| 噪声抑制的多极化SAR海冰图像分割 |
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| 引用本文： | 夏梦琴,杨学志,董张玉,郑鑫,李国强.噪声抑制的多极化SAR海冰图像分割[J].遥感学报,2015,19(5):856-863. |
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| 作者姓名： | 夏梦琴 杨学志 董张玉 郑鑫 李国强 |
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| 作者单位： | 合肥工业大学 计算机与信息学院, 安徽 合肥 230009,合肥工业大学 计算机与信息学院, 安徽 合肥 230009;光电控制技术重点实验室, 河南 洛阳 471009,合肥工业大学 计算机与信息学院, 安徽 合肥 230009,光电控制技术重点实验室, 河南 洛阳 471009,光电控制技术重点实验室, 河南 洛阳 471009 |
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| 基金项目： | 国家自然科学基金(编号:61371154,41076120,61271381);光电控制技术重点实验室和航空科学基金联合资助项目(编号:201301P4007);中央高校基本科研业务费专项基金(编号:2012HGCX0001) |
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| 摘 要： | 合成孔径雷达(SAR)海冰图像分割对全球气候研究和保证船舶航行安全具有重要意义。现有的基于区域的马尔可夫随机场(MRF)多极化SAR分割方法,由于受相干斑噪声影响,其区域划分不尽合理,不能有效完成分割。因此,提出一种噪声抑制的多极化SAR海冰图像分割算法,首先在极化总功率图上引入降低噪声的滤波算法,合理划分初始区域,其次考虑区域之间的差异度,从而实现多极化SAR海冰图像的准确分割。以RADARSAT-2和SIR-C获得的全极化海冰图像为实验数据进行验证,结果表明:和其他较先进算法相比,本文算法优势明显,既能高效保持图像连通性,又能增强图像的细节信息,具有更高的分割精度。
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| 关 键 词： | 海冰 多极化SAR Markov随机场 噪声抑制 区域差异度 |
| 收稿时间： | 2015/1/27 0:00:00 |
| 修稿时间： | 2015/4/12 0:00:00 |
Segmentation of polarimetric SAR sea ice image with noise suppression |
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| XIA Mengqin,YANG Xuezhi,DONG Zhangyu,ZHENG Xin and LI Guoqiang.Segmentation of polarimetric SAR sea ice image with noise suppression[J].Journal of Remote Sensing,2015,19(5):856-863. |
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| Authors: | XIA Mengqin YANG Xuezhi DONG Zhangyu ZHENG Xin and LI Guoqiang |
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| Institution: | School of Computer and Information, Hefei University of Technology, Hefei 230009, China,School of Computer and Information, Hefei University of Technology, Hefei 230009, China;Science and Technology on Electro-optic Control Laboratory, Luoyang 471009, China,School of Computer and Information, Hefei University of Technology, Hefei 230009, China,Science and Technology on Electro-optic Control Laboratory, Luoyang 471009, China and Science and Technology on Electro-optic Control Laboratory, Luoyang 471009, China |
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| Abstract: | SAR sea ice image segmentation is essential for climate variation research and navigation safety. However, current region-level MRF-based algorithms have been widely used in SAR sea ice image segmentation. Speckle noise is a phenomenon of the SAR imaging system produced by inherent defects of the system. This phenomenon seriously affects the accuracy of polarimetric SAR image interpretation and subsequent segmentation, classification, target detection, and treatment. Thus, polarimetric SAR image speckle should be inhibited. To effectively suppress the interference of speckle noise and preserve edge information, We proposed a new segmentation algorithm in this paper.A novel segmentation algorithm called polarimetric SAR sea ice image with noise suppression (NS-RMRF) is introduced. Speckle Reduction Anisotropic Diffusion (SRAD) filtering is used in polarization total power span before splitting the original image. The simulated overflow watershed segmentation algorithm is applied to generate initialized areas. After the initialized segmentation, a region adjacency graph is constructed. With the difference between the area and the adjacent area, the difference degree is introduced to the region MRF model based on the Wishart distribution. Simulated annealing is the optimization algorithm used to minimize the objective function.Two polarimetric SAR of sea ice image data obtained by RADARSAT-2 and SIR-C were used to verify the effectiveness of the proposed method. The classical polarimetric segmentation ML method of Lee, the region-WRMF segmentation method of Wu, and the PolarIRGS segmentation method of Yu were compared with the NS-RMRF method. The result indicates that the proposed algorithm exhibit advantages over other image segmentation methods.In a subjective perspective, the results of the NS-RMRF segmentation algorithm can indicate the true distribution of surface features. In an objective perspective, the proposed algorithm achieves better segmentation results than the three other methods based on the overall accuracy and Kappa coefficient.The segmentation algorithm of NS-RMRF is proposed in this study and a new calculation method that measures the regional difference is also presented. Noise-reduction filtering algorithm is used to establish a valid initial segmentation and maintain edge information by considering the difference between regions in a spatial context model. The proposed algorithm can effectively capture and maintain details and smoothen homogeneous areas more effectively than ML, region-based WMRF, and PolarIRGS algorithms. However, the proposed method are limited by deficiencies; thus, the proposed algorithm should be improved. |
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| Keywords: | sea ice polarimetric SAR MRF noise suppression regional difference |
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