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Dimensionality Reduction and Vegetation Monitoring on LISS III Satellite Image Using Principal Component Analysis and Normalized Difference Vegetation Index
Published in Institute of Electrical and Electronics Engineers Inc.
Analyzing the satellite images and their features is a crucial part in the area of remote sensing and Geographic Information System (GIS). Processing and testing the satellite images through preprocessing and classification had motivated the researchers to work efficiently in the field of remote sensing. Preprocessing was the initial part of accessing satellite images in remote sensing and GIS environment. The main objective of this paper was to reduce the dimensionality of the satellite image using Principal Component Analysis (PCA) and to monitor the vegetation by using the Normalized Difference Vegetation Index (NDVI). The satellite image was acquired from Bhuvan Indian Remote Sensing Satellite (IRS), i.e., from the LISS III sensor for the year 2016. In this work, PCA and NDVI methods were used for analyzing the LISS III satellite image, acquired from the region of Tirpattur district located in the northern part of Tamil Nadu, India. The variations in the original satellite image was observed and compared with all the derived principal component images. This work also provides information about the vegetation of the study area by using the NDVI method. © 2020 IEEE.