Abstract
In this paper, we have applied a recently developed complex-domain hyperspectral denoiser for the object recognition task, which is performed by the correlation analysis of investigated objects’ spectra with the fingerprint spectra from the same object. Extensive experiments carried out on noisy data from digital hyperspectral holography demonstrate a significant enhancement of the recognition accuracy of signals masked by noise, when the advanced noise suppression is applied.
Highlights
Hyperspectral imaging (HSI) is used to obtain the spectral distributions for each pixel of the image of a scene
HSI is extremely effective in obtaining spectral data in many applications such as earth remote sensing [1], terahertz imaging [2], and medical imaging [3]
Many rooting techniques are used for denoising in Hyperspectral Digital Holography (HSDH), but they do not help much in noise suppression, working for low noise levels [7,9], but failing in the high ones [10]
Summary
Hyperspectral imaging (HSI) is used to obtain the spectral distributions for each pixel of the image of a scene. Many rooting techniques are used for denoising in HSDH, but they do not help much in noise suppression, working for low noise levels [7,9], but failing in the high ones [10] It is explained by a slice-wise separate filtering which does not process all HS cube slices jointly. They fail due to high noise levels on spectral components with low intensity of a radiation source, which causes a low signal-to-noise ratio (SNR). We show that with the proper noise suppression by CCF , it is possible to significantly improve HSDH processing and to overcome problems of signal masking by background noise despite a high level of noise
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