Unsupervised ISODATA algorithm classification used in the landsat image for predicting the expansion of Salem urban, Tamil Nadu

Abstract

Objectives: To study the land cover change Salem city as a case study of urban expansion in India covering the span of 35 years from 1990 to 2025. Method: Remote sensing methodology is adopted to study the geographical land use changes occurred during the study period (year 1990-2025). Landsat images of TM and ETM+ of Salem city area are collected from the USGS Earth Explorer website. After image pre-processing, unsupervised image classification has been performed to classify the images into different land use categories. Seven land use classes have been identified as road, urban (Build up), vegetation, water bodies, fallow land, mines and barren land. Classification accuracy is also estimated using the field knowledge obtained from field surveys. Findings: The obtained accuracy is between 83 to 86% of all the classes. Change detection analysis shows the built-up area has been increased by 1.49 km 2 , vegetation area has been decreased by 11.55km 2. Application: Information on Urban growth, land use and land cover change study is very useful to local Government and Urban Planners for the betterment of the future plans of sustainable development of the city.

Comparison of Satellite Images Classification Techniques using Landsat-8 Data for Land Cover Extraction

“…Image classification in this research is divided into unsupervised (K-means and ISODATA) and supervised techniques (pixel-based and segment-based maximum likelihood classifiers). K-means algorithm is an unsupervised classifier that partition image pixels into K-clusters (classes) iteratively, where pixels are assigned to the cluster with the nearest mean in the feature space (Vimala et al, 2020;Abdu et al, 2014). The feature space is a two-dimensional space to measure the similarity in the clustering algorithm.…”