区域Gamma混合模型的SAR图像分割
Synthetic aperture radar image segmentation using a regional gamma mixture model
- 2014年18卷第5期 页码:1024-1033
纸质出版日期: 2014
DOI: 10.11834/jrs.20143230
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纸质出版日期: 2014 ,
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[1]李琴洁,杨学志,吴克伟,薛丽霞,郎文辉.区域Gamma混合模型的SAR图像分割[J].遥感学报,2014,18(05):1024-1033.
LI Qinjie, YANG Xuezhi, WU Kewei, et al. Synthetic aperture radar image segmentation using a regional gamma mixture model[J]. Journal of Remote Sensing, 2014,18(5):1024-1033.
针对传统Gamma混合模型用于SAR图像分割时忽略像素间空间相关性
导致分割结果不连续并产生大量误分割的现象
提出了区域Gamma混合模型的SAR图像分割算法。首先对图像进行分水岭分割
得到过分割区域块
然后将其作为输入样本进行基于Gamma混合模型的聚类
在模型的参数估计过程中进一步考虑区域间的空间相关性
设计邻域因子融入到迭代过程
得到邻域加权类分布概率。该算法充分利用像素间的空间相关性
能够降低噪声对分割结果的影响。通过合成图像和真实SAR图像的实验表明
本文算法能够实现SAR图像的准确分割。
The application of gamma mixture model on Synthetic Aperture Radar( SAR) image segmentation ignores spatial correlation between pixels. To solve this problem
this study proposes a new algorithm using regional gamma mixture model
which i mproves segmentation accuracy and reduces speckle noise effects on SAR images. The special imaging mechanism of SAR images results in severe distractions to speckle noise. The traditional gamma mixture model for SAR image segmentation only uses the gray information of images and ignores the spatial correlation between pixels
leading to segmentation result discontinuity and false segmentation. Watershed segmentation is a common initial segmentation algorithm. The defect of this algorithm is the over-segmentation phenomenon
which becomes more serious as the noise increases. Segmentation results cannot accurately represent the ground object features. Therefore
related algorithms should be used to combine segmentation results. A substantial amount of research has been conducted to solve the above problems. This study proposes a new segmentation algorithm that increases both segmentation accuracy and noise immunity. This algorithm involves two main steps: building of a regional gamma mixture model and designing of a neighborhood factor. SAR images are segmented by the watershed algorithm. The over-segmented results are used as initial clustering objects for the gamma mixture model. However
the watershed algorithm has a serious problem of over-segmentation. The watershed algorithm and gamma mixture model are combined to include regional information into calculation; however
inter-regional linkages remain missing
and segmentation accuracy remains poor. Therefore
considering the correlation between regional blocks in the parameter estimation
we include the neighborhood factor into the expectation-maximization iterative algorithm.The neighborhood factor is the weighted probability of each region belongs to each segmentation class. After several iterations
the factor is updated
and the image is segmented. Synthetic images and real SAR images demonstrate that the new algorithm can achieve more accurate segmentation results than the traditional gamma mixture model
regional Gaussian m ixture model and regional Markov random field algorithm. Upon visual observation
the segmentation results of the new algorithm are clearer and less affected by noise. Moreover
the results of the new algorithm have larger Kappa coefficient and better segmentation accuracy. This study proposes the RGaMM algorithm
which includes regional information and interregional contextual information into calculation. This algorithm can also effectively segment SAR image. In particular
under the effects of heavy speckle noise
the RGaMM algorithm has more advantages than the traditional algorithm. The proposed algorithm not only can a chieve accurate segmentation on SAR images
but also reduce speckle noise effects.
合成孔径雷达分水岭Gamma混合模型邻域因子EM算法
synthetic aperture radarwatershedgamma mixture modelneighborhood factorexpectation-maximization algorithm
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