Management and retrieval method for multi-resolution global reference base map database

Abstract

The invention relates to the technical field of satellite telemetry data processing, and provides a management and retrieval method for a multi-resolution global reference base map database aiming at the problem that reference base maps with different resolutions lack a unified coding system. Geographic space coordinates are converted into simple and regular digital codes, so that the base map quick retrieval method with the time complexity of O (1) is realized, and the problems of efficient management and quick retrieval of massive and multi-resolution reference base map data in the processes of registration and the like are solved.

Description

Technical Field

[0001] This invention relates to the field of satellite telemetry data processing technology. Background Technology

[0002] In the process of global large-area remote sensing image registration, the organization, management and rapid retrieval of reference base maps (i.e. reference images) are prerequisites and key links for image matching.

[0003] The management of reference base maps mainly includes encoding and storage. Since these base maps often originate from different satellite platforms, imaging sensors, and data production units, each entity formulates its own encoding rules according to its own business logic: some encodings focus on satellite model, imaging date, and area code as core dimensions; others emphasize resolution level, band information, and file format, resulting in a fragmented encoding structure. This makes it impossible to quickly establish spatial relationships between base maps of different resolutions in the same area through encoding, requiring significant time to manually compare coordinate ranges to confirm data correlation. Furthermore, base map data is difficult to manage and reuse systematically. Storage often employs file directory storage or simple database records, leading to problems such as irregular data segmentation, inconsistent naming conventions, and low query efficiency. Simultaneously, the aforementioned encoding differences also cause duplicate or missed data storage during data entry, consuming additional storage resources and increasing data management costs.

[0004] Existing retrieval methods are mostly range queries or spatial indexes based on geographic coordinates. However, due to problems such as inconsistent data block sizes and high overlap, the retrieval efficiency is low, and multiple candidate results are easily returned, requiring further filtering, which increases computational complexity and time cost.

[0005] The above problems are becoming increasingly prominent, especially when faced with the massive amounts of multi-resolution base map data on a global scale. This restricts the efficiency of base map data utilization, resulting in low automation of the remote sensing image registration process and unstable registration accuracy. Summary of the Invention

[0006] To address the technical problem of low storage and retrieval efficiency caused by the lack of a unified encoding system for reference base maps of different resolutions, this invention proposes a management and retrieval method for a multi-resolution global reference base map database. The specific technical solution includes the following steps: S1. Acquire global digital orthophoto data at different resolutions, perform tiling calculations to obtain tiling base maps.

[0007] Among them, the latitude and longitude intervals of the frame are calculated based on the resolution. ΔLon , ΔLat ), where n is the preset base map level; S2. Design a coding system so that each sub-map has a unique code: Calculate the encoded value of the top-left corner point of each sub-map and incorporate it into the custom resolution identifier R. For a top-left corner point with latitude and longitude of ( Lon_leftup , Lat_leftup The formulas for calculating the longitude code (CodeX) and latitude code (CodeY) of the base map are as follows: , in: Lon_leftup +180 and 90- Lat_leftup It can convert longitude range [-180°, 180°] and latitude range [-90°, 90°] to a non-negative interval starting from 0; ΔLon and ΔLat The latitude and longitude intervals calculated for S1; Base is a preset base constant used to ensure that the encoding intervals between different map sheets are fixed; S3. After encoding and calculating all image data, store it in the local system to complete the encoding and establishment of the base map database; S4. Obtain the geometric information of the image to be matched, and calculate the base map number according to the decoding rules: First, the coordinates of the four geographic corner points of the image to be matched are parsed. Then, a preset resolution identifier R corresponding to the image to be matched is determined based on the resolution. For each of the geographic corner point coordinates... After performing encoding calculations and deduplication, a set of base map numbers is obtained to determine the spatial relationship between the image to be matched and the sectional base map, thus completing the real-time retrieval.

[0008] Technical effects: This invention proposes a map-based encoding method using a global regular grid with equal latitude and longitude. By using a unified encoding rule that includes resolution identifiers, it achieves unified management of multi-resolution base map data. An encoding algorithm based on the latitude and longitude position of the image is designed. By using a fixed cardinality, the encoding interval between adjacent map sheets is ensured to be fixed, forming a regular encoding sequence. A unified encoding system suitable for base maps of different resolutions is established, supporting the collaborative organization and management of base maps with multiple resolutions from decimeter to hundred-meter, facilitating the use of cross-scale data.

[0009] Based on a unified coding system, automatic matching between the image to be registered and the reference base map is further realized. Through a direct mapping algorithm from the corner coordinates of the image to be registered to the base map code, a fast retrieval with a time complexity of O(1) is achieved, completely eliminating manual intervention and improving the automation level of the registration process. It ensures that the base map retrieval time is independent of the database size, meeting the current needs for processing massive base map data. At the same time, it provides a reliable data foundation for subsequent image registration, manual interpretation and other work. Attached Figure Description

[0010] Figure 1 This is an overall flowchart of an embodiment of the present invention. Detailed Implementation

[0011] The technical solutions in the embodiments of the present invention will be clearly and completely described below.

[0012] The entire implementation is divided into two core stages: the establishment of the reference base map database and the rapid retrieval of the reference base map.

[0013] The first stage is the establishment and encoding of the global reference base map database. This stage is the foundation for the efficiency of the entire method. Its goal is to organize the messy raw reference base map data into a database with a clear structure and standardized naming.

[0014] S1. Global digital orthophoto data preparation and grating calculation.

[0015] First, global digital orthophotos (DOMs) of various resolutions are acquired as the base map data source. This data typically contains multiple resolution levels (such as 2 meters, 5 meters, 10 meters, etc.). For unified management, a globally uniform grid is calculated based on user presets or the resolution of the data itself.

[0016] Calculation by section: The map is divided into equal latitude and longitude trapezoidal sections, with the latitude and longitude intervals between sections being ( ΔLon , ΔLat The interval is not a fixed value, but rather is related to the resolution, aiming to ensure that the amount of data (number of pixels) in each sub-map is within a reasonable and balanced range. For example, for high-resolution data (such as 2 meters), a smaller sub-map interval (such as 1.40625°) can be used to avoid individual files being too large; for low-resolution data (such as 10 meters), a larger sub-map interval (such as 5.625°) can be used to reduce the number of files and improve management efficiency.

[0017] The specific formula for calculating latitude and longitude intervals is as follows:

[0018] Furthermore, the corresponding latitude and longitude intervals, number of maps, and resolutions for each preset base map level are shown in the table below:

[0019] S2. Design a coding system so that each sub-map has a unique code: This step involves encoding and naming the base maps according to their level, ensuring that each map sheet has a globally unique code determined by its geographical extent and resolution. The encoding rules are as follows: Calculate the encoded value of the top left corner point of the map sheet: For a top left corner point with latitude and longitude of ( Lon_leftup , Lat_ leftup The formulas for calculating the longitude code (CodeX) and latitude code (CodeY) of the base map are as follows: , in: Lon_leftup +180 and 90- Lat_leftup This is to convert the longitude range [-180°, 180°] and the latitude range [-90°, 90°] to a non-negative interval starting with 0. ΔLon and ΔLat These are the longitude and latitude differences calculated in S1. Base is a base number, which is 256 in this embodiment to ensure that the encoding interval between different map sheets is fixed, thus facilitating subsequent rapid retrieval.

[0020] It also incorporates a custom resolution identifier R. To distinguish base maps of different resolutions, the final complete encoding will include a resolution identifier R. For example, R=16 can be set to represent a 2-meter resolution, R=14 to represent a 10-meter resolution, and so on.

[0021] Therefore, the final and unique name of a sectional base map is: TileID = CodeX_CodeY_R For example, a base map encoded as 55552_8192_16 represents an image file with a resolution level of 16 (corresponding to 2-meter resolution) and located within a specific latitude and longitude grid.

[0022] S3. Store the data in the local system to complete the encoding and creation of the base map database; All base map files are renamed according to the S2 encoding scheme and stored on local disks or distributed file systems. The database does not require a complex spatial database engine; efficient management is achieved solely through file system directories, ensuring the uniqueness and regularity of naming.

[0023] Phase Two: Real-time Retrieval of the Base Map S4. Obtain the geometric information of the image to be matched, and calculate the base map number according to the decoding rules: First, the coordinates of the four geographic corner points of the image to be matched are parsed, usually including the top left corner. Top right corner , lower left corner point and the bottom right corner These four corner points together precisely define the rectangular geographic area of ​​the image to be matched. Secondly, the system determines the preset resolution identifier R corresponding to the image to be matched based on the resolution. The value of the resolution identifier R corresponds one-to-one with a predefined specific spatial resolution level, which can be specified through the user interface or automatically determined by the system according to preset rules.

[0024] For each of the aforementioned geographic corner coordinates Perform encoding calculations using encoding functions that are identical to those used in the base map database construction phase.

[0025] The S4 encoding operation specifically involves: for any given corner coordinates... Its resolution identifier The corresponding base map number components are calculated using the following formula:

[0026]

[0027] in, and These are the resolution identifiers. The corresponding longitude and latitude intervals are given below, where Base is a preset base constant, which is 256 in this embodiment. This indicates a floor operation. Therefore, each corner point... This will correspond to a complete base map number, that is , Four corner points All the TileIDs obtained through the above calculations constitute an initial set. Through the analysis of Perform a deduplication operation to generate the final set of unique base map numbers. .

[0028] This set Number of elements This intuitively reflects the image to be registered. Spatial relationship with the base map. The value of N is 1, 2 or 4, which correspond to the image to be registered being completely located inside a single base map, crossing the boundary of two adjacent base maps, or covering the common corner area of ​​four base maps, respectively.

[0029] In subsequent processing, the system uses the final determined set of base map numbers. Each of them TileID According to predefined file path mapping rules The system generates the corresponding physical file path and verifies the file's actual existence in the storage system. If the file exists, the system immediately returns the path to the subsequent registration algorithm; if it does not exist (e.g., the area is ocean or there is no data), it returns a null value or processes it according to a preset strategy. All verified base map file paths are summarized and returned as input data for subsequent image registration processing.

[0030] The real-time retrieval in this embodiment ensures that for any image to be registered in any spatial location, the complete reference base map dataset required for registration can be obtained automatically and with high precision.

[0031] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

Claims

1. A method for managing and retrieving multi-resolution global reference base map databases, characterized in that: Includes the following steps: S1. Acquire global digital orthophoto data at different resolutions, perform tiling calculations to obtain tiling base maps. Among them, the latitude and longitude intervals of the frame are calculated based on the resolution. ΔLon , ΔLat ), where n is the preset base map level; S2. Design a coding system so that each sub-map has a unique code: Calculate the encoded value of the top-left corner point of each sub-map and incorporate it into the custom resolution identifier R. For a top-left corner point with latitude and longitude of ( Lon_leftup , Lat_leftup The formulas for calculating the longitude code (CodeX) and latitude code (CodeY) of the base map are as follows: ， in: Lon_leftup +180 and 90- Lat_leftup It can convert longitude range [-180°, 180°] and latitude range [-90°, 90°] to a non-negative interval starting from 0; ΔLon and ΔLat The latitude and longitude intervals calculated for S1; Base is a preset base constant used to ensure that the encoding intervals between different map sheets are fixed; S3. After encoding and calculating all image data, store it in the local system to complete the encoding and establishment of the base map database; S4. Obtain the geometric information of the image to be matched, and calculate the base map number according to the decoding rules: First, the coordinates of the four geographic corner points of the image to be matched are parsed. Then, a preset resolution identifier R corresponding to the image to be matched is determined based on the resolution. For each of the geographic corner point coordinates... After performing encoding calculations and deduplication, a set of base map numbers is obtained to determine the spatial relationship between the image to be matched and the sectional base map, thus completing the real-time retrieval.

2. The management and retrieval method for a multi-resolution global reference base map database according to claim 1, characterized in that, In S4, the coordinates of the four geographic corner points of the image to be matched typically include the top left corner. Top right corner , lower left corner point and the bottom right corner .

3. The management and retrieval method for a multi-resolution global reference base map database according to claim 2, characterized in that, The S4 encoding calculation is as follows: For any given corner coordinates Its resolution identifier The corresponding base map number components are calculated using the following formula: in, and These are the resolution identifiers. The corresponding longitude and latitude intervals are shown below, where Base is a preset base constant. This indicates the floor function; Four corner points The above calculations form an initial set. Through the analysis of Perform a deduplication operation to generate the final set of unique base map numbers. .

4. The management and retrieval method for a multi-resolution global reference base map database according to claim 3, characterized in that, The set Number of elements It intuitively reflects the spatial relationship between the image to be registered and the sectional base map. The value of N is 1, 2 or 4, which correspond to the image to be registered being completely located inside a single sectional base map, crossing the boundary of two adjacent sectional base maps, or covering the common corner area of ​​four sectional base maps, respectively.

5. The method for managing and retrieving a multi-resolution global reference base map database according to any one of claims 1 or 3, characterized in that, The value of Base is 256.

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