Efficient remote sensing image slicing-free service method based on cloud native

Abstract

The invention discloses an efficient remote sensing image slice-free service method based on cloud native. The method comprises the following steps: S1, calculating tile positions according to a service protocol; s2, calculating the offset of the intersection of the boundary of the remote sensing image and the tile, and obtaining an actual image tile; s3, analyzing the wave band type of the remote sensing image; s4, acquiring tile pixel data corresponding to different wavebands; s5, combining the pixel values of each wave band, and rendering into an image; and S6, performing map preview on the plurality of tiles on the whole. According to the method, only the slices of the specific area needed by the user need to be loaded and displayed, and the whole large file does not need to be loaded. Therefore, the loading speed of the remote sensing image and the user experience are greatly improved. The remote sensing image slicing-free service can also support dynamic zooming and navigation functions, and a user can check different geographic positions and detail levels through operations such as translation and zooming. Because only the required slices are loaded, the remote sensing image slice-free service can reduce the network bandwidth and the storage cost, and the data processing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image processing, and particularly to a method for efficient remote sensing image slicing-free service based on cloud native. Background Art

[0002] With the development of computer technology, computer processing technology has been increasingly applied to remote sensing image processing. After converting optical images into digital images, or directly obtaining digital remote sensing images through remote sensing sensors, the computer can be used to process remote sensing image data, and this processing technology becomes the digital processing method of remote sensing images. The digital processing method is simple to operate, can easily build a remote sensing image processing system that meets specific processing tasks, and can also be seamlessly integrated with other computer systems (such as geographic information systems and GPS systems) to form a comprehensive application of 3S technology.

[0003] The main characteristic of remote sensing images is their large size. Generally, a single image is 5-10GB, and a single satellite adds TB-level data per day, with the global data increasing by PB-level per day. With the increase in the accuracy of satellite sensors, the size of a single image file is getting larger and larger. The resulting problem is that it is not easy to store. If the local data center cannot store it, it has to be uploaded to the cloud.

[0004] However, after a large amount of remote sensing data is uploaded to the cloud, it faces many challenges: a large number of existing software cannot remotely read image files on the cloud (especially object storage). It must be downloaded to the local first and then can be opened for analysis; even if an S3 driver is added to the software in a timely manner to remotely analyze a file, the entire content of the file has to be read in full (because it is a linked list file). This operation is carried out through the network, so it greatly affects the efficiency. To separately extract a partial area (tile or thumbnail) from the image file, the entire file also has to be downloaded or accessed in full, even if the tile only accounts for 1 / N of the entire image. Summary of the Invention

[0005] To solve the existing problems, the present invention provides a method for efficient remote sensing image slicing-free service based on cloud native, and the specific solution is as follows:

[0006] A method for efficient remote sensing image slicing-free service based on cloud native includes the following steps:

[0007] S1, calculating the tile position according to the service protocol;

[0008] S2, calculating the offset of the intersection of the remote sensing image boundary and the tile to obtain the actual image tile;

[0009] S3, analyzing the band type of the remote sensing image;

[0010] S4, obtaining the tile pixel data corresponding to different bands;

[0011] S5. Merge the pixel values of each band and render them into an image;

[0012] S6. Display multiple tiles on the map for preview.

[0013] Preferably, the specific steps for calculating the tile position in step S1 are as follows:

[0014] S11. Create a grid and slice according to the grid;

[0015] S12. Calculate the number of tiles to be divided according to the zoom level;

[0016] S13. Obtain the four-point coordinates of the corresponding tile by combining the tile number.

[0017] Preferably, the method for obtaining the pixel data of the tiles corresponding to different bands in step S4 is as follows: Use the gdal.ReadRaster method to read the grid database and read the pixel values of the specified area from the dataset. Its basic syntax is data=dataset.ReadRaster(offsetX,offsetY,width,height); where the gdal.ReadRaster method is a method in the GDAL (Geospatial Data Abstraction Library) library for reading raster data; dataset is the dataset object from which data is to be read; offsetX and offsetY are the offsets of the starting pixel of the data to be read; width and height are the pixel width and height of the data to be read; the gdal.ReadRaster method returns the pixel values of the specified area.

[0018] Preferably, the algorithm for merging the pixel values of each band is as follows: / / Default setting is opaque

[0019] int alpha = 0xFF;

[0020] / / If the pixel value of a certain channel is 0, it means there is no pixel, and set alpha to 0, that is, transparent if(redPixel == 0 && greenPixel == 0 && bluePixel == 0){alpha = 0x00;}

[0021] / / Merge the pixel values of the red, green, and blue channels and add the value of the alpha channel

[0022] image = (alpha << 24) | (redPixel << 16) | (greenPixel << 8) | bluePixel;

[0023] Among them, << is the bitwise left shift operator, which is used to shift the binary representation of a number to the left by a specified number of bits;

[0024] Then, the merged image pixels are written into the picture and scaled to the specified size. A common tile size is 256 * 256, and the missing parts are filled with a transparent background color.

[0025] The present invention also discloses a computer-readable storage medium with a computer program stored thereon. After the computer program runs, it executes the method described in any one of the above.

[0026] The present invention also discloses a computer system, including a processor and a storage medium. The storage medium stores a computer program, and the processor reads and runs the computer program from the storage medium to execute the method described in any one of the above.

[0027] The beneficial effects of the present invention are as follows:

[0028] The present invention discloses a cloud-based remote sensing image non-slice service, which provides an online real-time service for map rendering. The positive effects are mainly reflected in three aspects:

[0029] 1. Efficient image data access. The remote sensing image non-slice service can transmit only the required part of the data, thereby shortening the processing time and creating a real-time workflow that was previously impossible.

[0030] 2. Reducing data duplication. Accessing the remote sensing image non-slice service through a cloud workflow enables different software to access a single file online without copying and caching the data;

[0031] 3. Legacy compatibility. Traditional GIS software can process cloud-optimized GeoTIFFs in the same way as ordinary GeoTIFFs, so data providers only need to generate one format.

[0032] In summary, the remote sensing image non-slice service uses cloud optimization and slicing technologies to make the browsing, processing, and sharing of large-scale remote sensing image data more efficient and convenient. It has broad application potential in the fields of geographic information systems, environmental monitoring, agriculture, urban planning, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0034] Figure 1Schematic diagram of the slicing rule when the zoom level is 2 in the embodiment;

[0035] Figure 2 Schematic diagram of the calculation of remote sensing image tiles in the embodiment. Detailed implementation manners

[0036] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0037] The present invention utilizes remote sensing image processing technology optimized based on the cloud to efficiently browse and process large-scale remote sensing image data. The remote sensing image slice-free service pre-segments and processes large GeoTIFF files through cloud optimization and preprocessing of remote sensing images, and then uploads the processed GeoTIFF to cloud storage.

[0038] In this way, during actual use, only the slices of the specific area required by the user need to be loaded and displayed, instead of loading the entire large file. This greatly improves the loading speed of remote sensing images and the user experience. The remote sensing image slice-free service also supports dynamic zooming and navigation functions, and users can view different geographical locations and levels of detail through operations such as panning and zooming. At the same time, since only the required slices are loaded, the remote sensing image slice-free service can reduce network bandwidth and storage costs and improve the efficiency of data processing.

[0039] The present invention discloses a method for an efficient remote sensing image slice-free service based on cloud native, and this method relies on two technologies:

[0040] 1. Saving tiles and overviews in the GeoTIFF image in addition to the original pixel data; 2. Obtaining partial content of a single file through a range request each time.

[0041] These two points together achieve the complete online processing of data by the remote sensing image slice-free service, because they can transmit the correct part of the GeoTIFF as needed without having to download the entire file.

[0042] A tile is to piece together the complete image Figure 1Samples are split into tiles according to the horizontal and vertical grids, and only load individual tiles within the required range instead of the entire large TIFF. Common image tiling technologies, such as the slicing of ArcMap / ArcGISServer and GeoServer, require pre-generated slices to be stored on disk, while the remote sensing image slice-free service stores tiles in TIFF files in tile format.

[0043] An overview is to create a resampled version of the same image at different resolutions. This means it has much less detail (the original image may have 100 or 1000 pixels corresponding to 1 pixel in the overview), but is also much smaller. Usually, a GeoTIFF will have many overviews to match different zoom levels. These increase the size of the entire file, but can be served faster. Those who have used ArcMap may be more familiar with the concept of image pyramids, which is similar in meaning to overviews.

[0044] Range requests allow the server to send only a part of the HTTP message to the client. Range requests are very useful when transferring large media files or in combination with the resume feature of file downloads. For cloud storage, the cloud storage protocol needs to support reading files on the cloud in the Range manner.

[0045] A method for an efficient slice-free remote sensing image service based on cloud-native includes the following steps:

[0046] S1. Calculate the tile positions according to the service protocol.

[0047] Specifically, calculating the tile positions specifically includes the following steps:

[0048] S11. Create a grid and slice according to the grid.

[0049] The slicing rules are as follows:

[0050] Slicing is actually a technical solution that uses a pyramid structure to slice a large amount of spatial data and transmit it back to the client for recombination on the client to form a visually complete map. Adopting this technical solution usually pre-cuts the map to accelerate the service access efficiency and reduces the repetitive computational overhead of the server through caching.

[0051] To slice, first establish a grid and slice according to the grid. The number of grids is inconsistent at different zoom levels, but basically follows the rule of 2 to the power of n. The slicing rules are almost the same, so the slicing rules are independent of the service type, whether it is WFS or MVT. The slicing rules have nothing to do with the projection coordinate system either, although projection is necessarily done before slicing. As long as its projection coordinate system is consistent and the slicing rules are the same, then the sliced tiles will be the same, and the positions of points, lines, and surfaces can all correspond.

[0052] S12. Calculate the number of tiles to be segmented according to the zoom level.

[0053] Example: As Figure 1 It shows how to slice the world map and the XY numbers of each tile when the zoom level is 2.

[0054] The number of tiles to be segmented can be calculated according to the zoom level. The calculation formula is as follows:

[0055] tileWidth = WorldWidth / 2 zoom

[0056] (The width of each tile in the horizontal coordinate direction = the width of the world map / the zoom power of 2)

[0057] tileHeight = WorldHeight / 2 zoom

[0058] (The height of each tile in the vertical coordinate direction = the height of the world map / the zoom power of 2)

[0059] S13. Obtain the four-point coordinates of the corresponding tile in combination with the tile number.

[0060] The calculation formula is as follows:

[0061] tileMinX = WorldMinX + X * tileWidth;

[0062] (The left horizontal coordinate = the minimum longitude of the world map + the X number * the tile width)

[0063] tileMaxX = WorldMinX + (X + 1) * tileWidth;

[0064] (The right horizontal coordinate = the minimum longitude of the world map + the X number plus one * the tile width)

[0065] tileMinY = WorldMinY + Y * tileHeight;

[0066] (The lower vertical coordinate = the minimum latitude of the world map + the Y number * the tile height)

[0067] tileMaxY = WorldMinY + (Y + 1) * tileHeight;

[0068] (The upper vertical coordinate = the minimum latitude of the world map + the Y number plus one * the tile height)

[0069] Finally, the four coordinates of the top, bottom, left, and right of the corresponding tile can be obtained through the three input parameters X, Y, and Z.

[0070] S2. Calculate the offset where the remote sensing image boundary intersects with the tile, and obtain the actual image tile.

[0071] As Figure 2 shown, assuming that the orange boundary is a remote sensing image data, then for the tile 3-2, the actual yellow part of the corresponding remote sensing image needs to be obtained. The intersection points of this yellow part with the slice are marked as ABCDEF. If you want to read only the yellow part of the remote sensing image, you need to first calculate the corresponding offset. As can be seen from the figure:

[0072] offsetX (starting position of the abscissa) = AB;

[0073] offsetY (starting position of the ordinate) = 0;

[0074] width (width of the abscissa) = BC;

[0075] height (height of the ordinate) = BE;

[0076] The specific calculation formula is as follows:

[0077] / / Four coordinates of the top, bottom, left, and right of the tile

[0078] int tileMinx = 100;

[0079] int tileMaxX = 200;

[0080] int tileMinY = 100;

[0081] int tileMaxY = 200;

[0082] / / Width and height of the remote sensing image

[0083] double tifwidth = 200;

[0084] double tifHeight = 100;

[0085] / / Four coordinates of the remote sensing image

[0086] double tifMinX = 50;

[0087] double tifMaxx = 300;

[0088] double tifMinY = 50;

[0089] double tifMaxY = 300;

[0090] / / Calculate the x / y pixel offset of the upper left corner of the tile on the source image. int offsetx = (int)((tileMinx - tifMinx) / tifwidth);

[0091] int offsety = (int)((tileMiny - tifMinY) / tifHeight);

[0092] / / Calculate the pixel width of the tile at this level on the source image. int width = (int)((tileMaxx - tileMinx) / tifwidth);

[0093] int height = (int)((tileMaxY - tileminY) / tifHeight);

[0094] / / If the offset is less than 0, the size needs to be adjusted. if (offsetx < 0) {

[0095] width = width + offsetX;

[0096] offsetX = 0;}

[0097] if (offsety < 0) {

[0098] height = height + offsetY;

[0099] offsetY = 0;}

[0100] / / Calculate the maximum offset

[0101] int maxoffsetX = (int)((tifMaxX - tifinx) / tifwidth);

[0102] int maxoffsetY = (int)((tifaxY - tifminY) / tifHeight);

[0103] / / If the offset is greater than the maximum value, the size needs to be adjusted

[0104] if ((offsetx + width) > maxoffsetx) {

[0105] width = maxoffsetX - offsetX;}

[0106] if ((offsety + height) > maxoffsetY) {

[0107] height = maxoffsety - offsetY;}

[0108] S3. Analyze the types of remote sensing image bands.

[0109] Specifically, remote sensing images generally include information in different bands such as red, green, and blue. In color remote sensing images, these bands are combined in a specific way to present a color image. In most cases, a single band is a black-and-white image, and a multi-band is a color image. Several common color models are as follows:

[0110] The RGB color model is a way of representing colors using three color channels: red, green, and blue. It is one of the most commonly used color models in computer color models.

[0111] The CMYK color is a color model based on the colors of printing inks, where C represents cyan, M represents magenta, Y represents yellow, and K represents black. This color model is mainly used in the fields of printing and printing.

[0112] The grayscale color model is a color model that only has brightness information and is used to represent black-and-white images or convert color images to black-and-white images.

[0113] S4. Obtain the tile pixel data corresponding to different bands;

[0114] gdal.ReadRaster is one of the methods in the GDAL (Geospatial Data Abstraction Library) library for reading raster data. GDAL is an open-source library for reading, writing, and processing geospatial data and supports remote sensing image data in multiple formats.

[0115] The specific method is as follows: Use the gdal.ReadRaster method to read the grid database and read the pixel values of the specified area from the dataset. Its basic syntax is data = dataset.ReadRaster(offsetX, offsetY, width, height); where the gdal.ReadRaster method is a method in the GDAL (Geospatial Data Abstraction Library) library for reading raster data; dataset is the dataset object from which to read the data; offsetX and offsetY are the offsets of the starting pixel of the data to be read; width and height are the pixel width and height of the data to be read; the gdal.ReadRaster method returns the pixel values of the specified area.

[0116] S5. Merge the pixel values of each band and render them into an image.

[0117] The algorithm for merging the pixel values of each band is as follows: / / Default setting is opaque

[0118] int alpha = 0xFF;

[0119] / / If the pixel value of a certain channel is 0, it means there is no pixel, and set alpha to 0, that is, transparent. if (redPixel == 0 && greenPixel == 0 && bluePixel == 0) { alpha = 0x00;}

[0120] / / Merge the pixel values of the red, green, and blue channels, and add the value of the alpha channel

[0121] image = (alpha << 24) | (redPixel << 16) | (greenPixel << 8) | bluePixel;

[0122] Among them, << is the bitwise left shift operator, which is used to shift the binary representation of a number to the left by a specified number of bits;

[0123] Then write the merged image pixels into the picture, and scale them to the specified size. The common tile size is 256 * 256, and fill in the missing parts with a transparent background color.

[0124] S6. Preview multiple tiles on the map as a whole.

[0125] The present invention discloses a cloud-based remote sensing image slicing-free service, which provides an online real-time service for map rendering. The positive effects are mainly reflected in three aspects:

[0126] 1. Efficient image data access. The remote sensing image slicing-free service can transmit only the required part of the data, thus shortening the processing time and creating a real-time workflow that was previously impossible.

[0127] 2. Reduced data duplication. Accessing the remote sensing image slicing-free service through a cloud workflow enables different software to access a single file online without copying and caching the data;

[0128] 3. Legacy compatibility. Traditional GIS software can process cloud-optimized GeoTIFFs in the same way as ordinary GeoTIFFs, so data providers only need to generate one format.

[0129] In summary, the remote sensing image slicing-free service utilizes cloud optimization and slicing technologies to make the browsing, processing, and sharing of large-scale remote sensing image data more efficient and convenient. It has extensive application potential in fields such as geographic information systems, environmental monitoring, agriculture, and urban planning.

[0130] The present invention also discloses a computer-readable storage medium and a computer system. Specifically, the computer-readable storage medium stores a computer program, and after the computer program runs, it executes the method described in any one of the above. The computer system includes a processor and a storage medium. The storage medium stores a computer program, and the processor reads and runs the computer program from the storage medium to execute the method described in any one of the above.

[0131] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, the various illustrative components, blocks, modules, circuits, and steps are described above in terms of their functionality in a generalized form. Whether such functionality is implemented as hardware or software depends upon the particular application and the design constraints imposed on the overall system. Skilled artisans may implement the described functionality in different ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

[0132] The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein can be implemented or executed with a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0133] The steps of a method or algorithm described in connection with the embodiments disclosed in this specification can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read from, and write to, the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0134] In one or more exemplary embodiments, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted via a computer-readable medium as one or more instructions or code. The computer-readable medium includes both a computer storage medium and a communication medium including any medium that facilitates transfer of a computer program from one place to another. A storage medium may be any available medium that can be accessed by a computer. By way of example and not limitation, such a computer-readable medium can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable medium.

[0135] The foregoing description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0136] Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for efficient remote sensing image slice-free service based on cloud native, characterized in that: The following steps are involved: S1, calculate tile position according to service agreement; S2, calculate the offset of the intersection between the remote sensing image boundary and the tile to obtain the actual image tile; S3, analyze remote sensing image band types; S4, obtaining tile pixel data corresponding to different bands; S5, merging pixel values ​​of each band and rendering into an image; S6, previewing the multiple tiles on the map as a whole.

2. The method according to claim 1, characterized in that Calculating the tile position in step S1 specifically includes the following steps: S11, creating a grid and slicing according to the grid; S12, calculate the number of tiles that need to be split according to the zoom level; S13, obtain the four-point coordinates of the corresponding tile in combination with the tile number.

3. The method according to claim 1, characterized in that The method of step S4 for obtaining tile pixel data corresponding to different bands is as follows: use the gdal.ReadRaster method to read the raster database, and read the pixel values ​​of the specified area from the dataset. Its basic syntax is data = dataset.ReadRaster (offsetX, offsetY, width, height); wherein the gdal.ReadRaster method is a method in the GDAL (Geospatial Data Abstraction Library) library for reading raster data; dataset is the dataset object to read data; offsetX and offsetY are the offsets of the starting pixels to read data; width and height are the pixel width and height of the data to be read; the gdal.ReadRaster method returns the pixel values ​​of the specified area.

4. The method according to claim 1, characterized in that The algorithm for merging pixel values ​​of each band is as follows: / / The default setting is opaque int alpha = 0xFF; / / If the pixel value of a channel is 0, it means there is no pixel, and alpha is set to 0, that is, transparent if(redPixel==0&&greenPixel==0&&bluePixel==0){alpha=0x00;} / / Merge the pixel values ​​of the red, green and blue channels and add the value of the alpha channel image=(alpha<<24)|(redPixel<<16)|(greenPixel<<8)|bluePixel; Among them, << is the bitwise left shift operator, which is used to shift the binary representation of a number to the left by a specified number of bits; The merged image pixels are then written into the picture and scaled to the specified size. The common tile size is 256*256, and the missing pixels are filled with a transparent background color.

5. A computer-readable storage medium, characterized in that: A computer program is stored on the medium, and after the computer program is run, the method according to any one of claims 1 to 4 is executed.

6. A computer system, characterized in that: The method comprises a processor and a storage medium, wherein a computer program is stored in the storage medium, and the processor reads and runs the computer program from the storage medium to execute the method as claimed in any one of claims 1 to 4.

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