Noise Reduction in Hyperspectral Images Through Spectral Unmixing
Milad Razmi1, Ali Rafiee2, Zoheir Kordrostami3

1Milad Razmi, Department of Electrical Engineering, Branch Bushehr slamic Azad University, Bushehr, Iran.
2Ali Rafiee, Electrical Engineering Department Kazeroun Branch Islamic Azad University, Kazeroun, Iran.
3Zoheir Kordrostami, Department of Electrical Engineering, Shiraz University of Technology, Shiraz, Iran

Manuscript received on October 11, 2013. | Revised Manuscript received on October 15, 2013. | Manuscript published on October 25, 2013. | PP:90-94 | Volume-1, Issue-12, October 2013. | Retrieval Number: L05131011213/2013©BEIESP

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Abstract: Spectral unmixing and denoising of hyperspectral images have always been regarded as separate problems. By considering the physical properties of a mixed spectrum, this letter introduces unmixing-based denoising, a supervised methodology representing any pixel as a linear combination of reference spectra in a hyperspectral scene. Such spectra are related to some classes of interest, and exhibit negligible noise influences, as hey are averaged over areas for which ground truth is available. After the unmixing process, the residual vector is mostly composed by the contributions of uninteresting materials, unwanted atmospheric influences and sensor-induced noise, and is thus ignored in the reconstruction of each spectrum. The proposed method, in spite of its simplicity, is able to remove noise effectively for spectral bands with both low and high signal-to-noise ratio. Experiments show that this method could be used to retrieve spectral information from corrupted bands, such as the ones placed at the edge between ultraviolet and visible light frequencies, which are usually discarded in practical applications. The proposed method achieves better results in terms of visual quality in comparison to competitors, if the mean squared error is kept constant. This leads to questioning the validity of mean squared error as a predictor for image quality in remote sensing applications.
Keywords: Denoising, hyperspectral images, image restoration, mean squared error, spectral unmixing.