结合多特征和SVM的SAR图像分割  

Multiple features and SVM combined SAR image segmentation

作　　者：钟微宇[1,2] 沈汀[1] 

机构地区：[1]中国科学院对地观测与数字地球科学中心,北京100094 [2]中国科学院大学,北京100049

出　　处：《计算机应用研究》2013年第9期2846-2851,共6页Application Research of Computers

摘　　要：为实现灰度共生矩阵(GLCM)多尺度、多方向的纹理特征提取,提出了一种结合非下采样轮廓变换(NSCT)和GLCM的纹理特征提取方法。先用NSCT对合成孔径雷达(SAR)图像进行多尺度、多方向分解;再对得到的子带图像使用GLCM提取灰度共生量;然后对提取的灰度共生量进行相关性分析,去除冗余特征量,并将其与灰度特征构成多特征矢量;最后,充分利用支持向量机(SVM)在小样本数据库和泛化能力方面的优势,由SVM完成多特征矢量的划分,实现SAR图像分割。实验结果表明,基于NSCT域的GLCM纹理提取方法和多特征融合用于SAR图像分割,可以提高分割准确率,获得较好的边缘保持效果。In order to implement multi-scale and multi-directional texture extraction, this paper proposed a texture feature ex- traction algorithm l which combined the nonsubsampled contourlet transform （NSCT） and gray level co-occurrence matric （GL- CM）. Firstly,it translated the SAR image to be segmented via NSCT. Then, it computed the gray co-occurrence features via GLCM for the decomposed sub-bands, and selected the features extracted by correlation analysis to remove redundant features. Meanwhile, it extracted gray features to constitute a multi-feature vector with the gray co-occurrence features. Finally, making full use of advantages of resolving the small-sample statistics and generalizing ability of support vector machines （ SVM）, it used SVM to divide the multi-feature vector to segment the SAR image. Experimental results show that the proposed method for SAR image segmentation can improve segmentation precision, and obtain better edge preservation results.

关 键 词：合成孔径雷达 图像分割 非下采样轮廓变换 灰度共生矩阵 支持向量机 特征选择 多特征融合 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]