基于ISOMAP的高光谱遥感数据的降维与分类

Dimensionality reduction and classification for hyperspectral remote sensing data using ISOMAP

为挖掘高光谱遥感数据内在的非线性结构特性,采用全局化流形学习算法等距特征映射(ISOMAP)对高光谱遥感数据进行非线性降维,并取得了优于常用的最小噪声分离(MNF)变换方法的结果,具有更好的数据压缩性能。将光谱角相似性度量方法用于ISOMAP算法,取得良好的降维效果。通过把ISOMAP降维算法和k-最邻近分类器相结合对降维后子空间特征进行分类,实验表明:ISOMAP利用较少的特征维数获得比MNF更高的分类精度,并达到较高稳定的分类精度,尤其对难以区分、光谱相似的两类别问题,ISOMAP的特征维数能够有效的提高两类别的可分性。

In order to address intrinsic nonlinearities of hyperspectral remote sensing data, isometric feature mapping （ISOMAP） is the most widely utilized global manifold learning approach for nonlinear dimensionality reduction. In this paper, it was employed to extract the inherent manifold of hyperspectral data and the experimental results show that ISOMAP provides a significantly more compact feature representation of hyperspectral data than the minimum noise fraction （MNF） transform. Considering the spectral information of hyperspectral data, spectral angle （SA） was applied to derive the neighborhood distances in ISOMAP algorithm, and the result was better. Extracted subspace features via ISOMAP algorithm were also implemented in conjunction with k Nearest Neighbor （kNN） classifier for classification. Experimental results show ISOMAP achieves higher classification accuracies than MNF transform, but with much smaller dimensionality. Especially, ISOMAP provides better discrimination for spectrally similar classes.