THE APPLICATION OF SELF-ORGANIZING NEURAL NETWORKS TO REMOTE SENSING IMAGERY CLASSIFICATION

doi:10.6046/gtzyyg.2004.04.05

Download: PDF(355 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract In comparison with the Kohonen neural networks, the structure of the compete study networks is relatively simple because it does not consider neighboring neural units. In experiments, the authors adopted this simplified neural structure and improved its study algorithm by using max-min distance means. Experimental results show that the classification accuracy and efficiency of the improved compete study networks are remarkably raised in unsupervised classification of remote sensing imagery, and hence the technique of compete study networks has the practical application value.

Issue Date: 02 August 2011

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

Cite this article:

LIU Xiu-guo, LUO Xiao-bo . THE APPLICATION OF SELF-ORGANIZING NEURAL NETWORKS TO REMOTE SENSING IMAGERY CLASSIFICATION[J]. REMOTE SENSING FOR LAND & RESOURCES,2004, 16(4): 14-18.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2004.04.05 OR https://www.gtzyyg.com/EN/Y2004/V16/I4/14

[1] 杨行峻,郑君里.人工神经网络与盲信号处理［J］.北京:清华大学出版社,2003,113-117.

[2] 孙丹峰,等.自组织神经网络在遥感土地覆盖分类中应用研究［J］.遥感学报,1999,3(2):139-144.

[3] 蔡煜东,等.遥感土地覆盖类型识别的自组织神经网络模型［J］.国土资源遥感,1994,(4):63-66.

[4] Kohonen T, et al.. Engineering application of the self-organizing map［J］.PIEEE,1996,84(10):1358-1384.

[5] Carpenter G A, et al..ARTMAP:supervised real-time learning and classification of nonstationary data by a self-organizing nerural network［J］.Neural networks,1991,4:578-590.

[6] Lihovidov V.Variational approach to unsupervised learning algorithm of neural networks［J］.Neural Networks,1997,10(2):278-287.

[7] Lin Simin, Sie Jennie.Weight-value convergence of the SOM algorithm for discrete input［J］.Neural Computation ,1998,10:807-814.

[8] Haese Karin.Self-organizing feature maps with self-adjusting leaning parameters［J］.IEEE Trans.On Neural Networks,1998,9(6):1272-1278.

[9] Kohonen T ,et al..Self Organization of a massive document collection［J］.Neural Networks, 2000,11(3):570-584.

[10] 杨凯,孙家炳,等.遥感图像处理原理和方法［M］.北京:测绘出版社, 1988,130-134.

[11] 骆剑承,等.基于径向基函数(RBF)映射理论的遥感影像分类模型研究［J］.中国图象图形学报,2000,5(2):95-99.

[12] 王京.人工神经网络方法在西藏羌塘盆地早期综合评价中的应用［J］.国土资源遥感,2000,(1):15-18.

[13] 江东,王建华.人工神经网络在遥感中的应用与发展［J］.国土资源遥感,1999,(2):12-18.