TY - JFULL AU - Najam Aziz and Nasru Minallah and Ahmad Junaid and Kashaf Gul PY - 2017/6/ TI - Performance Analysis of Artificial Neural Network Based Land Cover Classification T2 - International Journal of Marine and Environmental Sciences SP - 421 EP - 426 VL - 11 SN - 1307-6892 UR - https://publications.waset.org/pdf/10007011 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 125, 2017 N2 - Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier. ER -