Wavelet-based threshold denoising for imaging hyperspectral data

Yang Hao, Zhang Dongyan, Huang Linsheng, Zhao Jinling

Abstract


Imaging spectroradiometer is highly susceptible to noise. Accurately quantitative processing with higher quality is obligatory before any derivative analysis, especially for precision agricultural application. Using the self-developed Pushbroom Imaging Spectrometer (PIS), a wavelet-based threshold (WT) denoising method was proposed for the PIS imaging hyperspectral data. The WT with PIS was evaluated by comparing with other popular denoising methods in pixel scale and in regional scale. Furthermore, WT was validated by chlorophyll concentration retrieval based on red-edge position extraction. The result indicated that the determination coefficient R2 of the chlorophyll concentration inversion model of winter wheat leaves was improved from 0.586 to 0.811. It showed that the developed denoising method allowed effective denoising while maintaining image quality, and presented significant advantages over conventional methods.
Keywords: wavelet, denoising, spectral domain, Pushbroom Imaging Spectrometer, red edge position
DOI: 10.3965/j.ijabe.20140703.005

Citation: Yang H, Zhang D Y, Huang L S, Zhao J L. Wavelet-based threshold denoising for imaging hyperspectral data. Int J Agric & Biol Eng, 2014; 7(3): 36－42.

Keywords


wavelet, denoising, spectral domain, Pushbroom Imaging Spectrometer, red edge position

Full Text:

PDF

References


Mulla D J. Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems Engineering, 2013; 114(4): 358－371.

Lelong C C D, Pinet P C, Poilvé H. Hyperspectral imaging and stress mapping in agriculture: A case study on wheat in Beauce (France). Remote Sensing of Environment, 1998; 66(2): 179－191.

Huang W, Lamb D W, Liu Z, Zhang Y, Liu L, Wang J. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture, 2007; 8(4-5): 187－197.

Mariotto I, Thenkabail P S, Huete A, Slonecker E T, Platonov A. Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission. Remote Sensing of Environment, 2013; 139: 291－305.

Schaepman M E, Ustin S L, Plaza A J, Painter T H, Verrelst J, Liang S. Earth system science related imaging spectroscopy—An assessment. Remote Sensing of Environment, 2009; 113: S123－S137.

Haest M, Cudahy T, Rodger A, Laukamp C, Martens E, Caccetta M. Unmixing the effects of vegetation in airborne hyperspectral mineral maps over the Rocklea Dome iron-rich palaeochannel system (Western Australia). Remote Sensing of Environment, 2013; 129: 17－31.

Guo Z M, Huang W Q, Chen L P, Peng Y K, Wang X. Shortwave infrared hyperspectral imaging for detection of pH value in Fuji apple. International Journal of Agricultural and Biological Engineering, 2014; 7(2): 130－137.

Kamruzzaman M, ElMasry G, Sun D W, Allen P. Application of NIR hyperspectral imaging for discrimination of lamb muscles. Journal of Food Engineering, 2011; 104(3): 332－340.

ElMasry G, Sun D W, Allen P. Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging. Food Research International, 2011; 44(9): 2624－2633.

Martin M E, Wabuyele M B, Chen K, Kasili P, Panjehpour M, Phan M, et al. Development of an advanced hyperspectral imaging (HSI) system with applications for cancer detection. Annals of biomedical engineering, 2006; 34(6): 1061－1068.

Gramfort, A, Poupon, C, Descoteaux, M. Denoising and fast diffusion imaging with physically constrained sparse dictionary learning. Medical Image Analysis, 2014; 18(1): 36－49.

Luo J H, Zhu Y M. Denoising of medical images using a reconstruction-average mechanism. Digital Signal Processing, 2012; 22(2): 337－347.

Fang L Y, Li S T, Nie Q, Izatt J A, Toth C A, Farsiu S. Sparsity based denoising of spectral domain optical coherence tomography images. Biomedical Optics Express, 2012; 3(5): 927－942.

Mohan J, Krishnaveni V, Guo Y H. MRI denoising using nonlocal neutrosophic set approach of Wiener filtering. Biomedical Signal Processing and Control, 2013; 8(6): 779－791.

Wang S, Huang T Z, Liu J, Lv X G. An alternating iterative algorithm for image deblurring and denoising problems. Communications in Nonlinear Science and Numerical Simulation, 2014; 19(3): 617－626.

Jahangir A L A M, Chowdhury F A, Fasiul A L A M. Wiener denoising based on perceptual frequency weighting and noise spectrum shaping. IU-Journal of Electrical & Electronics Engineering, 2013; 13(1): 1589－1595.

Liu Y P, Gao G R, Gong N, Huang R H. Infrared spectrum denoising with combination of lifting wavelet domain thresholding and median filtering. Spectroscopy and Spectral Analysis, 2012; 32(8): 2085－2088.

Li H, Lin Q Z, Wang Q J, Liu Q J, Wu Y Z. Research on spectrum denoising methods based on the combination of wavelet package transformation and mathematical morphology. Spectroscopy and Spectral Analysis, 2010; 30(3): 644－648.

Axell E and Larsson E G. A bayesian approach to spectrum sensing, denoising and anomaly detection, proceedings of the 34th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'09), 2009; 2333－2336.

Lin X M, Wang J, Yao Q H. Wavelet analysis of near infrared spectral data in the application of denoising. Applied Mechanics and Materials, 2011; 1358: 48－49.

Zhang D Y, Song X Y, Ma Z H, Yang G J, Huang W J, Wang J H. Assessment of the developed Pushbroom Imaging Spectrometer in single leaf scale. Scientia Agricultura Sinica, 2010; 43(11): 2239－2245.

Chen J, Jönsson P, Tamura M, Gu Z H, Matsushita B, Eklundh L. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote sensing of Environment, 2004; 91(3): 332－344.

Said S E, Dickey D A. Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 1984; 71(3): 599－607.

Brownrigg D R K. The weighted median filter. Communications of the ACM, 1984; 27(8): 807－818.

David L D. and Iain M J. Ideal denoising in an orthonormal basis chosen from a library of bases. Academic Science Series (I), 1994; 319: 1317－1322.

Cho M A, Skidmore A K. A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sensing of Environment. 2006; 101: 181－193.




Copyright (c)



2023-2026 Copyright IJABE Editing and Publishing Office