Wavelet analysis and its application in denoising the spectrum of hyperspectral image

• Published in: Guang pu xue yu guang pu fen xi Vol. 29; no. 7; p. 1941
• Main Authors: Zhou, Dan, Wang, Qin-Jun, Tian, Qing-Jiu, Lin, Qi-Zhong, Fu, Wen-Xue
• Format: Journal Article
• Language: Chinese
• Published: China 01.07.2009
• ISSN: 1000-0593
• DOI: 10.3964/j.issn.1000-0593(2009)07-1941-05

In order to remove the sawtoothed noise in the spectrum of hyperspectral remote sensing and improve the accuracy of information extraction using spectrum in the present research, the spectrum of vegetation in the USGS (United States Geological Survey) spectrum library was used to simulate the performance of wavelet denoising. These spectra were measured by a custom-modified and computer-controlled Beckman spectrometer at the USGS Denver Spectroscopy Lab. The wavelength accuracy is about 5 nm in the NIR and 2 nm in the visible. In the experiment, noise with signal to noise ratio (SNR) 30 was first added to the spectrum, and then removed by the wavelet denoising approach. For the purpose of finding the optimal parameters combinations, the SNR, mean squared error (MSE), spectral angle (SA) and integrated evaluation coefficient eta were used to evaluate the approach's denoising effects. Denoising effect is directly proportional to SNR, and inversely proportional to MSE, SA and the integrated evaluation coefficien