An Evaluation of Performance of Change Detection of Land Use/Land Cover in Hyderabad city using Artificial Neural Network and Mahalanobis Classification to improve Accuracy

In this current research the aim of study is to predict changes happening in land by comparing an Innovative Artificial Neural Network (ANN) classifier and Mahalanobis Classifier (MC) by digital image processing and also comparing which algorithm gives more accuracy. For the years 2001 and 2011 Landsat7 ETM+(Enhanced Thematic Mapper plus) is used and Landsat 8 is used for 2021 of study region. These satellite images were classified into two groups which are ANN classifier and Mahalanobis classifier, each group contains 3 samples with a total of N=6 samples. The pretest power is assumed to be 80% and with alpha value of 0.05 and Confidence Interval of 95%. The land use and land cover changes have been analyzed with supervised classifiers and percentages of different types of region has been noted. An independent samples-t test from SPSS statistical analysis it was observed that from a single tail test $\mathrm{p} > 0.05$ hence there is no significance difference between two groups of classifiers, namely, ANN and MC. The mean and standard deviation of overall classification accuracy is $98.69\pm 1.24$ and $91.13\pm 6.47$ respectively. The mean and standard deviation for kappa coefficient is $0.97\pm 0.016$ and $0.87\pm 0.076$ for ANN and MC respectively. From this research, within the limits of the study, it can be concluded that Innovative Artificial Neural Network has performed better than Mahalanobis based classifier.