Abstract

Hyperspectral image visualization reduces color bands to three, but prevailing linear methods fail to address data characteristics, and nonlinear embeddings are computationally demanding. Qualitative evaluation of embedding is also lacking. We propose faithful stochastic proximity embedding (FSPE), which is a scalable and nonlinear dimensionality reduction method. FSPE considers the nonlinear characteristics of spectral signatures, yet it avoids the costly computation of geodesic distances that are often required by other nonlinear methods. Furthermore, we employ a pixelwise metric that measures the quality of hyperspectral image visualization at each pixel. FSPE outperforms the state-of-art methods by at least 12% on average and up to 25% in the qualitative measure. An implementation on graphics processing units is two orders of magnitude faster than the baseline. Our method opens the path to high-fidelity and real-time analysis of hyperspectral images.

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Version of record

DOI

10.1109/LGRS.2014.2324631
https://dx.doi.org/10.1109/LGRS.2014.2324631

Citation

BibTeX

@ARTICLE{FSPE,
	author={Safa A. Najim and Ik Soo Lim and Peter Wittek and Mark W. Jones},
	journal={Geoscience and Remote Sensing Letters, IEEE},
	title={{FSPE}: Visualization of Hyperspectral Imagery Using Faithful Stochastic Proximity Embedding},
	year={2015},
	month={1},
	volume={12},
	number={1},
	pages={18-22},
	doi={10.1109/LGRS.2014.2324631},
	ISSN={1545-598X}
}