[Wavelet analysis and its application in denoising the spectrum of hyperspectral image].
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 coefficient eta. Denoising results show that the sawtoothed noise in noisy spectrum was basically eliminated, and the denoised spectrum basically coincides with the original spectrum, maintaining a good spectral characteristic of the curve. Evaluation results show that the optimal denoising can be achieved by firstly decomposing the noisy spectrum into 3-7 levels using db12, db10, sym9 and sym6 wavelets, then processing the wavelet transform coefficients by soft-threshold functions, and finally estimating the thresholds by heursure threshold selection rule and rescaling using a single estimation of level noise based on first-level coefficients. However, this approach depends on the noise level, which means that for different noise level the optimal parameters combination is also diverse.
Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100086, China. dzhou@rsgs.ac.cn
Dan
D
Zhou
Qin-Jun
QJ
Wang
Qing-Jiu
QJ
Tian
Qi-Zhong
QZ
Lin
Wen-Xue
WX
Fu