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Novel Fusion Rules for Discrete Wavelet Transform based Image Fusion
Objective: The accuracy and reduction in speckle noise is an issue of major concern in change detection methods. In this paper, new fusion rules for Discrete Wavelet Transform (DWT) based image change detection have been proposed. Method: Multi-temporal images have been applied to Log Ratio and Mean Ratio operators to generate the source images. Both the source images are decomposed into wavelet coefficients through DWT. The fused image is obtained by applying the proposed fusion rules on the decomposed wavelet coefficients. The fusion rules for low frequency sub-band is based on addition of the average and maximum value of the wavelet coefficients while the fusion rule for high frequency sub-band is based on the neighborhood mean differencing of the coefficients. Findings: The difference image is generated by applying inverse wavelet transform on the fused coefficient map. The changed and unchanged areas have been classified by Fuzzy C Means (FCM) clustering. The results have been compared based upon parameters like Overall Error (OE), Percentage Correct Classification (PCC) and Kappa Coefficient (KC). The qualitative and quantitative results show that the proposed method offers least overall error. The accuracy and Kappa value of proposed method are also better than its preexistences. Application: The method has applications in remote sensing, medical diagnosis and disaster management.
Change Detection, Discrete Wavelet Transform, Fuzzy Clustering, Image Fusion, Log Ratio, Mean Ratio.
- Radke RJ, AndraS, Al-KofahiO, RoysamB. Image Change Detection Algorithms. A Systematic Survey. IEEE Transactions on Image Processing. 2005 March; 14(3): 294307. Crossref PMid:15762326
- Kettaf FZ, Bi D, Asselin de Beauville JP. A Comparison Study of Image Segmentation by Clustering Techniques. Beijing: Third International Conference on Signal Processing. 1996; 2:1280-3. Crossref
- Singh A. Digital Change Detection Techniques Using Remotely Sensed Data. International Journal of Remote Sensing. 1989; 10(6):989-1003. Crossref
- Rignot EJM, Van Zyl JJ. Change Detection Techniques for ERS-1 SAR Data. IEEE Transaction on Geoscience Remote Sensing. 1993 July; 31(4):896-906. Crossref
- Thamarai M, Mohanbabu K. An Improved Image Fusion and Segmentation using FLICM with GA for Medical Diagnosis. Indian Journal of Science and Technology. 2016 March; 9(12):1-10. Crossref
- Kuruo¢glu EE, Zerubia J. Modeling SAR Images with a Generalization of the Rayleigh Distribution. IEEE Transactions on Image Processing.2004; 13(4):527-33. Crossref PMid:15376587
- Sharma A, Gulati T. Review of Change Detection Techniques for Remotely Sensed Images. International Journal of Computer Science and Engineering. 2017 January; 5(1):22-5.
- Sharma A, Gulati T. Performance Analysis of Unsupervised Change Detection Methods for Remotely Sensed Images. International Journal of Computational Intelligence Research. 2017; 13(4):503-08.
- Zhang H, Cao X. A Way of Image Fusion Based on Wavelet Transform. IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks, Dalian. 2013; p. 498-501. Crossref
- Gong M, Zhou Z, Ma J. Change Detection in Synthetic Aperture Radar Images Based on Image Fusion and Fuzzy Clustering. IEEE Transaction on Image Processing. 2012 April; 21(4):2141-51. Crossref PMid:21984509
- Ma W, Li X, Wu Y, Jiao L, Xing D. Data Fusion and Fuzzy Clustering on Ratio Images for Change Detection in Synthetic Aperture Radar Images. Mathematical Problems in Engineering. 2014; 2014:1-14.
- Sharma A, Gulati T. Change Detection in Remotely Sensed Images Based on Image Fusion and Fuzzy Clustering. International Journal of Electronics Engineering Research. 2017; 9(1):141-50.
- Bezdek JC. Pattern Recognition with Fuzzy Objective Function. New York. Plenum.1981. Crossref 14. KrinidisS, ChatzisV. A Robust Fuzzy Local Information C-Means Clustering Algorithm. IEEE Transactions on Image Processing. 2010 May; 19(5):1328-37. Crossref PMid:20089475
- Celik T. Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and k Means Clustering. IEEE Geoscience and Remote Sensing Letters. 2009; 6(4):772-76. Crossref
- Da Cunha AL, ZhouJ, Do MN. The Nonsubsampled Contourlet Transform: Theory, Design, and Application. IEEE Transactions on Image Processing. 2006 October; 14(10):3089-101. Crossref
- SezginM, Sankur B. A Survey over Image Thresholding Techniques and Quantitative Performance Evaluation. Journal of Electronic Imaging. 2004 January; 13(1):146-65. Crossref
- Li S, Fang L, Yin H. Multitemporal Image Change Detection Using a Detail Enhancing Approach with Nonsubsampled Contourlet Transform. IEEE Geoscience and Remote Sensing Letters.2012 September; 9(5):836-40. Crossref
- Rosin PL, Ioannidis E. Evaluation of Global Image Thresholding for Change Detection. Pattern Recognition Letters. 2003; 24(14):2345-56. Crossref
- Rosenfield GH, Fitzpatrick-LinsA. A Coefficient of Agreement as a Measure of Thematic Classification Accuracy. Photogrammetric Engineering and Remote Sensing. 1986; 52(2):223-27.
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