Large-scale remote sensing image data high-performance visualization method and system and electronic equipment

Abstract

The invention discloses a high-performance visualization method and system for large-scale remote sensing image data and electronic equipment. The method comprises the following steps: S1, constructing a remote sensing image sky map corresponding relation library; s2, collecting remote sensing image data, extracting an adaptive sky map level corresponding to a remote sensing image resolution and a corresponding optimal GeoSOT level, slicing the remote sensing image data according to a grid of the optimal GeoSOT level, and storing the remote sensing image data as optimal level slicing data; s3, carrying out grid slicing and downsampling processing step by step from the optimal GeoSOT hierarchy according to the GeoSOT hierarchy, and sequentially obtaining slice data of each GeoSOT hierarchy; and S4, screening all slice data corresponding to the GeoSOT hierarchy based on the sky map hierarchy selected by the query viewport, and rendering to generate a visual picture. According to the method, the remote sensing image sky map corresponding relation library is constructed, the optimal GeoSOT hierarchy adaptation of the remote sensing image data is carried out on the basis, GeoSOT hierarchy slice expansion is carried out so as to adapt to more query viewport resolutions, and the visualization quality and efficiency are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing data processing, and in particular to a high-performance visualization method, system and electronic equipment for large-scale remote sensing image data. Background Art

[0002] Remote sensing images are images of the earth's surface obtained through remote sensing technology, which contain spectral information of surface features. With the development of cloud computing, remote sensing technology, and sensor technology, remote sensing science and technology are becoming increasingly important and are widely used in various fields, such as air pollution, environmental protection, ecological restoration, and urban planning. Among them, remote sensing image data visualization technology plays a vital role. As a national geographic information public service platform, Tianditu integrates the resources of geographic information departments at all levels, provides authoritative and unified online services, and is usually used as the base map for remote sensing image visualization services.

[0003] Remote sensing image data visualization usually follows OGC standards, such as WMS and WMTS. WMS renders remote sensing images into pictures and returns them through HTTP services, while WMTS divides images into tiles and pre-renders and stores them to improve access efficiency. In addition, there are non-OGC standard visualization methods such as TMS, which have similar principles but low standardization. The above visualization methods have the following three problems: First, the access efficiency is relatively low. If the amount of data is large, such as when the image resolution is very high, or when the network bandwidth is low, there is a huge performance bottleneck; second, the storage method is single and not easy to expand. Both the image itself and the image after image slicing must be stored on the disk. The organization and management of the file depends on the file system of the operating system, and the distributed file system cannot be well utilized. The read and write performance depends on the host performance, and there is a single point of failure risk; third, the resource occupation is serious, and the memory usage rate will continue to increase with the increase of services. With the development of remote sensing related technologies, the resolution of images that can be obtained is getting higher and higher, and the size is getting larger and larger. The above three problems have greatly restricted the use of images, and there is an urgent need for a visualization solution with high efficiency, distributed deployment, and low resource occupation. Summary of the invention

[0004] The purpose of the present invention is to solve the technical problems of low efficiency, single storage and difficulty in expansion, and serious resource occupation in the existing remote sensing image data visualization services, and to provide a high-performance visualization method, system and electronic equipment for large-scale remote sensing image data. Through GeoSOT grid coding and calculation, distributed file system, and map color rendering, it can be quickly accessed, flexibly stored and has the advantages of low resource consumption; no matter how high the resolution of the remote sensing image is or how large the size is, reliable storage, fast retrieval and efficient rendering visualization can be achieved.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] A high-performance visualization method for large-scale remote sensing image data, the method comprising:

[0007] S1. Construct a remote sensing image sky map correspondence library including sky map level, GeoSOT level and level correspondence; the sky map level includes sky map resolution, and the GeoSOT level includes GeoSOT size;

[0008] S2, collect remote sensing image data, extract remote sensing image resolution corresponding to the adaptation of the sky and earth map level and the corresponding optimal GeoSOT level, slice the remote sensing image data according to the grid of the optimal GeoSOT level and store it as optimal level slice data;

[0009] S3, starting from the optimal GeoSOT level, grid slicing and downsampling are performed step by step according to the GeoSOT level, and slice data of each GeoSOT level is obtained in turn;

[0010] S4. Based on the selected sky and earth layer of the query viewport, all slice data corresponding to the GeoSOT layer are filtered and rendered to generate a visualization image.

[0011] In order to better implement the present invention, step S3 also includes the following method:

[0012] The optimal level slice data and the slice data of each GeoSOT level are GeoSOT grid coded according to the grid.

[0013] Preferably, in step S1, the sky map resolution of the sky map level increases in sequence from level to level, and that of the GeoSOT level decreases in sequence from level to level, and the hierarchical correspondence is a one-to-one correspondence between the sky map level and the GeoSOT level; the highest sky map resolution of the sky map level corresponds to the lowest GeoSOT size of the GeoSOT level, and the lowest sky map resolution of the sky map level corresponds to the highest GeoSOT size of the GeoSOT level.

[0014] Preferably, in step S2, the sky map resolution of the sky map level is adapted according to the extracted remote sensing image resolution and the sky map level is adapted, and the corresponding GeoSOT level is found as the optimal GeoSOT level based on the remote sensing image sky map correspondence library; mask extraction slicing and grid coding processing are performed based on the grid of the optimal GeoSOT level to obtain the optimal level slicing data containing grid coding information.

[0015] Preferably, in step S3, the GeoSOT size is increased by one level based on the GeoSOT level being larger than the optimal GeoSOT level, and mask extraction slicing, grid coding, and downsampling processing are performed based on the grid of the increased GeoSOT level to obtain slice data containing grid coding information and the increased GeoSOT level; the GeoSOT level is increased step by step to obtain slice data of each GeoSOT level.

[0016] Preferably, step S3 also includes the following method:

[0017] Based on the GeoSOT level, the GeoSOT size is reduced by one level compared to the optimal GeoSOT level. Mask extraction slicing, grid encoding, and upsampling are performed based on the grid of the reduced GeoSOT level to obtain slice data containing grid coding information and reduced GeoSOT level; the GeoSOT level is reduced step by step to obtain slice data of the remaining GeoSOT levels.

[0018] Preferably, a distributed file system is used to perform distributed storage on all slice data.

[0019] Preferably, in step S4, all slice data at the GeoSOT level are screened out and the data content is extracted for standardization and color rendering to generate a rendered visualization image.

[0020] A high-performance visualization system for large-scale remote sensing image data includes a data acquisition module, a slice code storage system and a query viewport system. The slice code storage system has a remote sensing image sky map correspondence library, which includes a sky map hierarchy, a GeoSOT hierarchy and hierarchy correspondences. The sky map hierarchy includes a sky map resolution, and the GeoSOT hierarchy includes a GeoSOT size. The data acquisition module is used to collect remote sensing image data and input it into the slice code storage system. The slice code storage system extracts remote sensing image data and the remote sensing image resolution corresponds to the sky map hierarchy. and the corresponding optimal GeoSOT level, slice the remote sensing image data according to the grid of the optimal GeoSOT level and store it as the optimal level slice data; the slice encoding storage system performs grid slicing and downsampling processing step by step from the optimal GeoSOT level according to the GeoSOT level, and obtains the slice data of each GeoSOT level in turn; the query viewport system includes a screening module and a rendering module, the screening module is used to select the sky and earth map level and correspondingly screen all the slice data of the GeoSOT level to input the rendering module, and the rendering module uses all the slice data to render and generate a visual image. The query efficiency is steadily improved, and the storage can also be easily expanded. In addition, it does not need to be preloaded into the memory, which reduces resource usage.

[0021] An electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the steps of the high-performance visualization method for large-scale remote sensing image data of the present invention.

[0022] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0023] (1) The present invention extracts the adaptive sky-land map level and the corresponding optimal GeoSOT level of the remote sensing image resolution from the collected remote sensing image data, then performs grid slicing and downsampling processing step by step according to the GeoSOT level and stores all the slice data respectively, and filters all the slice data of the corresponding GeoSOT level by querying the sky-land map level selected by the viewport and renders to generate a visual image; by dividing the remote sensing image into multiple grids, the distributed storage of the image itself is realized, and the rapid query of the image range is realized through grid calculation; the queried grid is used to generate a picture in real time through map color matching, and finally the picture is rendered on the map, so as to quickly realize data query and display without occupying additional resources.

[0024] (2) The present invention provides a high-performance visualization method, system and electronic device for large-scale remote sensing image data. Through GeoSOT grid coding and calculation, distributed file system and map color rendering, it can achieve fast access, flexible storage and low resource consumption. No matter how high the resolution of the remote sensing image is or how large its size is, it can achieve reliable storage, fast retrieval and efficient rendering visualization.

[0025] (3) The present invention constructs a remote sensing image sky map correspondence library and uses it to perform optimal GeoSOT level adaptation of remote sensing image data, realizing the optimal visualization adaptation strategy of query viewport resolution and remote sensing image resolution, and then performs GeoSOT level slicing expansion to adapt to more query viewport resolutions, thereby improving the remote sensing image data screening, reading and downloading speed and subsequent rendering efficiency, greatly improving the visualization quality and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A method flow chart of a high-performance visualization method for large-scale remote sensing image data according to the present invention;

[0027] Figure 2 It is a schematic diagram of the principle of grid division and encoding in the embodiment;

[0028] Figure 3 It is a schematic diagram of the principle of slicing in a pyramid from the optimal GeoSOT level in the embodiment;

[0029] Figure 4 A schematic diagram of the principle of querying a viewport to filter slice data and render under a query request in an embodiment;

[0030] Figure 5 This is a principle structural block diagram of the high-performance visualization system for large-scale remote sensing image data of the present invention. DETAILED DESCRIPTION

[0031] The present invention is further described in detail below in conjunction with embodiments:

[0032] Example

[0033] like Figure 1 As shown, a high-performance visualization method for large-scale remote sensing image data includes:

[0034] S1. Construct a remote sensing image sky map correspondence library including sky map level, GeoSOT level and level correspondence; the sky map level includes sky map resolution, and the GeoSOT level includes GeoSOT size.

[0035] In step S1, the sky map resolution of the sky map level increases in sequence from level to level, and that of the GeoSOT level decreases in sequence from level to level, and the level correspondence is a one-to-one correspondence between the sky map level and the GeoSOT level; the highest sky map resolution of the sky map level corresponds to the lowest GeoSOT size of the GeoSOT level, and the lowest sky map resolution of the sky map level corresponds to the highest GeoSOT size of the GeoSOT level.

[0036] Assume that the map scale is 1:100 (to simplify the calculation, assume that the length and width of a pixel are 1CM each), query the viewport pixel resolution 100*100 (size is 100 pixels long and 100 pixels wide), that is, 1 pixel represents a geographical range of 100 meters long and 100 meters wide, and the viewport needs to display a geographical range of 100 pixels long and 100 meters wide = 10,000 meters. Assume that there are 4 map images with different resolutions (resolutions are 1 meter, 10 meters, 100 meters and 1000 meters respectively), that is, a pixel represents 1 meter, 10 meters, 100 meters and 1000 meters respectively. There are four ways to load imagery at this time: the first way is to obtain a resolution of 1 meter, which requires loading 10,000 meters in length and width / 1 meter = 10,000 pixels. According to the three bands of RGB, each pixel in each band can be expressed by one Byte (0-255), and the size of the converted image is: 10000*10000*3 / 1024 / 1024 = 286MB; the second way is to obtain a resolution of 10 meters, which requires loading 10,000 meters in length and width / 10 Meter = 1000 pixels, and the image size converted by the same method is: 1000*1000*3 / 1024 / 1024=2.86MB; the third method, to obtain a resolution of 100 meters, it is necessary to load 10000 meters / 100 meters = 100 pixels in length and width, and the image size converted by the same method is: 100*100*3 / 1024=29KB; the fourth method, to obtain a resolution of 1000 meters, it is necessary to load 10*10*3=300B in length and width. In other words, no matter what resolution, the front-end map (the query viewport of the present invention selects the world map) can be loaded, but it can be seen from the size of the picture that the lower the image resolution, the smaller the actual generated picture. For the front-end loading pictures through the network, in theory, the smaller the picture, the faster it loads.

[0037] Let's analyze the above 4 methods. The first method is to display 10,000 pixels in length and width in an area of ​​100 pixels in length and width, which is equivalent to displaying 100 pixels per pixel, like compressing a picture with a resolution of 10,000*10,000 to 100*100, which is very fine; the second method is equivalent to displaying 10 pixels per pixel, which will be finer; the third method does not require any compression or stretching, and highly restores the true situation of the image; the fourth method is equivalent to displaying 0.1 pixel per pixel, like stretching a 100*100 pixel picture to 1000*1000, which will show obvious granularity and appear very rough.

[0038] From the above analysis, we can see that: when the viewport size and map scale are determined, if you want fast loading, the lower the image resolution, the better; if you want good display effect, the higher the image resolution, the better. It is not difficult to draw a conclusion: the image resolution is just equal to the viewport pixel resolution for optimal performance. Visualization should still consider the display effect first. If the display is not distorted or even slightly fine, it is acceptable to load a slightly larger image.

[0039] Let's look at the effect of map scale on loading efficiency; the viewport size is set to 100 pixels in length and width, and the map scale is 1:100. Assuming the viewport size remains unchanged, the map scale becomes 1:10, which means that the map should display a geographic range of 100*10=1000 meters in length and width. The best way is to load a 10-meter resolution image, which is converted into a picture size of 1000 / 10*1000 / 10*3 / 1024=29KB. Similarly, if the scale is changed to 1:1000, the same result can be obtained. The same is true for other scales. It can be inferred that when the viewport is determined, the map scale does not affect the size of the loaded image.

[0040] Next, let's analyze the effect of viewport size on loading efficiency. The viewport size is set to 100 pixels in length and width. In reality, this viewport size is not always possible. The current mainstream screen resolution is 2K (resolution is 2560*1440), so the maximum viewport can be set to 2K. If the 2K viewport can load smoothly, other smaller viewports will not have any problems. Let's analyze the above hypothetical scenario, change the viewport size to 2K, and load it according to the third method. The range that the viewport needs to display is: 2560*100=256000 meters in length, 1440*100=144000 meters in width. The size that needs to be converted into the image is: 256000 / 100*144000 / 100*3 / 1024 / 1024=10.5MB, which is larger than the original one. In fact, the image size increases as the number of viewport pixels increases: 2560*1440 / (100*100)=10.5MB / 29KB.

[0041] That is to say, on a 2K screen, the maximum loaded image size is 10.5MB. If the bandwidth is 100Mbps, then the time required to download this image is about 10.5M / (100Mbps / 8)=0.84 seconds. Because the image is relatively large, the post-rendering (using H5 rendering) is also relatively time-consuming, about 0.1 seconds. Without considering other bandwidth occupation, the total display time is less than 1 second. The effect presented is that the interface freezes for about 1 second, and then suddenly appears. The intuitive feeling is not only slow, but also not smooth. However, corresponding optimization can be made: because different browsers have a concurrency of 4 to 6 for each domain name when loading images, a large image can be divided into multiple small images, each of which is loaded concurrently. The visual effect presented is that the small images are presented one by one in a waiting time of about 1 second, and the effect is relatively smooth. Consider dividing the large image into small square images of the same length and width:

[0042] (1) Divide the width of the small image into two parts, 1440 / 2 = 720 pixels, and the length is 2560 / 720 = rounded up to 4. A total of 2*4 = 8 images are needed, and the size of each image is 10.5MB / 8 = 1.3M.

[0043] (2) Divide the width of the image into three parts: 1440 / 3 = 480 pixels, and the length of the small image is 2560 / 480 = rounded up to 6. A total of 3*6 = 18 images are needed, and the size of each image is 10.5MB / 18 = 583KB.

[0044] (3) Divide the width of the small image into 4 parts with the width of 1440, then the length and width of the small image are 1440 / 4 = 360 pixels, and the length is 2560 / 360 = rounded up to 8. A total of 4*8 = 32 small images are needed, and the size of each image is 10.5MB / 32 = 328KB;

[0045] (4) Divide the width of 1440 into 5 parts. The length and width of the small picture are 1440 / 5 = 288 pixels. The length is 2560 / 288 = rounded up to 9. A total of 5*9 = 45 pictures are needed. The size of each picture is 10.5MB / 45 = 233KB.

[0046] …

[0047] Considering that the size of small images cannot be too large, (2), (3), and (4) meet the requirements. Considering that the number of concurrent browsers is limited (only 4-6), each concurrent loading of images cannot be too many, otherwise the sum of multiple small images will be very large and slow to load, so (2) and (3) are more in line with the requirements. Consider another situation: the resolution of images varies, and it is generally impossible to be the same as the pixel resolution corresponding to the map scale. In order to meet the display effect, images with a resolution greater than or equal to the pixel resolution will be loaded. If the image resolution is 7 meters and 12 meters, and the pixel resolution is 10 meters, then the 7-meter image is loaded. According to the above calculation method, the generated image size is: 100*10 / 7*100*10 / 7*3 / 1024=60KB. If we look at a similar situation on a 2K screen, (2) in this case, the size of a small picture can be roughly estimated to be 583*10 / 7=833KB, and (3) is estimated to be 328*10 / 7=469KB. Obviously, (3) has a larger margin and is a more desirable method.

[0048] Based on the above analysis, there is actually a corresponding relationship between viewport size, map scale, pixel resolution, image resolution, picture size, number of segmented pictures, and size of segmented pictures. Some examples are shown in Table 1 below (the viewport size is set to 2K):

[0049]

[0050]

[0051] Table 1

[0052] It can be seen that if the scale changes in intervals are not large, the image size can be controlled within a fixed range. In fact, if the scale is continuous enough, such as 1:10, 1:9, 1:8..., the image size can even be kept the same.

[0053] In fact, the zoom level setting of the map can ensure that the scale interval is within a certain range. Take Tiandi Map for example, it is divided into 24 levels from 0 to 23, with a scale ranging from 1:279541132 to 1:33. On a 2K screen, the pixel resolution ranges from 78184 meters to 0.009 meters. Some examples are shown in Table 2:

[0054]

[0055]

[0056] Table 2

[0057] Then we will examine GeoSOT and establish the relationship between it and the small image. GeoSOT is a way of segmenting images, which can be used as the segmentation method of the large image mentioned above. The goal is to select a GeoSOT segmentation level for different image resolutions, and the generated image is close to the 328KB discussed above.

[0058] By matching GeoSOT level 25 with Tiandi Map level 23, GeoSOT level 24 with Tiandi Map level 22, and GeoSOT level 23 with Tiandi Map level 21, we can get the following list 3 (partial example):

[0059]

[0060]

[0061] Table 3

[0062] Considering that the actual image resolution does not strictly correspond to the pixel resolution, the above table can be expressed in a range format (in the following table, (xy] means the value range is greater than x and less than or equal to y):

[0063]

[0064]

[0065] Table 4

[0066] Then calculate the size range of the generated image based on the range boundary (in the following table, (xy] means the value range is greater than x and less than or equal to y):

[0067]

[0068] Table 5

[0069] According to Table 5 (Table 5 is part of the remote sensing image sky map correspondence library), combined with the above discussion, under the 2K viewport, the image resolution with the same pixel resolution as the map loading has the best display effect.

[0070] S2. Collect remote sensing image data and extract the remote sensing image resolution corresponding to the world map level and the corresponding optimal GeoSOT level (for example, the remote sensing image resolution is 0.01 meters, which falls within the range expressed by the 22nd level of the world map, and the corresponding geosot level is 24). Slice the remote sensing image data according to the grid of the optimal GeoSOT level and store it as optimal level slice data.

[0071] In some embodiments, the sky map resolution of the sky map level is adapted according to the extracted remote sensing image resolution, and the sky map level is adapted, and the corresponding GeoSOT level is found as the optimal GeoSOT level based on the remote sensing image sky map correspondence library. Mask extraction slicing and grid coding processing are performed based on the grid of the optimal GeoSOT level to obtain the optimal level slice data containing grid coding information.

[0072] S3. According to the GeoSOT level, grid slicing and downsampling are performed step by step starting from the optimal GeoSOT level (to facilitate the generation of low-level slice data, until level 0 in Table 5), and slice data of each GeoSOT level is obtained in turn. The downsampling rate of this embodiment is 0.5, and the length and width resolution becomes 1 / 2 of the original. Through such a downsampling operation, not only a new resolution is generated, but also the generated slice size and size are guaranteed to be consistent with the original: for example, the original slice resolution is 100*100, and the merged resolution is 200*200. The resolution remains unchanged, the size becomes twice the original, and the size also becomes twice the original. At this time, a downsampling operation of 0.5 is performed to change the resolution to 100*100, keeping the size and size unchanged. Then, a lower-level slice is generated based on the generated first-level slice, which is consistent with the above method. This cycle is repeated until level 0. The remote sensing image that loads the pixel resolution consistent with the present invention is optimal (the optimal GeoSOT level has been obtained), such as Figure 3 As shown, this embodiment constructs a pyramid. When the map scale changes, it is necessary to be able to provide a remote sensing image of the corresponding resolution. If only one level is divided according to the first step, it can only correspond to one resolution, which cannot meet the demand. First, generate a slice of a lower level: the main process is to merge the original slices and downsample, and then generate a lower level slice based on the generated first-level slice, and so on. The merging method is roughly to merge 4 adjacent slices up, down, left and right into 1. According to the rules of geosot coding, only those with the same prefix can be merged. Each level of geosot coding has a fixed number of bits.

[0073] In some embodiments, the present invention further comprises the following method:

[0074] The optimal level slice data and the slice data of each GeoSOT level are processed by GeoSOT grid coding according to the grid. Figure 2 As shown in the figure, GeoSOT coding rules: only the same prefix can be merged, and each level of GeoSOT coding has a fixed number of bits: the longest code is 32 levels, divided into four sections, namely 9-bit degree grid code, 6-bit sub-grid code, 6-bit second grid code and 11-bit sub-second grid code. As shown below:

[0075] dddddddd-mmmmmm-ssssss.uuuuuuuuuuu

[0076] 9-digit degree code 6-digit grade code 6-digit second code 11-digit code below second

[0077] A further preferred embodiment of step S3 is to increase the GeoSOT size by one level based on the GeoSOT level being larger than the optimal GeoSOT level (as shown in Table 5, increasing the GeoSOT size by one level corresponds to reducing the number of geosot levels, that is, the direction of the number of geosot levels from 25 to 2 is the direction of increasing the GeoSOT size by one level), and perform mask extraction slicing, grid coding, and downsampling processing based on the grid of the increased GeoSOT level to obtain slice data containing grid coding information and the increased GeoSOT level; and obtain slice data of each GeoSOT level by increasing the GeoSOT level step by step.

[0078] In order to meet more diverse data requirements, step S3 of this embodiment further includes the following method:

[0079] Based on the GeoSOT level, the GeoSOT size is reduced by one level compared to the optimal GeoSOT level (for example, the remote sensing image resolution is 0.01 meters, and the 22nd level of Tiandi Map corresponds to the optimal GeoSOT level of 24. Through mask extraction of slices, grid coding, and downsampling processing, slice data of levels 23 to 2 can be obtained; this embodiment reduces the GeoSOT size by one level from the optimal GeoSOT level, that is, there are only 25 levels. In order to enrich the data, slice data of the 25th level GeoSOT level is also generated, and the 25th level slice data is obtained through mask extraction of slices, grid coding, and upsampling processing), mask extraction of slices, grid coding, and upsampling processing are performed based on the grid of the GeoSOT level reduced by one level to obtain slice data containing grid coding information and the GeoSOT level reduced by one level; the GeoSOT level is reduced step by step to obtain slice data of the remaining GeoSOT levels.

[0080] The present invention adopts a distributed file system to perform distributed storage of all slice data, and subsequent screening and rendering all adopt distributed data retrieval and rendering operations.

[0081] S4. Based on the query viewport (which can obtain the zoom level and viewport range of the current map), the selected sky and earth map level filters all slice data of the corresponding GeoSOT level and renders to generate a visualization image (the visualization service informs the map of the image information to be generated while generating the image, so the image generation and loading are carried out at the same time; the map calculates the position of each image based on the position of the starting image and the GeoSOT encoding information of each image, and renders it on the map).

[0082] In some embodiments, Figure 4 As shown, all slice data at the GeoSOT level are filtered out and the data content is extracted for standardization and color rendering. Color matching can be performed according to unique values, multiple color bands (color bands can be divided by value or by percentage) and RGB true colors to generate rendered visualization images.

[0083] like Figure 5 As shown, a high-performance visualization system for large-scale remote sensing image data includes a data acquisition module, a slice code storage system and a query viewport system. The slice code storage system has a remote sensing image world map correspondence library, which includes a world map hierarchy, a GeoSOT hierarchy and hierarchy correspondences. The world map hierarchy includes a world map resolution, and the GeoSOT hierarchy includes a GeoSOT size. The data acquisition module is used to collect remote sensing image data and input it into the slice code storage system. The slice code storage system extracts the remote sensing image resolution of the remote sensing image data and adapts the world map to the remote sensing image resolution. The query viewport system comprises a filtering module and a rendering module, wherein the filtering module is used to select a sky and earth map level and correspondingly filter all the slice data of the GeoSOT level and input them into the rendering module, and the rendering module uses all the slice data to render and generate a visual image.

[0084] An electronic device comprises: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the steps of the high-performance visualization method for large-scale remote sensing image data of the present invention.

[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 and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A high-performance visualization method for large-scale remote sensing image data, characterized by: The methods include: S1. Construct a remote sensing image sky map correspondence library including sky map level, GeoSOT level and level correspondence; the sky map level includes sky map resolution, and the GeoSOT level includes GeoSOT size; S2, collect remote sensing image data, extract remote sensing image resolution corresponding to the adaptation of the sky and earth map level and the corresponding optimal GeoSOT level, slice the remote sensing image data according to the grid of the optimal GeoSOT level and store it as optimal level slice data; S3, starting from the optimal GeoSOT level, grid slicing and downsampling are performed step by step according to the GeoSOT level, and slice data of each GeoSOT level is obtained in turn; S4. Based on the selected sky and earth layer of the query viewport, all slice data corresponding to the GeoSOT layer are filtered and rendered to generate a visualization image.

2. The high-performance visualization method for large-scale remote sensing image data according to claim 1, characterized in that: Step S3 also includes the following method: The optimal level slice data and the slice data of each GeoSOT level are GeoSOT grid coded according to the grid.

3. The high-performance visualization method for large-scale remote sensing image data according to claim 1, characterized in that: In step S1, the sky map resolution of the sky map level increases in sequence from level to level, and that of the GeoSOT level decreases in sequence from level to level, and the level correspondence is a one-to-one correspondence between the sky map level and the GeoSOT level; the highest sky map resolution of the sky map level corresponds to the lowest GeoSOT size of the GeoSOT level, and the lowest sky map resolution of the sky map level corresponds to the highest GeoSOT size of the GeoSOT level.

4. The high-performance visualization method for large-scale remote sensing image data according to claim 1, characterized in that: In step S2, the sky map resolution of the sky map level is adapted according to the extracted remote sensing image resolution, and the sky map level is adapted. Based on the remote sensing image sky map correspondence library, the corresponding GeoSOT level is found as the optimal GeoSOT level; mask extraction slicing and grid coding processing are performed based on the grid of the optimal GeoSOT level to obtain the optimal level slice data containing grid coding information.

5. The high-performance visualization method for large-scale remote sensing image data according to claim 1, characterized in that: In step S3, the GeoSOT size is increased by one level based on the GeoSOT level being larger than the optimal GeoSOT level, and mask extraction slicing, grid coding, and downsampling processing are performed based on the grid of the increased GeoSOT level to obtain slice data containing grid coding information and the increased GeoSOT level; the GeoSOT level is increased step by step to obtain slice data of each GeoSOT level.

6. The high-performance visualization method for large-scale remote sensing image data according to claim 5, characterized in that: Step S3 also includes the following method: Based on the GeoSOT level, the GeoSOT size is reduced by one level compared to the optimal GeoSOT level. Mask extraction slicing, grid encoding, and upsampling are performed based on the grid of the reduced GeoSOT level to obtain slice data containing grid coding information and reduced GeoSOT level; the GeoSOT level is reduced step by step to obtain slice data of the remaining GeoSOT levels.

7. The high-performance visualization method for large-scale remote sensing image data according to claim 6, characterized in that: A distributed file system is used to store all slice data in a distributed manner.

8. The high-performance visualization method for large-scale remote sensing image data according to claim 1, characterized in that: In step S4, all slice data at the GeoSOT level are screened and the data content is extracted for standardization and color rendering to generate a rendered visualization image.

9. A high-performance visualization system for large-scale remote sensing image data, characterized by: The invention comprises a data acquisition module, a slice code storage system and a query viewport system. The slice code storage system has a remote sensing image world map correspondence library, which includes a world map level, a GeoSOT level and a level correspondence. The world map level includes a world map resolution, and the GeoSOT level includes a GeoSOT size. The data acquisition module is used to collect remote sensing image data and input it into the slice code storage system. The slice code storage system extracts the remote sensing image resolution of the remote sensing image data, corresponds to the world map level and the corresponding optimal GeoSOT level, slices the remote sensing image data according to the grid of the optimal GeoSOT level and stores it as optimal level slice data. The slice code storage system performs grid slicing and downsampling processing step by step according to the GeoSOT level starting from the optimal GeoSOT level, and obtains slice data of each GeoSOT level in turn. The query viewport system comprises a screening module and a rendering module. The screening module is used to select the world map level and correspondingly screen all slice data of the GeoSOT level and input them into the rendering module. The rendering module uses all slice data to render and generate a visual image.

10. An electronic device, characterized in that: include: at least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of any method described in claims 1-8.

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