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A comparative study on linear regression-based noise estimation for hyperspectral imagery.
| Content Provider | CiteSeerX |
|---|---|
| Author | Gao, Lianru Du, Qian Zhang, Bing Yang, Wei Wu, Yuanfeng |
| Abstract | Abstract—In the traditional signal model, signal is assumed to be deterministic, and noise is assumed to be random, additive and uncorrelated to the signal component. A hyperspectral image has high spatial and spectral correlation, and a pixel can be well pre-dicted using its spatial and/or spectral neighbors; any prediction error can be considered from noise. Using this concept, several al-gorithms have been developed for noise estimation for hyperspec-tral images. However, these algorithms have not been rigorously analyzed with a unified scheme. In this paper, we conduct a com-parative study for such linear regression-based algorithms using simulated images with different signal-to-noise ratio (SNR) and real images with different land cover types. Based on experimental results, instructive guidance is concluded for their practical appli-cations. Index Terms—Hyperspectral, multiple linear regressions, noise estimation. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Linear Regression-based Noise Estimation Hyperspectral Imagery Comparative Study Real Image Spectral Neighbor Index Term Hyperspectral Hyperspec-tral Image Spectral Correlation Hyperspectral Image Noise Estimation Practical Appli-cations Traditional Signal Model Multiple Linear Regression Linear Regression-based Algorithm Com-parative Study Instructive Guidance Different Signal-to-noise Ratio Several Al-gorithms Unified Scheme High Spatial Signal Component Prediction Error Different Land Cover Type Experimental Result Simulated Image |
| Content Type | Text |
