Evaluation of Various Vegetation Indices for Multispectral Satellite Images
L. Gowri1, K.R. Manjula2
1L. Gowri, School of Computing SASTRA Deemed University, Thanjavur, Tamil Nadu, India.
2Dr. K.R. Manjula, School of Computing SASTRA Deemed University, Thanjavur, Tamil Nadu, India.
Manuscript received on 17 August 2019 | Revised Manuscript received on 21 August 2019 | Manuscript published on 30 August 2019 | PP: 3494-3500 | Volume-8 Issue-10, August 2019 | Retrieval Number: J91950881019 /19©BEIESP | DOI: 10.35940/ijitee.J9195.0881019
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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: Vegetation indices play a predominant role in the field of Remote processing systems which assimilate vital multispectral images. The digital numbers identify the spectral information in one or more spectral bands. It focuses mainly on two or more spectral regions and obtains different types of surfaces like vegetation, built-up, bare soil and water area. Different types of vegetation can be studied and analyzed using LANDSAT images. In this paper, comparison has been made on ten major vegetation indices such as RVI, DVI, NDVI, TNDVI, NDWI, MNDWI, NDBI, UI, SAVI, and NDMI using different spectral bands and different features are detected and extracted with the help of ArcGIS and MATLAB tools. This study reveals better classification accuracy.
Keywords: NDVI, NDBI, Landsat5, SAVI, UI
Scope of the Article: Performance Evaluation of Networks