BIG DATA PROCESSING USING HPC FOR REMOTE SENSING DISASTER DATA

被引：11

作者：

Bhangale, Ujwala M. ^{[1
]}

Kurte, Kuldeep R. ^{[1
]}

Durbha, Surya S. ^{[1
]}

King, Roger L. ^{[2
]}

Younan, Nicolas H. ^{[2
]}

机构：

[1] Indian Inst Technol, Ctr Studies Resources Engn, Bombay 400076, Maharashtra, India

[2] Mississippi State Univ, Dept Elect & Comp Engn, Starkville, MS USA

来源：

2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2016年

关键词：

CUDA; GPU; MPI; HPC; Big Data; Analytics; OIL-SPILL DETECTION; IMAGES;

D O I：

10.1109/IGARSS.2016.7730540

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Voluminous data (Multispectral, Hyperspectral) from Variety of sensors (Airborne sensors, space borne sensors) with Velocity (high temporal resolution) when used for decision making to support natural disasters such as earthquakes, floods, oil-spills etc., for near real time accurate responses, is a problem that needs Big Data Analytics. To gain rapid insight from this big data, high performance computing (HPC) with some scalable solution that reduces the execution time are in extreme demand. To serve this real time need, scalable hybrid parallelism approach based on state of art multi-core GPUs and Message Passing Interface (MPI) is explored for analyzing remote sensing disaster data. Spatio-temporal remote sensing data of oil-spill at Gulf of Mexico captured by LANDSAT 7 ETM+ is considered for analysis. The core objective includes performance evaluation of the analysis process across various parallel implementation platforms.

引用

页码：5894 / 5897

页数：4

共 16 条

[1]

[Anonymous], IEEE AER C

[2]

Bernabe S., 2015, SPIE REMOTE SENSING, V9646

[3] SMART DATA ANALYTICS METHODS FOR REMOTE SENSING APPLICATIONS [J].

Cavallaro, Gabriele ;

Riedel, Morris ;

Benediktsson, Jon Atli ;

Goetz, Markus ;

Runarsson, Tomas ;

Jonasson, Kristjan ;

Lippert, Thomas .

2014 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2014, :1405-1408

[4] A simple method for fitting of bounding rectangle to closed regions [J].

Chaudhuri, D. ;

Samal, A. .

PATTERN RECOGNITION, 2007, 40 (07) :1981-1989

[5] Earth-Observation Image Retrieval Based on Content, Semantics, and Metadata [J].

Espinoza-Molina, Daniela ;

Datcu, Mihai .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013, 51 (11) :5145-5159

[6]

Faur Daniela, 2015, 2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), DOI 10.1109/Multi-Temp.2015.7245752

[7] Characterizing and Counting Roofless Buildings in Very High Resolution Optical Images [J].

Gueguen, Lionel ;

Pesaresi, Martino ;

Gerhardinger, Andrea ;

Soille, Pierre .

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2012, 9 (01) :114-118

[8] High Performance Computing for Hyperspectral Remote Sensing [J].

Plaza, Antonio ;

Du, Qian ;

Chang, Yang-Lang ;

King, Roger L. .

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2011, 4 (03) :528-544

[9] Sparse Unmixing-Based Content Retrieval of Hyperspectral Images on Graphics Processing Units [J].

Sevilla, Jorge ;

Ignacio Jimenez, Luis ;

Plaza, Antonio .

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2015, 12 (12) :2443-2447

[10] Automated Image Registration Based on Pseudoinvariant Metrics of Dynamic Land-Surface Features [J].

Shah, Chintan A. ;

Sheng, Yongwei ;

Smith, Laurence C. .

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2008, 46 (11) :3908-3916

← 1 2 →