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Satellite image fusion using fuzzy logic. (English) Zbl 1404.68201
Summary: Image fusion is a method of combining the Multispectral (MS) and Panchromatic (PAN) images into one image contains more information than any of the input. Image fusion aim is to decrease unknown and weaken common data in the fused output image at the same time improving necessary information. Fused images are helpful in various applications like, remote sensing, computer vision, biometrics, change detection, image analysis and image classification. Conventional fusion methods are having some side effects like assertive spatial information and uncertain color information is an usually the problem in PCA and wavelet transform based fusion is a computationally in depth process. In order to overcome these side effects and to propose alternative soft computing fusion approach for conventional fusion methods we exploit image fusion using fuzzy logic technique to fuse two source images obtained from different sensors to enhance both spectral and spatial information. The proposed work here further compared with two common fusion methods like, principal component analysis (PCA) and wavelet transform along with quality assessment metrics. Exploratory outputs demonstrated in order that fuzzy based image fusion technique can actively retains more information compared to PCA and wavelet transform approaches while enhancing the spatial and spectral resolution of the satellite images.
68U10 Computing methodologies for image processing
62H25 Factor analysis and principal components; correspondence analysis
65T60 Numerical methods for wavelets
Full Text: DOI
[1] P. Balasubramaniam, V. P. Ananthi: Image fusion using intuitionistic fuzzy sets, Elsever Journal of Information Fusion 20 (2014) 21-30. ⇒245
[2] B. Biswas, K. N. Dey, A. Chakrabarti, Remote sensing image fusion using multithreshold Otsu method in Shearlet domain, Procedia Computer Science 57, (2015) 554-562. ⇒242
[3] Y. Chen, Z. Qin, PCNN-based image fusion in compressed domain, Mathematical Problems in Engineering, Vol. 2015. ⇒242
[4] A. Ellmauthaler, C. L. Pagliari, E. A. B. da Silva, Multiscale image fusion using the undecimated wavelet transform with spectral factorization and nonorthogonal filter banks, IEEE Transactions on Image Processing, 22, 3 (2013) 1005-1017. ⇒243 · Zbl 1373.94108
[5] D. L. A. Godse, D. S. Bormane, Wavelet based image fusion using pixel based maximum selection rule, International Journal of Engineering Science and Technology, 3, 7, (2011) 5572-5557. ⇒243
[6] R. Hassen, Z. Wang, M. M. A. Salama, Objective quality assessment for multi- exposure multifocus image fusion, IEEE Transactions on Image Processing 24, 9 (2015) 2712-2724. ⇒243
[7] K. Kannan, S. A. Perumal, K. Arulmozhi, Performance comparision of various levels of fusion of multi-focused images using wavelet transform, I. J. Computer Applications, 1, 6 (2010) 71-78. ⇒243
[8] S. Li, X. Kang, J. Hu, Image fusion with guided filtering, IEEE Transactions on Image Processing, 22, 7 (2013) 2864-2875. ⇒242, 249
[9] A. N. Myna, J. Prakash, A novel hybrid approach for multi-focus image fusion using fuzzy logic and wavelets, International Journal of Emerging Trends and Technology in Computer Science, (IJETTCS), 3, (2014⇒ 131-138. ⇒245
[10] D. S. Rao, M. Seetha, M. H. M. Krishna Prasad, Comparison of fuzzy and neuro fuzzy image fusion techniques and its applications, International Journal of Computer Applications, 43, 20 (2012) 31-37. ⇒242
[11] D. S. Rao, M. Seetha, M. H. M. Krishna Prasad, Quality assessment of pixel- level image fusion using fuzzy logic, International Journal on Soft Computing, 3, 1, (2012) 13-25. ⇒245, 247
[12] D. S. Rao, M. Seetha, M. H. M. Krishna Prasad, Novel approach for iterative image fusion using fuzzy and neuro fuzzy logic, International Journal of Geoinformatics 11, 2 (2015) 29-39. ⇒244, 245
[13] C. H. Seng, A. Bouzerdoum, F. H. C. Tivive, M. G. Amin, Fuzzy logic-based image fusion for multi-view through-the-wall radar, Int. Conf. Digital Image Computing: Techniques and Applications (DICTA), 2010, pp. 423-428. ⇒245
[14] M. Seetha, I. V. Murali Krishna, B. L. Deekshatulu, Data fusion performance analysis based on conventional and wavelet transform techniques, IEEE Proceedings on Geoscience and Remote Sensing Symposium 4 (2005) 2842-2845. ⇒247 [15] C. Yang, B. Yang, Efficient compressive multi-focus image fusion, Journal of Computer and Communications, 2 (2014) 78-86. ⇒242, 246
[15] Y. Yang, S. Huang, J. Gao, Z. Qian: Multi-focus image fusion using an effective discrete wavelet transform based algorithm, Measurement Science Review, 14, 2 (2014) 102-108. ⇒242
[16] Y. Yang, W. Zheng, S. Huang, Effective multifocus image fusion based on HVS and BP neural network, The Scientific World Journal, 2014, Article ID 281073, 10 pages. ⇒242
[17] M. Zhu, Y. Yang, A new image fusion algorithm based on fuzzy logic, Inter- national Conference on Intelligent Computation Technology and Automation, (ICICTA) 2008, pp. 83-86. ⇒245
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