Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony

Tapan Sharma, Vinod Shokeen and Sunil Mathur
Additional contact information
Tapan Sharma: Amity University, Uttar Pradesh, India
Vinod Shokeen: Amity University, Uttar Pradesh, India
Sunil Mathur: Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 2020, vol. 11, issue 2, 80-94

Abstract: The remote sensing domain has witnessed tremendous growth in the past decade, due to advancement in technology. In order to store and process such a large amount of data, a platform like Hadoop is leveraged. This article proposes a MapReduce (MR) approach to perform edge detection of satellite images using a nature-inspired algorithm Artificial Bee Colony (ABC). Edge detection is one of the significant steps in the field of image processing and is being used for object detection in the image. The article also compares two edge detection approaches on Hadoop with respect to scalability parameters such as scaleup and speedup. The experiment makes use of Amazon AWS Elastic MapReduce cluster to run MR jobs. It focuses on traditional edge detection algorithms like Canny Edge (CE) and the proposed MR based Artificial Bee Colony approach. It observes that for five images, the scaleup value of CE is 1.1 whereas, for MR-ABC, it is 1.2. Similarly, speedup values come out to be 1.02 and 1.04, respectively. The algorithm proposed by authors in this article scales comparatively better when compared to Canny Edge.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 8/IJSSMET.2020040105 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jssmet:v:11:y:2020:i:2:p:80-94

Access Statistics for this article

International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) is currently edited by Ahmad Taher Azar

More articles in International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) from IGI Global
Bibliographic data for series maintained by Journal Editor ().