TY - JOUR
T1 - Real-Time Big Data Analytical Architecture for Remote Sensing Application
AU - Rathore, Muhammad Mazhar Ullah
AU - Paul, Anand
AU - Ahmad, Awais
AU - Chen, Bo Wei
AU - Huang, Bormin
AU - Ji, Wen
N1 - Publisher Copyright:
© 2008-2012 IEEE.
PY - 2015/10
Y1 - 2015/10
N2 - The assets of remote senses digital world daily generate massive volume of real-time data (mainly referred to the term 'Big Data'), where insight information has a potential significance if collected and aggregated effectively. In today's era, there is a great deal added to real-time remote sensing Big Data than it seems at first, and extracting the useful information in an efficient manner leads a system toward a major computational challenges, such as to analyze, aggregate, and store, where data are remotely collected. Keeping in view the above mentioned factors, there is a need for designing a system architecture that welcomes both real-time, as well as offline data processing. Therefore, in this paper, we propose real-time Big Data analytical architecture for remote sensing satellite application. The proposed architecture comprises three main units, such as 1) remote sensing Big Data acquisition unit (RSDU); 2) data processing unit (DPU); and 3) data analysis decision unit (DADU). First, RSDU acquires data from the satellite and sends this data to the Base Station, where initial processing takes place. Second, DPU plays a vital role in architecture for efficient processing of real-time Big Data by providing filtration, load balancing, and parallel processing. Third, DADU is the upper layer unit of the proposed architecture, which is responsible for compilation, storage of the results, and generation of decision based on the results received from DPU. The proposed architecture has the capability of dividing, load balancing, and parallel processing of only useful data. Thus, it results in efficiently analyzing real-time remote sensing Big Data using earth observatory system. Furthermore, the proposed architecture has the capability of storing incoming raw data to perform offline analysis on largely stored dumps, when required. Finally, a detailed analysis of remotely sensed earth observatory Big Data for land and sea area are provided using Hadoop. In addition, various algorithms are proposed for each level of RSDU, DPU, and DADU to detect land as well as sea area to elaborate the working of an architecture.
AB - The assets of remote senses digital world daily generate massive volume of real-time data (mainly referred to the term 'Big Data'), where insight information has a potential significance if collected and aggregated effectively. In today's era, there is a great deal added to real-time remote sensing Big Data than it seems at first, and extracting the useful information in an efficient manner leads a system toward a major computational challenges, such as to analyze, aggregate, and store, where data are remotely collected. Keeping in view the above mentioned factors, there is a need for designing a system architecture that welcomes both real-time, as well as offline data processing. Therefore, in this paper, we propose real-time Big Data analytical architecture for remote sensing satellite application. The proposed architecture comprises three main units, such as 1) remote sensing Big Data acquisition unit (RSDU); 2) data processing unit (DPU); and 3) data analysis decision unit (DADU). First, RSDU acquires data from the satellite and sends this data to the Base Station, where initial processing takes place. Second, DPU plays a vital role in architecture for efficient processing of real-time Big Data by providing filtration, load balancing, and parallel processing. Third, DADU is the upper layer unit of the proposed architecture, which is responsible for compilation, storage of the results, and generation of decision based on the results received from DPU. The proposed architecture has the capability of dividing, load balancing, and parallel processing of only useful data. Thus, it results in efficiently analyzing real-time remote sensing Big Data using earth observatory system. Furthermore, the proposed architecture has the capability of storing incoming raw data to perform offline analysis on largely stored dumps, when required. Finally, a detailed analysis of remotely sensed earth observatory Big Data for land and sea area are provided using Hadoop. In addition, various algorithms are proposed for each level of RSDU, DPU, and DADU to detect land as well as sea area to elaborate the working of an architecture.
KW - Big Data
KW - data analysis decision unit (DADU)
KW - data processing unit (DPU)
KW - land and sea area
KW - offline
KW - real-time
KW - remote senses
KW - remote sensing Big Data acquisition unit (RSDU)
UR - http://www.scopus.com/inward/record.url?scp=84929598456&partnerID=8YFLogxK
U2 - 10.1109/JSTARS.2015.2424683
DO - 10.1109/JSTARS.2015.2424683
M3 - Article
AN - SCOPUS:84929598456
SN - 1939-1404
VL - 8
SP - 4610
EP - 4621
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IS - 10
M1 - 7109130
ER -