摘要:
該文描述了一種利用極化SAR圖像的Mueller矩陣分解系數(shù)進行非監(jiān)督聚類的算法。根據(jù)關(guān)于各種地貌目標散射電磁波機理的先驗知識,該算法可以在不需要任何實地勘測的條件下將圖像粗略地分割為三種完全不同的、物理含義明顯的類別,即建筑區(qū)域、茂密植被和微粗糙表面(例如水面)。與利用單極化灰度圖像的非監(jiān)督分類算法相比,該算法的突出特點是不僅僅將每個像素按照其特征緊密地聚集在一起,而且還能確定每個聚類的散射機理,進而確定目標類型。
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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