CN107767324A - A kind of large-scale remote sensing images fast cache method - Google Patents
A kind of large-scale remote sensing images fast cache method Download PDFInfo
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Abstract
The invention discloses a kind of large-scale remote sensing images fast cache method, belong to remote sensing images visualization field, it is mainly directed towards large-scale remote sensing images, provide a kind of remote sensing image data caching method based on memory block, remote sensing images are subjected to hierarchical block storage with different resolution in internal memory, establish the layer of correspondence image block, OK, row linking relationship, carry out the dynamic dispatching of memory block, so as to significantly improve the loading of large-scale remote sensing images and surfing, read in bottom data and memory cache layer is added among rendering pipeline, reduce disk reading frequency, the reading speed of internal memory is about 100 times of disk.So after memory cache mechanism is increased, system can be greatly improved to the data reading performance using redundancy cached.Reduce frequently memory fragmentation, lifting system performance caused by application internal memory.Memory leak issue can be avoided using internal memory cache region application and releasing memory.
Description
Technical Field
The invention belongs to the field of remote sensing image visualization, mainly aims at large remote sensing images, and relates to a method for quickly caching the large remote sensing images.
Background
With the rapid development of sensor technology and computer science technology, the ground resolution of the obtained remote sensing image is higher and higher, and the data volume is larger and larger. Therefore, the rapid reading, displaying and browsing of the large remote sensing image becomes an important function necessary for the professional processing software of the remote sensing image at present. When a single remote sensing image file is larger than 2GB, the traditional sampling method cannot apply for a memory space with the size exceeding 2GB, and cannot provide a pointer of the file. The Lujing and the Huweiloy adopt a memory mapping file technology to realize the rapid reading and display of mass images. The DuanLique converts different image formats into an intermediate image format, blocks the intermediate image, and stores image sequences with different resolutions in the blocked image by adopting an intermediate image sequence technology (namely a multi-resolution pyramid technology). However, the work of converting different image formats into intermediate image formats is very heavy, so that the file formats of different images are known clearly, and the reading and writing operations of different image files are very burdensome. GDAL (geographic Data Abstraction Library) supports various common image file formats, and can dynamically establish an image pyramid and arbitrarily read Data of a specified image block, so that design and development of image processing and analysis software are based on GDAL for a long time. The technology of the patent is based on GDAL data reading, an image fast cache area is established, remote sensing images are processed in blocks, the application requirement of large data volume can be met by adopting an image data dynamic scheduling method, the utilization efficiency of a system memory and the loading speed of the remote sensing images in the roaming and zooming processes are improved, and the fast reading and browsing display of the remote sensing large images are realized.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for quickly reading and displaying the remote sensing data of the large file based on the memory cache, and solves the problems that a large amount of work is consumed for the intermediate format file and the system is blocked and the user experience is poor due to the fact that the disk is frequently read at the bottom layer of the system in the prior art.
The invention adopts the following technical scheme to solve the technical problems
A large remote sensing image fast caching method specifically comprises the following steps:
step 1, setting the total number of layers of a remote sensing image fast cache region as N, performing roaming operation on a remote sensing image on an nth layer, wherein N is less than or equal to N, and both N and N are positive integers, judging whether the remote sensing image on the nth layer is in the fast cache region, if so, reading and displaying the remote sensing image on the nth layer, and finishing reading the remote sensing image; otherwise, performing the step 2;
step 2, judging whether the remote sensing image on the nth layer has a corresponding image block in a layer (n-1, 0) below the fast cache region: if the corresponding image blocks exist, the image block closest to the nth layer is found, the up-sampling processing is carried out in the memory fast cache region, the image block data of the image block is obtained and displayed, and the image block is added into the memory cache region; if the memory block data exceeds the set memory upper limit, removing the image blocks which are not frequently used or are positioned at the tail of the cache area queue until enough space is available for accommodating newly added image block data, and finishing reading the image blocks; if the image block does not correspond to the image block, performing step 3;
step 3, judging whether the remote sensing image on the nth layer exists in the (n +1, N-1) layer of the fast cache region, if the remote sensing image on the nth layer has the corresponding image block, finding out the block in the layer closest to the layer n, performing downsampling processing in the fast cache region to obtain the image block data of the image block, and performing step 4 after displaying the image block; if no corresponding image block exists, directly performing the step 4;
and step 4, reading image block data from the disk file, adding the image block data into a memory cache region, removing partial image block data if the memory upper limit is exceeded until enough space is available for accommodating the newly added image block data, and then displaying an image of the image block data.
As a further preferable scheme of the method for quickly caching the large remote sensing image, in step 1, the roaming operation includes translation, enlargement and reduction.
As a further preferable scheme of the method for quickly caching the large remote sensing images, in step 2, an LRU cache elimination algorithm is executed to remove image blocks which are not frequently used or are located at the tail of a cache area queue.
As a further preferable scheme of the method for rapidly caching the large remote sensing image, in step 2, the data stream of the memory cache region layer is divided into 4 layers:
a data reading layer: and reading data to obtain the original data of the remote sensing image from the magnetic disk.
A data caching layer: when data is read, an original image is subjected to blocking processing, and is divided into 256-by-256 image blocks, and the image blocks are organized in the forms of layers, rows and columns and are placed in a memory cache region.
A data scheduling layer: searching the image block in the memory cache region, and if the image block is hit, directly obtaining the image block from the memory cache region; if not, reading from the disk and adding into the memory cache region. After a certain image block is read out, the image block is added into the memory cache region, and can be directly obtained from the memory cache region when being used again.
Visual rendering layer: rendering visualization operation of the original data is carried out, and operations such as roaming, zooming and the like of the remote sensing image are realized.
As a further preferred scheme of the large remote sensing image fast caching method, the remote sensing image layering and blocking principle is as follows:
step a, setting an image block division sequence: from the top left corner, from left to right, from top to bottom;
step b, setting the width and height of the source image as W s And H s Object displayWidth and height of image W d And H d And each image block has a size W tile *H tile ，W tile And H tile Decreasing by a factor of 2 as the level Lod increases, thereby forming images at different resolutions;
wherein, the block number of any point (x, y) on the image formed under different resolutions is:
then the level of the target display image is:
wherein r is xSize The pixel width of a grid wave band of a source image;
block range of current level:
the pixel range in the image line direction is r start ～r end ：
Pixel range in image column direction is c start ～c end ：
Wherein s is left ,s top Starting values of pixels of the source data window at the leftmost side and the uppermost side, B x ,B y The length and width of the block are 256 tile And H tile Take 256 pixels.
As a further preferred scheme of the method for quickly caching the large remote sensing images, the LRU cache elimination algorithm comprises the following specific steps:
(1) Assuming that the memory size is 4GB, the upper limit is set to 3GB, and the size of each image block is 64KB;
(2) When a new image block is read, adding the new image block into a memory cache region;
(3) When the image block of the cache region is accessed, recording the number of times of access N, wherein N is less than or equal to K, and if the number of access is less than the maximum number of times of access K, moving the image block to the head of the cache region; when the cache region reaches the upper limit of the memory, discarding the image block at the tail part of the cache region;
(4) When the number of times of accessing the image blocks in the cache region reaches K, wherein 1 & ltK & gt is less than or equal to 3 times, deleting the image block index from the memory cache region, moving the image blocks into a cache queue, caching the image blocks, and reordering the image blocks in the cache region according to time;
(5) And when the data queue of the buffer area is accessed again, reordering.
As a further preferable scheme of the large remote sensing image fast caching method, the data reading layer adopts GDAL drive to read data.
As a further preferable scheme of the rapid cache method for the large remote sensing image, the data cache layer divides the original data of the remote sensing image into blocks and tiled tissues when the data is read, and divides the original data into 256 × 256 image blocks.
As a further preferable scheme of the large remote sensing image fast caching method, the data caching layer organizes image blocks in the form of wave bands, layers, rows and columns and stores the image blocks in the memory caching area.
As a further preferable scheme of the large remote sensing image fast caching method of the invention, in the step b, the size of each image block is W tile *H tile And W tile And H tile Take 256 pixels.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
(1) A memory cache layer is added between the bottom data reading pipeline and the rendering pipeline, so that the disk reading frequency is reduced, and the reading speed of a memory is about 100 times that of the disk, so that the reading efficiency of the cached data can be greatly improved by a system after a memory cache mechanism is added;
(2) Memory fragments caused by frequent memory application are reduced, and system performance is improved;
(3) The problem of memory leakage can be avoided by applying and releasing the memory by using the memory cache region.
Drawings
FIG. 1 is a block diagram of a method for rapidly caching remote sensing images;
FIG. 2 is a remote sensing image blocking strategy;
FIG. 3 is a pyramid model of a remote sensing image;
FIG. 4 is a diagram of remote sensing image memory cache scheduling technique;
fig. 5 is a flow chart of an implementation mechanism.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
the first embodiment is as follows:
the technical scheme is described in detail with reference to the accompanying drawings and the detailed description.
FIG. 1 is a main frame diagram of the method. The whole body is divided into four layers. The first layer is data reading based on GDAL drive, and original data of remote sensing images are obtained from a magnetic disk; the second layer is a fast cache layer, read data are processed in a blocking mode, the data blocks are organized in the modes of wave bands, layers, rows and columns, and a memory cache region is established; the third layer is a scheduling layer, and data blocks of corresponding images are acquired according to the resolution of the current window to carry out organization scheduling (replacement or addition); and the fourth layer is a rendering layer, and data in the cache region in the resolution ratio of the current window is rendered and visualized.
FIG. 2 is a block strategy diagram of a remote sensing image. Since the visible area is generally smaller than the whole range of the image when the image is displayed, only the block data of the visible area needs to be read. The method adopts a fixed-size blocking mode, the size of each block is fixed to tileWidth tileHeight (256 pixels by 256 pixels), and each block is identified.
FIG. 3 is a remote sensing image pyramid composition. Based on the image resolution and the window resolution, an image resampling method is adopted to sample a low-level image into an upper-level image, and the higher the Lod (Levels of detail) level is, the smaller the image is, and the lower the resolution is.
FIG. 4 is a strategy diagram for remote sensing image block scheduling. Firstly, reading image data (data cache nodes) by using a GDAL driver, adding the image data into a memory cache area pool, and recording related information of an image block; when the data of the cache region is read, whether the image block exists in the cache region or not is judged, and whether visual display or file reading is carried out or not is further judged.
Fig. 5 is a flow chart of the mechanism for implementing the method. In the data reading process, whether a corresponding memory cache region exists is checked in a manager which opens the current remote sensing image; if not, a corresponding memory cache region is created, if yes, the layer row and the line of the image corresponding to the display range are calculated, and whether the corresponding image block exists or not is searched in the memory cache region; if yes, directly taking out the image block, if not, reading the corresponding image block from the magnetic disk and adding the image block into a memory cache area; then, splicing and cutting the taken image blocks in a memory to obtain a data area to be displayed; and finally, performing down-sampling processing to obtain a final image to be displayed.
Example two:
the technical solution of the present invention and the scientific principles underlying it are explained in detail below.
1. The remote sensing image layering and blocking principle is as follows:
(1) It is assumed that the image block division order starts from the upper left corner, goes from left to right, and goes from top to bottom.
(2) Assume the source image has a width and height W s And H s The width and height of the target display image is W d And H d And each image block has a size W tile *H tile (W tile And H tile Typically 256 pixels or 512 pixels), W tile And H tile Decreasing by a factor of 2 as the level Lod increases, thereby forming images at different resolutions. As shown in fig. 2, the block number of any point (x, y) on the image is:
then the level of the target image is:
wherein r is xSize The pixel width of the grid band of the source image.
Block range of current level:
the pixel range in the image line direction is r start ～r end ：
Pixel range in image column direction is c start ～c end ：
Wherein s is left ,s top Starting values for the pixels of the source data window at the leftmost and uppermost sides, B x ,B y The values taken in this method are 256 for the length and width of the block.
2. The cache elimination principle is as follows:
(1) Assuming a memory size of 4GB, an upper limit is set to 3GB, and each block size is 64KB.
(2) When a new image block is read, it is added to the memory cache area.
(3) When an image block in the cache region is accessed, recording the number of times of access N (N is less than or equal to K), and if the access is less than the maximum number of times of access K (1-to-K is less than or equal to 3), moving the image block to the head of the cache region; and when the cache region reaches the upper limit of the memory, discarding the image block at the tail part of the cache region.
(4) And when the number of times of accessing the image blocks in the cache region reaches K times, deleting the image block index from the memory cache region, moving the image blocks to a cache queue, caching the image blocks, and reordering the image blocks in the cache region according to time.
(5) And when the data queue of the buffer area is accessed again, reordering.
(6) And when the image blocks need to be eliminated, eliminating the image blocks arranged at the tail in the cache area queue. Namely: the image block that is "the last kth visit longest now" is eliminated.
Example three:
the method for quickly caching the large remote sensing image comprises the following specific implementation steps:
(1) And assuming that the total number of the layers of the remote sensing image pyramid is N, and performing roaming operation on the image on the nth layer.
(2) Judging whether the image block has a corresponding image block in the layer [ n-1,0] below the fast cache region, if so, finding the image block closest to the layer of the layer n, performing up-sampling processing in a memory to obtain the block data, displaying the block image, adding the block image into the memory cache region, if the set upper limit of the memory is exceeded, executing an LRU (Least recent Used) cache elimination algorithm to remove some image blocks which are not frequently Used or are positioned at the tail of the cache region until enough space is available for accommodating newly added image block data, and finishing reading the block; and (4) if no corresponding block exists, performing the step (3).
(3) If the block exists in the [ n +1, N-1] layer of the fast cache region, if the corresponding block exists, finding the block in the layer closest to the layer n, and performing down-sampling processing in the memory to obtain the block data (realizing the gradual change effect, the amplified block data is not added into the memory cache region, but is read from the file and then added into the memory cache region), and performing the step (4) after displaying the block image; and (4) if no corresponding block exists, directly performing the step (4).
(4) Reading block data from a disk file, adding the image blocks into a memory cache region, if the upper limit of the memory is exceeded, executing an LRU cache elimination algorithm to remove some image blocks until enough space is available for accommodating the newly added image block data, then displaying the image of the block, and ending the block reading process.
Claims (10)
1. A large remote sensing image fast caching method is characterized in that: the method specifically comprises the following steps:
step 1, setting the total number of layers of a remote sensing image fast cache region as N, performing roaming operation on a remote sensing image on the nth layer, wherein N is less than or equal to N, and both N and N are positive integers, judging whether the remote sensing image on the nth layer is in the fast cache region, if so, reading and displaying the remote sensing image on the nth layer, and finishing reading the remote sensing image; otherwise, performing the step 2;
step 2, judging whether the remote sensing image on the nth layer has a corresponding image block in a layer (n-1, 0) below the fast cache region: if the corresponding image blocks exist, the image block closest to the nth layer is found, the up-sampling processing is carried out in the memory fast cache region, the image block data of the image block is obtained and displayed, and the image block is added into the memory cache region; if the memory upper limit is exceeded, removing the image blocks which are not frequently used or are positioned at the tail of the cache area queue until enough space is available for accommodating newly added image block data, and finishing reading the image blocks; if the image block does not correspond to the image block, performing step 3;
step 3, judging whether the remote sensing image on the nth layer exists in a layer (n +1, N-1) of a fast cache region, if the remote sensing image on the nth layer has a corresponding image block, finding out a block in a layer closest to the layer n, performing downsampling processing in the fast cache region to obtain image block data of the image block, and performing step 4 after displaying the image block; if no corresponding image block exists, directly performing the step 4;
and step 4, reading image block data from the disk file, adding the image block data into a memory cache region, removing partial image block data if the memory upper limit is exceeded until enough space is available for accommodating the newly added image block data, and then displaying an image of the image block data.
2. The large remote sensing image fast caching method according to claim 1, characterized in that: in step 1, the roaming operation includes panning, zooming in, and zooming out.
3. The large remote sensing image fast caching method according to claim 1, characterized in that: in step 2, an LRU cache eviction algorithm is executed to remove image blocks that are not frequently used or are located at the tail of the cache line.
4. The large remote sensing image fast caching method according to claim 1, characterized in that: in step 2, the data stream of the memory cache region layer is divided into 4 layers:
a data reading layer: and reading data to obtain the original data of the remote sensing image from the magnetic disk.
A data caching layer: when data is read, an original image is subjected to blocking processing, and is divided into 256-by-256 image blocks, and the image blocks are organized in the forms of layers, rows and columns and are placed in a memory cache region.
A data scheduling layer: searching the image block in the memory cache region, and if the image block is hit, directly obtaining the image block from the memory cache region; if not, reading from the disk and adding into the memory buffer zone. After a certain image block is read out, the image block is added into the memory cache region, and can be directly obtained from the memory cache region when being used again.
Visually rendering the layers: rendering visualization operation of the original data is carried out, and operations such as roaming, zooming and the like of the remote sensing image are realized.
5. The large remote sensing image fast caching method according to claim 1, characterized in that: the remote sensing image layering and blocking principle is as follows:
step a, setting an image block division sequence: from the top left corner, from left to right, from top to bottom;
step b, setting the width and height of the source image as W s And H s The width and height of the target display image is W d And H d And each image block has a size W tile *H tile ，W tile And H tile Decreasing by a factor of 2 as the level Lod increases, thereby forming images at different resolutions;
wherein, the block number of any point (x, y) on the image formed under different resolutions is:
then the level of the target display image is:
wherein r is xSize The pixel width of a grid wave band of a source image;
block range of current level:
the pixel range in the image line direction is r start ～r end ：
Pixel range in image column direction is c start ～c end ：
Wherein s is left ,s top Starting values for the pixels of the source data window at the leftmost and uppermost sides, B x ,B y The length and width of the block are 256 tile And H tile Take 256 pixels.
6. The large remote sensing image fast caching method according to claim 2, characterized in that: the LRU cache elimination algorithm comprises the following specific steps:
(1) Assuming that the memory size is 4GB, the upper limit is set to 3GB, and the size of each image block is 64KB;
(2) When a new image block is read, adding the new image block into a memory cache region;
(3) When the image block of the cache region is accessed, recording the number of times of access N, wherein N is less than or equal to K, and if the number of access is less than the maximum number of times of access K, moving the image block to the head of the cache region; when the cache region reaches the upper limit of the memory, discarding the image block at the tail part of the cache region;
(4) When the number of times of accessing the image blocks in the cache region reaches K, wherein 1 & ltk & gt K is less than or equal to 3 times, deleting the image block index from the memory cache region, moving the image blocks into a cache queue, caching the image blocks, and sequencing the image blocks in the cache region again according to time;
(5) And when the data queue of the buffer area is accessed again, reordering.
7. The large remote sensing image fast caching method according to claim 3, characterized in that: the data reading layer adopts GDAL drive to read data.
8. The large remote sensing image fast caching method according to claim 3, wherein the large remote sensing image fast caching method comprises the following steps: the data cache layer divides original data of the remote sensing image into blocks and tiles during data reading, and divides the original data into 256 × 256 image blocks.
9. The large remote sensing image fast caching method according to claim 3, characterized in that: the data cache layer organizes the image blocks in the form of wave bands, layers, rows and columns and stores the image blocks into a memory cache region.
10. The large remote sensing image fast caching method according to claim 4, characterized in that: in step b, the size of each image block is W tile *H tile And W tile And H tile Take 256 pixels.
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| CN112233011A (en) * | 2020-10-22 | 2021-01-15 | 中国科学院空天信息创新研究院 | Memory organization and processing method and device for remote sensing image |
| CN112965826A (en) * | 2021-04-07 | 2021-06-15 | 中国科学院空天信息创新研究院 | Large-size satellite remote sensing image display scheduling method |
| CN115482146A (en) * | 2022-08-31 | 2022-12-16 | 北京四维远见信息技术有限公司 | Method, device, equipment and storage medium for automatic cross-image pair roaming of stereoscopic image |
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