(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-5 Issue-40 March-2018
Full-Text PDF
DOI:10.19101/IJATEE.2018.539005
Paper Title : Performance assessment of neuro fuzzy based image fusion of satellite images
Author Name : Ch. Ramesh Babu, D. Srinivasa Rao, T. Ravi and G. Gopi
Abstract :

Image fusion is a technique to converge multispectral (MS) and panchromatic (PAN) images in to a one fused image which is moderately supplementary helpful compared to input images taken for fusion. Image fusion is an important task to recover an image which delivers as much as evidence of the same body part at the similar time it also assistances to decrease the storing capability to a particular image. In this paper an assessment is completed among conventional image fusion methods; principal component analysis (PCA), discrete wavelet transform (DWT), IHS transform based fusion, Brovey transform based fusion, and the projected neuro fuzzy based iterative image fusion techniques. The proposed neuro fuzzy based iterative fusion method utilizes fuzzy inference system (FIS) prepared by determining fuzzy rules and membership functions precisely. Experimentations have been finished on different datasets of multimodal satellite images. The projected technique is perceivably and significant related with the other fusion approaches. For the assessment of the fused image obtained from various fusion techniques ten diverse measures is prepared and utilized of, namely image quality index (IQI) and mutual information measure (MIM) with probability density.

Keywords : Image fusion, PCA, DWT, IHS, Brovey transform.
Cite this article : Ch. Ramesh Babu, D. Srinivasa Rao, T. Ravi and G. Gopi , " Performance assessment of neuro fuzzy based image fusion of satellite images " , International Journal of Advanced Technology and Engineering Exploration (IJATEE), Volume-5, Issue-40, March-2018 ,pp.43-49.DOI:10.19101/IJATEE.2018.539005
References :
[1]Petković D. Adaptive neuro-fuzzy fusion of sensor data. Infrared Physics & Technology. 2014; 67:222-8.
[Crossref] [Google Scholar]
[2]Zheng Y, Zheng P. Multisensor image fusion using fuzzy logic for surveillance systems. In international conference on fuzzy systems and knowledge discovery 2010 (pp. 588-92). IEEE.
[Crossref] [Google Scholar]
[3]Zhu M, Yang Y. A new image fusion algorithm based on fuzzy logic. In international conference on intelligent computation technology and automation 2008 (pp. 83-6). IEEE.
[Crossref] [Google Scholar]
[4]Ranjan R, Singh H, Meitzler T, Gerhart GR. Iterative image fusion technique using fuzzy and neuro fuzzy logic and applications. In annual meeting of the North American fuzzy information processing society 2005 (pp. 706-10). IEEE.
[Crossref] [Google Scholar]
[5]Das S, Kundu MK. A neuro-fuzzy approach for medical image fusion. IEEE Transactions on Biomedical Engineering. 2013; 60(12):3347-53.
[Crossref] [Google Scholar]
[6]Teng J, Wang S, Zhang J, Wang X. Neuro-fuzzy logic based fusion algorithm of medical images. In image and signal processing (CISP), 3rd international congress 2010 (pp. 1552-6). IEEE.
[Crossref] [Google Scholar]
[7]Naidu VP, Raol JR. Pixel-level image fusion using wavelets and principal component analysis. Defence Science Journal. 2008; 58(3):338-52.
[Google Scholar]
[8]Yang Y, Huang S, Gao J, Qian Z. Multi-focus image fusion using an effective discrete wavelet transform based algorithm. Measurement Science Review. 2014; 14(2):102-8.
[Crossref] [Google Scholar]
[9]El-Mezouar MC, Taleb N, Kpalma K, Ronsin J. An IHS-based fusion for color distortion reduction and vegetation enhancement in IKONOS imagery. IEEE Transactions on Geoscience and Remote Sensing. 2011; 49(5):1590-602.
[Crossref] [Google Scholar]
[10]Mandhare RA, Upadhyay P, Gupta S. Pixel-level image fusion using brovey transforme and wavelet transform. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. 2013; 2(6):2690-5.
[Google Scholar]
[11]Dammavalam SR, Maddala S, Prasad MK. Quality evaluation measures of pixel-level image fusion using fuzzy logic. In international conference on swarm, evolutionary, and memetic computing 2011 (pp. 485-93). Springer, Berlin, Heidelberg.
[Crossref] [Google Scholar]
[12]Mumtaz A, Majid A, Mumtaz A. Genetic algorithms and its application to image fusion. In international conference on emerging technologies 2008 (pp. 6-10). IEEE.
[Crossref] [Google Scholar]
[13]Dammavalam SR, Maddala S, Prasad MK. Iterative image fusion using fuzzy logic with applications. In advances in computing and information technology 2013 (pp. 145-52). Springer, Berlin, Heidelberg.
[Crossref] [Google Scholar]
[14]Rao DS, Seetha M, Hazarath M. Iterative image fusion using neuro fuzzy logic and applications. In international conference on machine vision and image processing 2012 (pp. 121-4). IEEE.
[Crossref] [Google Scholar]