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Spectral Object Recognition in Hyperspectral Holography with Complex-Domain Denoising
Igor Shevkunov1,2, Vladimir Katkovnik1, Daniel Claus3
1Faculty of Information Technology and Communication Sciences, Tampere University, FI-33101 Tampere, Finland.
Sensors (Basel, Switzerland)
|November 30, 2019
Summary
A new complex-domain hyperspectral denoiser significantly improves object recognition accuracy. This advanced noise suppression enhances signal detection in noisy digital hyperspectral holography data.
Area of Science:
- Optics and Photonics
- Computer Vision
- Signal Processing
Background:
- Object recognition relies on spectral analysis, often hindered by noise in hyperspectral data.
- Digital hyperspectral holography generates complex data requiring advanced processing for accurate analysis.
Purpose of the Study:
- To evaluate a novel complex-domain hyperspectral denoiser for object recognition.
- To assess the impact of advanced noise suppression on recognition accuracy in noisy hyperspectral datasets.
Main Methods:
- Application of a recently developed complex-domain hyperspectral denoiser.
- Object recognition via correlation analysis of spectral data against reference spectra.
- Experimental validation using noisy data from digital hyperspectral holography.
Main Results:
- The hyperspectral denoiser demonstrated significant noise suppression capabilities.
- Recognition accuracy for signals masked by noise was substantially enhanced.
- The method proved effective in improving object identification in challenging, noisy environments.
Conclusions:
- Complex-domain hyperspectral denoising is a powerful tool for improving object recognition.
- Advanced noise suppression techniques are crucial for reliable analysis of hyperspectral data.
- The developed denoiser offers a significant advantage for object recognition tasks in digital hyperspectral holography.
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