Unsupervised Discriminative Dimension Reduction for Hyperspectral Chemical Plume Segmentation | IEEE Conference Publication | IEEE Xplore

Unsupervised Discriminative Dimension Reduction for Hyperspectral Chemical Plume Segmentation


Abstract:

We propose a novel algorithm for unsupervised segmentation of hyperspectral imagery (HSI). Representative cluster modes are learned through the diffusion geometry of the ...Show More

Abstract:

We propose a novel algorithm for unsupervised segmentation of hyperspectral imagery (HSI). Representative cluster modes are learned through the diffusion geometry of the HSI, which is highly invariant to non-linearities present in HSI clusters. Mode detection is followed by partial least squares regression to project the data onto a low-dimensional space that discriminates between the learned modes and to assign labels in the low-dimensional space. We evaluate this method for unsupervised chemical plume segmentation in HSI, showing it performs competitively versus benchmark and state-of-the-art unsupervised learning techniques.
Date of Conference: 28 July 2019 - 02 August 2019
Date Added to IEEE Xplore: 14 November 2019
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Conference Location: Yokohama, Japan

References

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