Skip to main content
Log in

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

  • Research Article
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

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 wavelet-based 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  • Adjorlolo C, Mutanga O, Cho MA, Ismail R (2012) Challenges and opportunities in the use of remote sensing for C 3 and C 4 grass species discrimination and mapping. Afr J Range Fore Sci 29(2):47–61

    Article  Google Scholar 

  • Antoniadis A, Bigot J, Sapatinas T (2001) Wavelet estimators in nonparametric regression: a comparative simulation study. J Stat Softw 6:1–83

    Google Scholar 

  • Banskota A, Wynne RH, Kayastha N (2011) Improving within-genus tree species discrimination using the discrete wavelet transform applied to airborne hyperspectral data. Int J Remote Sens 32(13):3551–3563

    Article  Google Scholar 

  • Bao W, Zhou R, Yang J, Yu D, Li N (2009) Anti-aliasing lifting scheme for mechanical vibration fault feature extraction. Mech Syst Signal Process 23(5):1458–1473

    Article  Google Scholar 

  • Bilgin G, Erturk S, Yildirim T (2008) Multiscale windowed denoising and segmentation of hyperspectral images. In: International Conference of the IEEE Computational Intelligence for Measurement Systems and Applications (CIMSA), 33–37

  • Biradar CM, Thenkabail PS, Noojipady P, Li Y, Dheeravath V, Turral H, Velpuri M, Gumma MK, Gangalakunta ORP, Cai XL, Xiao X, Schull MA, Alankara RD, Gunasinghe S, Mohideen S (2009) A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing. Int J Appl Earth Obs Geoinf 11(2):114–129. doi:10.1016/j.jag.2008.11.002

    Article  Google Scholar 

  • Blackburn GA (2007) Wavelet decomposition of hyperspectral data: a novel approach to quantifying pigment concentrations in vegetation. Int J Remote Sens 28(12):2831–2855

    Article  Google Scholar 

  • Bruce LM, Li J (2001) Wavelets for computationally efficient hyperspectral derivative analysis. IEEE Trans Geosci Remote Sens 39(7):1540–1546

    Article  Google Scholar 

  • Bsoul M, Tamil L (2011) Using second generation wavelets for ECG characteristics points detection. In: Middle East Conference on Biomedical Engineering, Sharjah, United Arab Emirates. 375–378

  • Bsoul M, Minn H, Nourani M, Gupta G, Tamil L (2010) Real-time sleep quality assessment using single-lead ECG and multi-stage SVM classifier. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, Argentina. 1178–1181

  • Bsoul M, Minn H, Tamil L (2011) Apnea MedAssist: real-time sleep apnea monitor using single-lead ECG. IEEE Trans Inf Technol Biomed 15(3):416–427

    Article  Google Scholar 

  • Buaba R, Homaifar A, Gebril M, Kihn E, Zhizhin M (2011) Satellite image retrieval using low memory locality sensitive hashing in Euclidean space. Earth Sci Inf 4(1):17–28

    Article  Google Scholar 

  • Chai Y, Li HF, Qu JF (2010) Image fusion scheme using a novel dual-channel PCNN in lifting stationary wavelet domain. Opt Commun 283(19):3591–3602

    Article  Google Scholar 

  • Charles C, Rasson JP (2003) Wavelet denoising of Poisson-distributed data and applications. Comput Stat Data Anal 43(2):139–148

    Article  Google Scholar 

  • Chávez P, Zorogastúa P, Chuquillanqui C, Salazar LF, Mares V, Quiroz R (2009) Assessing potato yellow vein virus (PYVV) infection using remotely sensed data. Int J Pest Manag 55(3):251–256. doi:10.1080/09670870902862685

    Article  Google Scholar 

  • Chen X, Li X, Wang S, Yang Z, Chen B, He Z (2013) Composite damage detection based on redundant second-generation wavelet transform and fractal dimension tomography algorithm of lamb wave. IEEE Trans Instrum Meas 62(5):1354–1363

    Article  Google Scholar 

  • Chendong D, Qiang G (2008) A lifting undecimated wavelet transform and its applications. J Intell Manuf 19(4):433–441

    Article  Google Scholar 

  • Curran PJ, Dungan JL, Macler BA, Plummer SE, Peterson DL (1992) Reflectance spectroscopy of fresh whole leaves for the estimation of chemical concentration. Remote Sens Environ 39(2):153–166

    Article  Google Scholar 

  • Danandeh Mehr A, Kahya E, Bagheri F, Deliktas E (2013) Successive-station monthly streamflow prediction using neuro-wavelet technique. Earth Sci Inform:1–13

  • Daubechies I, Sweldens W (1998) Factoring wavelet transforms into lifting steps. J Fourier Anal Appl 4(3):247–269

    Article  Google Scholar 

  • Donoho DL, Johnstone IM (1994) Threshold selection for wavelet shrinkage of noisy data. In: Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the Annual International Conference of the Engineering in Medicine and Biology Society. 24–25 doi:10.1109/iembs.1994.412133

  • Duan L, Liu N, Tang y, Liu Y, Zhang Q (2012) Incipient Feature extraction based on singular value decomposition and undecimated lifting scheme packet. In: International Conference on Fuzzy Systems and Knowledge Discovery, Sichuan, China. 1829–1833 doi:10.1109/fskd.2012.6233752

  • Ebadi L, Shafri HM (2014) Compression of remote sensing data using second-generation wavelets: a review. Environ Earth Sci 71(3):1379–1387. doi:10.1007/s12665-013-2544-3

    Article  Google Scholar 

  • Ebadi L, Shafri HZM, Mansor SB, Ashurov R (2013) A review of applying second-generation wavelets for noise removal from remote sensing data. Environ Earth Sci 70(6):2679–2690

    Article  Google Scholar 

  • Estep L, Carter GA (2005) Derivative analysis of AVIRIS data for crop stress detection. Photogramm Eng Remote Sens 71(12):1417–1421

    Article  Google Scholar 

  • Gao L, Yang Z, Cai L, Wang H, Chen P (2011) Roller bearing fault diagnosis based on nonlinear redundant lifting wavelet packet analysis. Sensors 11(1):260–277

    Article  Google Scholar 

  • Garfagnoli F, Martelloni G, Ciampalini A, Innocenti L, Moretti S (2013) Two GUIs-based analysis tool for spectroradiometer data pre-processing. Earth Sci Inf 6(4):227–240. doi:10.1007/s12145-013-0124-4

    Article  Google Scholar 

  • Ge S, Carruthers RI, Kramer M, Everitt JH, Anderson GL (2011) Multiple-level defoliation assessment with hyperspectral data: integration of continuum-removed absorptions and red edges. Int J Remote Sens 32(21):6407–6422

    Article  Google Scholar 

  • Han X, Chang X (2013) An intelligent noise reduction method for chaotic signals based on genetic algorithms and lifting wavelet transforms. Inf Sci (NY) 218(0):103–118. doi:10.1016/j.ins.2012.06.033

    Article  Google Scholar 

  • Hosgood B, Jacquemoud S, Andreoli G, Verdebout J, Pedrini A, Schmuck G (2005) Leaf Optical Properties EXperiment 93 (LOPEX93). Report EUR 16095 EN (revised 2005). Ispra, Italy: European Commission, Joint Research Centre, Institute for Remote Sensing Applications

  • Huang JF, Blackburn GA (2011) Optimizing predictive models for leaf chlorophyll concentration based on continuous wavelet analysis of hyperspectral data. Int J Remote Sens 32(24):9375–9396

    Article  Google Scholar 

  • Huang Z, Turner BJ, Dury SJ, Wallis IR, Foley WJ (2004) Estimating foliage nitrogen concentration from HYMAP data using continuum removal analysis. Remote Sens Environ 93(1–2):18–29

    Article  Google Scholar 

  • Jacquemoud S, Baret F (1990) PROSPECT: a model of leaf optical properties spectra. Remote Sens Environ 34(2):75–91

    Article  Google Scholar 

  • Jia K, Wu B, Tian Y, Zeng Y, Li Q (2011) Vegetation classification method with biochemical composition estimated from remote sensing data. Int J Remote Sens 32(24):9307–9325

    Article  Google Scholar 

  • Ju CH, Tian YC, Yao X, Cao WX, Zhu Y, Hannaway D (2010) Estimating leaf chlorophyll content using red edge parameters. Pedosphere 20(5):633–644

    Article  Google Scholar 

  • Kempeneers P, De Backer S, Debruyn W, Coppin P, Scheunders P (2005) Generic wavelet-based hyperspectral classification applied to vegetation stress detection. IEEE Trans Geosci Remote Sens 43(3):610–614

    Article  Google Scholar 

  • King BM, Rosopa PJ, Minium EW (2011) Statistical reasoning in the behavioral sciences. Wiley, Hoboken

    Google Scholar 

  • Knight MI, Nunes MA, Nason GP (2012) Spectral estimation for locally stationary time series with missing observations. Stat Comput 22(4):877–895

    Article  Google Scholar 

  • Kokaly RF, Despain DG, Clark RN, Livo KE (2003) Mapping vegetation in Yellowstone national park using spectral feature analysis of AVIRIS data. Remote Sens Environ 84(3):437–456

    Article  Google Scholar 

  • Kolaczyk ED (1999) Wavelet shrinkage estimation of certain Poisson intensity signals using corrected thresholds. Stat Sin 9(1):119–135

    Google Scholar 

  • Kusuma KN, Ramakrishnan D, Pandalai HS, Kailash G (2010) Noise-signal index threshold: a new noise-reduction technique for generation of reference spectra and efficient hyperspectral image classification. Geocarto Int 25(7):569–580

    Article  Google Scholar 

  • Lee CS, Lee CK, Yoo KY (2000) New lifting based structure for undecimated wavelet transform. Electron Lett 36(22):1894–1895

    Article  Google Scholar 

  • Lelong CCD, Roger JM, Brégand S, Dubertret F, Lanore M, Sitorus NA, Raharjo DA, Caliman JP (2010) Evaluation of oil-palm fungal disease infestation with canopy hyperspectral reflectance data. Sensors 10(1):734–747

    Article  Google Scholar 

  • Li N, Zhou R (2012) Turbine machine fault diagnosis using modified redundant second generation wavelet packet transform. Proceedings of the World Congress on Intelligent Control and Automation, Beijing, China. 3126–3130

  • Li D, Guo S, Shi Z (2006) Redundant Lifted Fast Wavelet Transform for Signal Compression. Proceedings of the International Conference on Signal Processing, Beijing. 16–20

  • Li N, Zhou R, Hu Q, Liu X (2012) Mechanical fault diagnosis based on redundant second generation wavelet packet transform, neighborhood rough set and support vector machine. Mech Syst Signal Process 28:608–621

    Article  Google Scholar 

  • Liang S (2004) Quantitative remote sensing of land surfaces. John Wilcy & Sons

  • Liu H, He G (2007) Texture extraction of high resolution remote sensing image based on characteristic of image wavelet coefficients. In: Proceedings of SPIE - The International Society for Optical Engineering

  • Liu M, Liu X, Ding W, Wu L (2011) Monitoring stress levels on rice with heavy metal pollution from hyperspectral reflectance data using wavelet-fractal analysis. Int J Appl Earth Obs Geoinf 13(2):246–255

    Article  Google Scholar 

  • Lu X, Wang J (2011) Bearing fault diagnosis based on redundant second generation wavelet denoising and EEMD. Proceedings of the International Conference on Consumer Electronics, Communications and Networks, Xianning, China. 1090–1093

  • Ma HJ, Hu YH, Wu JF, Wang JG, Guo W (2009) Application of lifting scheme translation-invariant wavelet de-noising method in GPS/INS integrated navigation. Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, Baoding, China. 303–307

  • Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  Google Scholar 

  • Misiti M, Misiti Y, Oppenheim G, Poggi J-M (2007) Wavelets and their applications. ISTE Ltd., USA. doi:10.1002/9780470612491

    Book  Google Scholar 

  • Parrilli S, Poderico M, Angelino CV, Verdoliva L (2012) A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage. IEEE Trans Geosci Remote Sens 50(2):606–616

    Article  Google Scholar 

  • Plaza A, Plaza J, Vegas H (2010) Improving the performance of hyperspectral image and signal processing algorithms using parallel, distributed and specialized hardware-based systems. J Signal Process Syst 61(3):293–315

    Article  Google Scholar 

  • Pu R, Gong P, Biging GS, Larrieu MR (2003) Extraction of red edge optical parameters from hyperion data for estimation of forest leaf area index. IEEE Trans Geosci Remote Sens 41(4 PART II):916–921

    Google Scholar 

  • Pu R, Bell S, Baggett L, Meyer C, Zhao Y (2012) Discrimination of seagrass species and cover classes with in situ hyperspectral data. J Coast Res 28(6):1330–1344

    Article  Google Scholar 

  • Rollin EM, Milton EJ (1998) Processing of high spectral resolution reflectance data for the retrieval of canopy water content information. Remote Sens Environ 65(1):86–92

    Article  Google Scholar 

  • Schmidt KS, Skidmore AK (2004) Smoothing vegetation spectra with wavelets. Int J Remote Sens 25(6):1167–1184

    Article  Google Scholar 

  • Shafri HZM, Hamdan N (2009) Hyperspectral imagery for mapping disease infection in oil palm plantation using vegetation indices and red edge techniques. Am J Appl Sci 6(6):1031–1035

    Article  Google Scholar 

  • Shafri HZM, Mather PM (2005) Wavelet shrinkage in noise removal of hyperspectral remote sensing data. Am J Appl Sci 2(7):5

    Google Scholar 

  • Shafri HZM, Yusof MRM (2009) Determination of optimal wavelet denoising parameters for red edge feature extraction from hyperspectral data. J Appl Remote Sens 3 (1)

  • Shafri HZM, Salleh MAM, Ghiyamat A (2006) Hyperspectral remote sensing of vegetation using red edge position techniques. Am J Appl Sci 3(6):1864–1871

  • Shafri HZM, Anuar MI, Seman IA, Noor NM (2011) Spectral discrimination of healthy and ganoderma-infected oil palms from hyperspectral data. Int J Remote Sens 32(22):7111–7129

    Article  Google Scholar 

  • Sharma C, Thenkabail P, Sharma J (2011) Earth Observing Data and Methods for Advancing Water Harvesting Technologies in the Semi-arid Rain-Fed Environments of India. In: IEEE Global Humanitarian Technology Conference, Seattle, WA, USA. 189–193 doi:10.1109/GHTC.2011.68

  • Singh BN, Tiwari AK (2006) Optimal selection of wavelet basis function applied to ECG signal denoising. Digit Signal Process Rev J 16(3):275–287

    Article  Google Scholar 

  • Sreekala GB, Subodh SK (2011) Hyperspectral data mining. In: Thenkabail SP, Lyon JG, Huete A (eds) Hyperspectral remote sensing of vegetation. CRC Press, USA, pp 93–120. doi:10.1201/b11222-8

    Google Scholar 

  • Strang G, Nguyen T (1996) Wavelets and filter banks. Wellesley-Cambridge, USA

    Google Scholar 

  • Sweldens W (1996) The lifting scheme: a custom-design construction of biorthogonal wavelets. Appl Comput Harmon Anal 3(2):186–200

    Article  Google Scholar 

  • Sweldens W (1998) The lifting scheme: a construction of second generation wavelets. SIAM J Numer Anal 29(2):511–546

    Article  Google Scholar 

  • Tan L (2007) Digital signal processing fundamentals and applications. Elsevier, New York

    Google Scholar 

  • Thenkabail PS, Wu Z (2012) An automated cropland classification algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data. Remote Sens 4(10):2890–2918

    Article  Google Scholar 

  • Thenkabail PS, Enclona EA, Ashton MS, Van Der Meer B (2004) Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sens Environ 91(3–4):354–376. doi:10.1016/j.rse.2004.03.013

    Article  Google Scholar 

  • Thenkabail PS, Mariotto I, Gumma MK, Middleton EM, Landis DR, Huemmrich KF (2013) Selection of hyperspectral Narrowbands (HNBs) and composition of hyperspectral Twoband vegetation indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and hyperion/EO-1 data. IEEE J Sel Top Appl Earth Obs Remote Sens 6(2):427–439. doi:10.1109/JSTARS.2013.2252601

    Article  Google Scholar 

  • Tsai F, Philpot W (1998) Derivative analysis of hyperspectral data. Remote Sens Environ 66(1):41–51

    Article  Google Scholar 

  • Uss ML, Vozel B, Lukin VV, Chehdi K (2011) Local signal-dependent noise variance estimation from hyperspectral textural images. IEEE J Sel Top Signal Process 5(3):469–486. doi:10.1109/JSTSP.2010.2104312

    Article  Google Scholar 

  • Vaiphasa C, Skidmore AK, de Boer WF, Vaiphasa T (2007) A hyperspectral band selector for plant species discrimination. ISPRS J Photogramm Remote Sens 62(3):225–235

    Article  Google Scholar 

  • Wang W, Zeng J, Yin S, Wang X (2001) Image fusion on redundant lifting non-separable wavelet transforms. Proceedings of SPIE – The International Society for Optical Engineering, California, USA doi:10.1117/12.872336

  • Wang L, Qu JJ, Hao X, Hunt ER Jr (2011) Estimating dry matter content from spectral reflectance for green leaves of different species. Int J Remote Sens 32(22):7097–7109

    Article  Google Scholar 

  • Wu C, Niu Z, Tang Q, Huang W (2008) Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation. Agric For Meteorol 148(8–9):1230–1241

    Article  Google Scholar 

  • Xiu-bi W (2009) Image Edge Detection Based on Lifting Wavelet. In: International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 25–27

  • Yang Y, Mason AJ (2011) Implantable neural spike detection using lifting-based stationary wavelet transform. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA

  • Yang Y, Kamboh A, Mason AJ (2010) Adaptive threshold spike detection using stationary wavelet transform for neural recording implants. In: IEEE Biomedical Circuits and Systems Conference, Paphos, Cyprus. 9–12

  • Yang Z, Cai L, Gao L, Wang H (2012) Adaptive redundant lifting wavelet transform based on fitting for fault feature extraction of roller bearings. Sensors 12(4):4381–4398

    Article  Google Scholar 

  • Yu XC, Ni F, Long SL, Pei WJ (2012) Remote sensing image fusion based on integer wavelet transformation and ordered nonnegative independent component analysis. GISci Remote Sens 49(3):364–377

    Article  Google Scholar 

  • Zhou F (2010) Fault diagnosis method of gear of wind turbine gearbox based on undecimated wavelet transformation. In: International Conference on Computer Design and Applications, Qinhuangdao, China. 606–609 doi:10.1109/iccda.2010.5540743

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ladan Ebadi.

Additional information

Communicated by: H. A. Babaie

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ebadi, L., Shafri, H.Z.M. A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum. Earth Sci Inform 8, 411–425 (2015). https://doi.org/10.1007/s12145-014-0168-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-014-0168-0

Keywords

Navigation