Land Use and Land Cover Classification Using Deep Belief Network for LISS-III Multispectral Satellite Images
T. Vignesh1, K. K. Thyagharajan2
1T. Vignesh, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai (Tamil Nadu), India.
2K. K. Thyagharajan, RMD Engineering College, Kavaraipettai, Chennai (Tamil Nadu), India.
Manuscript received on 22 November 2019 | Revised Manuscript received on 03 December 2019 | Manuscript Published on 14 December 2019 | PP: 94-98 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10221191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1022.1191S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Multispectral Images, Deep Belief Network, Spectral Features, Spatial Features, Land use and Land Classification, LISS III.
Scope of the Article: Image Security