eISSN: 2618-6446
Latest Issue Archive Future Issues About Us JOURNALS

SETSCI - Volume 3 (2018)
ISAS2018-Winter - 2nd International Symposium on Innovative Approaches in Scientific Studies, Samsun, Turkey, Nov 30, 2018

Modified Synthetic Variable Ratio Pansharpening Method (ISAS2018-Winter_16)
Volkan Yılmaz1*, Çiğdem Şerifoğlu Yılmaz2, Oğuz Güngör3
1Karadeniz Teknik University, Trabzon, Turkey
2Karadeniz Teknik University, Trabzon, Turkey
3Karadeniz Teknik University, Trabzon, Turkey
* Corresponding author: volkanyilmaz.jdz@gmail.com
Published Date: 2019-01-14   |   Page (s): 92-96   |    25     5

ABSTRACT Pansharpening, which is transferring the spatial content of a high-resolution panchromatic (PAN) band into a lowerresolution multispectral (MS) image to produce a spatially enhanced MS image, has always been one of the hottest topics of
image processing. Numerous studies have focused on developing approaches to inject the spatial details with minimum spectral
distortion. This study utilized the Genetic Algorithms (GA) to improve the performance of the Synthetic Variable Ratio (SVR),
which is one of the most conventional pansharpening methods. This method was modified such that the weight of each MS band
was estimated by means of a GA to achieve the optimum result. The spectral quality of the image produced by the proposed
approach was compared against those of the images obtained from widely-used pansharpening algorithms Principal Component
Analysis (PCA), Modified IHS (MIHS), Gram-Schmidt (GS), Nearest Neighbor Diffuse (NND), High-Pass Filtering (HPF) and
conventional SVR. The quantitative evaluation of the pansharpening results revealed that the proposed approach resulted in
superior spectral quality, compared to the other methods.  
KEYWORDS pansharpening, genetic algorithm, synthetic variable ratio, image enhancement, image fusion
REFERENCES 1] C. Pohl, and J. van Genderen, Remote sensing image fusion: A practical guide, Crc Press, 2016.
[2] W. A. Hallada, and S. Cox, “Image sharpening for mixed spatial and spectral resolution satellite systems,” in Proc. 17th International
Symposium on Remote Sensing of Environment, 1983, pp. 1023-1032.
[3] D. Pradines, “Improving SPOT images size and multispectral resolution,” in Earth Remote Sensing Using the Landsat Thermatic Mapper and SPOT Sensor Systems, International Society for Optics and Photonics, 1986, pp. 98-103.
[4] C. K. Munechika, J. S. Warnick, C. Salvaggio, and J. R. Schott, “Resolution enhancement of multispectral image data to improve classification accuracy,” Photogramm Eng. Remote Sens., vol. 59, no. 1, pp. 67-72, 1993.
[5] P. S. Chavez, and A. Y. Kwarteng, “Extracting spectral contrast in Landsat Thematic Mapper 23 image data using selective principal component analysis,” Photogramm. Eng. Remote Sens., vol. 55, pp. 339–348, 1989.
[6] C. A. Laben, and B. V. Brower, “Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening,” U.S. Patent 6 011 875, Washington, DC: U.S. Patent and Trademark Office, 2000.
[7] Erdas Imagine Field Guide, Leica Geosystems.
[8] R. Haydn, G. W. Dalke, J. Henkel, and J. E. Bare, Application of the IHS color transform to the processing of multisensor data and image enhancement. In Proc. International Symposium on Remote Sensing of Environment, First Thematic Conference: Remote sensing of arid and semi-arid lands, 1982.
[9] W. Sun, B. Chen, and D. Messinger, “Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images,” Opt. Eng., vol. 53, no. 1, 2014.
[10] R. A. Schowengerdt, “Reconstruction of multispatial, multispectral image data using spatial frequency content, Photogramm Eng. Remote Sens., vol. 46, no. 10, pp. 1325-1334, 1980.
[11] H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor: The University of Michigan Press, 1975.
[12] M. Yu, “Image segmentation using genetic algorithm and morphological operations,” Master’s Thesis, Iowa State University, 1998.
[13] K. S. Tang, K .F. Man, S. Kwong, and Q. He, “Genetic algorithms and their applications,” IEEE Signal Process. Mag., vol. 13, no. 6, pp. 22- 37, 1996.
[14] L. Wald, “Fusion of Images of Different Spatial Resolutions,” Presses de l'Ecole, Ecole des Mines de Paris France, 2002.
[15] Z. Wang and Q. Li, “Information content weighting for perceptual image quality assessment,” IEEE Trans. Image Process., vol. 20, pp. 1185–1198, 2011.
[16] Z. Wang, and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett., vol. 9, pp. 81–84, 2002.
[17] M. B. Haghighat, A. A. Aghagolzadeh, and H. Seyedarabi, “A NonReference Image Fusion Metric Based on Mutual Information of Image Features,” Comput. Electr. Eng., vol. 37, no. 5, pp. 744-756, 2011.
[18] A. Liu, W. Lin, and M. Narwaria, “Image quality assessment based on gradient similarity,” IEEE Trans. Image Process., vol. 21, pp. 1500– 1512, 2012.

SET Technology - Turkey

eISSN  : 2618-6446

E-mail : info@set-science.com
+90 533 2245325

Tokat Technology Development Zone Gaziosmanpaşa University Taşlıçiftlik Campus, 60240 TOKAT-TURKEY
©2018 SET Technology