基于Mueller矩陣分解的非監(jiān)督聚類算法
UNSUPERVISED CLUSTERING ALGORITHM BASED ON THE DECOMPOSITION OF MUELLER MATRIX
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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(jìn)行非監(jiān)督聚類的算法。根據(jù)關(guān)于各種地貌目標(biāo)散射電磁波機(jī)理的先驗(yàn)知識(shí),該算法可以在不需要任何實(shí)地勘測(cè)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(diǎn)是不僅僅將每個(gè)像素按照其特征緊密地聚集在一起,而且還能確定每個(gè)聚類的散射機(jī)理,進(jìn)而確定目標(biāo)類型。
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關(guān)鍵詞:
- SAR; 聚類; 矩陣分解
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. -
J.J.Van Zyl,Unsupervised classification of scattering behavior using radar polarimetry data,IEEE Trans.On Geosci,Remote Sensing,1989,27(1),36-45.[2]M.Borgraud,R.T.Shin,J.A.Kong.Theoretical models for polarimetric radar clutter,Journalof Electromagnetic Waves and Applications,1987,1(1),73-89.[3]王之禹,朱敏慧,白有天.基于散射模型的極化SAR數(shù)據(jù)分解,電子科學(xué)學(xué)刊,待發(fā)[4]O.Rice,Reflection of electromagnetic waves from slightly rough surfacws,Prue Appl.Math.1951,4(3),351-378.[5]S.B.Serpico,P.Pellegretti,L.Bruzzone,Feature-selection for remote-sensing data classification SPIE,2315,1994,564-577. -
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