  Hyperspectral Imagery (and Remote Sensing in general) captured from UAVs or
satellites are highly voluminous in nature due to the large spatial extent and
wavelengths captured by them. Since analyzing these images requires a huge
amount of computational time and power, various dimensionality reduction
techniques have been used for feature reduction. Some popular techniques among
these falter when applied to Hyperspectral Imagery due to the famed curse of
dimensionality. In this paper, we propose a novel approach, LEt-SNE, which
combines graph based algorithms like t-SNE and Laplacian Eigenmaps into a model
parameterized by a shallow feed forward network. We introduce a new term,
Compression Factor, that enables our method to combat the curse of
dimensionality. The proposed algorithm is suitable for manifold visualization
and sample clustering with labelled or unlabelled data. We demonstrate that our
method is competitive with current state-of-the-art methods on hyperspectral
remote sensing datasets in public domain.
