Title: Remote sensing image retrieval using object-based, semantic classifier techniques

Authors: N. Suresh Kumar; M. Arun; Mukesh Kumar Dangi

Addresses: School of Computing Science and Engineering, VIT University, Vellore, Tamil Nadu, India ' School of Electronics Engineering, VIT University, Vellore, Tamil Nadu, India ' School of Electronics Engineering, VIT University, Vellore, Tamil Nadu, India

Abstract: Data captured by satellites is increasing exponentially for agriculture and crop management, health, climate changes and future prediction of plants. Nowadays, it is required to have reliable, automated, satellite image classification and recovering system. Every day there is a massive amount of remotely-sensed data being collected and sent by satellite. Many retrieval systems are proposed for image content and information retrieval. However, the output of these approaches is generally not up to expectation. In this work, a new approach, remote sensing image retrieval scheme by content base image retrieval with grid computing and advanced database concepts are used and this will help to speed up both input processing and system response time. This paper presents the idea of the parallel processing of input data, queries, and storing images in the database using advanced database concept like B+ or BST trees.

Keywords: two-dimensional multi-resolution hidden Markov model; 2-D MHMM; remote sensing images; semantic network.

DOI: 10.1504/IJICT.2018.090432

International Journal of Information and Communication Technology, 2018 Vol.13 No.1, pp.68 - 82

Received: 04 Feb 2015
Accepted: 04 Jul 2015
Published online: 19 Mar 2018 *

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