节点文献
多波段遥感数据的自组织神经网络降维分类研究
Dimension Reduction of Self-organized Neural Network Classification for Multi-band Satellite Data
【摘要】 介绍了基于聚类分析的自组织特征映射神经网络分类方法 ,神经网络的输出层结构选用了 3D结构 ,可以更好地保持多波段遥感数据中的内在拓扑结构 ;并选择天津大港地区的ASTER数据中的 9个波段作为试验数据 ,通过对验证点的统计 ,分类精度达到了 94 %以上
【Abstract】 To meet the productive application of satellite data in land cove classification the higher resolution, the more bands are used, the more accurate results can be produced. A clustering in 3D self-organized neural nodes is used. ASTER data is a new kinds of sensors, 3 bands with 15m resolution and 6 bands with 30m resolutions. Dagang region in Tianjing is selected as a case study area. The Wavelet fusion is applied to fused different bands with different resolutions, then the self-organized neural network classification for land use is performed. Finally classification results is compared with that of the maximum likelihood classification with the same training samples. The accuracy of validation result is over 94%.
【Key words】 classification； wavelet fusion； self-organizing feature map； neural network； principal components transform；
- 【文献出处】 武汉大学学报(信息科学版) ,Editorial Board of Geomatics and Information Science of Wuhan University , 编辑部邮箱 ,2004年05期
- 【分类号】TP751
- 【被引频次】29
- 【下载频次】409