摘要
为了克服传统的基于合成孔径雷达(SAR)图像局部统计特性的降斑算法的缺点,提出了基于区域分类、自适应滑动窗和结构检测的联合降斑算法.首先,联合降斑算法对当前区域进行区域分类,并直接保留强边缘结构和点目标.接着,联合降斑算法对均匀区域和弱边缘结构区域进行滑动窗的自适应增长,从而获得合适的滤波窗口.最后,联合降斑算法对新的滤波窗口使用区域分类.如果滤波窗口属于均匀区域,则直接使用均值滤波;如果滤波窗口属于边缘结构区域,则进一步使用结构检测,并且选择窗口内的均匀子区域作为最终的滤波区域.降斑实验表明,联合降斑算法可以有效滤除均匀区域和边缘区域的斑点,同时对强边缘结构信息和点目标有较好的保留作用.
In order to overcome the shortcomings of the conventional local statistical speckle filters for synthetic aperture radar （SAR） images,a combined despeckling algorithm is proposed based on region classification,adaptive windowing,and structure detection. Firstly,the combined despeckling algorithm classifies the local region and directly preserves the strong edges and structures as well as the point targets. Then,the homogeneous region and the weak edge and structure region grow adaptively to obtain appropriate filtering windows. Lastly,the new filtering windows are classified. For the homogeneous region,the averaged filter is directly used. For the edge and structure region,the structure information is detected and the homogeneous sub-window is selected as the final filtering region. Despeckling experiments demonstrate that,the combined despeckling algorithm effectively suppresses the speckles in homogeneous region and edge region,and well preserves the strong edge and structure information as well as the point targets.
出处
《物理学报》
SCIE
EI
CAS
CSCD
北大核心
2010年第5期3210-3220,共11页
Acta Physica Sinica
基金
国家重点基础研究发展计划(973)项目(批准号:2007CB311006)
国家高技术研究发展计划(863)项目(批准号:2006AA01Z126)
国家自然科学基金(批准号:60602026)
高等学校博士学科点专项科研基金(批准号:20070698002)资助的课题~~
关键词
区域分类
自适应滑动窗
结构检测
联合降斑算法
region classification
adaptive windowing
structure detection
combined despeckling algorithm
