Estimation of Number of Spectrally Distinct Signal Sources in Hyperspectral Imagery
University of Maryland, Baltimore County · Texas A&M University – Kingsville
Abstract
With very high spectral resolution, hyperspectral sensors can now uncover many unknown signal sources which cannot be identified by visual inspection or a priori. In order to account for such unknown signal sources, we introduce a new definition, referred to as virtual dimensionality (VD) in this paper. It is defined as the minimum number of spectrally distinct signal sources that characterize the hyperspectral data from the perspective view of target detection and classification. It is different from the commonly used intrinsic dimensionality (ID) in the sense that the signal sources are determined by the proposed VD based only on their distinct spectral properties. These signal sources may include unknown…
Citation impact
- FWCI
- 34.04
- Percentile
- 100%
- References
- 28
Authors
2- CCC.-I. ChangCorresponding
University of Maryland, Baltimore County
- QDQian Du
Texas A&M University – Kingsville
Topics & keywords
- Hyperspectral imaging
- Computer science
- Artificial intelligence
- Curse of dimensionality
- Thresholding
- Pattern recognition (psychology)
- SIGNAL (programming language)
- Noise (video)