Classification of hyperspectral remote sensing image based on nonlinear kernel mapping and artificial immune network

CHEN Shan-Jing; HU Yi-Hua; SUN Du-Juan; XU Shi-Long

doi:10.3724/sp.j.1010.2014.00289

[1] Plaza J A,Benediktsson J， Boardman W, et al. Recent advances in techniques for hyperspectral image processing［J］. Remote Sens. Environ., 2009,113(S1)： S110S122.

[2] WANG Y, GUO L, LIANG N. Improving the classification precision of spectral angle mapper algorithm［C］. in Proceedings of SPIE conference on Remote Sensing and GIS Data Processing and Other Applications, 2009,7498： 271276.

[3] Elbakary M I， Alam M S. Mine detection in multispectral imagery data using constrained energy minimization［C］. in Proceedings of SPIE conference on Optical Pattern Recognition, 2008,6977： 7179.

[4] Begüm Demir, Sarp Ertürk. Clustering-based extraction of border training patterns for accurate SVM classification of hyperspectral images［J］. IEEE Geosci. and Remote Sens. Lett., 2009,6(4)： 840844.

[5] Wesam Sakla, Andrew Chan, Jim Ji, et al. An SVDD-based algorithm for target detection in hyperspectral imagery［J］. IEEE Geosci. and Remote Sens. Lett., 2011,8(2)： 384388.

[6] Dasgupta D，Yu S， Nino F. Recent Advances in Artificial Immune Systems： Models and Applications［J］. Applied Soft Computing, 2011,11(2)： 15741587.

[7] Hunt J E,Cooke D E. Learning using an artificial immune system［J］. Journal of Network and Computer Applications, 1996,19(2)： 189212.

[8] Dasgupta D, Artificial immune systems and their application［M］. Berlin, Germany： Springer-Verlag, 1999.

[9] De Castro L N， Timmis J. Artificial immune systems： a new computational intelligence approach［M］. London, U.K.： Springer-Verlag, 2002.

[10] ZHONG Y F, ZHANG L P, HUANG B, et al. An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery［J］. IEEE Trans. Geosci. Remote Sens., 2006,44(2)： 420431.

[11] ZHONG Y F, ZHANG L P, HUANG B, et al. A resource limited artificial immune system algorithm for supervised classification of multi/hyper-spectral remote sensing imagery［J］. Int. J. Remote Sens., 2007,28(7)： 16651686.

[12] ZHONG Y F, ZHANG L P, GONG J Y, et al. A supervised artificial immune classifier for remote-sensing imagery［J］. IEEE Trans. Geosci. Remote Sens., 2007,45(12)： 39573966.

[13] Zhong Y F， Zhang L. Unsupervised remote sensing image classification using an artificial immune network［J］. Int. J. Remote Sens., 2011,32(19)： 54615483.

[14] ZHONG Y F, ZHONG Y, HUANG B J， et al. Dimensionality reduction based on clonal selection for hyperspectral imagery［J］. IEEE Trans. Geosci. Remote Sens., 2007,45(12)： 41724186.

[15] ZHONG Y F, ZHANG L P. An adaptive artifcial immune network for supervised classification of multi-/hyperspectral remote sensing imagery［J］. IEEE Trans. on Geosci. and Remote Sens., 2012,50(3)： 894909.

[16] GONG B, Im J， Mountrakis G. An artificial immune network approach to multi-sensor land use/land cover classification［J］. Remote Sens. Environ., 2011,115： 600614.

[17] Vapnik V N. The Nature of Statistical Learning Theory［M］. Springer Verlag, 1995.

[18] Jerne N K. The immune system［J］. Sci. Amer., 1973,229(1)： 5160.

[19] Jerne N K, Towards a network theory of the immune system［J］. Annu. Immunol., 1974,125(c)： 5160.

[20] Galeano J， Veloza A， González F. A comparative analysis of artificial immune network models［C］. in Proceedings of GECCO, 2005： 361368.