Band-Specified Virtual Dimensionality for Band Selection: An Orthogonal Subspace Projection Approach

Chunyan Yu, Li Chien Lee, Chein I. Chang, Bai Xue, Meiping Song, Jian Chen

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


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 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 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 BS and finds desired bands simultaneously and progressively.

Original languageEnglish
Pages (from-to)2822-2832
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number5
Publication statusPublished - 2018 May

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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