Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 6, Issue 2, Pages 488-498Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2012.2227245
Keywords
Hyperspectral; multiple linear regressions; noise estimation
Categories
Funding
- National Key Technology R&D Program of China [2011BAH12B04]
- National Natural Science Foundation of China [40901225, 41201356]
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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 predicted using its spatial and/or spectral neighbors; any prediction error can be considered from noise. Using this concept, several algorithms have been developed for noise estimation for hyperspectral images. However, these algorithms have not been rigorously analyzed with a unified scheme. In this paper, we conduct a comparative 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 applications.
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