A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum

Abstract

Hyperspectral vegetation spectrum is normally contaminated with noise and the presence of noise affects the results of vegetation studies, such as species discrimination and classification, disease detection, stress assessment and the estimation of vegetation's biophysical and biochemical characteristics. Additionally, hyperspectral signals are usually studied using the derivative analysis method that is very sensitive to noise in the data. This study investigates denoising of the hyperspectral vegetation spectrum using different waveletbased methods. A test signal and several real-world vegetation spectra are denoised using four wavelet methods: traditional discrete wavelet transform (DWT); stationary wavelet transform (SWT); lifting wavelet transform (LWT); and a combination of SWT and LWT, which in this paper is called stationary lifting wavelet transform (SLWT). SLWT incorporates the advantages of both SWT and LWT methods, including a translation invariance property and a fast simple algorithm. Experimental results show that SLWT highly outperforms other wavelet-based methods in terms of accuracy and visual quality. Furthermore, this research reveals the following novel results: SLWT 1) for different levels of decomposition of the wavelet transform gives similar results and its denoising results is independent to the selection of decomposition level; 2) generates stable statistical results; 3) can make use of mother wavelets with small filter size (i.e., low-order mother wavelets) that are suitable for preserving subtle features in vegetation spectrum; and 4) its denoising results do not depend on the selection of the mother wavelet when applying low-order mother wavelets.

Comparative analysis of denoising techniques in burn depth discrimination from burn hyperspectral images

“…The discrete wavelet transform (WT) decomposes the image into a set of wavelet coefficients at different decomposition levels so the noise concentrated in the low channels can be removed [24].…”

Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration

“…WT is a method for local signal analysis in both time domain and frequency domain. It can locate useful information in huge signal data and conduct accurate time-domain and frequency-domain analysis [ 31 ]. In addition, the origin spectra were also applied for a comparative analysis.…”

Comparativo de Algoritmos de Machine Learning para Identificação de Moscas da Espécie Drosophila suzukii Através de Imagens das Asas

“…A waveleté capaz de ser executada em diferentes escalas ou resoluções. Segundo [16],é uma técnica para estudo de particularidades invisíveis em certas escalas nos componentes da wavelet estabelecidos em espaço e escala, devido sua estrutura de representação combinada de tempo e frequência. Esse tipo de transformada pode analisar séries contínuas ou discretas de escalas, denominadas respectivamente de Transformada Wavelet Contínua (TWC) e Transformada Wavelet Disceta (TWD).…”