Welcome to the InfoSci Platform
Reference Hub3
Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony

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

Tapan Sharma, Vinod Shokeen, Sunil Mathur
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 15
ISSN: 1947-959X|EISSN: 1947-9603|EISBN13: 9781799806349|DOI: 10.4018/IJSSMET.2020040105
Cite Article Cite Article

MLA

Sharma, Tapan, et al. "Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony." IJSSMET vol.11, no.2 2020: pp.80-94. http://doi.org/10.4018/IJSSMET.2020040105

APA

Sharma, T., Shokeen, V., & Mathur, S. (2020). Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 11(2), 80-94. http://doi.org/10.4018/IJSSMET.2020040105

Chicago

Sharma, Tapan, Vinod Shokeen, and Sunil Mathur. "Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET) 11, no.2: 80-94. http://doi.org/10.4018/IJSSMET.2020040105

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.