Method and device for establishing space grid system and remote sensing image processing system

Abstract

The invention provides a method and device for establishing a space grid system and a remote sensing image processing system. The method belongs to the technical field of remote sensing images, and carries out unified structured management on remote sensing image data by constructing a three-dimensional holographic management framework composed of a time grid, a space grid and a spectrum grid. The space grids adopt global subdivision grids and are endowed with unique codes, the time grids establish indexes according to years, seasons and months, and the spectrum grids are divided according to professional spectrum segments. The corresponding device comprises a data receiving module, a grid coding module, a storage module, a scheduling module and a processing module. The system supports micro-service cluster deployment, visual low-loss compression and open API integration. According to the method, pixel-level scheduling, dynamic processing and real-time service release of the remote sensing data are realized, the bottlenecks that the traditional file-level management efficiency is low, the preprocessing is tedious, and the instant service demand of massive high-resolution images is difficult to meet are overcome, and the data management, processing and service efficiency is remarkably improved.

Description

A method, apparatus, and remote sensing image processing system for establishing a spatial grid system Technical Field

[0001] This invention relates to the field of remote sensing image technology, and in particular to a method, apparatus and remote sensing image processing system for establishing a spatial grid system. Background Technology

[0002] Traditional remote sensing image processing systems rely on file-level data organization and management, which cannot meet the needs of efficient management and rapid processing of massive amounts of high-resolution remote sensing imagery. A new data organization and management method is needed to achieve efficient storage, rapid scheduling, and dynamic processing of remote sensing imagery.

[0003] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to provide a technical solution that enables efficient storage, rapid scheduling, and dynamic processing of remote sensing images.

[0005] To achieve the above objectives, the present invention provides the following solution: a method for establishing a spatial grid system, comprising the following steps: constructing a three-dimensional holographic management framework composed of a temporal grid, a spatial grid, and a spectral grid; the spatial grid adopts a globally segmented grid, which is subdivided downwards in degrees to form a set of globally segmented grids, and each grid is assigned a unique code; the temporal grid establishes a time index at the level of year, quarter, month, and day; the spectral grid is divided according to remote sensing professional spectral bands such as red, green, blue, infrared, and panchromatic; and remote sensing image data is placed into the three-dimensional holographic management framework to achieve structured management and rapid retrieval of the data.

[0006] Optionally, the unique code of the spatial grid is generated through a mathematical transformation method to establish a globally unified spatial index.

[0007] Optionally, it also includes: achieving pixel-level data scheduling based on the spatial grid encoding; supporting multi-rule, multi-level grid definitions to adapt to the needs of different application scenarios.

[0008] Optionally, it also includes: using the time grid and spatial grid to achieve unified scheduling and dynamic mosaicking of multi-temporal remote sensing data; and by extending the WMTS protocol, supporting time parameter filtering to achieve dynamic single-map service for multi-temporal data.

[0009] Optionally, it also includes: based on the spectral grid, realizing dynamic band combination, dynamic stretching enhancement and dynamic light and color homogenization processing; data processing is completed in memory in real time without generating intermediate files.

[0010] An apparatus for establishing a spatial grid system includes: a data receiving module for receiving remote sensing image data; a grid coding module for assigning a unique code to each spatial grid, temporal grid, and spectral grid; a data storage module for storing remote sensing image data and its metadata; a data scheduling module for scheduling data according to the grid codes; and a data processing module for dynamically processing the scheduled data, including projection transformation, band combination, stretching enhancement, light and color homogenization, and dynamic tile generation.

[0011] Optionally, the data scheduling module supports parallel reading based on grid coding and pixel-level data retrieval, improving data reading speed and processing efficiency.

[0012] Optionally, the data processing module also supports: dynamic fusion of panchromatic and multispectral images; dynamic generation of multi-specification map services, supporting various projections and tile rules; and real-time publishing as OGC standard services without pre-tiling.

[0013] A remote sensing image processing system includes the aforementioned device for establishing a spatial grid system, a cluster deployment module with a microservice architecture that supports horizontal scaling and high availability; a visualization low-loss compression module for compressing the original image to less than 4% of its original volume and supporting dynamic service publishing; and an open API interface that supports third-party system integration and custom service orchestration.

[0014] Optional features also include: real-time publishing of L1-level raw data, integration of dynamic RPC correction and metadata extraction functions; provision of dual-mode OGC services, including dynamic WMTS and static thematic WMTS services; implementation of a unified service interface for multi-temporal data, supporting timeline switching and historical backtracking functions.

[0015] Compared with existing technologies, the present invention has the following advantages: it achieves efficient storage and management: it organizes massive remote sensing data in a structured manner through a three-dimensional grid system and establishes a unified index using discrete grid coding, which significantly improves data storage efficiency and retrieval speed.

[0016] Supports rapid dynamic processing: Pixel-level data scheduling and parallel reading are achieved based on grid encoding. Combined with real-time memory processing technology, dynamic band combination, stretching enhancement, light and color homogenization, and tile generation are completed without the need to generate intermediate files, greatly shortening the processing flow.

[0017] Improved service publishing and application efficiency: Adopting pre-tiling-free technology, it supports parameter-driven multi-specification map services and multi-temporal data "dynamic single map" services. By extending the WMTS protocol, it enables on-demand real-time publishing, reducing data publishing time from days / weeks to minutes, meeting the real-time service needs of high concurrency and large data volumes.

[0018] Enhanced system scalability and flexibility: Based on microservice architecture and cluster deployment, it supports horizontal scaling and high availability; it provides open APIs for easy integration with third-party systems and custom service orchestration, adapting to various application scenarios. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments 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.

[0020] Figure 1 is a schematic diagram of the method for establishing a spatial grid system provided in an embodiment of the present invention.

[0021] Figure 2 is a schematic diagram of the spatial grid system device structure provided in an embodiment of the present invention.

[0022] Figure 3 is a schematic diagram of the overall technical route provided by the embodiments of the present invention.

[0023] Figure 4 is a schematic diagram of the data inspection and database construction process provided in an embodiment of the present invention.

[0024] Figure 5 is a technical flowchart of the real-time prototype release system provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] The purpose of this invention is to provide a technical solution that enables efficient storage, rapid scheduling, and dynamic processing of remote sensing images.

[0027] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] Example 1: This example provides a method and apparatus for establishing a spatial grid system, as shown in Figure 1. The method includes: constructing a three-dimensional holographic management framework composed of a temporal grid, a spatial grid, and a spectral grid; the spatial grid adopts a global subdivision grid, which is subdivided downwards in degrees to form a global subdivision grid set, and each grid is assigned a unique code; the temporal grid establishes a time index at the year, quarter, and month levels; the spectral grid is divided according to remote sensing professional spectral bands such as red, green, blue, infrared, and panchromatic; and remote sensing image data is placed into the three-dimensional holographic management framework to achieve structured management and rapid retrieval of data.

[0029] Furthermore, the unique code of the spatial grid is generated through mathematical transformation methods and used to establish a globally unified spatial index.

[0030] Furthermore, it also includes: achieving pixel-level data scheduling based on the spatial grid encoding; supporting multi-rule, multi-level grid definitions to adapt to the needs of different application scenarios.

[0031] Furthermore, it also includes: utilizing the time grid and spatial grid to achieve unified scheduling and dynamic mosaicking of multi-temporal remote sensing data; and extending the WMTS protocol to support time parameter filtering, thereby realizing a dynamic single-map service for multi-temporal data.

[0032] Furthermore, it also includes: based on the spectral grid, realizing dynamic band combination, dynamic stretching enhancement, and dynamic light and color homogenization processing; data processing is completed in memory in real time without the need to generate intermediate files.

[0033] As shown in Figure 2, the device includes: a data receiving module for receiving remote sensing image data; a grid coding module for assigning a unique code to each spatial grid, temporal grid, and spectral grid; a data storage module for storing remote sensing image data and its metadata; a data scheduling module for scheduling data according to the grid coding; and a data processing module for dynamically processing the scheduled data, including projection transformation, band combination, stretching enhancement, light and color homogenization, and dynamic tile generation.

[0034] The specific tasks of establishing a national geographic conditions monitoring remote sensing image processing system based on a spatial grid system include six main parts: data collection, data distribution, data quality inspection, data database construction, data management and backup, and the construction of a prototype system for real-time release of results.

[0035] Data collection refers to the acquisition of satellite remote sensing images; data distribution refers to the development of remote sensing image data distribution service plans for different regions, specifying the distribution time and method of satellite image data, providing data distribution services according to the established distribution service plan, providing technical support for image use, and ensuring the timely and effective use of remote sensing data.

[0036] Data quality inspection refers to the quality inspection of the completeness and standardization of various data products, such as whole-scene orthophoto data and metadata, transferred by each production unit, and timely feedback of problems to the production unit for modification, improvement, and re-collection.

[0037] Data database construction refers to establishing a geographic national condition monitoring remote sensing image database based on the collection and organization of domestic and international remote sensing image data, extracting metadata from various satellite images, constructing a unified remote sensing image management model, and efficiently managing remote sensing image data. Simultaneously, the acquired image data undergoes quality checks, and only the quality-checked image data is used to build the database.

[0038] Data management backup refers to the online management and distribution services of metadata and image data based on data database construction, and the research and design of a storage backup scheme that combines online and offline methods to achieve effective data storage and secure management.

[0039] The construction of the real-time results publishing prototype system refers to the system's use of satellite remote sensing image processing technology as a foundation, combined with CPU-GPU collaborative processing and streaming computing methods to achieve rapid processing of remote sensing images. Simultaneously, leveraging database principles and dynamic tile map service technology, the real-time computing results are published via WMTS service.

[0040] Technical Approach: After compiling orthophoto data from all provinces and cities across the country, the project team's technical approach included five parts: data inspection, metadata collection and database construction, data storage and backup, management system development, and application service technology research. See Figure 3 for details.

[0041] (1) Data inspection: This mainly involves quality inspection of the data deliverables transferred by each production unit to ensure that the data meets the project requirements and database establishment requirements. First, it is necessary to check whether the data submitted by each production unit is consistent and standardized; then, through a combination of automation and manual methods, the integrity, metadata and data quality of the data are checked, and an inspection log is generated and promptly reported back to the production units.

[0042] Regarding data reception, inspection, and database construction, the main processes include data integrity checks, availability checks, metadata checks, database construction, and image quality checks. The specific process is shown in Figure 4.

[0043] (a) Data integrity check: Compare the number of map sheets in the data transfer list and vector map with the actual number of metadata files and image data files to ensure data integrity. Any problems found should be promptly reported to the production unit for resubmission.

[0044] (b) Data Availability Check: An automated process checks the availability of documents submitted by each production unit. Key checks include whether the document can be opened and whether the projection is correct. If any issues are found, the production unit is notified to submit replacement documents.

[0045] (c) Data Metadata Inspection: An automated program checks whether metadata names are correct, whether metadata data items are complete, and whether any required metadata items are missing. Problematic metadata is returned to the production unit for modification.

[0046] (d) Database creation and preprocessing: For image data files, a mosaic dataset is created using ArcGIS software.

[0047] (e) Post-image processing: For qualified images, an index map is generated based on the effective coverage of the image using automated software. At the same time, a quick image view is generated for image preview and fast, seamless browsing of large areas, respectively.

[0048] (2) Metadata collection and database construction involves preprocessing image files using a mosaic dataset method to generate index maps and quick views, and storing them in the database. This process involves checking qualified metadata files and metadata information generated during the database construction process.

[0049] (3) Data storage backup mainly involves storing all examined image files and metadata files using a combination of online and offline methods. Online storage is primarily for the convenience of data distribution and application; offline storage is achieved through tape backup to ensure data security.

[0050] (4) Development of the Real-Time Publishing Prototype System: The real-time publishing prototype system is based on satellite remote sensing image processing technology, and combines CPU-GPU collaborative processing and streaming computing methods to achieve rapid processing of remote sensing images. At the same time, using database principles and dynamic tile map service technology, the real-time computing results are published as WMTS service. The technical process of the real-time publishing prototype system is shown in Figure 5.

[0051] (a) Real-time preprocessing: This process is based on remote sensing image processing technology and establishes a computational model for the entire processing flow from light and color equalization to mosaicking. Subsequent real-time calculations and rendering directly utilize the established real-time model for rapid calculations and rendering.

[0052] (b) Real-time model library: Create a real-time model library, manage the real-time model library and create spatial grid, temporal grid and spectral grid indexes to realize fast data retrieval and serve real-time computing and rendering processing.

[0053] (c) Real-time computing and rendering: Real-time processing of remote sensing images is achieved through CPU-GPU collaborative processing and memory chain streaming computing. At the same time, load balancing technology is used to achieve parallel processing of multiple servers and multiple threads, thereby improving the processing efficiency of real-time computing and rendering.

[0054] (d) Real-time publishing of dynamic tiles: The results of real-time calculation and rendering are used to generate dynamic mosaic datasets and dynamic tiles, and then the map tile service is published in the form of WMTS.

[0055] The solution provided in this embodiment addresses the problems of large data storage, management, and sharing difficulties of massive high-resolution remote sensing image data. It combines virtualization cloud computing technology, distributed storage technology, and distributed computing technology to construct a high-resolution remote sensing image storage model in a cloud environment, and conducts research on technologies such as non-slicing to avoid the "information island" phenomenon formed by remote sensing image data. This enables the storage management and online service of massive multi-source heterogeneous remote sensing images for national geographic conditions monitoring.

[0056] Based on a holographic multidimensional data organization model using a mesh-based approach, a multidimensional data cube encompassing time, space, and spectral dimensions was constructed, and a spatial grid engine was developed. Key technical challenges in grid data scheduling were overcome, enabling remote sensing processing to shift from file-oriented to grid-oriented, achieving "content-oriented structured management of remote sensing data." This allows image stretching, band combination rendering, and other processing to be dynamically completed in memory, realizing parameter-driven multi-specification dynamic map services. Through a combination of spatiotemporal search and dynamic mosaicking techniques, unified scheduling and service of multi-temporal remote sensing data were achieved, realizing a new application model of "dynamic single map." The successful integration of grid scheduling and remote sensing analysis algorithms enabled online and real-time operation of remote sensing data information extraction. Finally, the data scheduling mode with discrete data logic facilitated the adoption of distributed data storage and processing technologies, constructing a clustered distributed service system that effectively guarantees service performance under massive data volumes and large-scale access.

[0057] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0058] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for establishing a spatial grid system, characterized in that, Includes the following steps: A three-dimensional holographic management framework consisting of a temporal grid, a spatial grid, and a spectral grid is constructed. The spatial grid adopts a globally segmented grid, which is subdivided stepwise in degrees to form a set of globally segmented grids, and each grid is assigned a unique code. The temporal grid is indexed by year, quarter, month, and day. The spectral grid is divided according to remote sensing professional spectral bands such as red, green, blue, infrared, and panchromatic. Remote sensing image data is placed into the three-dimensional holographic management framework to achieve structured management and rapid retrieval of data.

2. The method for establishing a spatial grid system according to claim 1, characterized in that, The unique code of the spatial grid is generated through mathematical transformation methods and is used to establish a globally unified spatial index.

3. The method for establishing a spatial grid system according to claim 1, characterized in that, Also includes: Pixel-level data scheduling is achieved based on the aforementioned spatial grid coding; It supports multi-rule and multi-level grid definitions, which can adapt to the needs of different application scenarios.

4. The method for establishing a spatial grid system according to claim 1, characterized in that, Also includes: By utilizing the aforementioned time grid and spatial grid, unified scheduling and dynamic mosaicking of multi-temporal remote sensing data can be achieved; By extending the WMTS protocol, time parameter filtering is supported, enabling a dynamic single-image service for multi-temporal data.

5. The method for establishing a spatial grid system according to claim 1, characterized in that, Also includes: Based on the aforementioned spectral grid, dynamic band combination, dynamic stretching enhancement, and dynamic light and color homogenization processing are achieved. Data processing is completed in real time in memory, without the need to generate intermediate files.

6. An apparatus for establishing a spatial grid system, characterized in that, include: The data receiving module is used to receive remote sensing image data; The grid coding module is used to assign a unique code to each spatial grid, temporal grid, and spectral grid. The data storage module is used to store remote sensing image data and its metadata; The data scheduling module schedules data according to the grid encoding; the data processing module performs dynamic processing on the scheduled data, including projection transformation, band combination, stretching enhancement, light and color homogenization, and dynamic tile generation.

7. The apparatus for establishing a spatial grid system according to claim 6, characterized in that, The data scheduling module supports parallel reading based on grid coding and pixel-level data retrieval, improving data reading speed and processing efficiency.

8. The apparatus for establishing a spatial grid system according to claim 6, characterized in that, The data processing module also supports: dynamic fusion of panchromatic and multispectral imagery; dynamic generation of multi-specification map services, supporting various projections and tile rules; and real-time publishing as OGC standard services without pre-tiling.

9. A remote sensing image processing system, characterized in that, The device includes the apparatus as described in any one of claims 6 to 8, and: a cluster deployment module for a microservice architecture that supports horizontal scaling and high availability; and a visual low-loss compression module for compressing the original image to less than 4% of its original volume and supporting dynamic service publishing. An open API interface supports third-party system integration and custom service orchestration.

10. The remote sensing image processing system according to claim 9, characterized in that, It also supports: real-time publishing of L1 level raw data, integration of dynamic RPC correction and metadata extraction functions; and provides dual-mode OGC services, including dynamic WMTS and static thematic WMTS services. Implement a unified service interface for multi-temporal data, supporting timeline switching and historical backtracking functions.

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