Dimensionality Reduction on Hyperspectral Images:A Comparative Review Based on Artificial Datas

摘要：

In this research we address the problem of highdimensional in hyperspectral images, which may contain rare /anomaly vectors introduced in the subspace observation that we wish to preserve. Linear techniques Principal Component Analysis(PCA), and non linear techniques Kernel PCA, Isomap, Multidimensional scaling (MDS), Local Tangent Space Alignment (LTSA), Diffusion maps, Sammon mapping, Symmetric Stochastic Neighbor Embedding (SymSNE), Stochastic Neighbor Embedding (SNE), Locally Linear Embedding(LLE), Locality Preserving Projection(LPP), Neighborhood Preserving embedding (NPE), Linear Local Tangent Space Alignment (LLTSA) was presented. Classical approaches criterion based on the norm ld, derivative spectral, nearest neighbors and quality criteria are used for obtaining a good preservation of these vectors in the reduction dimension. We have observed from the results obtained that Sammon and Isomap are less sensitive to these rare vectors compared to the other presented methods.

关键词： component Dimensionality reduction manifold learning rare vectors quality criteria

作者: Jihan Khodr Rafic Younes

作者单位: Laboratoire Tsi2m UPRES JE 2529 Universite de Renne 1 - ENSSAT Lannion, France Laboratoire R.I.T.C.H. Faculty of Engineering, Lebanese University Beirut, Lebanon

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 1910-1918

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）