CN110855638A - Remote sensing satellite data decompression processing system and method based on cloud computing - Google Patents
Remote sensing satellite data decompression processing system and method based on cloud computing Download PDFInfo
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Abstract
A remote sensing satellite data decompression processing system based on cloud computing comprises an input format component, a block reading component, a data dividing component, a decompression Mapper component, a decompression Shuffle component, a decompression Reducer component, an original data decompression component and an image splicing component; the method comprises the steps of performing parallel granularity division on original data of each sensor image according to a remote sensing data division description file and a data compression frame format, so that the original data can be processed in parallel; and performing high-speed parallel decompression processing on the segmented original data by adopting the optimized Map-Reduce calculation model, wherein the image data of one sensor can be subjected to parallel decompression processing by a plurality of nodes, and the data decompression, the image data sorting and the image data splicing are sequentially completed to form complete image data.
Description
Technical Field
The invention relates to a system and a method for decompressing and processing original data of a satellite remote sensing image, belongs to the field of remote sensing data processing, and is suitable for remote sensing satellite image processing.
Background
The original data downloaded from the satellite are packed into compressed code streams with an agreed format, and the ground must be provided with a de-format decompression device to complete corresponding inverse processing so as to recover the original image data.
At present, the original data decompression of the domestic remote sensing satellite mainly adopts a single-node single-step processing mode, and each special node is responsible for the decompression of data of one image virtual channel. From data acquisition, the original data is decompressed through the steps of frame synchronization, descrambling, format analysis, data segmentation, decompression, image splicing, data playback and the like. With the development of the remote sensing satellite technology in China, the satellite data transmission code rate and the performance of the satellite-borne compression encoder are both greatly improved. The single-node single-step flow is used for processing the high-speed data transmission code stream, the operation amount is extremely large, the single node is easy to cause system problems, and the reliability and the efficiency are extremely low. In order to ensure the reliability of data decompression, the special node can only reduce the decompression rate, cannot meet the real-time acquisition requirement of a user on the remote sensing image, and increases the system maintenance cost.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the invention adopts a cloud computing architecture, overcomes the performance bottleneck problem of a single node mode, provides a remote sensing satellite data decompression system and method based on cloud computing, and solves the problems of poor timeliness and low reliability of the current remote sensing satellite data decompression processing. The method has huge performance and reliability advantages in the aspect of mass remote sensing data decompression, and can efficiently and reliably complete the decompression processing of the whole-orbit dual-channel original data without speed reduction decompression; meanwhile, the performance of the system can be further improved by adding cloud computing nodes, and the system has high expansibility.
The technical scheme adopted by the invention is as follows: a remote sensing satellite data decompression processing system based on cloud computing comprises an input format component, a block reading component, a data dividing component, a decompression Mapper component, a decompression Shuffle component, a decompression Reducer component, an original data decompression component and an image splicing component;
an input format component: according to the set size and position of original data blocks, the original data is cut into block data, a block reading component is called, key, value pairs of the original block data key values which can be processed by a decompression Mapper component are generated and distributed to Map processing nodes;
a data partitioning component: recording the size and the position of each original block data;
a block reading component: generating a key value and a value according to the original block data, and combining the key value pair < key, value > of the original block data;
decompression Mapper component: receiving key value pairs of original block data, performing data extraction operation, calling an original data decompression component, decompressing the original block data to generate image block data, and reorganizing the image block data into a key value pair (key, value) form;
decompression Shuffle component: receiving the image block data key value pair, sorting according to the image frame number and the intra-frame block number, and outputting to a decompression Reducer component;
decompression Reducer component: receiving the sequenced image block data, calling an image splicing component to splice the image data, recovering auxiliary data and verifying the data;
raw data decompression component: performing data decompression by taking the original block data as a unit, and outputting decompressed data;
the image splicing component: and sequencing the image blocks and the auxiliary data blocks with the same number according to the block numbers, splicing, and finally forming the image data and the auxiliary data which conform to the satellite data format specification.
A remote sensing satellite data decompression processing method based on cloud computing comprises the following steps:
(1) determining the input data block length for parallel calculation according to the original data format characteristics of each remote sensing satellite, calling a data division component, and generating an original data division information description file;
(2) according to an input original data segmentation information description file, calling an input format component, a block reading component and a data partitioning component, partitioning original data, partitioning the original data into a set of key value pairs (key, value) of the original block data, and distributing data nodes for storing each original block data;
(3) the Hadoop platform calculates each original block data key value pair distributed to the optimal Map processing node according to the load of each Map node and the storage position of the original block data;
(4) each decompression Mapper component processes key value pairs of original block data, performs data extraction operation, calls an original data decompression component at the same time, decompresses the original block data to generate image block data, recombines the image block data into a form of key value pairs of the image block data, and transmits processing results to a decompression Shuffle component in a data stream mode; if the node has an abnormal error, scheduling to other nodes for recalculation;
(5) the decompression Shuffle component receives the data stream, sorts the key value pairs according to the image amplitude numbers and the intra-amplitude block numbers, and transmits the key value pairs to each decompression Reducer component in a data stream mode;
(6) and each decompression Reducer component sorts the image blocks and the auxiliary data blocks with the same number according to the block numbers, splices the image data and the auxiliary data with the same number, simultaneously checks the image data and the auxiliary data, and finally generates complete image data and auxiliary data of each type of sensor of the remote sensing satellite.
The structure of the segmentation information description file is as follows:
<split>
< id > Block number </id >
< start > Start coordinate </start >
< end > end coordinate </end >
</split>
Wherein id is a data block sequence number, start is a data block start byte value, and end is a data block end byte value.
The input format component: according to the set size and position of the original data block, the original data is cut into block data, a block reading component is called, and key value pairs < key, value > of the original block data which can be processed by the decompression Mapper component are generated and distributed to each Map processing node.
The data partitioning component: and recording the size and the position of each original block data.
The block reading component: and generating a key value and a value according to the original block data, and combining the key value pair < key, value > of the original block data.
Compared with the prior art, the invention has the advantages that:
(1) according to the invention, the cloud computing technology is utilized to divide the large-scale remote sensing satellite original data into data blocks which can be processed in parallel, and high-speed parallel decompression processing is carried out.
(2) According to the invention, each data block is distributed to different server nodes of the cloud computing cluster for decompression processing, so that the calculation amount of each server node is greatly reduced, and the stability and reliability of the system are greatly improved.
(3) The invention can improve the system performance by increasing the cloud computing cluster server nodes, and has higher expansibility. Through the test of a plurality of remote sensing satellite data, the decompression performance of the algorithm can completely meet the requirements of massive original data of the remote sensing satellite in the future on decompression timeliness and stability.
(4) The method comprises the steps of performing parallel granularity division on original data of each sensor image according to a remote sensing data division description file and a data compression frame format, so that the original data can be processed in parallel; and performing high-speed parallel decompression processing on the segmented original data by adopting the optimized Map-Reduce calculation model, wherein the image data of one sensor can be subjected to parallel decompression processing by a plurality of nodes, and the data decompression, the image data sorting and the image data splicing are sequentially completed to form complete image data.
Drawings
Fig. 1 is a block diagram of a high-performance decompression algorithm based on cloud computing;
fig. 2 is a flow chart of a remote sensing satellite decompression method based on cloud computing.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The concept to which the present invention relates is defined first.
Cloud computing: the core idea of cloud computing is 'divide-and-conquer', mass data are subdivided into M parts, the M parts are mapped to M nodes for computing, iterative operation of the data is carried out in the period, intermediate data are collected to N nodes for computing final results, and large-scale data are utilized to accelerate program operation in parallel.
MapReduce model: MapReduce is a programming model for parallel computation of large-scale datasets. The model firstly appoints a Map function, maps an input key value pair set into a group of new key value pair sets, transfers the value sets with the same key value to a Reduce function, the Reduce function merges the value values, and the model finally outputs a merged and simplified key value pair set.
Decompression: original image data are decoded and restored from original data with formats downloaded from a remote sensing satellite through a certain algorithm.
Aiming at the characteristics of original data of a remote sensing satellite and a MapReduce cloud computing model, a bottom layer framework of MapReduce is redesigned to form a high-performance decompression algorithm module based on cloud computing, as shown in figure 1, the algorithm module is independent of satellite models and data types, can support block processing of data of multiple types of satellites and multiple frame formats, and is high in data processing speed and high in stability.
The remote sensing satellite data decompression processing system based on cloud computing comprises an input format component, a block reading component, a data dividing component, a decompression Mapper component, a decompression Shuffle component, a decompression Reducer component, an original data decompression component and an image splicing component. Each modular component is provided with an input stream and an output stream. The functions of the components are as follows:
an input format component: according to the set size and position of the original data block, the original data is cut into block data, a block reading component is called, and key value pairs < key, value > of the original block data which can be processed by the decompression Mapper component are generated and distributed to each Map processing node.
Wherein, key represents a serial number, and value represents a numerical value;
a block reading component: and generating a key value and a value according to the original block data, and combining the key value pair < key, value > of the original block data.
A data partitioning component: and recording the size and the position of each original block data.
Decompression Mapper component: and receiving key value pairs of the original block data, performing data extraction operation, calling an original data decompression component, decompressing the original block data to generate image block data, and reorganizing the image block data into a key value pair (key, value) form.
Decompression Shuffle component: and receiving the key value pairs of the image block data, sequencing the image block data according to the image frame number and the intra-frame block number, and outputting the image block data to a decompression Reducer component.
Decompression Reducer component: and receiving the sequenced image block data, and calling the image splicing component to perform image data splicing, auxiliary data recovery and data verification.
Raw data decompression component: and decompressing the data by taking the original block data as a unit, and outputting the decompressed data.
The image splicing component: and sequencing the image blocks and the auxiliary data blocks with the same number according to the block numbers, splicing, and finally forming the image data and the auxiliary data which conform to the satellite data format specification.
The remote sensing satellite data decompression method based on cloud computing comprises the following steps as shown in figure 2:
(1) according to the original data format characteristics of each remote sensing satellite, determining the input data block length for parallel calculation, calling a data division component, and generating an original data division information description file. The structure of the segmentation information description file is as follows:
<split>
< id > Block number </id >
< start > Start coordinate </start >
< end > end coordinate </end >
</split>
id is the serial number of the data block, start is the start byte value of the data block, and end is the end byte value of the data block.
(2) According to the input original data segmentation information description file, calling an original data partitioning component (an input format component, a block reading component and a data partitioning component), partitioning original data, segmenting the original data into a set of key value pairs < key, value >, and simultaneously distributing data nodes stored by each original block data.
(3) And the Hadoop platform allocates each original block data key value pair to the optimal Map processing node for operation according to the load condition of each Map node and the storage position of the original block data.
(4) Each decompression Mapper component processes the key value pair of the original block data to complete the work of auxiliary data extraction and decompression, and recombines the key value pair of the image block data into a form of key, value, and the processing result is transmitted to the decompression Shuffle component in a streaming way; and if the node has an abnormal error, scheduling to other nodes for re-operation.
(5) The decompression Shuffle component receives the data stream, sorts the key value pairs according to the image amplitude numbers and the intra-amplitude block numbers, and transmits the sorted key value pairs to each decompression Reducer component in a streaming mode.
(6) And the decompression Reducer component sorts the image blocks and the auxiliary data blocks with the same number according to the block numbers, splices the image data and the auxiliary data with the same number, simultaneously checks the image data and the auxiliary data, and finally generates complete image data and complete auxiliary data of each type of sensor of the satellite.
The present invention has not been described in detail, partly as is known to the person skilled in the art.
Claims (6)
1. A remote sensing satellite data decompression processing system based on cloud computing is characterized by comprising an input format component, a block reading component, a data dividing component, a decompression Mapper component, a decompression Shuffle component, a decompression Reducer component, an original data decompression component and an image splicing component;
an input format component: according to the set size and position of original data blocks, the original data is cut into block data, a block reading component is called, key, value pairs of the original block data key values which can be processed by a decompression Mapper component are generated and distributed to Map processing nodes;
a data partitioning component: recording the size and the position of each original block data;
a block reading component: generating a key value and a value according to the original block data, and combining the key value pair < key, value > of the original block data;
decompression Mapper component: receiving key value pairs of original block data, performing data extraction operation, calling an original data decompression component, decompressing the original block data to generate image block data, and reorganizing the image block data into a key value pair (key, value) form;
decompression Shuffle component: receiving the image block data key value pair, sorting according to the image frame number and the intra-frame block number, and outputting to a decompression Reducer component;
decompression Reducer component: receiving the sequenced image block data, calling an image splicing component to splice the image data, recovering auxiliary data and verifying the data;
raw data decompression component: performing data decompression by taking the original block data as a unit, and outputting decompressed data;
the image splicing component: and sequencing the image blocks and the auxiliary data blocks with the same number according to the block numbers, splicing, and finally forming the image data and the auxiliary data which conform to the satellite data format specification.
2. A remote sensing satellite data decompression processing method based on cloud computing is characterized by comprising the following steps:
(1) determining the input data block length for parallel calculation according to the original data format characteristics of each remote sensing satellite, calling a data division component, and generating an original data division information description file;
(2) according to an input original data segmentation information description file, calling an input format component, a block reading component and a data partitioning component, partitioning original data, partitioning the original data into a set of key value pairs (key, value) of the original block data, and distributing data nodes for storing each original block data;
(3) the Hadoop platform calculates each original block data key value pair distributed to the optimal Map processing node according to the load of each Map node and the storage position of the original block data;
(4) each decompression Mapper component processes key value pairs of original block data, performs data extraction operation, calls an original data decompression component at the same time, decompresses the original block data to generate image block data, recombines the image block data into a form of key value pairs of the image block data, and transmits processing results to a decompression Shuffle component in a data stream mode; if the node has an abnormal error, scheduling to other nodes for recalculation;
(5) the decompression Shuffle component receives the data stream, sorts the key value pairs according to the image amplitude numbers and the intra-amplitude block numbers, and transmits the key value pairs to each decompression Reducer component in a data stream mode;
(6) and each decompression Reducer component sorts the image blocks and the auxiliary data blocks with the same number according to the block numbers, splices the image data and the auxiliary data with the same number, simultaneously checks the image data and the auxiliary data, and finally generates complete image data and auxiliary data of each type of sensor of the remote sensing satellite.
3. The remote sensing satellite data decompression processing method based on cloud computing as claimed in claim 2, wherein the structure of the segmentation information description file is as follows:
<split>
< id > Block number </id >
< start > Start coordinate </start >
< end > end coordinate </end >
</split>
Wherein id is a data block sequence number, start is a data block start byte value, and end is a data block end byte value.
4. The remote sensing satellite data decompression processing method based on cloud computing as claimed in claim 2 or 3, wherein the input format component: according to the set size and position of the original data block, the original data is cut into block data, a block reading component is called, and key value pairs < key, value > of the original block data which can be processed by the decompression Mapper component are generated and distributed to each Map processing node.
5. The remote sensing satellite data decompression processing method based on cloud computing as claimed in claim 4, wherein the data partitioning component: and recording the size and the position of each original block data.
6. The remote sensing satellite data decompression processing method based on cloud computing as claimed in claim 5, wherein the block reading component: and generating a key value and a value according to the original block data, and combining the key value pair < key, value > of the original block data.
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