CLASSIFICATION OF MULTI-LOOK POLARIMETRIC SAR IMAGERY-BASED ON COMPLEX WISHART DISTRIBUTION
CLASSIFICATION OF MULTI-LOOK POLARIMETRIC SAR IMAGERY-BASED ON COMPLEX WISHART DISTRIBUTION
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DOI：
10.1080/01431169408954244
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发表时间：
1994-07-20
影响因子：
3.4
通讯作者：
KWOK, R
中科院分区：
文献类型：
--
作者：
LEE, JS;GRUNES, MR;KWOK, R
多视极化SAR（合成孔径雷达）数据可以用Mueller矩阵形式或复协方差矩阵形式表示。后者具有复杂的Wishart分布。基于Wishart分布，开发了一种根据地形类型分割极化SAR数据的最大似然分类器。该算法不仅适用于只包含强度信息的SAR数据，也适用于多频多视极化SAR数据。在此基础上，提出了一种非监督分类方法，并利用Monte Carlo方法对多视极化SAR数据进行了分类误差评估。使用的训练集和单看数据的分类错误的比较。用NASA/JPL的P-、L-和C-波段极化SAR数据演示了该算法的应用。
Multi-look polarimetric SAR (synthetic aperture radar) data can be represented either in Mueller matrix form or in complex covariance matrix form. The latter has a complex Wishart distribution. A maximum likelihood classifier to segment polarimetric SAR data according to terrain types has been developed based on the Wishart distribution. This algorithm can also be applied to multi-frequency multi-look polarimetric SAR data, as well as to SAR data containing only intensity information. A procedure is then developed for unsupervised classification.The classification error is assessed by using Monte Carlo simulation of multi-look polarimetric SAR data, owing to the lack of ground truth for each pixel. Comparisons of classification errors using the training sets and single-look data are also made. Applications of this algorithm are demonstrated with NASA/JPL P-, L- and C-band polarimetric SAR data.