Assessment on the Potential of Multispectral and Hyperspectral Datasets for Land Use/Land Cover Classification

Abstract

Land use/land cover (LULC) is a significant factor which plays a vital role in defining an urban ecosystem. Interpretations of LULC are eased in recent times by utilizing hyperspectral and multispectral datasets obtained from various platforms. An attempt is made to comparatively assess the potentiality of AVIRIS NG with Sentinel 2 data through applied classification techniques for Kalaburagi urban sphere. Spectral responses of both datasets were analyzed to derive reflectance spectra. A standard supervised classification algorithm associated with dimensionality reduction techniques is applied. For performance evaluation, results are validated to check which dataset outperforms well and provides better accuracy.

An Assessment of Land Cover Change Dynamics of Gaja Cyclone in Coastal Tamil Nadu, India Using Sentinel 1 Sar Dataset

“…It adds randomness to the data and builds decision trees those bags together to attain maximum accuracy. With respect to the concept of boot strapping, RF yields carts with lower variance (Nivedita et al, 2019). Using the dual polarization ratio a profile is set for the image to obtain the original colour composite in order to collect training datasets for ease of classification.…”