摘要:
该文描述了一种利用极化SAR图像的Mueller矩阵分解系数进行非监督聚类的算法。根据关于各种地貌目标散射电磁波机理的先验知识,该算法可以在不需要任何实地勘测的条件下将图像粗略地分割为三种完全不同的、物理含义明显的类别,即建筑区域、茂密植被和微粗糙表面(例如水面)。与利用单极化灰度图像的非监督分类算法相比,该算法的突出特点是不仅仅将每个像素按照其特征紧密地聚集在一起,而且还能确定每个聚类的散射机理,进而确定目标类型。
Abstract:
An unsupervised clustering algorithm is described in this paper, which utilizes the coefncient of decomposition of the Mueller matrix of the polarimetric SAR image. The algorithm can classify the image into three distinct categories, i.e., building area, vegetated area, and slightly rough surface (e.g. water) without any terrain measurement according to the various experienced knowledge about scattering mechnism of terrain targets. Compared with other unsupervised clustering algorithm based on the single polarimetric gray-scale image, this
algorithm is characterized that it can not only cluster every pixel according to its character, but also determine the scattering mechnism of every class, and the type of targets.
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