Spatial Domain Display for Interference Image Dataset
WANG Cai-ling1，LI Yu-shan2*，LIU Xue-bin1，HU Bing-liang1，JING Juan-juan1，WEN Jia1
1. Key Lab of Spectral Imaging in Xi’an Institute of Optics and Precision Mechanics of Chinese Academy of Sciences，Xi’an 710119，China 2. CAD Lab of Xidian University, Xi’an 710069，China
Abstract：The requirements of imaging interferometer visualization is imminent for the user of image interpretation and information extraction. However, the conventional researches on visualization only focus on the spectral image dataset in spectral domain. Hence, the quick show of interference spectral image dataset display is one of the nodes in interference image processing. The conventional visualization of interference dataset chooses classical spectral image dataset display method after Fourier transformation. In the present paper, the problem of quick view of interferometer imager in image domain is addressed and the algorithm is proposed which simplifies the matter. The Fourier transformation is an obstacle since its computation time is very large and the complexion would be even deteriorated with the size of dataset increasing. The algorithm proposed, named interference weighted envelopes, makes the dataset divorced from transformation. The authors choose three interference weighted envelopes respectively based on the Fourier transformation, features of interference data and human visual system. After comparing the proposed with the conventional methods, the results show the huge difference in display time.
[1] Rupert S, Sharp M, Sweet J, et al. Geoscience and Remote Sensing Symposium, 2001, 1: 94. [2] Bryant J. Photogramm. Eng. Remote Sens., 1988，54(12)：1739. [3] Di L，Rundquist D C. Photogramm. Eng. Remote Sens., 1988, 54(12): 1745. [4] Durand J M, Kerr Y H. IEEE Trans. Geosci. Remote Sens., 1989, 27(5): 611. [5] Lin Q，Allebach J. Proc. IEEE Int. Geosci. Remote Sens. Symp., 1990. 357. [6] Robertson P K, O’Callaghan J F. IEEE Trans. Geosci. Remote Sens., 1988，26(1)：49. [7] Qaid Ali M，Basavarajappa H T. American-Eurasian Journal of Scientific Research, 2008, 3(1): 84. [8] Du H, Qi H, Wang X, et al. Proc. 32nd Applied Imagery Pattern RecognitionWorkshop, 2003. 93. [9] Ready P J，Wintz P A. IEEE Trans. Commun., 1973，21(10)：1123. [10] Tyo J S, Konsolakis A, Diersen D I, et al. IEEE Trans. Geosci. Remote Sens., 2003，41(3)：708. [11] Jacobson N P, Gupta M R. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(11): 2684. [12] Jia Xiuping, Richards John A. IEEE Trans. Geosci. Remote Sens., 1999，37(1)：538. [13] Du Qian, Raksuntorn Nareenart, Cai Shangshu，et al. IEEE Trans. Geosci. Remote Sens., 2008，46(6)：1858. [14] Roger R E. IEEE Trans. Geosci. Remote Sens., 1994，32(6)：1194. [15] Chang C I，Du U. IEEE Trans. Geosci. Remote Sens., 1999，37(9)：2387. [16] Chen S, Mulgrew B, Grant P M. IEEE Trans. Neural Networks, 1993，4：570. [17] Wyszecki G，Stiles W S. Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd ed. New York: Wiley, 2000. [18] Polder G，van der Heijden G W A N. Presented at the SPIE Conf. Visualization and Optimization Techniques, 2001.
