Land use and land cover (LULC) provides a way to classify objects on the surface of Earth. This paper aims to
identify the varying land cover classes by stacking of 6 spectral bands and 10 different generated indices from those bands
together. We have considered the multispectral images of Landsat 7 for our research. It is seen that instead of using only basic
spectral bands (blue, green, red, nir, swir1 and swir2) for classification, stacking relevant indices of multiple target classes like
ndvi, evi, nbr, BU, etc. with basic bands generates more precise results. In this study, we have used automated clustering
techniques for generating 5 different class labels for training the model. These labels are further used to develop a
predictive model to classify LULC classes.
