Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32451
Performance Analysis of Artificial Neural Network Based Land Cover Classification

Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul


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.

Keywords: Landcover classification, artificial neural network, remote sensing, SPOT-5.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1465


[1] S. Wheeler and P. N. Misra, “Crop classification with Landsat multispectral scanner data II”, Pattern Recognition, vol. 12, pp. 219-228, 1980.
[2] B R Deilmai, K D Kanniah1, A W Rasib and A Ariffin, “Comparison of pixel –based and Artificial neural networks classification methods for detecting forest cover changes in Malaysia Department of Geoinformation, Faculty of Geoinformation and Real Estate,
[3] Universiti Teknologi Malaysia, 81310 Johor, Malaysia
[4] S. W. Buechel, W. R. Philipson, and W. D. Philpot, “The effects of a complex environment on crop separability with Landsat TM”, Remote Sensing of Environment, vol. 27, pp. 261-271, 1989.
[5] A. Aziz; M. Muhammad; A. Manzoor; Y. Muhammad; U. Sadiq; K. Shahbaz, "Mahalanobis distance and maximum likelihood based classification for identifying tobacco in Pakistan," in Recent Advances in Space Technologies (RAST), 2015 7th International Conference on, vol., no., pp.255-260, 16-19 June 2015
[6] J. A. Richards and J. Richards, Remote sensing digital image analysis vol. 3: Springer, 1999.
[7] R. R. Macleod, R. G. Congalton “A quantitative comparison of change-detection algorithms for monitoring Eelgrass from remotely sensed data”. Photogramm. Eng Rem S 64 207-216 1998.
[8] J F Mas, J J Flores “The application of artificial neural networks to the analysis of remotely sensed data”. J. Remote. Sens 29 617-663, 2008
[9] V. E Neagoe, M. Neghina, and M. Datcu “Neural Network Techniques for Automated Land-Cover Change Detection in Multispectral Satellite Time Series Imagery”. Int. J. Math. Models Methods. Appl. Sci 131-139, 2012.
[10] G. Pajares, "A Hopfield Neural Network for Image Change Detection," in IEEE Transactions on Neural Networks, vol. 17, no. 5, pp. 1250-1264, Sept. 2006.
[11] H. Yuan, Van Der Wiele C F and S.Khorram “An automated artificial neural network system for land use/land cover classification from Landsat TM imagery”. Remote. Sens1 243-265, 2009.
[12] H. Ibrahim, N. S. P. Kong, and T. F. Ng, "Simple adaptive median filter for the removal of impulse noise from highly corrupted images," Consumer Electronics, IEEE Transactions on, vol. 54, pp. 1920-1927, 2008.
[13] G. M. Foody, "Thematic Map Comparison," Photogrammetric Engineering & Remote Sensing, vol. 70, pp. 627-633, 2004
[14] K Perumal and R Bhaskaran, “Supervised Classification Performance of Multispectral Images”, Journal of Computing, Volume 2, Issue 2, February2010, ISSN 2151-9617
[15] J.A.Richards, 1999, “Remote Sensing Digital Image Analysis” Springer- Verlag, Berlin p.240.
[16] Yu-guo Wang; Hua-peng Li, "Remote sensing image classification based on artificial neural network: A case study of Honghe Wetlands National Nature Reserve," in Computer, Mechatronics, Control and Electronic Engineering (CMCE), 2010 International Conference on , vol.5, no., pp.17-20, 24-26 Aug. 2010, 10.1109/CMCE.2010.5610049
[17] A. Khobragade, P. Athawale, M. Raguwanshi., "Optimization of statistical learning algorithm for crop discrimination using remote sensing data," in Advance Computing Conference (IACC), 2015 IEEE International, pp.570-574, 12-13 June 2015, 10.1109/IADCC.2015.7154771
[18] L. H. Thai, T. S. Hai, N. T. Thuy., "Image Classification using Support Vector Machine and Artificial Neural Network", in International Journal of Information Technology and Computer Science(IJITCS), IJITCS Vol. No. 5,pp. 32-38, May 2012