Classification of remote sensed data using Artificial Bee Colony algorithm

Egyptian Journal of Remote Sensing and Space Science

Volumn 18, Issue 1, 2015, Pages 119-126

  Jayanth, J ^a   Koliwad, Shivaprakash ^b   Ashok Kumar, T ^c  

^a GSSS Institute of Engineering and Technology for Women   (India)

^b Department of Electronics and Communication VCET   (India)

^c PESITM   (India)

Author keywords

Artificial Bee Colony; Classification onlooker bees; MLC; Remote sensing data

Indexed keywords

MAXIMUM LIKELIHOOD; NEURAL NETWORKS; OBJECT RECOGNITION; OPTIMIZATION; REMOTE SENSING; SUPPORT VECTOR MACHINES; SWARM INTELLIGENCE;

ARTIFICIAL BEE COLONIES; ARTIFICIAL BEE COLONY ALGORITHMS; ARTIFICIAL BEE COLONY ALGORITHMS (ABC); CLASSIFICATION OF REMOTE SENSED DATA; MAXIMUM LIKELIHOOD CLASSIFIERS; REMOTE SENSING DATA; STATISTICAL CLASSIFICATION TECHNIQUES; SWARM INTELLIGENCE TECHNIQUES;

CLASSIFICATION (OF INFORMATION);

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL INTELLIGENCE; IMAGE ANALYSIS; IMAGE CLASSIFICATION; MAXIMUM LIKELIHOOD ANALYSIS; OPTIMIZATION; PIXEL; REMOTE SENSING; SATELLITE DATA; SPECTRAL RESOLUTION;

APOIDEA;

EID: 84943352410     PISSN: 11109823     EISSN: 20902476     Source Type: Journal    
DOI: 10.1016/j.ejrs.2015.03.001     Document Type: Article

References (24)

1. Ashok Kumar, T., 2011. Advanced Image Processing Techniques for Land Feature Classification. Lambert Academic Publishing AG & CO-KG (Dec-2011).
2. Bandyopadhyay, S., Maulik, U., 2002. Genetic clustering for automatic evolution of clusters and application to image classification. IEEE Pattern Recognit. 35, 1197-1208.
3. Banerjee, Srideepa, Bharadwaj, Akanksha, Gupta, Daya, Panchal, V.K., 2012. Remote sensing image classification using artificial bee colony algorithm. Int. J. Comp. Sci. Inf. 2 (3), 67-71.
4. Baraldi, A., Parmiggiani, F., 1995. A neural network for unsupervised categorization of multivalued input pattern: an application to satellite image clustering. IEEE Trans. Geosci. Remote Sens. 33 (2), 305-316.
5. Chen, C., 1999. Fuzzy training data for fuzzy supervised classification of remotely sensed images. In: Proceedings of 20th Asian Conference on Remote Sensing (ACRS 1999): 460-465.
6. Cuevas, E., Sección-echauri, F., Zaldivar, D., Pérez-cisneros, M., 2011. Multi-circle detection on images using artificial bee colony (ABC) optimization. Soft Comput. 22, 15-26.
7. Dorigo, M., Stützle, T., 2005. Ant Colony Optimization. Prentice Hall of India Private Limited, New Delhi, India.
8. El-Asmar, Hesham M., Hereher, Mohamed E., El Kafrawy, Sameh B., 2013. Surface area change detection of the Burullus Lagoon, North of the Nile Delta, Egypt, using water indices: a remote sensing approach. Egypt. J. Remote Sens. Space Sci. 16 (2), 119-123.
9. Karaboga, D., Basturk, B., 2008. On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8, 687-697.
10. Karaboga, D., Ozturk, C., 2011. A novel clustering approach: artificial bee colony (ABC) algorithm. Appl. Soft Comput. 11, 652-657.
11. Lee, S., Han, J., Chi, K., Suh, J., Lee, H., Miyazaki, M., Akizuki, K., 1999. A neuro-fuzzy classifier for land cover classification. Proc. IEEE Int. Conf. Fuzzy Syst. 20, 1063-1068.
12. Liu, Xiaoping, Li, Xia, Liu, Lin, He, Jinqiang, Ai, Bin, 2008. An innovative method to classify remote-sensing images using ant colony optimization. IEEE Trans. Geosci. Remote Sens. 46 (12), 4198-4208.
13. Mondal, Saptarshi, Jeganathan, C., Sinha, Nitish Kumar, Rajan, Harshit, Roy, Tanmoy, Kumar, Praveen, 2014. Extracting seasonal cropping patterns using multi-temporal vegetation indices from IRS LISS-III data in Muzaffarpur District of Bihar, India. Egypt. J. Remote Sens. Space Sci. 17 (2), 111-121.
14. Pan, Q.K., Fatih-tasgetiren, M., Suganthan, P.N., Chua, T.J., 2010. A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Inf. Sci. 26, 65-78.
15. Rawat, J.S., Biswas, Vivekanand, Kumar, Manish, 2013. Changes in land use/cover using geospatial techniques: a case study of Ramnagar town area, district Nainital, Uttarakhand, India. Egypt. J. Remote Sens. Space Sci. 16 (1), 111-117.
16. Sabat, S.L., Udgata, S.K., Abraham, A., 2010. Artificial bee colony algorithm for small signal model parameter extraction of MESFET. Eng. Appl. Artif. Intell. 23, 689-694.
17. Singh, Prafull, Gupta, Ankit, Singh, Madhulika, 2014. Hydrological inferences from watershed analysis for water resource management using remote sensing and GIS techniques. Egypt. J. Remote Sens. Space Sci. 17 (2), 111-121.
18. Stathakis, D., Vasilakos, A., 2008. Comparison of computational intelligence based classification techniques for remotely sensed optical image classification. IEEE Trans. Geosci. Remote Sens. 44 (8), 2305-2318.
19. Sylla, L., Xiong, D., Zhang, H.Y., Bangoura, S.T., 2012. A GIS technology and method to assess environmental problems from land use/cover changes: Conakry, Coyah and Dubreka region case study. Egypt. J. Remote Sens. Space Sci. 15 (2), 31-38.
20. Tereshko, V., 2000. Reaction diffusion model of a honeybee colony's foraging behavior. In: Schoenauer, M. (Ed.), Parallel Problem Solving from Nature VI, Lecture Notes in Computer Science. Springer-Verlag, Berlin, pp. 17-19.
21. Voisin, Aurelie, Krylov, Vladimir A., Moser, Gabriele, 2013. Supervised classification of multi-sensor and multi-resolution remote sensing images with a hierarchical copula-based approach. IEEE Trans. Geosci. Remote Sens. 20, 1-18.
22. Xu, C., Duan, H., Liu, F., 2010. Chaotic artificial bee colony approach to uninhabited combat air vehicle (UCAV) path planning. Aerosp. Sci. Technol. 14, 535-541.
23. Yang, X.-S., Deb, S., 2010. Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optim. 1 (4), 330-343.
24. Zhang, C., Ouyang, D., Ning, J., 2010. An artificial bee colony approach for clustering. Expert Syst. Appl. 37, 4761-4767.