Parallel Acceleration of SAM Algorithm and Performance Analysis

Qu, Haicheng; Zhang, Junping; Lin, Zhouhan; Chen, Hao

doi:10.1109/jstars.2013.2239261

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…Some research has been done in the GPU-based processing of remote sensing imagery. For example, references [4][5][6][7][8][9][10][11][12] explored hyperspectral image processing by the GPU with a focus on handling hundred spectral bands on the same image pixel. References [13][14][15][16][17][18] proposed the GPU methods for geocorrection and orthorectification (also called geo-referencing) for imagery acquired by UAS commercial off-the-shelf cameras, an airborne pushbroom imager, and high resolution satellite sensors.…”

Section: Introductionmentioning

confidence: 99%

The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing

Huang

Gibeaut

et al. 2016

Geoinformatica

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Unmanned aerial systems (UAS) have been used as a robust tool for agricultural and environmental applications in recent years. Remote sensing systems based on UAS typically acquire massive hyper-spatial images in its short turnaround. This paper takes advantage of graphics processing unit (GPU) massive parallel computation in order to process the huge data timely and efficiently. More specifically, this paper presents an index array approach for lens distortion correction and geo-referencing. They are the two essential components in UAS hyper-spatial image processing. The index array approach is also capable of parallelizing image file I/O and the orthoimage generation. In addition, this paper presents the dual tiled similarity algorithm for the image co-registration. The index array approach and the dual tiled similarity algorithm were evaluated using two UAS remote sensing datasets of South Padre island shorelines. The results show that this index array approach was able to speed up at least 10 times the lens distortion correction and the geo-referencing relative to the central processing unit (CPU) computation. This dual tiled algorithm could provide 12 times speedup compared with the CPU similarity computation.

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Section: Introductionmentioning

confidence: 99%

The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing

Huang

Gibeaut

et al. 2016

Geoinformatica

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“…On the other hand, in order to accelerate the compression and decompression processing, we are currently working on efficient parallel implementations using specialized hardware devices such as commodity graphics processing units (GPUs) [12][13][14][15][16] and field-programmable gate array (FPGA) [17].…”

Section: Discussionmentioning

confidence: 99%

A Pipelined-Based Multi-Thread Acceleration Method for Remote Sensing Image Progressive Transmission

Zhang

Meng³

2016

IJSIP

Self Cite

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In order to meet the different data browsing requests for different users to the visual quality of remote sensing images in heterogeneous network especially under narrow bandwidth environments, an online remote sensing image progressive transmission model is constructed in which remote sensing image compression and decompression are synchronized with transmission. At the same time, a pipeline-based multi-threaded acceleration method has been proposed through solving the asynchronous problem between compression decompression and transmission to improve the efficiency of remote sensing progressive transmission. At last, an idea of retry broken downloads transmission interruption has been implemented to improve end-user interactive experience.Experimental results show that the whole processing speed has been improved nearly twice without reducing image transmission quality and the amount of data transmission was reduced evidently by using the proposed progressive transmission and real-time compression model.

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“…GPU is a specialized circuit dedicated to graphical processing preliminarily. After it has had a brilliant rise in manipulating computer graphics and image processing in recent years, this technology gets used for developing parallel algorithms on RS image data widely [38][39][40]. RS complex algorithms should be rewritten to benefit from GPU parallelism by using thousands of simultaneous threads.…”

Section: Graphic Processing Unitsmentioning

confidence: 99%

Performance-Aware High-Performance Computing for Remote Sensing Big Data Analytics

Pekturk¹,

Ünal²

2018

Data Mining

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The incredible increase in the volume of data emerging along with recent technological developments has made the analysis processes which use traditional approaches more difficult for many organizations. Especially applications involving subjects that require timely processing and big data such as satellite imagery, sensor data, bank operations, web servers, and social networks require efficient mechanisms for collecting, storing, processing, and analyzing these data. At this point, big data analytics, which contains data mining, machine learning, statistics, and similar techniques, comes to the help of organizations for end-to-end managing of the data. In this chapter, we introduce a novel high-performance computing system on the geo-distributed private cloud for remote sensing applications, which takes advantages of network topology, exploits utilization and workloads of CPU, storage, and memory resources in a distributed fashion, and optimizes resource allocation for realizing big data analytics efficiently.

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Parallel Acceleration of SAM Algorithm and Performance Analysis

Cited by 10 publications

References 16 publications

The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing

The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing

A Pipelined-Based Multi-Thread Acceleration Method for Remote Sensing Image Progressive Transmission

Performance-Aware High-Performance Computing for Remote Sensing Big Data Analytics

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