability information of multi-source remote sensing image metadata, which is used to solve the problem that the information recorded in remote sensing image metadata cannot meet the complex traceability requirements.

[0006] In a first aspect, this invention discloses a method for organizing source information of multi-source remote sensing image metadata, the method comprising:

[0007] Acquire source information from multi-source remote sensing images in different scenarios; abstract the source information from multi-source remote sensing images into four categories of elements: events, entities, relationships, and attributes, and establish a conceptual model of the source information from multi-source remote sensing images in a graph-based manner;

[0008] The dimensions of the remote sensing image metadata model (UMM) are summarized. Based on the image source, processing process and inter-image relationship in the conceptual model, the source information is embedded into the remote sensing image metadata model (UMM) to obtain a metadata organization model with enhanced source expression.

[0009] Based on the above technical solutions, preferably, the method for obtaining multi-source remote sensing image source tracing information based on original remote sensing image data includes:

[0010] Based on the hierarchical relationship between remote sensing images, model from top to bottom and create source tracing information in batches;

[0011] Remote sensing data processing tools are used to automatically capture the algorithms used, input / output, and execution time information, and PROV-O is used to record traceability information fragments;

[0012] Users manually enter the source information of remote sensing data through an interactive interface;

[0013] Based on the semantic relationships of the source map, source relationship mining is performed to automatically complete the source information.

[0014] Based on the above technical solutions, preferably, the abstraction of multi-source remote sensing image source tracing information into four categories of elements—events, entities, relationships, and attributes—specifically includes:

[0015] The processing of remote sensing images is abstracted into event elements, which have attribute information including start time and end time.

[0016] The image datasets, individual remote sensing images, processing algorithms, and individuals / organizations involved in the remote sensing image processing are all abstracted as entity elements.

[0017] Relationship elements include relationships between entities and relationships between entities and events;

[0018] Attribute elements are the semantic information contained in event elements, entity elements, or relationship elements.

[0019] Based on the above technical solutions, preferably, the relationship between entities includes the inclusion relationship between an image dataset and a single remote sensing image, the derivation or substitution relationship between image datasets, the derivation or substitution relationship between single remote sensing images, and the attribution relationship between an image dataset or a single remote sensing image and an individual / organization.

[0020] A coding scheme is used to express the changing dimensional information in the derivation relationship. The coding order from left to right represents the spatial range, band information, resolution, and data type, respectively.

[0021] Based on the above technical solutions, preferably, the step of dimensionally summarizing the remote sensing image metadata model UMM, and embedding the source information into the remote sensing image metadata model UMM based on the image source, processing process, and inter-image relationships in the conceptual model to obtain a metadata organization model with enhanced source representation specifically includes:

[0022] The Remote Sensing Image Metadata Model (UMM) is summarized into eight dimensions: identification dimension, time dimension, spatial dimension, source dimension, platform dimension, data dimension, quality dimension, and access dimension.

[0023] By analyzing the image sources, processing procedures, and inter-image relationships in the conceptual model, the source information is embedded into the source dimension of the remote sensing image metadata model UMM, resulting in a metadata organization model with enhanced source representation.

[0024] Based on the above technical solutions, preferably, the method further includes:

[0025] A mapping framework between the conceptual model and the PROV model is established. PROV-O is used to express the source information of the conceptual model as RDF data, and the source information of remote sensing images is shared in the Web environment based on the RDF data.

[0026] Based on the above technical solutions, preferably, the establishment of the mapping framework between the conceptual model and the PROV model specifically includes:

[0027] Map the event elements in the conceptual model to the activities in the PROV model;

[0028] The entity elements in the conceptual model are mapped to entities or agents in the PROV model; where image datasets and individual remote sensing images are mapped to entities in the PROV model, and individuals / institutions and software are mapped to agents in the PROV model.

[0029] Map the relational elements in the conceptual model to the seven relations in the PROV model.

[0030] In a second aspect, the present invention discloses a multi-source remote sensing image metadata traceability information management system, the system being based on the method described in the first aspect of the present invention, the system comprising:

[0031] Source tracing information import module: used to import source tracing information, and supports the import of PROV-O format source tracing information fragments;

[0032] Metadata storage module: Used to organize and store traceability information and metadata in the form of a metadata organization model;

[0033] Source tracing information query module: used for tracing the source of remote sensing images and searching and retrieving metadata based on the metadata organization model;

[0034] Source traceability information visualization module: used to display source traceability information in a graphical and visual manner.

[0035] The present invention has the following advantages over the prior art:

[0036] 1) This invention expresses the events, entities, relationships, and attribute information in the remote sensing image derivation process in a graph-like manner, constructs a conceptual model for traceability information, embeds the traceability model into the metadata model, designs a metadata organization model to enhance traceability information, enriches the metadata content, and through this metadata organization model, it is possible to easily realize image traceability and metadata search and retrieval, meet complex traceability needs, and make the remote sensing image processing process open and transparent, thus providing a guarantee for the traceability of remote sensing image product quality;

[0037] 2) The conceptual model of this invention for source tracing information records information such as the steps, algorithms, software environment, executors, and changes in the derived data generated after processing from the data source to the processing process. It expresses the dimensional information of changes in the derived relationship through an encoding scheme, which can be used for comprehensive source tracing and improve the usability and reliability of remote sensing image source tracing.

[0038] 3) This invention proposes a mapping method between the source tracing conceptual model and the PROV source tracing model, which extends the W3CPROV model and improves the interoperability of remote sensing image source tracing information in the Web domain, facilitating data sharing. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of the multi-source remote sensing image metadata traceability information organization method of the present invention;

[0041] Figure 2 This is a conceptual model diagram of remote sensing image source tracing information according to the present invention;

[0042] Figure 3 This invention encodes changes in remote sensing image metadata.

[0043] Figure 4 This is a diagram showing the mapping framework between the conceptual model of remote sensing image source tracing information and the W3C PROV model of the present invention;

[0044] Figure 5 This is a diagram illustrating the remote sensing image metadata model extension method of the present invention. Detailed Implementation

[0045] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0046] Please see Figure 1 This invention proposes a method for organizing source information of multi-source remote sensing image metadata, the method comprising:

[0047] S1. Obtain source information of multi-source remote sensing images in different scenarios.

[0048] Remote sensing image provenance information refers to a complete record of all processes a remote sensing image undergoes from its inception to its disappearance, including the data source, producer information, and the processing steps and algorithms involved in the data production process. This invention proposes four methods for obtaining remote sensing image provenance information:

[0049] 1) Top-down modeling: Based on the hierarchical relationship between remote sensing images, model from top to bottom to create source information in batches; for example, Figure 2 In the Landsat Collection 2 dataset, image data L2 is derived from image data L1.

[0050] 2) Automatic capture by remote sensing data processing tools: When processing remote sensing images using remote sensing data processing software tools, the algorithm used, input / output, execution time and other information are automatically captured, and traceability information fragments are recorded using PROV-O.

[0051] 3) Manual input: Users manually input the source information of remote sensing data through an interactive interface;

[0052] 4) Source Relationship Mining: Based on the semantic relationships of the source graph, source relationship mining is performed to automatically complete the source information.

[0053] S2. Abstract the source information of multi-source remote sensing images into four categories of elements: events, entities, relationships, and attributes, and establish a conceptual model of the source information of multi-source remote sensing images in a graph-based manner.

[0054] This invention constructs a conceptual model of source information using a graph-based approach, abstracting source information into four categories of elements: events, entities, relationships, and attributes. Specifically, the processing of remote sensing images is abstracted as an event element, possessing attribute information including start and end times. The image datasets (Collection), individual remote sensing images (Granule), processing algorithms, and involved individuals / organizations involved in the remote sensing image processing are all abstracted as entity elements. Relationship elements describe the relationships between entities and between entities and events. Attribute elements describe the semantic information contained within event elements, entity elements, or relationship elements.

[0055] Figure 2 The diagram shown is a conceptual model of remote sensing image source tracing information based on Landsat data, which records the steps, algorithms, software environment, and executors involved in the process from the data source to the processing, as well as the changes in spatial range, resolution, and band information after processing. Figure 2 In this model, the image datasets Collection1L1 and Collection2L1 obtained from the Landsat data source are interchangeable. Collection2L1 further derives into Collection2L2. The single image data Granule2 in Collection2L1 is processed by atmospheric correction to obtain single image data Granule3. After water extraction, single image data Granule3 is processed to obtain single image data Granule4. This model also includes various attribution, association, and derivation relationships. Finally, a complete conceptual model of remote sensing image source information is constructed, which has the advantages of comprehensive source information and clear tracking path.

[0056] Table 1 lists the concepts of four categories of elements: events, entities, relationships, and attributes, and the elements they contain.

[0057] Table 1. Elements of the Source Tracing Information Conceptual Model

[0058]

[0059]

[0060] Table 1 shows that the relationships between entities include the inclusion relationship between an image dataset and a single remote sensing image, the derivation or substitution relationship between image datasets, the derivation or substitution relationship between single remote sensing images, and the attribution relationship between an image dataset or a single remote sensing image and an individual / institution. The derivational relationships between images can be identified by the "change code" attribute, which indicates changes in the image's dimensions, such as spatial dimension changes (cropping operations, etc.), spatial resolution dimension changes (resampling, etc.), and image band dimension changes (normalized vegetation index calculation, etc.).

[0061] This invention uses an encoding scheme to express the changing dimensional information in a derived relationship, encoding the changed information as "01" and the unchanged dimensions as "00". For example... Figure 3 The example shown is a specific encoding method. The encoding order from left to right represents the spatial range, band information, resolution, and data type, etc. The encoding method can be expanded according to actual needs.

[0062] by Figure 2 Taking the image processing process as an example, the image Granule4 and image Granule3 have undergone water extraction processing, which is achieved by calculating the water index through NDWI. The image band information has changed, so the metadata change information is encoded as "00010000". The encoding order from left to right represents the spatial range, band information, resolution and data type, respectively.

[0063] S3. Establish a mapping framework between the conceptual model and the PROV model, use PROV-O to express the source information of the conceptual model as RDF data, and share the source information of remote sensing images in the Web environment based on the RDF data.

[0064] The W3C PROV model defines three cores and seven relationships. The cores include Entity, Agent, and Activity. The seven relationships define the relationships between these three cores and between each other, such as the derivative relationship between output and input data, the representation relationship between individuals and organizations, and the use or generation relationship between processing and data.

[0065] This invention achieves traceability information sharing in a distributed environment by establishing a mapping framework between the conceptual model of traceability information and the W3C PROV traceability model. Specifically, event elements in the conceptual model are mapped to activities in the PROV model; entity elements in the conceptual model are mapped to entities or agents in the PROV model; among them, entities such as image datasets (Collection) and single remote sensing images (Granule) are entities in the PROV model, while individuals / organizations and software are mapped to agents in the PROV model; relational elements in the conceptual model are mapped to the seven types of relations in the PROV model. If two entities have only slight differences or no differences, they can be considered interchangeable.

[0066] like Figure 4 The diagram illustrates the mapping framework constructed in this invention. This invention uses elements of different shapes to represent the three elements in the PROV model: Entity, Activity, and Agent, and further maps the relationships between these elements. For example, an ellipse represents an Entity in the PROV model, which can include an image collection, a single remote sensing image (Granule), and an algorithm, etc.; a rectangle represents an Activity in the PROV model, which can represent the image processing procedure; and a hexagon represents an Agent in the PROV model, which can be the organization or individual to which the remote sensing image data source belongs, or the executor of the processing procedure, etc.

[0067] Using the PROV Ontology (PROV-O) released by W3C, source tracing information can be expressed as RDF (Resource Description Framework) data, enabling the representation of source tracing knowledge. The PROV model-based representation of source tracing information improves the interoperability of remote sensing image source tracing information in a web environment, facilitating convenient sharing of source tracing information.

[0068] S4. The metadata information of the remote sensing image metadata model UMM is dimensionally summarized. Based on the image source, processing process and inter-image relationship of the source information in the conceptual model, the source information is embedded into the remote sensing image metadata model UMM to obtain a metadata organization model with enhanced source expression.

[0069] NASA's Remote Sensing Image Metadata Model (UMM) is an extensible metadata model that primarily comprises seven configuration files: UMM-C, UMM-G, UMM-S, UMM-Var, UMM-Vis, UMM-T, and UMM Common. It serves as a bridge for mapping between metadata standards supported by the Common Metadata Repository (CMR). This invention extends the UMM metadata model by embedding source information into the metadata model and designs a metadata organization model with enhanced source representation.

[0070] First, the metadata information of the remote sensing image metadata model UMM is summarized into eight dimensions: identification dimension, time dimension, spatial dimension, source dimension, platform dimension, data dimension, quality dimension, and permission dimension. The metadata information mainly included in the eight dimensions is shown in Table 2.

[0071] Table 2. Dimensions of the Metadata Organization Model

[0072]

[0073]

[0074] Then, the image source, processing process, and inter-image relationship information in the source tracing information of the conceptual model established in step S2 are analyzed. The source tracing information is embedded into the source tracing dimension of the remote sensing image metadata model UMM, resulting in a metadata organization model with enhanced source tracing representation. For example... Figure 4 The image shows an example of the extended metadata organization model, where the dashed box contains the embedded traceability information, specifically including information such as the relationship between images, the relationship type, single image traceability, image processing process, image processing events, image processing data source, and image set traceability.

[0075] S5. Visualization, tracing, and metadata lookup and retrieval of remote sensing images.

[0076] This invention summarizes the metadata information of the Remote Sensing Image Metadata Model (UMM) from multiple dimensions and aggregates the metadata information for each dimension. It embeds traceability information into the metadata information, enriches the metadata content, and improves the ability of remote sensing image metadata to express traceability information. Through this organizational model, the traceability of images and the search and retrieval of metadata can be realized, making the image processing process open and transparent, and providing a guarantee for the traceability of remote sensing image product quality.

[0077] Based on the above-mentioned method for organizing multi-source remote sensing image metadata traceability information, this invention also proposes a multi-source remote sensing image metadata traceability information management system, the system comprising:

[0078] Source tracing information import module: used to import source tracing information, and supports the import of PROV-O format source tracing information fragments;

[0079] Metadata storage module: used to organize and store traceability information and metadata in the form of metadata organization model; the specific organization method is the same as steps S1 to S4 of the aforementioned method embodiment.

[0080] Source tracing information query module: used for tracing the source of remote sensing images and searching and retrieving metadata based on the metadata organization model;

[0081] Source traceability information visualization module: used to display source traceability information in a graphical and visual manner.

[0082] The above system adopts a B / S architecture, with the backend developed using Java and the Spring Boot framework, and the frontend developed using OpenLayers. The system uses the open-source relational database software PostgreSQL to store remote sensing image metadata, with spatial dimension information stored in PostGIS, a spatial extension of PostgreSQL. The system supports importing PROV-O format source information fragments and also provides a remote sensing image source information query interface and a map-based visualization method.

[0083] The above system embodiments are based on the method embodiments. For a brief description of the system embodiments, please refer to the method embodiments.

[0084] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, meaning they can be distributed across multiple network units. Those skilled in the art can select some or all of the modules to achieve the purpose of this embodiment without any inventive effort, based on actual needs.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for organizing source information traceability data of multi-source remote sensing images, characterized in that, The method includes: Acquire source information from multi-source remote sensing images in different scenarios; abstract this source information into four categories: events, entities, relationships, and attributes; and establish a conceptual model of the source information in a graph-based manner. Specifically, abstracting the source information into these four categories includes: The processing of remote sensing images is abstracted into event elements, which have attribute information including start time and end time. The image datasets, individual remote sensing images, processing algorithms, and individuals / organizations involved in the remote sensing image processing are all abstracted as entity elements. Relationship elements include relationships between entities and relationships between entities and events; Attribute elements are the semantic information contained in event elements, entity elements, or relationship elements; The metadata information of the remote sensing image metadata model UMM is dimensionally summarized. Based on the source information of the image, processing process and inter-image relationship in the conceptual model, the source information is embedded into the remote sensing image metadata model UMM to obtain a metadata organization model with enhanced source expression.

2. The method for organizing source information of multi-source remote sensing image metadata according to claim 1, characterized in that, The methods for obtaining source tracing information of multi-source remote sensing images in different scenarios include: Based on the hierarchical relationship between remote sensing images, model from top to bottom and create source tracing information in batches; Remote sensing data processing tools are used to automatically capture the algorithms used, input / output, and execution time information, and PROV-O is used to record traceability information fragments; Users manually enter the source information of remote sensing data through an interactive interface; Based on the semantic relationships of the source map, source relationship mining is performed to automatically complete the source information.

3. The method for organizing source information of multi-source remote sensing image metadata according to claim 1, characterized in that, The relationships between entities include the inclusion relationship between an image dataset and a single remote sensing image, the derivation or substitution relationship between image datasets, the derivation or substitution relationship between single remote sensing images, and the attribution relationship between an image dataset or a single remote sensing image and an individual / organization. An encoding scheme is used to express the changing dimensional information in the derivation relationship. The encoding order from left to right represents the spatial range, band information, resolution, and data type, respectively.

4. The method for organizing source information of multi-source remote sensing image metadata according to claim 3, characterized in that, The step of dimensional summarizing the remote sensing image metadata model UMM, based on the image source, processing process, and inter-image relationships in the conceptual model, embedding the source information into the remote sensing image metadata model UMM to obtain a metadata organization model with enhanced source representation specifically includes: The Remote Sensing Image Metadata Model (UMM) is summarized into eight dimensions: identification dimension, time dimension, spatial dimension, source dimension, platform dimension, data dimension, quality dimension, and access dimension. By analyzing the image sources, processing procedures, and inter-image relationships in the conceptual model, the source information is embedded into the source dimension of the remote sensing image metadata model UMM, resulting in a metadata organization model with enhanced source representation.

5. The method for organizing source information of multi-source remote sensing image metadata according to claim 1, characterized in that, The method further includes: A mapping framework between the conceptual model and the PROV model is established. PROV-O is used to express the source information of the conceptual model as RDF data, and the source information of remote sensing images is shared in the Web environment based on the RDF data.

6. The method for organizing source information of multi-source remote sensing image metadata according to claim 5, characterized in that, The framework for establishing the mapping between the conceptual model and the PROV model specifically includes: Map the event elements in the conceptual model to the activities in the PROV model; The entity elements in the conceptual model are mapped to entities or agents in the PROV model; where image datasets and individual remote sensing images are mapped to entities in the PROV model, and individuals / institutions and software are mapped to agents in the PROV model. Map the relational elements in the conceptual model to the seven relations in the PROV model.

7. A multi-source remote sensing image metadata traceability information management system using the multi-source remote sensing image metadata traceability information organization method according to any one of claims 1 to 6, characterized in that, The system includes: Source tracing information import module: used to import source tracing information, and supports the import of PROV-O format source tracing information fragments; Metadata storage module: Used to organize and store traceability information and metadata in the form of a metadata organization model; Source tracing information query module: used for tracing the source of remote sensing images and searching and retrieving metadata based on the metadata organization model; Source traceability information visualization module: used to display source traceability information in a graphical and visual manner.

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