Title: Comparative study of satellite multispectral image data processing with MapReduce and classification algorithm

Authors: Ch. Rajyalakshmi; Katta Subba Rao; R. Rajeswara Rao

Addresses: Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Hyderabad, Telangana, India ' Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Hyderabad, Telangana, India ' Department of Computer Science and Engineering, Jawaharlal Nehru Technological University (JNTUK), Kakinada, Andhra Pradesh, India; University College of Engineering Vizianagaram (UCEV), Vizianagaram, Andhra Pradesh, India

Abstract: Now that Big Data has amassed a significant amount of data, it is available in both structured and unstructured formats. Unstructured data processing is difficult to generate by individuals (e.g. Twitter data) or even sensors (e.g. satellites, videos) with data sizes ranging from gigabytes to terabytes and peta-bytes. We can easily analyse and classify different trends in unstructured data sets if the right analytical approach is used, while keeping data quality and size in mind. Early warning forecasts, which are based on satellite imagery and radar sensor data, are a major problem in the real world. To obtain a better understanding of Big data a proper architecture for the analysis of various classifications of satellite imagery patterns using Hadoop technology should be proposed. Different classification methods for different satellite imagery pattern classification methods are segregated in the proposed architecture to reduce time and improve efficiency and will get scalability results to increase the performance of classification patterns to large data sets.

Keywords: C4.5 algorithm; satellite image; Hadoop; Google's MapReduce; big data.

DOI: 10.1504/IJCAT.2022.124944

International Journal of Computer Applications in Technology, 2022 Vol.68 No.3, pp.223 - 227

Received: 23 Apr 2021
Accepted: 22 May 2021
Published online: 18 Aug 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article