一种改进的大尺度高光谱流形降维算法

Improved Dimensionality Reduction Algorithm of Large-Scale Hyperspectral Scenes Using Manifold

经典流形算法等距映射(ISOMAP)和局部线性嵌入(LLE)可以对高光谱数据进行降维,但不能解决大尺度高光谱图像的流形降维难题。详细论述了ISOMAP和LLE在大尺度高光谱流形降维中遇到的问题,提出了一种基于增量等距映射(IISOMAP)和LLE结合的高光谱流形降维算法IISOMAP-LLE,并针对流形降维算法较线性降维算法最小噪声分离(MNF)可以更好地发掘出高光谱数据中的非线性结构的优点,通过AVIRIS和OMIS-II数据实验验证了算法的可行性和优越性,并证明了IISOMAP-LLE算法可以避免增强型等距映射(ENH-ISOMAP)中由于Landmark点选取不当而造成的本征维数较大时类间可分性反而低于MNF的缺点。

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.