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Object-based spectral quality assessment of high-resolution pan-sharpened satellite imageries: new combined fusion strategy to increase the spectral quality

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Abstract

Panchromatic and multispectral images produced by the earth observation satellites are fused; hence, a high-resolution multispectral image is obtained. Spectral quality of the fused images is of great importance since the quality of a large number of remote sensing products mainly depends on this feature. Due to the importance of the spectral quality of the fused images, its assessment is of particular significance as well. This article proposes an object-based strategy for the spectral quality assessment of the fused images to eliminate the limitations of the current pixel-based method. This kind of assessment is performed by focusing on homogeneous objects with similar spectral and textural behaviors. After determining an optimal metric, the object-based scheme was applied to five datasets from four types of satellite sensors and the spectral behavior of fusion methods was examined within the image classes. Although the spectral behavior of the fusion methods was not regular, the best methods in each class were determined using statistical analysis. Furthermore, a scheme was proposed to combine the results of different fusion methods to obtain a fused image with the best possible spectral quality. The obtained results indicate that this image enjoys 37% better quality than the best-fused image selected based on the pixel-based quality assessment.

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Notes

  1. Note that Entropy and standard deviation metrics are applicable when the reference image is not available, and the rest of the above-mentioned indices need a reference image (Jagalingam and Hegde 2015).

References

  • Agudelo-Medina OA, Benitez-Restrepo HD, Vivone G, Bovik A (2019) Perceptual quality assessment of pan-sharpened images. Remote Sens 11(7):877

    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 Sens 40(10):2300–2312

    Google Scholar 

  • Aiazzi B, Alparone L, Baronti S, Garzelli A, Selva M (2003) An MTF-based spectral distortion minimizing model for pan-sharpening of very high resolution multispectral images of urban areas. In: 2003 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas. IEEE, pp 90–94

  • 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 Sens 72(5):591–596

    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

    Google Scholar 

  • Alimuddin I, Sumantyo JTS, Kuze H (2012) Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data. Int J Appl Earth Obs Geoinf 18:165–175

    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

    Google Scholar 

  • 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

    Google Scholar 

  • Benz UC, Hofmann P, Willhauck G, Lingenfelder I, Heynen M (2004) Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogramm Remote Sens 58(3–4):239–258

    Google Scholar 

  • Blasch E, Li X, Chen G, Li W (2008) Image quality assessment for performance evaluation of image fusion. In: 2008 11th International Conference on Information Fusion. IEEE, pp 1–6

  • Buntilov V, Bretschneider T (2004) Objective content-dependent quality measures for image fusion of optical data. In: IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium. IEEE

  • Chavez P, 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(3):295–303

    Google Scholar 

  • Chen Y, Blum RS (2005) Experimental tests of image fusion for night vision. In: 2005 7th International Conference on Information Fusion. IEEE p 8 pp

  • 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

    Google Scholar 

  • Choi M (2006) A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter. IEEE Transactions on Geoscience and Remote sensing 44(6):1672–1682

  • DadrasJavan F, Samadzadegan F (2014) An object-level strategy for pan-sharpening quality assessment of high-resolution satellite imagery. Adv Space Res 54(11):2286–2295

    Google Scholar 

  • DadrasJavan F, Samadzadegan F, Fathollahi F (2018) Spectral and spatial quality assessment of IHS and wavelet based pan-sharpening techniques for high resolution satellite imagery. Advances in image and video processing 6(2):01. https://doi.org/10.14738/aivp.62.4226

    Article  Google Scholar 

  • de Béthune S, Muller F, Donnay J-P (1998) Fusion of multispectral and panchromatic images by local mean and variance matching filtering techniques. Fusion of Earth Data:28–30

  • Dou W (2018) Image degradation for quality assessment of pan-sharpening methods. Remote Sens 10(1):154

    Google Scholar 

  • Duran J, Buades A, Coll B, Sbert C, Blanchet G (2017) A survey of pansharpening methods with a new band-decoupled variational model. ISPRS J Photogramm Remote Sens 125:78–105

    Google Scholar 

  • eCognition Developer T (2014) 9.0 User Guide. Trimble Germany GmbH: Munich, Germany

  • Fonseca L, Namikawa L, Castejon E, Carvalho L, Pinho C, Pagamisse A (2011) Image fusion for remote sensing applications. In: Image fusion and its applications. IntechOpen

  • Garzelli A, Nencini F, Alparone L, Aiazzi B, Baronti S (2004) Pan-sharpening of multispectral images: a critical review and comparison. In: IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium. IEEE,

  • Garzelli A, Nencini F, Capobianco L (2008) Optimal MMSE pan sharpening of very high resolution multispectral images. IEEE Trans Geosci Remote Sens 46(1):228–236

    Google Scholar 

  • Ghahremani M, Ghassemian H (2016) Nonlinear IHS: a promising method for pan-sharpening. IEEE Geosci Remote Sens Lett 13(11):1606–1610

    Google Scholar 

  • Ghassemian H (2016) A review of remote sensing image fusion methods. Information Fusion 32:75–89

    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 Sens 42(6):1291–1299

    Google Scholar 

  • González-Audícana M, Otazu X, Fors O, & Seco A (2005) Comparison between Mallat's and the ‘à trous’ discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images. International Journal of Remote Sensing, 26(3):595–614

  • Hasanlou M, Saradjian MR (2016) Quality assessment of pan-sharpening methods in high-resolution satellite images using radiometric and geometric index. Arab J Geosci 9(1):45

    Google Scholar 

  • Jagalingam P, Hegde AV (2015) A review of quality metrics for fused image. Aquatic Procedia 4:133–142

    Google Scholar 

  • Javan F, Samadzadegan F, Reinartz P (2013) Spatial quality assessment of pan-sharpened high resolution satellite imagery based on an automatically estimated edge based metric. Remote Sens 5(12):6539–6559

    Google Scholar 

  • Kang X, Li S, Benediktsson JA (2014) Pansharpening with matting model. IEEE Trans Geosci Remote Sens 52(8):5088–5099

    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 Lett 5(1):98–102

    Google Scholar 

  • 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

    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

    Google Scholar 

  • Klonus S, Ehlers M (2009) Performance of evaluation methods in image fusion. In: 2009 12th International Conference on Information Fusion. IEEE, pp 1409-1416

  • Liu J (2000) Smoothing filter-based intensity modulation: a spectral preserve image fusion technique for improving spatial details. Int J Remote Sens 21(18):3461–3472

    Google Scholar 

  • Mandhare RA, Upadhyay P, Gupta S (2013) Pixel-level image fusion using Brovey transforme and wavelet transform. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 2(6):2690–2695

    Google Scholar 

  • Meng X, Shen H, Li H, Zhang L, Fu R (2019) Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: practical discussion and challenges. Information Fusion 46:102–113

    Google Scholar 

  • Mucherino A, Papajorgji PJ, Pardalos PM (2009) K-nearest neighbor classification. In: Data mining in agriculture. Springer, pp 83–106

  • 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

    Google Scholar 

  • Padwick C, Deskevich M, Pacifici F, Smallwood S (2010) WorldView-2 pan-sharpening. In: Proceedings of the ASPRS 2010 Annual Conference, San Diego, CA, USA

  • Pandit VR, Bhiwani R (2015) Image fusion in remote sensing applications: a review. International journal of computer applications 120(10):22–32

    Google Scholar 

  • Piella G, Heijmans H (2003) A new quality metric for image fusion. In: Proceedings 2003 International Conference on Image Processing (Cat. No. 03CH37429). IEEE, pp III-173

  • Ranchin T, Wald L (2000) Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation. Photogramm Eng Remote Sens 66(1):49–61

    Google Scholar 

  • Restaino R, Vivone G, Dalla Mura M, Chanussot J (2016) Fusion of multispectral and panchromatic images based on morphological operators. IEEE Trans Image Process 25(6):2882–2895

    Google Scholar 

  • Rodríguez-Esparragón D, Marcello J, Eugenio F, García-Pedrero A, Gonzalo-Martín C (2017) Object-based quality evaluation procedure for fused remote sensing imagery. Neurocomputing 255:40–51

    Google Scholar 

  • Samadzadegan F, DadrasJavan F (2011) Evaluating the sensitivity of image fusion quality metrics to image degradation in satellite imagery. Journal of the Indian Society of Remote Sensing 39(4):431–441

    Google Scholar 

  • Samadzadegan F, Javan FD (2011) New object level strategy for image fusion quality assessment of high resolution satellite imagery. Photogrammetrie-Fernerkundung-Geoinformation 2011(3):123–133

    Google Scholar 

  • Seetha M, MuraliKrishna I, Deekshatulu B (2008) Content dependent quality measures for image fusion. Int J Geoinform 4(1)

  • Shi W, Zhu C, Tian Y, Nichol J (2005) Wavelet-based image fusion and quality assessment. Int J Appl Earth Obs Geoinf 6(3–4):241–251

    Google Scholar 

  • Snehmani GA, Ganju A, Kumar S, Srivastava P (2017) A comparative analysis of pansharpening techniques on QuickBird and WorldView-3 images. Geocarto International 32(11):1268–1284

    Google Scholar 

  • Thomas C, Wald L (2006) Analysis of changes in quality assessment with scales. In: In Proceedings of Fusion06, pp 10–13

    Google Scholar 

  • Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46(sup1):234–240

    Google Scholar 

  • Tu T, Lee Y-C, Chang C-P, Huang PS (2005) Adjustable intensity-hue-saturation and Brovey transform fusion technique for IKONOS/QuickBird imagery. Opt Eng 44(11):116201

    Google Scholar 

  • Vijayaraj V, Younan NH, O'Hara CG (2006) Quantitative analysis of pansharpened images. Opt Eng 45(4):046202

    Google Scholar 

  • Vivone G, Alparone L, Chanussot J, Dalla Mura M, Garzelli A, Licciardi GA, Restaino R, Wald L (2015) A critical comparison among pansharpening algorithms. IEEE Trans Geosci Remote Sens 53(5):2565–2586

    Google Scholar 

  • Wald L (2000) Quality of high resolution synthesised images: is there a simple criterion? In: Third conference “" Fusion of earth data: merging point measurements, raster maps and remotely sensed images”. SEE/URISCA, pp 99-103

  • Wald L, & Ranchin T (2002) Liu‘Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details’. International Journal of Remote Sens 23(3):593-597

  • Wang Z, Bovik AC (2002) A universal image quality index. IEEE signal processing letters 9(3):81–84

    Google Scholar 

  • Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Google Scholar 

  • Xu J, Guan Z, Liu J (2008) An improved IHS fusion method for merging multi-spectral and panchromatic images considering sensor spectral response. Int Arch Photogramm Remote Sens Spat Inf Sci 37:1169–1174

    Google Scholar 

  • Xydeas CA, Petrovic V (2000) Objective image fusion performance measure. Electron Lett 36(4):308–309

    Google Scholar 

  • Zeng D, Hu Y, Huang Y, Xu Z, & Ding X (2016) Pan-sharpening with structural consistency and ℓ1/2 gradient prior. Remote Sensing Letters, 7(12):1170–1179

  • Zhang Y (2008) Methods for image fusion quality assessment—a review, comparison and analysis. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37(PART B7):1101-1109

  • Zhang X, Han J (2004) Multiscale contrast image fusion scheme with performance measures. Opt Appl 34(3):453–461

    Google Scholar 

  • Zhang Y, Hong G (2005) An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and QuickBird images. Information Fusion 6(3):225–234

    Google Scholar 

  • Zhu XX, Bamler R (2013) A sparse image fusion algorithm with application to pan-sharpening. IEEE Trans Geosci Remote Sens 51(5):2827–2836

    Google Scholar 

  • Zoran LF (2009) Quality evaluation of multiresolution remote sensing images fusion. UPB Sci Bull Series C 71:38–52

    Google Scholar 

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Authors and Affiliations

  1. School of Surveying and Geospatial Engineering, University College of Engineering, University of Tehran, Tehran, Iran

    Ahmad Toosi, Farzaneh Dadrass Javan, Farhad Samadzadegan & Soroosh Mehravar

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  1. Ahmad Toosi
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  2. Farzaneh Dadrass Javan
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  3. Farhad Samadzadegan
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  4. Soroosh Mehravar
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Correspondence to Farzaneh Dadrass Javan.

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Toosi, A., Javan, F.D., Samadzadegan, F. et al. Object-based spectral quality assessment of high-resolution pan-sharpened satellite imageries: new combined fusion strategy to increase the spectral quality. Arab J Geosci 13, 499 (2020). https://doi.org/10.1007/s12517-020-05523-3

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