An Artificial Immune Network Clustering Algorithm For Mangroves Remote Sensing Image

[1]D. M. Alongi, “Present state and future of the world’s mangrove forests,” Environmental Conservation, 2002, 29(3), pp. 331-349.10.1017/S0376892902000231 Search in Google Scholar

[2]B. W. Heumann, “Satellite remote sensing of mangrove forests: Recent advances and future opportunities,” Progress in Physical Geography, 2011, 35(1), pp. 87-108.10.1177/0309133310385371 Search in Google Scholar

[3]C. Giri, E. Ochieng, L. Tieszen, Z. Zhu, A. Singh, T. Loveland, J. Masek, N. Duke, “Status and distribution of mangrove forests of the world using earth observation satellite data,” Global Ecology and Biogeography, 2011, 20(1), pp. 154-159.10.1111/j.1466-8238.2010.00584.x Search in Google Scholar

[4]K. Liu, X. Li, X. Shi, S. Wang, “Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning,” Wetlands, 2008, 28(2), pp. 336-346.10.1672/06-91.1 Search in Google Scholar

[5]B. W. Heumann, “An Object-Based Classification of Mangroves Using a Hybrid Decision Tree--Support Vector Machine Approach,” Remote Sensing, 2011, 3(11), pp. 2440-2460.10.3390/rs3112440 Search in Google Scholar

[6]Y. Luo, M. Liao, J. Yan, C. ZHANG, “A multi-features fusion support vector machine method (MF-SVM) for classification of mangrove remote sensing image,” Journal of Computational Information Sys-terns, 2012, 8(1), pp. 323-334. Search in Google Scholar

[7]V. Rodríguez-Galiano, F. Abarca-Hernández, B. Ghimire, M. Chica-Olmo, P. Atkinson, C. Jeganathan, “Incorporating spatial variability measures in land-cover classification using Random Forest,” Procedia Environmental Sciences, 2011, pp. 344-349.10.1016/j.proenv.2011.02.009 Search in Google Scholar

[8]Y. Zhong, L. Zhang, J. Gong, P. Li, “A supervised artificial immune classifier for remotesensing imagery,” Geoscience and Remote Sensing, IEEE Transactions on, 2007, 45(12), pp. 3957-3966.10.1109/TGRS.2007.907739 Search in Google Scholar

[9]Y. Luo, M. Liao, J. Yan, C. Zhang, S. Shang, “Development and demonstration of an artificial immune algorithm for mangrove mapping using landsat TM,” Geoscience and Remote Sensing Letters, IEEE, 2013, 10(4), pp. 751-755.10.1109/LGRS.2012.2221675 Search in Google Scholar

[10]N. K. Jerne, “Towards a network theory of the immune system,” Annales d’immunologie, 1974. Search in Google Scholar

[11]D. Dasgupta, Z. Ji, F. Gonzalez, “Artificial immune system (AIS) research in the last five years,” CEC’03. The 2003 Congress on Evolutionary Computation, 2003. Search in Google Scholar

[12]J. Hunt, J. Timmis, E. Cooke, M. Neal, C. King. Jisys, “The Envelopment of an Artificial Immune System for Real World Applications,”. City: Springer, 1999.10.1007/978-3-642-59901-9_9 Search in Google Scholar

[13]J. Timmis, M. Neal, “A resource limited artificial immune system for data analysis,” Knowledge-Based Systems, 2001, 14(3), pp. 121-130.10.1016/S0950-7051(01)00088-0 Search in Google Scholar

[14]L. Nunes de Casto, F. J. Von Zuben, “An evolutionary immune network for data clustering,” Neural Networks, 2000. Proceedings. Sixth Brazilian Symposium on, 2000. Search in Google Scholar

[15]G. B. Bezerra, L. N. de Castro, “Bioinformatics data analysis using an artificial immune network,” City: Springer, 2003.10.1007/978-3-540-45192-1_3 Search in Google Scholar

[16]F. O. de França, F. J. Von Zuben, L. N. de Castro, “An artificial immune network for multimodal function optimization on dynamic environments,” Proceedings of the 2005 conference on Genetic and evolutionary computation, 2005.10.1145/1068009.1068057 Search in Google Scholar

[17]R. M. Haralick, K. Shanmugam, I. H. Dinstein, “Textural features for image classification,” Systems, Man and Cybernetics, IEEE Transactions on, 1973, 3(6), pp. 610-621.10.1109/TSMC.1973.4309314 Search in Google Scholar