【摘要】 本文提出了一种利用全极化SAR数据的极化特征获取海冰密集度算法。首先，对全极化SAR数据进行多视化及滤波等预处理，以获取相干矩阵与协方差矩阵；其次，通过相干矩阵与协方差矩阵获取若干极化特征，对这些极化特征进行相关性与冗余性分析，构建最优特征空间；然后，将最优特征空间作为输入量放入神经网络分类器中，得到海冰分类结果；最后，根据海冰分类结果提取海冰密集度。选用拉布拉多南部海域2景全极化Radarsat-2影像获取海冰密集度，与业务化海冰密集度产品ASI-3125进行对比研究。本文算法结果与ASI-3125海冰密集度分布趋势基本一致，总体上略大于ASI-3125海冰密集度，标准差值分布为3.46%和6.82%,说明利用高分辨率全极化SAR数据在监测边缘区域小尺寸破碎海冰方面具有优势。更多还原

【Abstract】 This paper proposes an algorithm to obtain sea ice concetration(SIC) from fully polarimetric SAR data based on polarization features. Firstly, multilookprocess and filtering are performed on the fully polarimetric SAR data to obtain the coherence matrix and covariance matrix. Secondly, a number of polarization features are obtained through the coherence matrix and covariance matrix, and the correlation and redundancy analysis of these polarization features is carried out to construct the optimal feature space.Then, put the optimal feature space as input into the neural network classifier to obtain the SIC result. Finally, extract the sea ice concentration according to the SIC result. In this paper, two fully polarimetric Radarsat-2 images in the southern waters of Labrador are used to obtain the SIC. Compared with the commercial the SIC product of ASI-3125, the algorithm results of this paper are basically consistent with the distribution trend of the SIC product of ASI-3125,and generally slightly larger than the SIC product of ASI-3125. The standard deviation distributions are 3.46% and 6.82%, indicating that the use of high-resolution fully polarimetric SAR data has advantages in monitoring small-sized broken sea ice in the marginal area.更多还原