Evaluation of Classification Techniques for Land Use Change Mapping of Indian Cities

Abstract

This study looks into the development of multi-level classification approach for land use change mapping in Indian cities using Landsat imageries. In this study, we mapped 47 Indian cities at different time frames 1990, 2000, 2010, and 2017. We started with traditional classification methods, but results provided unsatisfactory accuracy levels. Thus, we employed multiple classification techniques to achieve results with higher accuracy. The paper captures the evaluation of different classification techniques—hybrid, unsupervised, decision tree classification (DTC), and object-based image analysis (OBIA). The results suggest improvement in accuracy levels by using multi-level classification for different cities at different stages of the classification process. The most prominent is the hybrid classification technique; 14 cities out of 47 reached to accuracy above 72% through hybrid classification. For problematic classes, we used DTC, OBIA, and unsupervised classification techniques after masking the datasets. DTC was used in cities with a greater number of problems in datasets. For example, in the case of Kochi City, the accuracy at the initial level was reported 51% through unsupervised classification which improved to 77% (supervised classification), and finally, it reached 90% by DTC technique. The overall accuracy achieved through the multi-level classification approach described in this paper for the 47 Indian cities ranges from 81 to 93%.