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Band-Specified Virtual Dimensionality for Band Selection: An Orthogonal Subspace Projection Approach.
- Source :
-
IEEE Transactions on Geoscience & Remote Sensing . May2018, Vol. 56 Issue 5, p2822-2832. 11p. - Publication Year :
- 2018
-
Abstract
- This paper develops a new Neyman–Pearson detection approach, to be called band-specified virtual dimensionality (BSVD), to estimating the number of bands required by band selection (BS), $n_{\mathrm {BS}}$ , as well as finding desired bands at the same time. Its idea is derived from target-specified virtual dimensionality (TSVD) where targets under hypotheses as signal sources in TSVD are replaced with bands as signal sources and the test statistics derived for a Neyman–Pearson detector (NPD) is signal-to-noise ratio (SNR) that is used to derive orthogonal subspace projection (OSP) approach for hyperspectral image classification and dimensionality reduction. Accordingly, the resulting virtual dimensionality is referred to as OSP-based BSVD. Several benefits resulting from BSVD cannot be offered by the traditional BS methods. One is its direct approach to dealing with $n_{\mathrm {BS}}$. Another is no-search strategy needed for finding optimal bands. Instead, it uses NPD to determine and rank desired bands for band prioritization. Most importantly, it determines $n_{\mathrm {BS}}$ and finds desired bands simultaneously and progressively. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 56
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 129949299
- Full Text :
- https://doi.org/10.1109/TGRS.2017.2784372