基于流形学习的新高光谱图像降维算法

提出了一种新的基于图像块距离的邻域选择方法，并将其应用于流形学习中，得到一类新的高光谱图像非线性降维算法。该类算法利用高光谱图像物理特性，结合图像的光谱信息和空间信息，在最大限度减小图像信息冗余的基础之上，很好地保持了原始数据集的特性。与其它高光谱图像的降维算法相比，改进的流形学习算法不仅考虑到高光谱图像本身的空间关系，而且利用图像块距离更好地保持了数据点之间的局部特性，从而有效地去除原始数据集光谱维和空间维的冗余信息。实际高光谱数据的实验结果表明，所提出的算法在应用于高光谱图像分类时，与其它方法相比具有更高的分类精度。

New dimensionality reduction algorithms for hyperspectral imagery based on manifold learning

A new neighborhood selection method was proposed based on the image patch distance and applied to the manifold learning. Thus, a new nonlinear methods for hyperspectral dimensionality reduction was obtained. Considering the physical characters of hyperspectral imagery, the proposed methods combined both spectral and spatial information and, thus, kept the original characters of dataset well with the less loss in the useful information and less distortion on the data structure. Compared with other dimensionality reduction methods for hyperspectral imagery, the proposed methods can reserve effectively the spatial relationships between observation pixels in hyperspectral imagery after transformation. Meanwhile, the proposed methods can discard efficiently the redundant information of original data sets along both spectral and spatial dimensions. Experimental results on real hyperspectral data demonstrate that the proposed methods have higher classification accuracy than the other methods when applied to the classification of hyperspectral imagery after dimensionality reduction.