GPU-Accelerated Computation for Electromagnetic Scattering of a Double-Layer Vegetation Model

Su, Xiang; Wu, Jiaji; Huang, Bormin; Wu, Zhensen

doi:10.1109/jstars.2012.2219508

Cited by 21 publications

(12 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. Some investigators used the GPU to accelerate computation of radiative transfer model [19,20], and segmentation and classification of remotely sensed imagery [21][22][23].…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Nonlinear problems particularly benefit from the NVIDIA SFU (Special Function Units) that calculate several transcendental functions (such as log(), exp(), sin(), cos(), and others) approximately 25 times faster than conventional processors [11] . CUDA is gramming model invented by NVIDIA which enables dramatic increases in computing performance by harnessing the power of the GPU [12] . CDUA is a heterogeneous computing networks comprised by the CPU and GPU.…”

Section: R R Rmentioning

confidence: 99%

Optimizing of Real-time Multi-scale Display of DICOM Image Based on CUDA

Wang¹,

Jiang²,

Shi³

2015

Proceedings of the First International Conference on Information Science and Electronic Technology

View full text Add to dashboard Cite

Abstract-The multi-scale display of digital imaging and communications in Medicine (DICOM) is the important step of detecting the lesions and making the radiotherapy plan. It is achieved by the dynamic regulation of the multi-scale display which also is called window operation to observe the organ tissues with different densities. The traditional way utilizing the linear function fails to be consistent with the characteristics of the human eye and loses image details. In this paper, nonlinear multi-scale display method is utilized to extend the gray level of the region of interest. In order to solve the contradiction between the long computing time of the nonlinear function and the real time of the dynamic window operation, the parallel generation algorithm of the images is implemented on the CUDA platform. The experiment results have shown that the optimization method has reached more elaborate display and at least an order of magnitude faster than traditional algorithms.

show abstract

“…Efremenko et al developed and compared multicore and GPU implementations of a radiative transfer model based on the discrete ordinate solution method [12]. Su et al proposed a GPU implementation for the Monte-Carlo-based electromagnetic scattering of a double-layer vegetation model [13]. For Infrared Atmospheric Sounding Interferometer (IASI) in operational numerical weather prediction systems, Mielikainen et al developed a GPU-based radiative transfer model [14].…”

Section: Introductionmentioning

confidence: 99%

Multicore Processors and Graphics Processing Unit Accelerators for Parallel Retrieval of Aerosol Optical Depth From Satellite Data: Implementation, Performance, and Energy Efficiency

Liu

Feld

Xue

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Quantitative retrieval is a growing area in remote sensing due to the rapid development of remote instruments and retrieval algorithms. The aerosol optical depth (AOD) is a significant optical property of aerosol which is involved in further applications such as the atmospheric correction of remotely sensed surface features, monitoring of volcanic eruptions or forest fires, air quality, and even climate changes from satellite data. The AOD retrieval can be computationally expensive as a result of huge amounts of remote sensing data and compute-intensive algorithms. In this paper, we present two efficient implementations of an AOD retrieval algorithm from the moderate resolution imaging spectroradiometer (MODIS) satellite data. Here, we have employed two different high performance computing architectures: multicore processors and a graphics processing unit (GPU). The compute unified device architecture C (CUDA-C) has been used for the GPU implementation for NVIDIA's graphic cards and open multiprocessing (OpenMP) for threadparallelism in the multicore implementation. We observe for the Manuscript GPU accelerator, a maximal overall speedup of 68.x for the studied data, whereas the multicore processor achieves a reasonable 7.x speedup. Additionally, for the largest benchmark input dataset, the GPU implementation also shows a great advantage in terms of energy efficiency with an overall consumption of 3.15 kJ compared to 58.09 kJ on a CPU with 1 thread and 38.39 kJ with 16 threads. Furthermore, the retrieval accuracy of all implementations has been checked and analyzed. Altogether, using the GPU accelerator shows great advantages for an application in AOD retrieval in both performance and energy efficiency metrics. Nevertheless, the multicore processor provides the easier programmability for the majority of today's programmers. Our work exploits the parallel implementations, the performance, and the energy efficiency features of GPU accelerators and multicore processors. With this paper, we attempt to give suggestions to geoscientists demanding for efficient desktop solutions.Index Terms-Aerosol optical depth (AOD), graphics processing unit (GPU), High performance computing (HPC), OpenMP, quantitative remote sensing retrieval.

show abstract

GPU-Accelerated Computation for Electromagnetic Scattering of a Double-Layer Vegetation Model

Cited by 21 publications

References 23 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

Optimizing of Real-time Multi-scale Display of DICOM Image Based on CUDA

Multicore Processors and Graphics Processing Unit Accelerators for Parallel Retrieval of Aerosol Optical Depth From Satellite Data: Implementation, Performance, and Energy Efficiency

Contact Info

Product

Resources

About