  Dimensionality reduction (DR) methods have attracted extensive attention to
provide discriminative information and reduce the computational burden of the
hyperspectral image (HSI) classification. However, the DR methods face many
challenges due to limited training samples with high dimensional spectra. To
address this issue, a graph-based spatial and spectral regularized local
scaling cut (SSRLSC) for DR of HSI data is proposed. The underlying idea of the
proposed method is to utilize the information from both the spectral and
spatial domains to achieve better classification accuracy than its spectral
domain counterpart. In SSRLSC, a guided filter is initially used to smoothen
and homogenize the pixels of the HSI data in order to preserve the pixel
consistency. This is followed by generation of between-class and within-class
dissimilarity matrices in both spectral and spatial domains by regularized
local scaling cut (RLSC) and neighboring pixel local scaling cut (NPLSC)
respectively. Finally, we obtain the projection matrix by optimizing the
updated spatial-spectral between-class and total-class dissimilarity. The
effectiveness of the proposed DR algorithm is illustrated with two popular
real-world HSI datasets.
