Name
Abstract | ||
|---|---|---|
In hyperspectral imagery, noise reduction is a vital and common pre-processing step that needs to be executed accurately and efficiently. Until recently, hyperspectral data was modeled using linear stochastic processes and the noise was assumed to manifest itself in a narrow spatial frequency band. The signal and noise are thus considered independent and most of the proposed noise reduction algorithms transform the hyperspectral data linearly from one space to another for noise and signal separation. Hyperspectral data, however, exhibits nonlinear characteristics making the noise frequency and signal dependent. Therefore, to accurately reduce the noise in hyperspectral data, a nonlinear noise reduction algorithm, such as the one we propose in this paper, must be considered. The algorithm, however, is computationally expensive and requires parallelization. To this end, we offer a framework which we have implemented and evaluated. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/IGARSS.2010.5651507 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
image denoising,source separation,stochastic processes,efficiently parallelizing nonlinear noise reduction,hyperspectral data,hyperspectral imagery,linear stochastic process,narrow spatial frequency band,noise frequency,nonlinear characteristics,signal separation,Nonlinear noise reduction,analysis,denoising,grid computing,nonlinear,parallelization,time series | Noise reduction,Value noise,Median filter,Noise (signal processing),Noise measurement,Computer science,Artificial intelligence,Computer vision,Mathematical optimization,Signal-to-noise ratio,Algorithm,Hyperspectral imaging,Gradient noise | Conference |
ISSN | ISBN | Citations |
2153-6996 E-ISBN : 978-1-4244-9564-1 | 978-1-4244-9564-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David G. Goodenough | 1 | 84 | 19.70 |
| tian han | 2 | 31 | 10.85 |
| Belaid Moa | 3 | 5 | 5.53 |
| Kelsey Lang | 4 | 0 | 0.68 |
| Hao Chen | 5 | 45 | 7.54 |
| Amanpreet Dhaliwal | 6 | 0 | 0.34 |
| Ashlin Richardson | 7 | 5 | 2.87 |