  The graph embedding (GE) methods have been widely applied for dimensionality
reduction of hyperspectral imagery (HSI). However, a major challenge of GE is
how to choose proper neighbors for graph construction and explore the spatial
information of HSI data. In this paper, we proposed an unsupervised
dimensionality reduction algorithm termed spatial-spectral manifold
reconstruction preserving embedding (SSMRPE) for HSI classification. At first,
a weighted mean filter (WMF) is employed to preprocess the image, which aims to
reduce the influence of background noise. According to the spatial consistency
property of HSI, the SSMRPE method utilizes a new spatial-spectral combined
distance (SSCD) to fuse the spatial structure and spectral information for
selecting effective spatial-spectral neighbors of HSI pixels. Then, it explores
the spatial relationship between each point and its neighbors to adjusts the
reconstruction weights for improving the efficiency of manifold reconstruction.
As a result, the proposed method can extract the discriminant features and
subsequently improve the classification performance of HSI. The experimental
results on PaviaU and Salinas hyperspectral datasets indicate that SSMRPE can
achieve better classification accuracies in comparison with some
state-of-the-art methods.
