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27 May 2019 Fusion of heterogeneous bands and kernels in hyperspectral image processing
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Hyperspectral imaging is a powerful technology that is plagued by large dimensionality. Our study explores a way to combat that hindrance via noncontiguous and contiguous (simpler to realize sensor) band grouping for dimensionality reduction. Our approach is different in the respect that it is flexible and it follows a well-studied process of visual clustering in high-dimensional spaces. Specifically, we extend the improved visual assessment of cluster tendency and clustering in ordered dissimilarity data unsupervised clustering algorithms for supervised hyperspectral learning. In addition, we propose a way to extract diverse features via the use of different proximity metrics (ways to measure the similarity between bands) and kernel functions. The discovered features are fused with ℓ  ∞  -norm multiple kernel learning. Experiments are conducted on two benchmark data sets and our results are compared to related work. These data sets indicate that contiguous or not is application specific, but heterogeneous features and kernels usually lead to performance gain.
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2019/$25.00 © 2019 SPIE
Muhammad Aminul Islam, Derek T. Anderson, John E. Ball, and Nicolas H. Younan "Fusion of heterogeneous bands and kernels in hyperspectral image processing," Journal of Applied Remote Sensing 13(2), 026508 (27 May 2019).
Received: 14 January 2019; Accepted: 3 May 2019; Published: 27 May 2019

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