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Takagi–Sugeno Fuzzy System and MTF-based Panchromatic Sharpening

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Abstract

The panchromatic sharpening or pansharpening refers to the fusion process of high-resolution panchromatic image and low- resolution multi-spectral images. Modulation Transfer Function (MTF) of satellite sensors has also been used for pansharpening. We investigate the use of Takagi–Sugeno fuzzy systems in MTF-based pansharpening algorithms. Traditional pansharpening schemes can result in spatial and/or spectral distortion during the fusion process. A fuzzy integrated fusion scheme is proposed to overcome this limitation. Spectral dissimilarities between panchromatic and multi-spectral bands are also taken into account. While preserving low-resolution multi-spectral information, Takagi–Sugeno fuzzy is introduced to inject appropriate spatial details in the pansharpened image. The local features of panchromatic image are also exploited to preserve the spatial and spectral content. Experiments conducted on Pl\(\acute{e}\)iades, Spot-5 and WorldView-2 data set demonstrate the superior fusion quality of the proposed scheme.

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References

  • Aiazzi B, Alparone L, Baronti S, Garzelli A (2002) Context-driven fusion of high spatial and spectral resolution images based on over sampled multiresolution analysis. IEEE Trans Geosci Remote Sens 40(10):2300–2312

    Article  Google Scholar 

  • Aiazzi B, Alparone L, Baronti S, Garzelli A, Selva M (2006) MTF tailored multiscale fusion of high-resolution MS and Pan imagery. Photogramm Eng Remote Sensing 72(5):591–596

    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 Sens 45(10):3230–3239

    Article  Google Scholar 

  • Aiazzi B, Baronti S, Lotti F, Selva M (2009) A comparison between global and context-adaptive pansharpening of multisepctral image. IEEE Geosci Remote Sens Lett 6(2):302–306

    Article  Google Scholar 

  • Ali SS, Riaz MM, Ghafoor A (2013) Hybrid component substitution and wavelet based image fusion. IEEE Int Conf Acoust Speech Signal Process, Vancouver, Canada, 2498–2502

  • Ali SS, Riaz MM, Ghafoor A (2014) Fuzzy logic and additive wavelet based panchromatic sharpening. IEEE Geosci Remote Sens Lett 11(1):357–360

    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 Lett 1(4):313–317

    Article  Google Scholar 

  • Alparone L, Wald L, Chanussot J, Thomas C, Gamba P, Bruce LM (2007) Comparison of pansharpening algorithms: outcome of the 2006 GRS-S data-fusion contest. IEEE Trans Geosci Remote Sens 45(10):3012–3021

    Article  Google Scholar 

  • Alparone L, Aiazzi B, Baronti S, Garzelli A, Nencini F, Selva M (2008) Mutltispectral and panchromatic data fusion assessment without reference. Photogramm Eng Remote Sensing 74(2):193–200

    Article  Google Scholar 

  • Choi J, Yu K, Kim Y (2011) A new adaptive component-substitution-based satellite image fusion by using partial replacement. IEEE Trans Geosci Remote Sens 49(1):295–309

    Article  Google Scholar 

  • Gang L, Zhong-liang J, Shao-yuan S (2006) Multiresolution image fusion scheme based on fuzzy region feature. J Zhejiang Univ Sci 7(2):117–122

    Article  Google Scholar 

  • Hung JC (2014) Robust Kalman filter based on a fuzzy GARCH model to forecast volatility using particle swarm optimization. Soft Comput J 1–9

  • Khan MM, Alparone L, Chanussot J (2009) Pansharpening quality assessment using the modulation transfer functions of instruments. IEEE Trans Geosci Remote Sens 47(11):3880–3891

    Article  Google Scholar 

  • Kim Y, Lee C, Han D, Kim Y, Kim Y (2011) Improved additive-wavelet image fusion. IEEE Geosci Remote Sens Lett 8(2):263–267

    Article  MathSciNet  Google Scholar 

  • Kim Y, Eo Y, Kim Y, Kim Y (2011) Generalized IHS-based satellite imagery fusion using spectral response functions. ETRI J 33(4):494–505

    Article  Google Scholar 

  • Leu FY, Liu JC, Hsu YT, Huang YL (2014) The simulation of an emotional robot implemented with fuzzy logic. Soft Comput J 1–15

  • Li S, Kwok JT, Wang Y (2001) Combination of images with diverse focuses using the spatial frequency. Inf Fusion 2:169–176

    Article  Google Scholar 

  • Ling Y, Ehlers M, Usery L, Madden M (2007) FFT-enhanced IHS transform method for fusing high-resolution satellite images. ISPRS J Photogramm Remote Sens 61(6):381–392

    Article  Google Scholar 

  • Nunez J, Otazu X, Fors O, Prades A, Pala V, Arbiol R (1999) Multiresolution-based image fusion with additive wavelet decomposition. IEEE Trans Geosci Remote Sens 37(3):1204–1211

    Article  Google Scholar 

  • Otazu X, Audicana MG, Fors O, Nunez J (2005) Introduction of sensor spectral response into image fusion method. Application to wavelet-based methods. IEEE Trans Geosci Remote Sens 43(10):2376–2385

    Article  Google Scholar 

  • Padwick C, Deskevich M, Pacifici F, Smallwood S (2010) WorldView-2 pan-sharpening. ASPRS Ann Conf, San Diego, California

  • Riaz MM, Ghafoor A (2013) Spectral and textural weighting using Takagi–Sugeno fuzzy system for through wall image enhancement. Prog Electromagn Res B 48:115–130

    Article  Google Scholar 

  • Seng C, Bouzerdoum A, Amin M, Phung S (2013) Two-Stage fuzzy fusion with applications to through-the-wall radar imaging. IEEE Trans Geosci Remote Sens Lett 10(4):687–691

    Article  Google Scholar 

  • Sohn M, Jeong S, Lee HJ (2014) Case-based context ontology construction using fuzzy set theory for personalized service in a smart home environment. Soft Comput J 1–14

  • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15:116–132

    Article  MATH  Google Scholar 

  • Thomas C, Ranchin T, Wald L, Chanussot J (2008) Synthesis of multispectral images to high spatial resolution: a critical review of fusion methods based on remote sensing physics. IEEE Trans Geosci Remote Sens 46(5):1301–1312

    Article  Google Scholar 

  • Tu TM, Huang PS, Hung CL, Chang CP (2004) A fast intensityhuesaturation fusion technique with spectral adjustment for IKONOS imagery. IEEE Geosci Remote Sens Lett 1(4):309–312

    Article  Google Scholar 

  • Wald L (2000) Quality of high resolution synthesised images: is there a simple criterion. Fusion Earth Data Merg Point Meas Raster Maps Remote Sense Images 99–103

  • Wang Z, Bovik AC (2002) A universal image quality index. IEEE Signal Process Lett 9(3):81–84

    Article  Google Scholar 

  • Wang Z, Ziou D, Armenakis C, Li D, Li Q (2005) A comparative analysis of image fusion methods. IEEE Trans Geosci Remote Sens 43(6):1391–1402

    Article  Google Scholar 

  • Zhang DG, Kang X, Wang J (2012) A novel image de-noising method based on spherical coordinates system. EURASIP J Adv Signal Process 1–10

  • Zhang Y (2008) Methods for image fusion quality assessment—a review, comparison and analysis. Int Arch Photogramm Remote Sens Spat Inf Sci, Beijing, China, XXXVII:1101–1109

  • Zhang DG, Zhang XD (2012) Design and implementation of embedded un-interruptible power supply system (EUPSS) for web-based mobile application. Enterp Inf Syst 6(4):473–489

  • Zhang DG (2012) A new approach and system for attentive mobile learning based on seamless migration. Appl Intell 36(1):75–89

    Article  Google Scholar 

  • Zhang DG, Li G, Pan Z (2014) An energy-balanced routing method based on forward-aware factor for wireless sensor network. IEEE Trans Industr Inform 10(1):766–773

    Article  MathSciNet  Google Scholar 

  • Zhu M, Yang Y (2008) A new image fusion algorithm based on fuzzy logic. Intell Comput Technol Autom 2:83–86

    Google Scholar 

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Correspondence to A. Ghafoor.

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Communicated by Y.-S. Ong.

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Ali, S.S., Riaz, M.M., Ghafoor, A. et al. Takagi–Sugeno Fuzzy System and MTF-based Panchromatic Sharpening. Soft Comput 20, 4695–4708 (2016). https://doi.org/10.1007/s00500-014-1526-z

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