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ase on SIFT are also unsuitable to our application because common key points can hardly be found. The important reason is that the key points are wholly dependent on the image intensities. With regard to the running speed, though the images are only zoomed out at 2 times, a higher running efficiency is obtained. The changeable resolution algorithm only takes less than one fortieth running time of the standard pixel by pixel cross correlation method, but they have same registration results, so the changeable resolution algorithm is a fast and effective realization of the NCC method. 6. Conclusion The paper presented a fast realization algorithm of automatic optical and infrared image registration. It reduces the computation considerably by utilizing changeable resolution algorithm and has obtained inspiring results at a fast running speed. It can also be used in other applications. As we mentioned above, the image registration is only the first work to do, the aim is to estimate the canopy temperature. But delightful thing is that it is our ongoing research, and some great progresses have been made. Moreover, how to get more accurate registration and how to make algorithm suit to more variational background are also our ongoing research. 7. Acknowledgement We would like to thank Mr Ashley Wheaton and Dr Nicola Cooley for their providing images and some useful discussions. References: [1] H.G. Jones. Use of thermography for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces. Journal of Plant, Cell and Environment (1999) 22, 1043– 1055. [2] H.G. Jones. Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Journal of Agricultural and Forest Meteorology 95 (1999) 139-149. [3] L. Guilioni , H.G. Jones, I. Leinonen, J.P. Lhomme. On the relationships between stomatal resistance and leaf temperatures in thermography. Journal of Agricultural and Forest Meteorology 148 (2008): 1908 – 1912. [4] O. Grant, L. Tronina1, H. G. Jones and M. M. Chaves (2007) Exploring thermal imaging variables for the detection of stress responses in grapevine under different irrigation regimes. Journal of Experimental Botany, Vol. 58, No. 4, pp. 815–825, 2007. [5] Wheaton, A.D., Cooley, N., Dunn, G., Goodwin, I., Needs, S., (2007) Evaluation of infrared thermography to determine the crop water status of Cabernet Sauvignon grapevines. Poster paper. 13th Australian Wine Industry Technical Conference, Adelaide, 28 July – 2 August. [6] Du-Ming Tsai, Chien-Ta Lin. Fast normalized cross correlation for defect detection. Journal of Pattern Recognition Letters 24 (2003) 2625–2631. [7] A. Roche, G. Malandain, etc. The correlation ratio as a new similarity measure for multimodal image registration. Medical Image Computing and Computer Assisted Intervention - MICCAI'98, 1496: 1115-1124,1998. [8] S. Klein. Evaluation of Optimization Methods for Nonrigid Medical Image Registration Using Mutual Information and B-Splines. IEEE Transactions on Image Processing, 16(12): 2879-2890, 2007. [9] Jian Chen, Jie Tian. Real-time multi-modal rigid registration based on a novel symmetric-SIFT descriptor. Progress in Natural Science 19 (2009): 643–651. [10] Yang Weiping, Wang Xuezhi, etc, Automatic optical and IR image fusion for plant water stress analysis, Proc of 12 th International Conference on Information Fusion, 1053-1059, 2009. 32
Impulsive Interference Detection Method Based on Morlet Wavelet and Maximum Likelihood Estimation Yuan Y. He 1 2 * , Chang J. Yu 2 , and Tai F. Quan 2 * Presenter 1. Harbin Institute of Technology, Harbin, 150001, China 2. The University of Melbourne, Parkville, VIC 3010, Australia The performance of the frequency monitor system (FMS) of high frequency (HF) radar is degraded by signal corruption due to impulsive interferences such as lightning and meteor echoes. These interferers raise the FMS spectrum noise level, as a result, FMS can’t pick out the real minimum disturbance frequency for HF radar. It is desirable to extract the impulsive interference components from the FMS data without removing any other information. However, Current wavelet detecting impulsive interference techniques, not matching impulsive interference very well, always lead to non-impulsive interference components degraded and lost. In this paper, we construct a more suitable impulsive interference detection method, by using the Morlet wavelet, matching well the shape of the impulsive interference signal, decomposition algorithm and maximum likelihood estimation thresholding rule, specifically designed to detect impulsive interference, so as to less disruption to the nonimpulsive interference information when extracting the impulsive interference. The effectiveness of the proposed technique has been proved by both simulated and practical experiments. 33
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