In this paper, we study the Locally Linear Embedding (LLE) for
nonlinear dimensionality reduction of hyperspectral data. We
improve the existing LLE in terms of both computational
complexity and memory consumption by introducing a spatial
neighbourhood window for calculating the k nearest neighbours.
The improved LLE can process larger hyperspectral images than
the existing LLE and it is also faster. We conducted
experiments of endmember extraction to assess the effectiveness
of the dimensionality reduction methods. Experimental results
show that the improved LLE is better than PCA and the existing
LLE in identifying endmembers. It finds more endmembers than
PCA and the existing LLE when the Pixel Purity Index (PPI)
based endmember extraction method is used. Also, better results
are obtained for detection.