Title: Enhanced classification of LISS-III satellite image using rough set theory and ANN

Authors: Anand Upadhyay; Jyotsna Anthal; Shashank Shukla

Addresses: Department of Information Technology, Thakur College of Science and Commerce, Thakur Village, Kandivali (E), Mumbai 400101, Maharashtra, India; AMET University, Plot No.135, East Coast Road, Kanathur, Chennai, Tamil Nadu 603112, India ' Department of Information Technology, Thakur College of Science and Commerce, Thakur Village, Kandivali (E), Mumbai 400101, Maharashtra, India ' Department of Information Technology, Thakur College of Science and Commerce, Thakur Village, Kandivali (E), Mumbai 400101, Maharashtra, India

Abstract: Land use and land cover classification are one of the major aspects to detect land coverage in particular area. Same goes for water, forest, and mangroves. So by keeping these parameters in mind, our objective is to identify water, land, forest, and mangroves from a LISS-III satellite image by using rough set theory and artificial neural network. LISS-III is multi-spectral camera operating in four different bands. There are many problems related to the classification of the satellite image i.e., universal classifiers, parameter setting of classifiers and features. The classification accuracy is one of the major issues related to classification of satellite image therefore in this paper rough set-based artificial neural network is used for classification of the satellite image. The rough set theory is used to reduce the number of the feature vector for improved classification of satellite image using the artificial neural network.

Keywords: LISS-III; linear imaging and self scanning sensor; classification; satellite image; accuracy; etc.

DOI: 10.1504/IJCC.2019.103928

International Journal of Cloud Computing, 2019 Vol.8 No.3, pp.249 - 257

Received: 04 Jun 2018
Accepted: 26 Apr 2019
Published online: 02 Dec 2019 *

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