摘要
应用聚类方法对合成孔径雷达(SAR)图像进行分割时,受SAR图像斑点噪声的影响,在聚类过程中应当既要考虑聚类原型的空间自适应性,又要考虑像素间强相关性,这就要求聚类隶属度与空间信息的有效结合.针对此问题提出了基于方向流场构建自适应邻域空间加权可能性c-均值聚类(PCM)算法,通过可操纵小波变换和视觉特征预测编码模型建立方向流场,用基于方向流场的Markov随机场(MRF)描述当前像素与其邻域像素间的相互关系,在像素的聚类隶属度估计中直接引入这种邻域信息,使聚类隶属度得到有效修正.实验表明这种方法在抑制噪声的同时可以保留图像的细节信息,对SAR图像有较好的分割结果.
Because of the influence of the speckle in the synthetic aperture radar （SAR） image, both the statistical dependencies among neighboring pixels and spatial adaptation of the clustering prototype should be considered during the process of SAR image segmentation. So it is required that the membership and spatial information should be combined. The adaptive spatial neighbor weighted possibilistic e-means （PCM） clustering based on the direction flow field is proposed in the paper. The direction flow field is constructed by combining the predictive coding model with the visual character and steerable wavelet transform. The relationship between the pixel under test and the pixels of neighborhood is described through Markov random fields （MRF） based on the direction flow field. Because the context information is considered, membership is adjusted efficiently. The experimental results on real SAR images demonstrate the merit of the proposed method, especially in despeckling and the preservation of details within a SAR image.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2008年第4期624-631,共8页
Journal of Xidian University
基金
国家自然科学基金资助(6067309760703109)
国家部委科技项目资助(A1420060172
51307040103)
关键词
合成孔径雷达图像分割
可能性c-均值聚类
可操纵小波变换
方向流场
synthetic aperture radar （SAR） image segmentation
possibilistic c-means clustering
steerable wavelet transform
direction flow field
