基于改进小波域隐马尔可夫模型的遥感图像分割
Remote-Sensing Image Segmentation Based on Improved Wavelet-Domain Hidden Markov Models
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摘要: 该文提出了一种基于改进小波域隐马尔可夫树(HMT)模型进行图像分割的方法。该方法利用基于希尔伯特变换对的二维方向小波,这种小波变换具有平移不变性、方向检测性好的特点。同时该方法还利用拓展HMT对该改进小波域中尺度间的小波系数相关性进行建模,并结合多背景融合技术进行遥感图像的分割,得到了优于已有文献的分割结果,而且与同类算法相比,降低了算法所需的计算量。Abstract: Improved wavelet-domain HMT based remote-sensing image segmentation algorithm is proposed in this paper, The algorithm is based on 2-D directional wavelet, which is implemented via Hilbert transform pairs. The 2-D directional wavelet can provide both shift invariance and good directional selectivity. In this paper, the dependence of wavelet coefficients lied in inter scale is modeled efficiently, and a new segmentaion algorithm is produced by combining this with multicontext fusion method. A better segmentation result for remote-sensing image with smaller computational burden is obtained,
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Choi H, Bararniuk R G. Multiscale image segmentation using wavelet-domain hidden Markov models[J].IEEE Trans. on Image Processing.2001, 10(9):1309-[2]Selesnick I W. The design of Hilbert transform pairs of wavelet bases via the flat delay filter. In Proc. IEEE Int. Conf. on Acoustic,peech and Signal Processing, ICASSP01, Salt Lake City, UT,May 2001:3673 - 3676.[3]Kingsbury N G. Image processing with complex wavelets. Phil.Trans. Royal Society London 4, 1999, 357(9): 2543 - 2560.[4]Crouse M S, Nowark R D, Baraniuk R G. Wavelet-based statistical signal processing using hidden Markov models[J].IEEE Trans. on Signal. Proc.1998, 46 (4):886-[5]Kingsbury N G. A dual-tree complex wavelet transform with improved orthogonality and symmetry properties. In Proc. IEEE Int. Conf. Image Processing, Vancouver, Canada, September 11-13, 2000:375 - 378.[6]Choi H, Romberg J, Baraniuk R, Kingsbury N G. Hidden Markov tree modeling of complex wavelet transform. Proc. ICASSP 2000,Istantbul, June 6-9, 2000:133 - 136[7]Fan G, Xia X G. A joint multi-context and multiseale approach to Bayesian image segmentation[J].IEEE Trans. on Geoscience and Remote Sensing.2001, 39(12):2680-[8]Bouman C A, Shapiro M. A multiseale random field model for Bayesian image segmentation[J].IEEE Trans. on Image Processing.1994, 3(2):162- 期刊类型引用(9)
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