
Land Use and Land Cover Classification Using Deep Belief Network for LISS-III Multispectral Satellite Images
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a1022.1191s19
Subject(s) - multispectral image , deep belief network , land cover , artificial intelligence , pattern recognition (psychology) , multispectral pattern recognition , computer science , feature (linguistics) , remote sensing , feature extraction , satellite , contextual image classification , deep learning , land use , geography , image (mathematics) , engineering , linguistics , philosophy , civil engineering , aerospace engineering
Land Use and Land Cover (LULC) classification is one of the familiar applications of geographical monitoring. Deep learning techniques like deep belief networks (DBN), are used for the purpose of feature extraction and classification of multispectral images. In this proposed framework, by applying DBN, spatial and spectral features were extracted and classified with high level of classification accuracy. LISS III images of Kottayam district, Kerala were used as experimental images. This proposed framework proved that, DBN has a high ability to extract the feature and classify the multispectral images with high accuracy than traditional methods.