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http://dx.doi.org/10.3837/tiis.2017.03.021

An Improved Remote Sensing Image Fusion Algorithm Based on IHS Transformation  

Deng, Chao (School of Physics and Electronic Information Engineering, Henan Polytechnic University)
Wang, Zhi-heng (Computer Science and Technology, Henan Polytechnic University)
Li, Xing-wang (School of Physics and Electronic Information Engineering, Henan Polytechnic University)
Li, Hui-na (School of Physics and Electronic Information Engineering, Henan Polytechnic University)
Cavalcante, Charles Casimiro (Wireless Telecommunications Research Group, Federal University of Ceara)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.11, no.3, 2017 , pp. 1633-1649 More about this Journal
Abstract
In remote sensing image processing, the traditional fusion algorithm is based on the Intensity-Hue-Saturation (IHS) transformation. This method does not take into account the texture or spectrum information, spatial resolution and statistical information of the photos adequately, which leads to spectrum distortion of the image. Although traditional solutions in such application combine manifold methods, the fusion procedure is rather complicated and not suitable for practical operation. In this paper, an improved IHS transformation fusion algorithm based on the local variance weighting scheme is proposed for remote sensing images. In our proposal, firstly, the local variance of the SPOT (which comes from French "Systeme Probatoire d'Observation dela Tarre" and means "earth observing system") image is calculated by using different sliding windows. The optimal window size is then selected with the images being normalized with the optimal window local variance. Secondly, the power exponent is chosen as the mapping function, and the local variance is used to obtain the weight of the I component and match SPOT images. Then we obtain the I' component with the weight, the I component and the matched SPOT images. Finally, the final fusion image is obtained by the inverse Intensity-Hue-Saturation transformation of the I', H and S components. The proposed algorithm has been tested and compared with some other image fusion methods well known in the literature. Simulation result indicates that the proposed algorithm could obtain a superior fused image based on quantitative fusion evaluation indices.
Keywords
Intensity-Hue-Saturation transformation; spectrum distortion; local variance weight; image fusion; multi-spectral images;
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1 F. Al-Wassai, N. V. Kalyankar, and A. A. Al-Zaky, "Multisensor images fusion based on feature- level," International Journal of Latest Technology in Engineering, Management and Applied Science, vol. 1, no. 5, pp. 124-138, 2012.
2 R. Gharbia, A. T. Azar, A. E. Baz, A. E. Hassanien, Image Fusion Techniques in Remote Sensing. [online]: available: https://arxiv.org/abs/1403.5473.
3 T. Ranchin, L. Wald, M. Mangolini, "The ARSIS method: a general solution for improving spatial resolution of images by the means of sensor fusion," Special Oil & Gas Reservoirs, vol. 41, no. 2, pp. 358-361, 1996.
4 T. Ranchin, L. Wald, M. Mangolini, C. Penicand, "On the assessment of merging processes for the improvement of the spatial resolution of multispectral SPOT XS images," in Proc. of the conference, Cannes, France, February 6-8, 1996, published by SEE/URISCA, Nice, France, pp. 59-67, 1996.
5 A. A. Goshtasby, S. Nikolov, "Image fusion: advances in the state of the art," Information Fusion, vol. 8, no. 2, pp. 114-118, 2007.   DOI
6 T. M. Tu, W. C. Cheng, C. P. Chang, P. S. Huang, J. C. Chang, "Best tradeoff for high-resolution image fusion to preserve spatial details and minimize color distortion," IEEE Geoscience and Remote Sensing Letters, vol. 4, no. 2, pp. 302-306, 2007.   DOI
7 B. A. Harrison, D. L. B. Jupp, MicroBRIAN resource manual: Introduction to image processing, CSIRO Australia, Division of Water Resources Press, 1990.
8 B. Garguet-Duport, J. Girel, J.-M. Chassery, G. Pautou, "The use of multiresolution analysis and wavelets transform for merging SPOT panchromatic and multispectral image data," Photogrammetric Engineering & Remote Sensing, vol. 62, no. 9, pp. 1057-1066, 1996.
9 C. Pohl, J. L. Van Genderen, "Multisensor image fusion in remote sensing: concepts, methods and applications," International Journal of Remote Sensing, vol. 19, no. 5, pp. 823-854, 1998.   DOI
10 D. L. Hall, J. Llinas, "An introduction to multisensor data fusion," in Proc. of the IEEE, vol. 85, no. 1, pp. 6-23, 1997.   DOI
11 C. Pohl, J. L. Van Genderen, "Multisensor image fusion in remote sensing: concepts, methods and applications," International Journal of Remote Sensing, vol. 19, no. 5, pp. 823-854, 1998.   DOI
12 P. K. Varshney, "Multisensor data fusion," Electronics and Communication Engineering Journal, vol. 9, no. 6, pp. 245-253, 1997.   DOI
13 J. Lu, B. M. Zhang, Z. H. Gong, E. Li, H. Y. Liu, "The remote-sensingimage fusion based on GPU," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 37, Part B7, pp. 1233-1238, Beijing 2008.
14 J. G. Liu, "Smoothing filter-based intensity modulation: a spectral preserve image fusion technique for improving spatial details," International Journal of Remote Sensing, vol. 21, no. 18, pp.3461-3472, 2000.   DOI
15 D. G. Clayton, "Gram-Schmidt orthogonalization," Applied Statistics, vol. 20, no. 3, pp. 335-338, 1971.   DOI
16 E. M. Schetselaar, "On preserving spectral balance in image fusion andits advantages for geological image interpretation," Photogrammetric Engineering & Remote Sensing, vol. 4, no. 2, pp. 925-934, 2001.
17 S. T. Li, J. T. Kwok, Y. N. Wang, "Using the discrete wavelet frame transform to merge landsat TM and SPOT panchromatic images," Information Fusion, vol. 3, no. 1, pp. 17-23, 2002.   DOI
18 Z. J. Wang, D. Ziou, C. Armenakis, D. R. Li, Q. Q. Li, "A comparativeanalysis of image fusion methods," IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 6, pp. 1391-1402, 2005.   DOI
19 X. H. Yang, L. C. Jiao, "Fusion algorithm for remote sensing images based on nonsubsampled contourlet transform," ACTA AUTOMATICA SINICA, vol. 34, no. 3, pp. 274-281, 2008.   DOI
20 S. L. Hsu, P. W. Gau, I. L. Wu, J. H. Jeng, "Region-based image fusion with artificial neural network," World Academy of Science, Engineering and Technology, vol. 29, pp. 156-159, 2009.
21 W. J. Carper, T. W. Lilesand, R. W. Kieffer, "The use of Intensity-Hue-Saturation transformation for merging SPOT panchromatic and multispectral image data," Photogrammetric Engineering and Remote Sensing, vol. 56, no. 4, pp. 459-467, 1990.
22 M. Gonzalez-Audicana, J. L. Saleta, R. G. Catalan, R. Garcia, "Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition," IEEE Transaction on Geoscience and Remote Sensing, vol. 42, no. 6, pp.1291-1299, 2004.   DOI
23 Y. Zhang, G. Hong, "An IHS and wavelet integrated approach to improvepan-sharpening visual quality of natural colour IKONOS and Quick Bird images," Information Fusion, vol. 6, no. 3, pp. 225-234, 2005.   DOI
24 P. S. Chavez, Jr., S. C. Sides, J. A. Anderson, "Comparison of three different methods to merge multire solution data: Landsat TM and SPOT Panchromatic," Photogrammetric Engineering & Remote Sensing, vol. 57, no. 3, pp. 295-303, 1991.
25 M. Choi, "A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter," IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 6, pp. 1672-1682, 2006.   DOI
26 T. M. Tu, P. S. Huang, C. L. Hung, C. P. Chang, "A fastintensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery," IEEE Geoscience and Remote Sensing Letter, vol. 1, no. 4, pp. 309-312, 2004.   DOI
27 I. De, B. Chanda, "A simple and efficient algorithm for multifocus image fusion using morphological wavelets," Signal Processing, vol. 86, no. 5, pp. 924-936, 2006.   DOI
28 M. N. Do, M. Vetterli, "The contourlet transform: an efficient directional multire solution image representation," IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2091-2106, 2005.   DOI
29 R. A. Schowengerdt, "Reconstruction of multi-spatial, multispectral image data using spatial frequency content," Photogrammetric Engineering & Remote Sensing, vol. 46, no. 10, pp. 1325-1334, 1980.
30 J. G. Liu, "Smoothing Filter-based Intensity Modulation: a spectral preserve image fusion technique for improving spatial details," International Journal of Remote Sensing, vol. 21, no. 18, pp.3461-3472, 2000.   DOI
31 J. R. Jensen, Introductory digital image processing: a remote sensing perspective, Prentice Hall Press, 2005.
32 O. Gungor, J. Shan, "Colour-based and criteria-based methods for image fusion," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 37, Part B7, pp. 1057-1064, Beijing, 2008.
33 R. Redondo, F. Sroubek, S. Fischer, G. Cristobal, "Multifocusimage fusion using the log-Gabor transform and a multisize windows technique," Information Fusion, vol. 10, no. 2, pp. 163-171, 2009.   DOI
34 X. F. Wen, "Image fusion based on improved IHS transform with weighted average," in Proc. of International Conference on Computational and Information Sciences (ICCIS), pp. 111-113, Chengdu, 2011.
35 F. A. Al-Wassai, N. V. Kalyankar, A. A. Al-Zuky, "The IHS transformations based image fusion," Journal of Global Research in Computer Science, vol. 2, no. 5, pp. 70-77, 2011.
36 E. M. Schetselaar, "Fusion by the IHS transform: Should we use cylindrical or spherical coordinates," International Journal of Remote Sensing, vol. 19, no. 4, pp. 759-765, 1998.   DOI
37 M. Zribi, "Non-parametric and region-based image fusion with bootstrap sampling," Information Fusion, vol. 11, no. 2, pp. 85-94, 2010.   DOI
38 S. Daneshvar, H. Ghassemian, "MRI and PET image fusion by combiningIHS and retina-inspired models," Information Fusion, vol. 11, no. 2, pp. 114-123, 2010.   DOI