Classification of Satellite Images Based on Color Features Using Remote Sensing

  • Assad H. Thary Al-Ghrairi Electronic Computer Center, Al-Karkh University of Science, Baghdad, Iraq
  • Zahraa H. Abed Dept. Computer Science, University of Baghdad, Baghdad, Iraq
  • Fatimah H. Fadhil Dept. Computer Science, University of Baghdad, Baghdad, Iraq
  • Faten K. Naser Dept. Computer Science, University of Baghdad, Baghdad, Iraq
Keywords: k-Means, Image features, Remote sensing, Color Moments, Satellite Image Classification, Landcover.


The aim of this paper is to classify satellite imagery using moment's features extraction with K-Means clustering algorithm in remote sensing. Clustering is the assignment of objects into groups called clusters so that objects from the same cluster are more similar to each other than objects from different clusters. In this research, the study area chosen is to cover the area of Baghdad city in Iraq taken by landsat 8. The proposed work consists of two phases: training and classification. The training phase aims to extract the moment features (mean, standard deviation, and skewness) for each block of the satellite imagery and store as dataset used in classification phase to compute the similarity measurement.  The experimental result of classification showed that the image contains five distinct classes (rivers, agriculture area, buildings with vegetation, buildings without vegetation, and bare lands). The classification result assessment was carried out by comparing the result with a reference classified image achieved by Iraqi Geological Surveying Corporation (GSC). It is observed that both the user accuracy and producers' accuracy and hence overall classification accuracy are enhanced with percent 92.12447%.


Chijioke, G. E. " Satellite Remote Sensing Technology in Spatial Modeling Process: Technique and Procedures", International Journal of Science and Technology, Vol. 2, No.5, P.309-315, May 2012.

Schowengerdt D.andA.Robert.“Remote sensing: models and methods for image processing”, Academic Press (3rd ed.) ISBN 978-0-12-369407-2, 02Pp, 2007.

Liu.S., L. Bruzzone. F. Bovolo, M. Zanettiand P. Du., “Sequential Spectral Change Vector Analysis for Iteratively Discovering and Detecting Multiple Changes in Hyperspectral Images,” Transactions on Geoscience and Remote Sensing, IEEE, Vol. 53, 8:4363–4378, 2015.

Quanfang Wang, Haiwen Zhang, Hangzhou Sun, “New Logic for Large-scale Land Cover Classification Based on Remote Sensing”, Geoinformatics17th international conference,ISBN 978-1-4244-4562-2,Pg 1-5,2009.

Baboo, Capt. Dr.S S., and Thirunavukkarasu,"Image Segmentation using High Resolution Multispectral Satellite Imagery implemented by FCM Clustering Techniques", IJCSI International Journal of Computer Science Issues, ISSN (Print): 1694-0814 | ISSN (Online): 1694-0784, vol. 11, Issue 3, no 1, S., May 2014.

Sunitha A., and Suresh B. G., " Satellite Image Classification Methods and Techniques: A Review", International Journal of Computer Applications, pp. 0975 – 8887, Volume 119 – No.8, June 2015.

Venkateswaran. K., N. Kasthuri. K. Balakrishnan. and K. Prakash, “Performance Analysis of K-Means Clustering For Remotely Sensed Images,” International Jour.of Computer Applications (0975–8887) Vol.84, 12, (2013).

Márcio L. Gonçalves1, Márcio L.A. Netto, and José A.F. Costa, "A Three-Stage Approach Based on the Self-organizing Map for Satellite Image Classification", Springer-Verlag Berlin Heidelberg, pp. 680–689, 2007.

Sathya, P., and Malathi, L., "Classification and Segmentation in Satellite Imagery Using Back Propagation Algorithm of ANN and K-Means Algorithm", International Journal of Machine Learning and Computing, vol. 1, no. 4, October 2011.

Anil Z Chitadeand Dr. S K. Katiyar,”Color based image segmentation using K-Means Clustering”, Department of civil Engineering, International Journal of Engineering Science and Technology, Vol. 2(10, 5319-5325), 2010.

Ankayarkanni and Ezil S. L., "A Technique for Classification of High Resolution Satellite Images Using Object-Based Segmentation", Journal of Theoretical and Applied Information Technology, Vol. 68, No.2, and ISSN: 1992-8645, 2014.

Harikrishnan.R, and S. Poongodi, "Satellite Image Classification Based on Fuzzy with Cellular Automata", International Journal of Electronics and Communication Engineering (SSRG-IJECE), ISSN: 2348 – 8549, volume 2 Issue 3, March 2015.

Thwe Z. P., Aung S. K., Hla M. T., " Classification of Cluster Area For satellite Image", International Journal of Scientific & Technology Research Volume 4, Issue 06, 2015.

S.Manthira Moorthi, Indranil Misra, “Kernel based learning approach for satellite image classification using support vector machine”, Recent Advances in Intelligent Computational Systems (RAICS), IEEE, DOI: 10.1109/RAICS.2011.6069282, ISBN: 978-1-4244-9478-1, 03 November 2011.

A. M. Chandra and S. K . Ghosh “Remote Sensing and Geographical Information systems”, Narosa publishing House, New Delhi, 2007.

Mohammed S. Mahdi Al-Taei, Assad H. Thary Al-Ghrairi,"Satellite Image Classification Using Moment and SVD Method", International Journal of Computer (IJC) Volume 23, No 1, pp 10-34, 2016.

Jain., A. K. “Data clustering: 50 years beyond K-Means,” Journal of Pattern Recognition, Vol. 31,651-666, Elsevier Science Inc. New York, NY, US, 2010.

Mahdi, M. S. and Abdul Hassan, A. A., Satellite Images Classification in Rural Areas Based on Fractal Dimension, Journal of Engineering, vol. 22, no. 4, pp. 147-157, 2016.

Chang S.K., and Hsu A. (1992), “Image information systems: where do we go from here?” IEEE Trans. On Knowledge and Data Engineering, Vol. 5, No. 5, pp. 431-442.

M.Stricker and M.Orengo, “Similarity of color images”, Storage and Retrieval for Image and Video Databases III (SPIE) 1995: 381-392.

.J. R. Landis, G.G. Koch, "The Measurement of Observer Agreement for Categorical Data. Biometrics", 2012, 33(1):159- 174.

How to Cite
H. Thary Al-Ghrairi, A., H. Abed, Z., H. Fadhil, F., & K. Naser, F. (2018). Classification of Satellite Images Based on Color Features Using Remote Sensing. International Journal of Computer (IJC), 31(1), 42-52. Retrieved from