Title: Hybrid learning model for analysing the Uppal earth region, in Telangana state, using multispectral Landsat-8 OLI images

Authors: P. Aruna Sri; V. Santhi

Addresses: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

Abstract: Remote Sensing (RS) and Geographical Information Systems (GIS) are being widely used to carry out analysis of the Earth's surface. In this paper, a hybrid learning model is proposed for the classification and analysis of the Uppal earth region, located nearby Hyderabad in Telangana state. In the hybrid learning model, the ISODATA clustering algorithm is combined with the Normalised Vegetation Index (NDVI) and K-means learning model. In this proposal, the spectral features of the Uppal region are extracted from the satellite images and used for further analysis. The obtained accuracy of the proposed Merged-ISODATA algorithm is 74.33% and the Kappa value is 0.64. The obtained accuracy and Kappa value for existing ISODATA clustering and K-Means algorithm are 71.5% and 0.58. These values imply that the obtained results of the proposed algorithm are better than the results obtained in existing approaches.

Keywords: Landsat-8 OLI; remote sensing; normalised vegetation index; accuracy.

DOI: 10.1504/IJCAT.2023.131589

International Journal of Computer Applications in Technology, 2023 Vol.71 No.2, pp.167 - 180

Received: 03 Mar 2022
Received in revised form: 29 Jun 2022
Accepted: 14 Jul 2022
Published online: 20 Jun 2023 *

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