Improved dimensionality reduction algorithm of large-scale hyperspectral scenes using manifold

被引：0

作者：

Zhang, Jingjing ^{[1
,2
]}

Zhou, Xiaoyong ^{[1
]}

Liu, Qi ^{[1
]}

机构：

[1] College of Electrical Engineering and Automation, Anhui University, Hefei

[2] Key Laboratory of Optical Calibration and Characterization, Chinese Academy of Sciences, Hefei

来源：

Guangxue Xuebao/Acta Optica Sinica | 2013年 / 33卷 / 11期

关键词：

Enhanced isometric mapping; Hyperspectral remote sensing; Incremental isometric mapping; Manifold dimensionality reduction of large-scale scenes; Minimum noise frarction; Remote sensing;

D O I：

10.3788/AOS201333.1128001

中图分类号：

学科分类号：

摘要：

It is practicable for dimensionality reduction of hyperspectral scenes using manifold algorithm such as isometric mapping (ISOMAP) and local linear embedding (LLE). However the two classical manifold algorithm are not suitable for solving the large-scale hyperspectral scenes. We elaborate the problems encountered in applying ISOMAP and LLE to dimensionality reduction of large-scale hyperspectral scenes, then an improved algorithm called IISOMAP-LLE, which is based on incremental isometric mapping (IISOMAP) and LLE, is proposed to represent the nonlinear structure of hyperspectral imagery that linear algorithm minimum noise fraction (MNF) could not discover. At last we demonstrate two experiments using large-scale AVIRIS and OMIS-II hyperspectral scenes to illustrate the approach. Experimental results prove that the IISOMAP-LLE not only is much better than linear algorithm MNF but also can avoid superiority decline of separability compared with MNF that encounterd in enhanced isometric mapping.

引用

共 24 条

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