Fractional Brownian motion models for synthetic aperture radar imagery scene segmentation
Fractional Brownian motion models for synthetic aperture radar imagery scene segmentation
复制标题
用于合成孔径雷达图像场景分割的分数布朗运动模型
作者：
C. Stewart;B. Moghaddam;K. Hintz;L. Novak
应用分形随机过程模型及其相关的缩放参数作为特征，在高分辨率，极化合成孔径雷达（SAR）图像的杂波的分析和分割。具体来说，自然杂波源，如草和树木的分形维数计算，并作为贝叶斯分类器的纹理特征。SAR阴影分割在一个单独的方式使用原始的后向散射功率作为判别。建议的分割过程产生一个三级分割图的场景中考虑这项研究（与三个杂波类型：阴影，树木和草）。研究了高相干斑SAR图像纹理度量的计算困难。特别是，一个两步的预处理方法，包括偏振最小散斑滤波，然后通过非相干空间平均使用。由此产生的分割地图的相关性恒虚警率（CFAR）雷达目标检测技术进行了讨论。>
The application of fractal random process models and their related scaling parameters as features in the analysis and segmentation of clutter in high-resolution, polarimetric synthetic aperture radar (SAR) imagery is demonstrated. Specifically, the fractal dimension of natural clutter sources, such as grass and trees, is computed and used as a texture feature for a Bayesian classifier. The SAR shadows are segmented in a separate manner using the original backscatter power as a discriminant. The proposed segmentation process yields a three-class segmentation map for the scenes considered in this study (with three clutter types: shadows, trees, and grass). The difficulty of computing texture metrics in high-speckle SAR imagery is addressed. In particular, a two-step preprocessing approach consisting of polarimetric minimum speckle filtering followed by noncoherent spatial averaging is used. The relevance of the resulting segmentation maps to constant-false-alarm-rate (CFAR) radar target detection techniques is discussed. >