Abstract
In this paper, we present a novel DFT- and wavelet-based estimation scheme for hyperspectral imagery. Optimal hyperspectral image estimation relies on the ability to decorrelate the signal in both space and channel at the cost of requiring second-order signal statistics. This statistical requirement is removed by the proposed estimator, which approximately decorrelates the signal in space using a 2-D discrete wavelet transform and in channel using a discrete Fourier transform. In addition to allowing extremely efficient estimation, the proposed estimator vastly improves visual quality and yields typical signal-to-noise ratio gains of over 14 dB.
| Original language | English (US) |
|---|---|
| Pages | 743-745 |
| Number of pages | 3 |
| State | Published - 2003 |
| Event | 2003 IGARSS: Learning From Earth's Shapes and Colours - Toulouse, France Duration: Jul 21 2003 → Jul 25 2003 |
Other
| Other | 2003 IGARSS: Learning From Earth's Shapes and Colours |
|---|---|
| Country/Territory | France |
| City | Toulouse |
| Period | 7/21/03 → 7/25/03 |
ASJC Scopus subject areas
- Computer Science Applications
- General Earth and Planetary Sciences