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26 May 2016 Spectral restoration for hyperspectral images
Zihui Tan, Guorui Jia, Huijie Zhao
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Proceedings Volume 9874, Remotely Sensed Data Compression, Communications, and Processing XII; 987404 (2016) https://doi.org/10.1117/12.2223534
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States
Abstract
Traditional Wiener filtering has been widely used to restore single-band images. However, it has not been discussed yet how to specially use Wiener filtering to get a spectral restoration effect for a 3-Dimensional hyperspectral image. Modeling the measured spectrum to be the result of a convolution with the Spectral Response Function (SRF) and noise-adding process, a method to apply spectral Wiener filtering to hyperspectral images is proposed. Spectral Wiener filtering aims to get an optimal estimation of real spectrum which considers the effect of both noise and SRF. For doing this, the spectral signal-to-noise ratio (SNR) is calculated using a decorrelation method. In an experiment based on simulated hyperspectral image cube, spectral Wiener filtering in a pixel by pixel way achieved a 1.38% increase in the average depth of spectral signature and a 15.4% increase in image sharpness. As a comparison, spatial Wiener filtering band by band achieved a 0.49% decrease in the average depth of spectral signature and a 21.6% increase in image sharpness. The results suggest that spatial and spectral degradation of hyper-spectral image are inter-coupled, and spectral Wiener filter is more suitable to restore spectrum while the spatial Wiener filter is more suitable to restore single-band image.
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Zihui Tan, Guorui Jia, and Huijie Zhao "Spectral restoration for hyperspectral images", Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 987404 (26 May 2016); https://doi.org/10.1117/12.2223534
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KEYWORDS
Signal to noise ratio
Hyperspectral imaging
Image processing
Point spread functions
Electronic filtering
Filtering (signal processing)
Image filtering
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