Open AccessCNN-Enhanced Multi-Indices Patch-Based Classification: A Case Study of Guwahati City
Author(s) -
Arindom Ain
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.39593
Subject(s) - pattern recognition (psychology) , land cover , support vector machine , computer science , artificial intelligence , multispectral pattern recognition , multispectral image , classifier (uml) , cover (algebra) , stacking , spectral bands , remote sensing , cluster analysis , normalized difference vegetation index , land use , geography , geology , engineering , mechanical engineering , civil engineering , physics , nuclear magnetic resonance , oceanography , climate change
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. The proposed classifier is compared with the SVM and KNN classifiers. The results show that this proposed strategy gives preferable outcomes over other techniques. After training the model over 50 epochs, an accuracy of 93.29% is achieved. Keywords: Land use, land cover, CNN, ISODATA, indices