Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image

Full Text (PDF, 352KB), PP.11-19

Views: 0 Downloads: 0


J. Senthilnath 1,* Nitin Karnwal 2 D. Sai Teja 3

1. Department of Aerospace Engineering, Indian Institute of Science, Bangalore- 560012, India

2. Instrumentation and Control Engineering, National Institute of Technology, Trichy- 620015, India

3. Computer Engineering, National Institute of Technology, Surathkal, Mangalore- 575025, India

* Corresponding author.


Received: 4 Apr. 2014 / Revised: 22 May 2014 / Accepted: 9 Jul. 2014 / Published: 8 Aug. 2014

Index Terms

Hierarchical clustering, k-means, Self Organizing Map, Artificial Immune System


This paper presents a hierarchical clustering algorithm for crop type classification problem using multi-spectral satellite image. In unsupervised techniques, the automatic generation of clusters and its centers is not exploited to their full potential. Hence, a hierarchical clustering algorithm is proposed which uses splitting and merging techniques. Initially, the splitting method is used to search for the best possible number of clusters and its centers using non-parametric technique i.e., clonal selection method. Using these clusters, a merging method is used to group the data points based on a parametric method (K-means algorithm). The performance of the proposed hierarchical clustering algorithm is compared with two unsupervised algorithms (K-means and Self-Organizing Map) that are available in the literature. A performance comparison of the proposed algorithm with the conventional algorithms is presented. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is more accurate.

Cite This Paper

J. Senthilnath, Nitin Karnwal, D. Sai Teja,"Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image", IJIGSP, vol.6, no.9, pp.11-19, 2014. DOI: 10.5815/ijigsp.2014.09.02


[1]Panigrahy, S., Sharma, S.A. Mapping of crop rotation using multidate Indian Remote Sensing satellite digital data. Journal of Photogrammetry and Remote Sensing. 52(2), pp. 85-91, 1997.

[2]Li, F., Tian, G. Research on Remote sensing- Meteorological model for wheat yield estimation., AARS, ACRS, 1991.

[3]Omkar, S.N., Senthilnath, J., Mudigere, D., Kumar, M.M. Crop classification using biologically inspired techniques and high resolution satellite image. Journal of Indian Society for Remote Sensing. 36, pp. 175-182, 2008.

[4]Omkar, S.N., Sivaranjani, V., Senthilnath, J., Suman, M. Dimensionality Reduction and Classification of Hyperspectral Data. 2(3), pp. 157-163. 2010.

[5]Foody, G.M., Curran, P.J., Groom, G.B., Munro, D.C. Crop Classification with Multi-Temporal X-Band SAR data. Proceeding of IGARSS ’88 Symposium, Edinburgh, Scotland, 1988.

[6]Schowengerdt, R.A., Remote Sensing: Models and Methods for Image Processing. 2nd ed. Academic Press, San Diego, CA, 1997.

[7]Lu, D., Weng, O. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing. 28(5), pp. 823-827, 2007.

[8]Yoshida, T., Omatu S. Neural network approach to land cover mapping. IEEE Trans. Geosci. Remote Sens. 32(5), pp. 1103-1108, 1994.

[9]Foody, G.M., Mathur, A. A relative evaluation of multiclass image classification of support vector machines. IEEE Trans. Geosci. Remote Sens. 42(6), pp. 1335-1343, 2004.

[10]Rajan, N., Stephan, J.M. Mapping ground cover using airborne multispectral digital imagery. Precision Agriculture. Springer. 10(4), pp. 304-318, 2009.

[11]Chenghai, Y., James H.E., Dale, M. Evaluating high resolution SPOT 5 satellite imagery for crop identification. Computers and Electronics in Agriculture. 75(2), pp. 347-354, 2011.

[12]Goel, P.K., Prasher, S.O., Patel, R.M., Landry, J.M., Bonnell, R.B., Viau, A.A., Classification of Hyperspectral Data by Decision Trees and Artificial Neural Networks to Identify Weed Stress and Nitrogen Status of Corn. Computers and Electronics in Agriculture, 39, pp. 67-93, 2003.

[13]Maulik, U., Bandyopadhyay, S., Pakhira, M.K. Clustering using annealing evolution: Application to pixel classification of satellite images. 3rd Indian Conference on Computer Vision, Graphics and Image Processing, Online ICVGIP -2002 Proceedings. 2002.

[14]Turner, D., Gower, S., Cohen, W.B., Gregory, M., Maiersperger, T. Effects of spatial variability in light use efficiency on satellite-based NPP monitoring. Remote Sensing of Environment 80, pp. 397-405, 2002.

[15]Everitt, J.H., Yang, C., Fletcher R.S., Drawe, D.L. Evalutaion of high resolution satellite imagery for assessing rangeland resources in South Texas. Rangeland Ecol. Manag., pp. 30-37, 2006.

[16]Ersahin, K., Scheuchl, B., Cumming, I. Incorporating texture information into polarimetric radar classification using neural networks. In IEEE International Geoscience and Remote Sensing Symposium, IGARSS ’04, 2004.

[17]Senthilnath, J., Omkar, S.N., Mani, V., Tejovanth, N., Diwakar, P.G., Archana, S.B. Multi-Spectral Satellite Image Classification using Glowworm Swarm Optimization. Proceeding of IEEE International Geoscience and Remote Sensing Symposium (IGARSS'11), Vancouver, Canada, 2011.

[18]Senthilnath, J., Omkar, S.N., Mani, V., Nitin, K. Hierarchical artificial immune system for crop stage classification. Proceeding IEEE INDICON'11, Hyderabad, India, 2011.

[19]Hartigan, J.A., Wong, M.A. A k-means Clustering Algorithm. Applied Statistics,1979.

[20]Kohonen, T. Self-Organizing Maps, Springer Series in Information Sciences, vol 30, Berlin. 1995.

[21]Vesanto, J., Alhoniemi, E. Clustering of the Self Organizing Map. IEEE Trans. Neural Networks, 11, pp 586-600, 2000.

[22]Fort, J.C., Letremy, P., Cottrell, M. Advantages and drawbacks of the Batch Kohonen algorithm, in ESANN’2002, M. Verleysen (ed.), D Facto, pp. 223-230, 2002.

[23]Senthilnath, J., Vipul, D., Omkar, S.N., Mani, V. Clustering using Levy Flight Cuckoo Search. Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Application (Eds. J.C. Bansal et al.). Advances in Intelligent Systems and Computing, Springer India. 202, pp. 65 – 75, 2013.

[24]Cao, Y., Dasgupta, D. An Immunogenic Approach in Chemical Spectrum Recognition In Advances in Evolutionary Computing. Springer-Verlag, 2003.

[25]Knight, T., Timmis, J. A multi-Layered Immune Inspired Approach to Data Mining. Proceedings of the 4th International Conference on Recent Advances in Soft Computing, 2002.

[26]Bradley, D., Tyrell, A. Immunotronics: Hardware fault tolerance inspired by the immune system in Proceedings of the 3rd International Conference on Evaluable Systems (ICES2000), 1801, Springer-verlag Inc., 2000.

[27]Senthilnath, J., Omkar, S.N., Mani, V., Nitin, K., Shreyas, P.B. Crop Stage Classification of Hyperspectral Data using Unsupervised Techniques. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (IJSTARS), 6(2), pp. 861 - 866, 2013.

[28]Yanfei, Z., Liangpei, Z., Bo H., Pingxiang, L. An Unsupervised Artificial Immune Classifier for Multi/Hyperspectral Remote Sensing Imagery. IEEE Transactions On Geoscience And Remote Sensing, 44(2), pp. 420-431, 2006.

[29]Tao, L., Yan, Z., Zhifeng, H., Zhijie, W. A New Clustering Algorithm Based on Artificial Immune System. Fifth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 347 – 351, 2008.

[30]Yanfei, Z., Liangpei Z., Pingxiang, L., Huanfeng, S. A Sub-Pixel Mapping Algorithm Based On Artificial Immune Systems for Remote Sensing Imagery. Geoscience and Remote Sensing Symposium, IEEE International, 2009.

[31]Yundong, W.U., Geng, L. Research on Computation Model and Key Parameters of AIRS Supervised Classification in Remote Sensing Images. International Conference on Environmental Science and Information Application Technology, 2009.

[32]MacQueen, J. Some methods for classification and analysis of multivariate observations,” in Proc. 5th Berkeley Symp. pp. 281–297, 1967.

[33]Sap, M.N.M., Mohebi, E. Hybrid self-organizing map for overlapping clusters. International Journal for Signal Processing, Image Processing and Pattern Recognition (IJSIP), 1(1), pp. 11-20, 2008.

[34]Erik, B., Joaquin, S. The Parameter-Less SOM Algorithm. In ANZIIS 2003, pp. 159-164. 2003

[35]Timmis, J. Artificial immune systems: A novel data analysis technique inspired by the immune network theory. PhD thesis, 2000.

[36]Sutton, R.S., Barto, A.G. Reinforcement Learning an Introduction’’, A Bradford Book, 1998.

[37]Jeme, N.K. Towards a Network Theory of the Immune System. Ann. Itnmunol. (Inst. Pasteur), pp. 373-389, 1974.

[38]Castro, D.E., Von, Z. Artificial Immune Systems: Part I-Basic Theory and Applications. Technical Report-RT DCA 01/99, URL: lnunes.

[39]Suresh, S., Sundararajan, N., Saratchandran, P.A. Sequential Multi-Category Classifier using Radial Basis Function Networks. Neurocomputing. 71(7-9), pp.1345-1358, 2008.

[40]Fawcett, T. Roc Graphs: Notes and Practical Considerations for Researchers, Technical Report HPL-2003-4, HP Labs, 2006.

[41]Ashoka, V., Omkar, S.N., Akhilesh, K., Devesh. Aerial Video Processing for Land Use and Land Cover Mapping. I.J. Image, Graphics and Signal Processing. 8, pp. 45-54, 2013.