Unsupervised Dimensionality Reduction for Hyperspectral Imagery via Local Geometric Structure Feature Learning

Volume: 17, Issue: 8, Pages: 1425 - 1429
Published: Aug 1, 2020
Abstract
Hyperspectral images (HSIs) possess a large number of spectral bands, which easily lead to the curse of dimensionality. To improve the classification performance, a huge challenge is how to reduce the number of spectral bands and preserve the valuable intrinsic information in the HSI. In this letter, we propose a novel unsupervised dimensionality reduction method called local neighborhood structure preserving embedding (LNSPE) for HSI...
Paper Details
Title
Unsupervised Dimensionality Reduction for Hyperspectral Imagery via Local Geometric Structure Feature Learning
Published Date
Aug 1, 2020
Volume
17
Issue
8
Pages
1425 - 1429
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