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A review of potential image fusion methods for remote sensing-based irrigation management: part II

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Abstract

Satellite-based sensors provide data at either greater spectral and coarser spatial resolutions or lower spectral and finer spatial resolutions due to complementary spectral and spatial characteristics of optical sensor systems. In order to overcome this limitation, image fusion has been suggested to obtain higher spatial and spectral resolution images at the same time. Image fusion has been a valuable technique in digital image analysis and comparison because of the availability of multi-spatial and multispectral images from satellite and airborne sensors. It has been applied to merge coarser spatial resolution of multispectral images with a finer spatial resolution panchromatic image to enhance visual apprehension and to provide images that are more informative. Part I companion paper presented and discussed the image downscaling methods. In this paper (part II), the main objective is to review existing image fusion methods for their capability to downscale coarser spatial resolution images for irrigation management applications. A literature review indicated that image fusion methods have not been actively used in obtaining high-resolution land surface temperature (LST) and evapotranspiration (ET) images for irrigation management. However, there is a great potential for applying image fusion methods to retrieve finer LST and ET images from coarser thermal images by fusing them with finer non-thermal color or panchromatic images for irrigation scheduling and management purposes.

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Abbreviations

ARSIS:

Amélioration de la résolution spatiale par injection de structures meaning improvement of the spatial resolution by injection of structures

ASTER:

Advanced spaceborne thermal emission and reflection radiometer

BDF:

Bayesian data fusion

CS:

Component substitution

DFB:

Directional filter bank

DI:

Disturbance index

DN:

Digital number

DSCK:

Downscaling co-kriging

DWT:

Discrete wavelet transform

ENVI:

ENvironment for Visualizing Images

ERGAS:

Erreur relative globale adimensionelle de synthése meaning relative dimensionless global error in synthesis

ET:

Evapotranspiration

ETM+:

Enhanced thematic mapper plus

GLP:

Generalized Laplacian pyramid

GP:

Gaussian pyramid

GS:

Gram–Schmidt

H:

Hue

HPF:

High-pass filter

HWT:

Haar wavelet transform

I:

Intensity

IHS:

Intensity, hue, and saturation

LHS:

Lightness, hue, and saturation

LP:

Laplacian pyramid

LPF:

Low pass filter

LST:

Land surface temperature

MODIS:

Moderate resolution imaging spectroradiometer

MRA:

Multi-resolution analysis

MS:

Multispectral

NDVI:

Normalized difference vegetation index

PAN:

Panchromatic

PBIM:

Pixel block intensity modulation

PCA:

Principal component analysis

PFS:

Pyramidal in Fourier space

PL:

Pyramidal Laplacian

PSF:

Point spread function

RASE:

Relative average spectral error

RGB:

Red, green, and blue

RMSE:

Root mean square error

ROLPP:

Ratio of low pass pyramid

S:

Saturation

SAM:

Spectral angle mapper

SFIM:

Smoothing filter-based intensity modulation

SNR:

Signal-to-noise ratio

SPOT:

Systeme pour l’observation de la terre

STAARCH:

Spatial temporal adaptive algorithm for mapping reflectance change

STARFM:

Spatial and temporal adaptive reflectance fusion model

TM:

Thematic mapper

WT:

Wavelet transform

References

  • Acerbi-Junior FW, Clevers JGPW, Schaepman ME (2006) The assessment of multi-sensor image fusion using wavelet transforms for mapping the Brazilian Savanna. Int J Appl Earth Obs 8:278–288

    Article  Google Scholar 

  • Aiazzi B, Alparone L, Baronti S (1998) Multiresolution loca-statistics speckle filtering based on a ratio Laplacian pyramid. IEEE Trans Geosci Remote 36:1466–1476

    Article  Google Scholar 

  • Aiazzi B, Alparone L, Baronti S, Garzelli A (2002) Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis. IEEE Trans Geosci Remote 40:2300–2312

    Article  Google Scholar 

  • Aiazzi B, Baronti S, Selva M (2007) Improving component substitution pansharpening through multivariate regression of MS+ Pan data. IEEE Trans Geosci Remote 45:3230–3239

    Article  Google Scholar 

  • Aiazzi B, Baronti S, Lotti F, Selva M (2009) A comparison between global and context-adaptive pansharpening of multispectral images. IEEE Geosci Remote Sens 6:302–306

    Article  Google Scholar 

  • Alparone L, Baronti S, Garzelli A, Nencini F (2004) A global quality measurement of pan-sharpened multispectral imagery. IEEE Geosci Remote Sens 1:313–317

    Article  Google Scholar 

  • Atkinson PM, Pardo-Iguzquiza E, Chica-Olmo M (2008) Downscaling cokriging for super-resolution mapping of continua in remotely sensed images. IEEE Trans Geosci Remote 46:573–580

    Article  Google Scholar 

  • Blanc P, Blu T, Ranchin T, Wald L, Aloisi R (1998) Using iterated rational filter banks within the ARSIS concept for producing 10 m Landsat multispectral images. Int J Remote Sens 19:2331–2343

    Article  Google Scholar 

  • Burt PJ, Adelson EH (1983) The Laplacian pyramid as a compact image code. IEEE Trans Commun 31:532–540

    Article  Google Scholar 

  • Candès EJ (1999) Harmonic analysis of neural networks. Appl Comput Harmon A 6:197–218

    Article  Google Scholar 

  • Candès EJ, Donoho DL (2000) Curvelets—a surprisingly effective nonadaptive representation for objects with edges. In: Cohen A, Rabut C, Schumaker CC (eds) Curves and surfaces. Vanderbilt University Press, Nashville, pp 105–120

  • Carper WJ, Lillesand TM, Kiefer RW (1990) The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogramm Eng Rem Sens 56:459–467

    Google Scholar 

  • Cetin M, Musaoglu N (2009) Merging hyperspectral and panchromatic image data: qualitative and quantitative analysis. Int J Remote Sens 30:1779–1804

    Article  Google Scholar 

  • Chavez PS Jr, Kwarteng AY (1989) Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis. Photogramm Eng Rem S 55:339–348

    Google Scholar 

  • Chavez PS Jr, Sides SC, Anderson JA (1991) Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic. Photogramm Eng Remote Sens 57:295–303

    Google Scholar 

  • Chen D, Li Q (2005) The use of complex contourlet transform on fusion scheme. Proc World Acad Sci E 7:342–347

    Google Scholar 

  • Choi M (2006) A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter. IEEE Trans Geosci Remote 44:1672–1682

    Article  Google Scholar 

  • Choi M, Kim RY, Nam M-R, Kim HO (2005) Fusion of multispectral and panchromatic satellite images using the curvelet transform. IEEE Geosci Remote Sens 2:136–140

    Google Scholar 

  • Do MN, Vetterli M (2003) Framing pyramids. IEEE Trans Signal Process 51:2329–2342

    Article  Google Scholar 

  • Do MN, Vetterli M (2005) The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans Image Process 14:2091–2106

    Article  PubMed  Google Scholar 

  • Donoho DL (1999) Wedgelets: nearly minimax estimation of edges. Ann Stat 27:859–897

    Article  Google Scholar 

  • Dou W, Chen Y, Li X, Sui DZ (2007) A general framework for component substitution image fusion: an implementation using the fast image fusion method. Comput Geosci 33:219–228

    Article  Google Scholar 

  • Edwards K, Davis PA (1994) The use of intensity-hue-saturation transformation for producing color shaded-relief images. Photogramm Eng Rem S 60:1369–1374

    Google Scholar 

  • ERDAS (2009) ERDAS field guide. November 2009 version, Leica Geosystems, ERDAS Inc., Norcross

  • Fasbender D, Radoux J, Bogaert P (2008a) Bayesian data fusion for adaptable image pansharpening. IEEE Trans Geosci Remote 46:1847–1857

    Article  Google Scholar 

  • Fasbender D, Tuia D, Bogaert P, Kanevski M (2008b) Support-based implementation of Bayesian data fusion for spatial enhancement: applications to ASTER thermal images. IEEE Geosci Remote Sens 5:598–602

    Article  Google Scholar 

  • Gangkofner UG, Pradhan PS, Holcomb DW (2008) Optimizing the high-pass filter addition technique for image fusion. Photogramm Eng Remote Sens 74:1107–1118

    Google Scholar 

  • Gao F, Masek J, Schwaller M, Hall F (2006) On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans Geosci Remote 44:2207–2218

    Article  Google Scholar 

  • Garguet-Duport B, Girel J, Chassery J-M, Pautou G (1996) The use of multiresolution analysis and wavelets transform for merging SPOT panchromatic and multispectral image data. Photogramm Eng Remote Sens 62:1057–1066

    Google Scholar 

  • Gautam RS, Singh D, Mittal A (2007) Application of principal component analysis and information fusion technique to detect hotspots in NOAA/AVHRR images of Jharia coalfield, India. J Appl Remote Sens 1: Art. No. 013523

  • Gillespie AR, Kahle AB, Walker RE (1987) Color enhancement of highly correlated images, II. Channel ratio and “chromaticity” transformation techniques. Remote Sens Environ 22:343–365

    Article  Google Scholar 

  • González-Audícana M, Saleta JL, Catalán RG, García R (2004) Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition. IEEE Trans Geosci Remote 42:1291–1299

    Article  Google Scholar 

  • Hauter A, Chang KC, Karp S (1997) Polarimetric fusion for synthetic aperture radar target classification. Pattern Recogn 30:769–775

    Article  Google Scholar 

  • Hilker T, Wulder MA, Coops NC, Linke J, McDermid G, Masek JG, Gao F, White JC (2009) A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sens Environ 113:1613–1627

    Article  Google Scholar 

  • Hong G, Zhang Y, Mercer B (2009) A wavelet and IHS integration method to fuse high resolution SAR with moderate resolution multispectral images. Photogramm Eng Remote Sens 75:1213–1223

    Google Scholar 

  • Kang TJ, Zhang XC, Wang HY (2008) Assessment of the fused image of multi-spectral and panchromatic images of SPOT5 in the investigation of geological hazards. Sci China Ser E 51:144–153

    Article  Google Scholar 

  • Karathanassi V, Kolokousis P, Ioannidou S (2007) A comparison study on fusion methods using evaluation indicators. Int J Remote Sens 28:2309–2341

    Article  Google Scholar 

  • Khan MM, Chanussot J, Condat L, Montanvert A (2008) Indusion: fusion of multispectral and panchromatic images using the induction scaling technique. IEEE Geosci Remote Sens 5:98–102

    Article  Google Scholar 

  • Klonus S, Ehlers M (2007) Image fusion using the Ehlers spectral characteristics preservation algorithm. GISci Remote Sens 44:93–116

    Article  Google Scholar 

  • Kyriakidis PC, Yoo E-H (2005) Geostatistical prediction and simulation of point values from areal data. Geogr Anal 37:124–151

    Article  Google Scholar 

  • Laben CA, Brower BV (2000) Process for enhancing the spatial resolution of multispectral imagery using Pan-sharpening (Gram–Schmidt). US Patent 6,011,875, 4 Jan 2000. Technical report, Eastman Kodak Company

  • Le Pennec E, Mallat S (2000) Image compression with geometrical wavelets. IEEE Image Proc 1:661–664

    Google Scholar 

  • Le Pennec E, Mallat S (2005a) Sparse geometric image representations with bandelets. IEEE Trans Image Process 14:423–438

    Article  PubMed  Google Scholar 

  • Le Pennec E, Mallat S (2005b) Bandelet image approximation and compression. Multiscale Model Simul 4:992–1039

    Article  Google Scholar 

  • Li H, Manjunath BS, Mitra SK (1995) Multisensor image fusion using the wavelet transform. Graph Model Image Proc 57:235–245

    Article  Google Scholar 

  • Liu JG (2000) Smoothing filter-based intensity modulation: a spectral preserve image fusion technique for improving spatial details. Int J Remote Sens 21:3461–3472

    Article  Google Scholar 

  • Liu JG, Moore JM (1998) Pixel block intensity modulation: adding spatial detail to TM band 6 thermal imagery. Int J Remote Sens 19:2477–2491

    Article  Google Scholar 

  • Mallat SG (1998) A wavelet tour of signal processing. Academic Press, San Diego

  • Marlik MH, Gilani SAM, Haq A-U (2008) Adaptive image fusion scheme based on contourlet transform, kernel PCA and support vector machine. In: Elleithy K (ed) Innovations and advanced techniques in systems. Computing sciences and software engineering, pp 313–318

  • Martínez B, Gilabert MA (2009) Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sens Environ 113:1823–1842

    Article  Google Scholar 

  • Mascarenhas NDA, Banon GJF, Candeias ALB (1996) Multispectral image data fusion under a Bayesian approach. Int J Remote Sens 17:1457–1471

    Article  Google Scholar 

  • Meyer F, Coifman RR (1997) Brushlets: a tool for directional image analysis and image compression. Appl Comput Harmon A 4:147–187

    Article  Google Scholar 

  • Mitianoudis N, Stathaki T (2006) Adaptive image fusion using ICA bases. IEEE international conference on acoustics, speech, and signal processing (ICASSP), Toulouse, France, pp II-829–II-832

  • Moulin P (2005) Multiscale image decompositions and wavelets. In: Bovik AC (ed) Handbook of image and video processing, 2nd edn. Academic Press, New York

    Google Scholar 

  • Nencini F, Garzelli A, Baronti S, Alparone L (2007) Remote sensing image fusion using the curvelet transform. Inf Fusion 8:143–156

    Article  Google Scholar 

  • Nikolakopoulos KG (2008) Comparison of nine fusion techniques for very high resolution data. Photogramm Eng Remote Sens 74:647–659

    Google Scholar 

  • Núñez J, Otazu X, Fors O, Prades A, Palà V, Arbiol R (1999) Multiresolution-based image fusion with additive wavelet decomposition. IEEE Trans Geosci Remote 37:1204–1211

    Article  Google Scholar 

  • Olkkonen H, Pesola P (1996) Gaussian pyramid wavelet transform for multiresolution analysis of images. Graph Model Image Proc 58:394–398

    Article  Google Scholar 

  • Pardo-Igúzquiza E, Chica-Olmo M, Atkinson PM (2006) Downscaling cokriging for image sharpening. Remote Sens Environ 102:86–98

    Article  Google Scholar 

  • Park JH, Kang MG (2004) Spatially adaptive multi-resolution multispectral image fusion. Int J Remote Sens 25:5491–5508

    Article  Google Scholar 

  • Petrovic V, Cootes T (2007) Objectively adaptive image fusion. Inf Fusion 8:168–176

    Article  Google Scholar 

  • Pohl C, van Genderen JL (1998) Multisensor image fusion in remote sensing: concepts, methods and applications. Int J Remote Sens 19:823–854

    Article  Google Scholar 

  • Price JC (1999) Combining multispectral data of differing spatial resolution. IEEE Trans Geosci Remote 37:1199–1203

    Article  Google Scholar 

  • Qu X, Yan J, Xie G, Zhu Z, Chen B (2007) A novel image fusion algorithm based on bandelet transform. Chin Opt Lett 5:569–572

    Google Scholar 

  • Ranchin T, Wald L (2000) Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation. Photogramm Eng Remote Sens 66:49–61

    Google Scholar 

  • Ranchin T, Aiazzi B, Alparone L, Baronti S, Wald L (2003) Image fusion-the ARSIS concept and some successful implementation schemes. ISPRS J Photogramm 58:4–18

    Article  Google Scholar 

  • Schowengerdt RA (1980) Reconstruction of multispatial, multispectral image data using spatial frequency content. Photogramm Eng Remote Sens 46:1325–1334

    Google Scholar 

  • Shah VP, Younan NH, King RL (2008) An efficient pan-sharpening method via a combined adaptive PCA approach and contourlets. IEEE Trans Geosci Remote 46:1323–1335

    Article  Google Scholar 

  • Starck J-L, Candès EJ, Donoho DL (2002) The curvelet transform for image denoising. IEEE Trans Image Process 11:670–684

    Article  PubMed  Google Scholar 

  • Starck J-L, Murtagh F, Candès EJ, Donoho DL (2003) Gray and color image contrast enhancement by the curvelet transform. IEEE Trans Image Process 12:706–717

    Article  PubMed  Google Scholar 

  • Toet A, van Ruyven LJ, Valeton JM (1989) Merging thermal and visual images by a contrast pyramid. Opt Eng 28:789–792

    Article  Google Scholar 

  • Unser M, Blu T (2003) Mathematical properties of the JPEG2000 wavelet filters. IEEE Trans Image Process 12:1080–1090

    Article  PubMed  Google Scholar 

  • Velisavljević V, Beferull-Lozano B, Vetterli M, Dragotti PL (2006) Directionlets: anisotropic multidirectional representation with separable filtering. IEEE Trans Image Process 15:1916–1933

    Article  PubMed  Google Scholar 

  • Wald L, Baleynaud J-M (1999) Observing air quality over the city of Nantes by means of Landsat thermal infrared data. Int J Remote Sens 20:947–959

    Article  Google Scholar 

  • Wald L, Ranchin T, Mangolini M (1997) Fusion of satellite images of different spatial resolutions: assessing the quality of resulting images. Photogramm Eng Remote Sens 63:691–699

    Google Scholar 

  • Wang Z, Ziou D, Armenakis C, Li D, Li Q (2005) A comparative analysis of image fusion methods. IEEE Trans Geosci Remote 43:1391–1402

    Article  Google Scholar 

  • Yocky DA (1995) Image merging and data fusion by means of the discrete two-dimensional wavelet transform. J Opt Soc Am Opt Image Sci Vis 12:1834–1841

    Article  Google Scholar 

  • Yocky DA (1996) Multiresolution wavelet decomposition image merger of Landsat Thematic Mapper and SPOT panchromatic data. Photogramm Eng Remote Sens 62:1067–1074

    Google Scholar 

  • Zhang Y (2004) Understanding image fusion. Photogramm Eng Remote Sens 70:657–661

    Google Scholar 

  • Zhao Y-Q, Pan Q, Zhang H-C (2006) New polarization imaging method based on spatially adaptive wavelet image fusion. Opt Eng 45:123202

    Article  Google Scholar 

  • Zhou J, Civco DL, Silander JA (1998) A wavelet transform method to merge Landsat TM and SPOT panchromatic data. Int J Remote Sens 19:743–757

    Article  Google Scholar 

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Acknowledgments

Funding for this study was provided by USDA-ARS and NASA Terrestrial Hydrology Program (Proposal No. 08-THP07-0053). Authors are grateful to reviewers who provided valuable comments.

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  1. Conservation and Production Research Laboratory, USDA-ARS, P.O. Drawer 10, Bushland, TX, 79012, USA

    Wonsook Ha, Prasanna H. Gowda & Terry A. Howell

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  1. Wonsook Ha
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  3. Terry A. Howell
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Correspondence to Wonsook Ha.

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Communicated by I. Dodd.

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Ha, W., Gowda, P.H. & Howell, T.A. A review of potential image fusion methods for remote sensing-based irrigation management: part II. Irrig Sci 31, 851–869 (2013). https://doi.org/10.1007/s00271-012-0340-6

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