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A review of applying second-generation wavelets for noise removal from remote sensing data

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Abstract

The processing of remotely sensed data includes compression, noise reduction, classification, feature extraction, change detection and any improvement associated with the problems at hand. In the literature, wavelet methods have been widely used for analysing remote sensing images and signals. The second-generation of wavelets, which is designed based on a method called the lifting scheme, is almost a new version of wavelets, and its application in the remote sensing field is fresh. Although first-generation wavelets have been proven to offer effective techniques for processing remotely sensed data, second-generation wavelets are more efficient in some respects, as will be discussed later. The aim of this review paper is to examine all existing studies in the literature related to applying second-generation wavelets for denoising remote sensing data. However, to make a better understanding of the application of wavelet-based denoising methods for remote sensing data, some studies that apply first-generation wavelets are also presented. In the part of hyperspectral data, there is a focus on noise removal from vegetation spectrum.

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References

  • Amolins K, Zhang Y, Dare P (2007) Wavelet based image fusion techniques—an introduction, review and comparison. ISPRS J Photogramm Remote Sens 62(4):249–263

    Article  Google Scholar 

  • Bacour C, Jacquemoud S, Tourbier Y, Dechambre M, Frangi JP (2002) Design and analysis of numerical experiments to compare four canopy reflectance models. Remote Sens Environ 79(1):72–83

    Article  Google Scholar 

  • Borsdorf A, Raupach R, Flohr T, Hornegger J (2008) Wavelet based noise reduction in CT-images using correlation analysis. Med Imaging IEEE Trans 27(12):1685–1703

    Article  Google Scholar 

  • Bose NK, Chappalli MB (2004) A second-generation wavelet framework for super-resolution with noise filtering. Int J Imaging Syst Technol 14(2):84–89

    Article  Google Scholar 

  • Bréon FM, Vermote E (2012) Correction of MODIS surface reflectance time series for BRDF effects. Remote Sens Environ 125:1–9

    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 

  • Cannata A, Giudice G, Gurrieri S, Montalto P, Alparone S, Di Grazia G, Favara R, Gresta S, Liuzzo M (2010) Relationship between soil CO2 flux and volcanic tremor at Mt. Etna: implications for magma dynamics. Environ Earth Sci 61(3):477–489

    Article  Google Scholar 

  • Chen G, Qian SE (2009) Denoising and dimensionality reduction of hyperspectral imagery using wavelet packets, neighbour shrinking and principal component analysis. Int J Remote Sens 30(18):4889–4895

    Article  Google Scholar 

  • Chen G, Qian SE (2011) Denoising of hyperspectral imagery using principal component analysis and wavelet shrinkage. IEEE Trans Geosci Remote Sens 49(3):973–980

    Article  Google Scholar 

  • Chen M, Weng F (2012) Kramers-Kronig analysis of leaf refractive index with the PROSPECT leaf optical property model. J Geophy Res D Atmosph 117 (17). doi:10.1029/2012JD017477

  • Chen J, Jönsson P, Tamura M, Gu Z, Matsushita B, Eklundh L (2004) A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens Environ 91(3–4):332–344

    Article  Google Scholar 

  • Chen J, Lin H, Shao Y, Yang L (2006) Oblique striping removal in remote sensing imagery based on wavelet transform. Int J Remote Sens 27(8):1717–1723

    Article  Google Scholar 

  • Chen S, Hu X, Peng S (2012) MAP-based denoising of hyperspectral imagery using 3-D edge-preserving priors. In: 2012 2nd International Conference on Remote Sensing, Environment and Transportation Engineering, RSETE 2012—Proceedings, 21:469–489

  • Chen X, Zhang L, Zhang X, Liu B (2013) Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data. Int J Remote Sens 34(6):2200–2215

    Article  Google Scholar 

  • Chinarro D, Villarroel JL, Cuchí JA (2012) Wavelet analysis of Fuenmayor karst spring, San Julián de Banzo, Huesca, Spain. Environ Earth Sci 65(8):2231–2243

    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 

  • Dawson TP, Curran PJ (1998) A new technique for interpolating the reflectance red edge position. Int J Remote Sens 19(11):2133–2139

    Article  Google Scholar 

  • De Backer S, Pizurica A, Huysmans B, Philips W, Scheunders P (2008) Denoising of multicomponent images using wavelet least-squares estimators. Image Vis Comput 26(7):1038–1051

    Article  Google Scholar 

  • Deledalle CA, Tupin F, Denis L (2010) A non-local approach for SAR and interferometric SAR denoising. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp 714–717

  • Demir B, Erturk S, Kemal Gullu M (2009) Wavelet shrinkage denoising of intrinsic mode functions of hyperspectral image bands for classification with high accuracy. In: International Geoscience and Remote Sensing Symposium (IGARSS), pp III983–III986

  • Depczynski U, Jetter K, Molt K, Niemöler A (1999) The fast wavelet transform on compact intervals as a tool in chemometrics. II. Boundary effects, denoising and compression. Chemomet Intell Lab Syst 49(2):151–161

    Article  Google Scholar 

  • Ebadi L, Shafri HZM (2010) Optimal Daubechies Wavelet Parameters for Noise Removal of Red-edge Region in Vegetation Spectrum. Kuala Lumpur, 2010. MRSSIC, p 13

  • 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 

  • Gleich D, Kseneman M, Datcu M (2010) Despeckling of terraSAR-X data using second-generation wavelets. IEEE Geosci Remote Sens Lett 7(1):68–72

    Article  Google Scholar 

  • Han N, Hu J, Zhang W (2010) Multi-spectral and SAR images fusion via Mallat and à trous wavelet transform. In: 2010 18th international conference on geoinformatics, Geoinformatics 2010, pp 1–4

  • Hernández-Clemente R, Navarro-Cerrillo RM, Zarco-Tejada PJ (2012) Carotenoid content estimation in a heterogeneous conifer forest using narrow-band indices and PROSPECT + DART simulations. Remote Sens Environ 127:298–315

    Article  Google Scholar 

  • Hu B, Li Q, Smith A (2009) Noise reduction of hyperspectral data using singular spectral analysis. Int J Remote Sens 30(9):2277–2296

    Article  Google Scholar 

  • Huang X, Zhang L (2012) A multiscale urban complexity index based on 3D wavelet transform for spectral-spatial feature extraction and classification: an evaluation on the 8-channel WorldView-2 imagery. Int J Remote Sens 33(8):2641–2656

    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 

  • Jacquemoud S, Ustin SL, Verdebout J, Schmuck G, Andreoli G, Hosgood B (1996) Estimating leaf biochemistry using the PROSPECT leaf optical properties model. Remote Sens Environ 56(3):194–202

    Article  Google Scholar 

  • Kang J, Zhang W (2008) QuickBird remote sensing image denoising using wavelet packet transform. In: Proceedings—2008 2nd International Symposium on Intelligent Information Technology Application, IITA, pp 315–318

  • 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 

  • 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 Intern 25(7):569–580

    Article  Google Scholar 

  • Landgrebe DA (2003) Signal Theory Methods in Multispectral Remote Sensing. Wiley, Hoboken

  • Letexier D, Bourennane S (2008) Noise removal from hyperspectral images by multidimensional filtering. Geosci Remote Sens IEEE Trans 46(7):2061–2069

    Article  Google Scholar 

  • Li B, Jiao RH, Li YC (2007) Fast adaptive wavelet for remote sensing image compression. J Comput Sci Technol 22(5):770–778

    Article  Google Scholar 

  • Li B, Yang R, Jiang H (2011) Remote-sensing image compression using two-dimensional oriented wavelet transform. IEEE Trans Geosci Remote Sens 49(1 Part 1):236–250

    Article  Google Scholar 

  • Liang S (2004) Quantitative remote sensing of land surfaces. Wiley, Hoboken

  • Lili J, Xiaomei C, Guoqiang N, Shule G (2008) Wavelet threshold denoising for hyperspectral data in spectral domain. In: Proceedings of SPIE—the International Society for Optical Engineering, 2008

  • 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, Liu R, Liu J, Liang S (2007) Removal of noise by wavelet method to generate high quality temporal data of terrestrial MODIS products. Photogramm Eng Remote Sens 73(10):1129–1139

    Google Scholar 

  • Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. Pattern analysis and machine intelligence. IEEE Trans 11(7):674–693

    Google Scholar 

  • Mallat S (2008) A wavelet tour of signal processing: the sparse way, 3rd edn. Academic Press, San Diego

    Google Scholar 

  • Mao J (2012) Noise reduction for lidar returns using local threshold wavelet analysis. Opt Quant Electron 43(1–5):59–68

    Article  Google Scholar 

  • Miao C, Yang L, Liu B, Gao Y, Li S (2011) Streamflow changes and its influencing factors in the mainstream of the Songhua River basin, Northeast China over the past 50 years. Environ Earth Sci 63(3):489–499

    Article  Google Scholar 

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

  • Narayanan RM, Ponnappan SK, Reichenbach SE (2001) Effects of uncorrelated and correlated noise on image information content. In: International Geoscience and Remote Sensing Symposium (IGARSS), 2001, pp 1898–1900

  • Othman H, Shen-En Q (2006) Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage. Geosci Remote Sens IEEE Trans 44(2):397–408

    Article  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 

  • Pizurica A, Philips W, Scheunders P (2005) Wavelet domain denoising of single-band and multiband images adapted to the probability of the presence of features of interest. In: Proceedings of SPIE—the International Society for Optical Engineering, pp 1–14

  • Pradhan B, Sandeep K, Mansor S, Ramli AR, Sharif ARBM (2007) Second-generation wavelets based GIS terrain data compression using Delaunay triangulation. Eng Comput (Swansea, Wales) 24(2):200–213

    Article  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 Coastal 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 

  • Ruffin C, King RL (1999) Analysis of hyperspectral data using Savitzky–Golay filtering—theoretical basis (Part 1). In: international geoscience and remote sensing symposium (IGARSS), pp 756–758

  • Scheunders P (2004) Wavelet thresholding of multivalued images. Image Process IEEE Trans 13(4):475–483

    Article  Google Scholar 

  • Scheunders P, De Backer S (2007) Wavelet denoising of multicomponent images using gaussian scale mixture models and a noise-free image as priors. Image Process IEEE Trans 16(7):1865–1872

    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, 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). doi:10.1117/1.3155804

  • 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

    Article  Google Scholar 

  • Shafri HZM, Hamdan N, Izzuddin Anuar M (2011) Detection of stressed oil palms from an airborne sensor using optimized spectral indices. Int J Remote Sens 33(14):4293–4311

    Article  Google Scholar 

  • Song X, Zhou C, Hepburn DM, Zhang G, Michel M (2007) Second-generation wavelet transform for data denoising in PD measurement. IEEE Trans Dielectr Electr Insul 14(6):1531–1537

    Article  Google Scholar 

  • Sui YP, Yang CY, Liu YJ, Wang J, Wei ZH, He X (2008) Remote sensing image compression algorithm based on wavelet sub-bands entropy. Guangdian Gongcheng Opto-Electronic Eng 35(2):61–65 133

    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 Math Anal 29(2):511–546

    Article  Google Scholar 

  • Sweldens W, Schröder P (1996) Building your own wavelets at home. In: Wavelets in computer graphics, ACM SIGGRAPH course notes, pp 15–87

  • Tian BF, Sun RC, Xu SY (2006) Lossy compression algorithm of remotely sensed multispectral images based on lifting scheme. Guangxue Jishu Optical Tech 32(Suppl):560–562 565

    Google Scholar 

  • Tieniu W, Guangyong L (2012) Climatic sub-cycles recorded by the fourth paleosol layer at Luochuan on the Loess Plateau. Environ Earth Sci 66(5):1329–1335

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Vaiphasa C (2006) Consideration of smoothing techniques for hyperspectral remote sensing. ISPRS J Photogramm Remote Sens 60(2):91–99

    Article  Google Scholar 

  • Vidakovic B (1998) Nonlinear wavelet shrinkage with Bayes rules and Bayes factors. J Am Statist Assoc 93(441):173–179

    Article  Google Scholar 

  • Wang H (2011) Sar image denoising based on dual tree complex wavelet transform. Communications in Computer and Information Science, vol 159, CCIS, USA

  • Wang W, Li Y (2009) Bayesian denoising for remote sensing image based on undecimated discrete wavelet transform. In: Proceedings—2009 international conference on information engineering and computer science, ICIECS, USA

  • Wang YP, Wang Y, Spencer P (2006) A differential wavelet-based noise reduction approach to improve the clustering of hyperspectral raman imaging data. In: 2006 3rd IEEE international symposium on biomedical imaging: from nano to macro—proceedings, pp 988–991

  • Wang XT, Shi GM, Niu Y (2008a) Image denoising based on improved adaptive directional lifting wavelet transform. Intern Conf Signal Process Proc ICSP, In, pp 1112–1115

    Google Scholar 

  • Wang Z, Yu X, Zhang L (2008b) A remote sensing image fusion algorithm based on integer wavelet transform. In: Proceedings of the world congress on intelligent control and automation (WCICA), pp 5950–5954

  • Wang Y, He Z, Zi Y (2009) Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform. Mech Syst Signal Proc 24(1):119–137

    Article  Google Scholar 

  • Weber B, Olehowski C, Knerr T, Hill J, Deutschewitz K, Wessels DCJ, Eitel B, Büdel B (2008) A new approach for mapping of Biological Soil Crusts in semidesert areas with hyperspectral imagery. Remote Sens Environ 112(5):2187–2201

    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 

  • Xiao J, Wu C (2004) Interference multispectral image compression using a new JPEG2000 region-of-interest coding method. Opt Eng 43(4):838–842

    Article  Google Scholar 

  • Yang G, Zheng N, Guo S (2007) Optimal wavelet filter design for remote sensing image compression. J Electron 24(2):276–284

    Google Scholar 

  • Yao H, Huang Y, Hruska Z, Thomson SJ, Reddy KN (2012) Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate. Comput Electron Agric 89:145–157

    Article  Google Scholar 

  • Yusof MRM (2012) Improved Wavelet Denoising of Hyperspectral Reflectance using Level-independent Wavelet Thresholding. Universiti Putra Malaysia, Malaysia

  • Zelinski AC, Goyal VK (2006) Denoising hyperspectral imagery and recovering junk bands using wavelets and sparse approximation. In: Geoscience and remote sensing symposium, 2006. IGARSS 2006. IEEE International Conference on, July 31 2006-Aug. 4 2006, pp 387–390

  • Zhang J, Liu G (2007) A novel lossless compression for hyperspectral images by context-based adaptive classified arithmetic coding in wavelet domain. IEEE Geosci Remote Sens Lett 4(3):461–465

    Article  Google Scholar 

  • Zhang B, Zheng Y-g, Fang W, Cui L-m (2010) A new image fusion algorithm based on second-generation wavelet transform. In: Computational intelligence and natural computing proceedings (CINC), 2010 Second International Conference on, 13–14, pp 390–393

  • Zhang F, Tiyip T, Ding J, Sawut M, Tashpolat N, Kung H, Han G, Gui D (2012a) Spectral reflectance properties of major objects in desert oasis: a case study of the Weigan-Kuqa river delta oasis in Xinjiang, China. Environ Monit Assess 184(8):5105–5119

    Article  Google Scholar 

  • Zhang J, Li G, Liang S (2012b) The response of river discharge to climate fluctuations in the source region of the Yellow River. Environ Earth Sci 66(5):1505–1512

    Article  Google Scholar 

  • Zhao B, He B, Cong Y (2010) Destriping method using lifting wavelet transform of remote sensing image. In: 2010 international conference on computer, mechatronics, control and electronic engineering, CMCE, pp 110–113

  • Zhou GZ, Yang FJ, Wang CZ (2008) Vegetation field spectrum denoising via lifting wavelet transform. J Coal Sci Eng 14(1):131–135

    Article  Google Scholar 

  • Zhu L, Meng J (2010) Study on rainfall variations in the middle part of Inner Mongolia, China during the past 43 years. Environ Earth Sci 60(8):1661–1671

    Article  Google Scholar 

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Ebadi, L., Shafri, H.Z.M., Mansor, S.B. et al. A review of applying second-generation wavelets for noise removal from remote sensing data. Environ Earth Sci 70, 2679–2690 (2013). https://doi.org/10.1007/s12665-013-2325-z

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