I. J. of Electrical & Electronics Research
Research Article | 
A High-Performance Infrastructure for Remote Sensing Data Applications Using HPC Paradigms
Author(s) : W. T. Chembian1, D. Hemanand2, A. Thomas Paul Roy3, P. Deepak Franklin4, G. Krishna Kumari5, N. C. Sendhil Kumar6, S. V. Hemanth7 and A. Gnana Soundari8
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 2, Special Issue on IEEE-SD
Publisher : FOREX Publication
Published : 30 June 2022
e-ISSN : 2347-470X
Page(s) : 394-398
Abstract
Individuals and businesses are currently involved in the administration of remote sensing data that was previously handled only by government agencies. There is a lot more information in remote sensing data than go through the eye, and retrieving it is time-consuming and computationally expensive. Clusters, distributed networks, and specialized hardware devices are essential to speeding up remote sensing data extraction calculations. HPC advances in remote sensing applications are examined in this research. High-performance computing (HPC) concepts for instance FPGAs and GPUs as well as large-scale and heterogeneous computer networks are examined (GPUs). Using HPC paradigms, remote sensing applications are examined in these sections.
Keywords: Remote sensing, Distributed Computing, High Performance Computing, Accelerators
W. T. Chembian, Associate Professor, Department of Computer Science and Engineering, Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College (Autonomous), Avadi – Vel Tech Road Vel Nagar Avadi, Chennai, Tamil Nadu, India
D. Hemanand, Professor, Department of Computer Science and Engineering, S.A. Engineering College (Autonomous), Thiruverkadu, Chennai, Tamil Nadu, India; Email: d.hemanand@gmail.com
A. Thomas Paul Roy, Professor, Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Kothandaraman Nagar, Tamil Nadu, India
P. Deepak Franklin, Assistant Professor, Department of Electrical and Electronics Engineering, Sriram Engineering College, Perumalpattu, Veppampattu (R.S.) Tiruvallur,Tamil Nadu, India
G. Krishna Kumari, Professor, Department of Humanities and Science (Mathematics),Vardhaman College of Engineering, Ranga Reddy, Telangana
N. C. Sendhil Kumar, Professor, Department of Electronics and Communication Engineering, Sri Indu College of Engineering & Technology, Sheriguda, Hyderabad, Telangana, India
S. V. Hemanth, Assistant Professor, Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management, Medchal-Malkajgiri, Hyderabad, Telangana, India
A. Gnana Soundari, Professor, Department of Information Technology, Jeppiaar Engineering College, Chennai, Tamil Nadu, India
[1] Benediktsson, A. Plaza, J. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, J. Gualtieri, M. Marconcini, J. C. Tilton, and G. Trianni, “Recent advances in techniques for hyperspectral image processing,” Remote Sens. Environ., vol. 113, pp. 110–122, 2009.[Cross Ref]
[2] Carvajal, W. Rivera, and W. Lugo, “Service oriented architecture grid-based environment for hyperspectral imaging analysis,” Int. J. Inf. Technol., vol. 11, no. 4, pp. 104–111, 2005.[Cross Ref]
[3] Chang and Hsueh, “Field programmable gate arrays (FPGA) for pixel purity index using blocks of skewers for endmember extraction in hyperspectral imagery,” Int. J. High Performance Comput. Applicat., vol. 22, pp. 408–423, 2008.[Cross Ref]
[4] Chang and A. Plaza, High Performance Computing in Remote Sensing. Boca Raton, FL: CRC Press, 2007.[Cross Ref]
[5] Dongarra and A. Lastovetsky, High-Performance Heterogeneous Computing. New York: Wiley, 2009.[Cross Ref]
[6] Fisk, R. Brightwell, D. Greenberg, T. Hudson, M. Levenhagen, A. Maccabe, and R. Riesen, “Massively parallel computing using commodity components,” Parallel Comput., vol. 26, p. 243266, 2000.[Cross Ref]
[7] Goldberg, L. Zhou, C. Barnet, Z. Cheng, F. Chung, W. Wolf, T. King, X. Liu, H. Sun, and M. Divakarla, “Regression of surface spectral emissivity from hyperspectral instruments,” IEEE Trans. Geosci. Remote Sens., vol. 46, pp. 328–333, 2008.[Cross Ref]
[8] Hauck, “The roles of FPGAs in reprogrammable systems,” Proc. IEEE, vol. 86, pp. 615–638, 1998.[Cross Ref]
[9] Itoh, “Massively parallel fourier-transform spectral imaging and hyperspectral image processing,” Opt. Laser Technol., vol. 25, p. 202, 1993.[Cross Ref]
[10] Kaewpijit, T. El-Ghazawi, and J. L. Moigne, “Parallel and adaptive reduction of hyperspectral data to intrinsic dimensionality,” Cluster Comput., vol. 1, pp. 102–110, 2001.[Cross Ref]
[11] Lastovetsky, D. Valencia, M. O’Flynn, A. Plaza, and J. Plaza, “Parallel processing of remotely sensed hyperspectral images on heterogeneous networks of workstations using HeteroMPI,” Int. J. High Perform. Comput. Applicat., vol. 22, no. 4, pp. 386–407, 2008.[Cross Ref]
[12] Nekovei and Du “Fast real-time onboard processing of hyperspectral imagery for detection and classification,” J. Real-Time Image Process., vol. 22, pp. 438–448, 2009.[Cross Ref]
[13] Neville, Brazile, K. Staenz, D. Schlaepfer, L. Sun, and K. I. Itten, “Cluster versus grid for operation generation of ATCOR’s MODTRAN-based look up table,” Parallel Comput., vol. 34, pp. 32–46, 2008.[Cross Ref]
[14] Nickolls, E. Lindholm, S. Oberman, and J. Montrym, “NVIDIA Tesla: A unified graphics and computing architecture,” IEEE Micro, vol. 28, pp. 39–55, 2008.[Cross Ref]
[15] Palencia, J. Dorband, and U. Ranawake, “Commodity computing clusters at goddard space flight center,” J. Space Commun., vol. 3, p. 1, 2003.[Cross Ref]
[16] Plaza, A. Plaza, and D. Valencia, “Impact of platform heterogeneity on the design of parallel algorithms for morphological processing of high-dimensional image data,” J. Supercomput., vol. 40, no. 1, pp. 81–107, 2007.[Cross Ref]
[17] Rosenbaum, U. Thomas, F. Kurz, S. Suri, and P. Reinartz, “A new software/hardware architecture for real time image processing of wide area airborne camera images,” J. Real-Time Image Process., vol. 5, pp. 229–244, 2009.[Cross Ref]
[18] Valencia, Plaza, J. Plaza, and P. Martinez, “Commodity clusterbased parallel processing of hyperspectral imagery,” J. Parallel Distrib. Comput. vol. 66, no. 3, pp. 345–358, 2006.[Cross Ref]
[19] Valencia, Plaza, and J. Plaza, “An experimental comparison of parallel algorithms for hyperspectral analysis using homogeneous and heterogeneous networks of workstations,” Parallel Comput., vol. 34, no. 2, pp. 92–114, 2008.[Cross Ref]
[20] Zhang, S. Kalluri, J. JaJa, S. Liang, and J. Townshend, “Characterizing land surface anisotropy from AVHRR data at a global scale using high performance computing,” Int. J. Remote Sens., vol. 22, pp. 2171–2191, 2001.[Cross Ref]
[21] Zhao, S. Tehranian, T. Harvey, A. Swaroop, and K. Mckenzie, “A robust framework for real-time distributed processing of satellite data,” J. Parallel Distrib. Comput., vol. 66, pp. 403–418, 2006.[Cross Ref]
W. T. Chembian, D. Hemanand, A. Thomas Paul Roy, P. Deepakfranklin, N. C. Sendhil Kumar, G. Krishna Kumari, S. V. Hemanth and A. Gnana Soundari (2022), A High-Performance Infrastructure for Remote Sensing Data Applications Using HPC Paradigms. IJEER 10(2), 394-398. DOI: 10.37391/IJEER.100255.