摘要
针对传统的基于变换域隐马尔可夫树(Hidden Markov Tree,HMT)模型的SAR图像分割方法不能得到较满意的区域一致性结果和较准确的分割边缘的问题,提出了一种基于第二代Bandelet域HMT-3S模型的SAR图像分割方法(BHMT-3Sseg).HMT-3S模型是一种融合了子带间相关性的HMT模型,在描述图像纹理特征时,更具合理性.BHMT-3Sseg方法采用HMT-3S模型对图像的第二代Bandelet系数建模,通过HMT-3S模型参数的训练、各尺度似然值的计算和基于邻域背景的多尺度融合,实现对SAR图像的分割,既能得到较为准确和连续的边缘,也增强了分割结果的区域一致性.实验表明,本文方法BHMT-3Sseg对SAR图像分割是可行有效的.
Since the segmentation results of SAR images by traditional transform domain hidden Markov tree （HMT） model were unsatisfactory in homogenous regions and exact edges,a new segmentation method based on second generation bandelet-domain HMT-3S model was proposed.The method was called BHMT-3Sseg shortly.HMT-3S is a special kind of HMT which combines the correlation of different subbands.It is more reasonable to characterize texture regions than HMT model.BHMT-3Sseg modeled the second generation Bandelet coefficients of an image by using HMT-3S,and the SAR image segmentation results were obtained by training the parameters of HMT-3S and computing the likelihood of each scale and multiscale fusion based on a contextual model.The segmentation results by BHMT-3Sseg not only have more exact and more continuous edges,but also retain better region information.The experiments show that BHMT-3Sseg is efficient and effective for SAR image segmentation.
出处
《红外与毫米波学报》
SCIE
EI
CAS
CSCD
北大核心
2010年第2期145-149,共5页
Journal of Infrared and Millimeter Waves
基金
国家自然科学基金(6097112
60672126
60673097
60702062)
"863计划"项目(2007AA12Z136)
科技部"973计划"重点项目(2006CB705707)资助项目
