论文标题
利曼尼亚最接近的降级子空间分类，用于极化SAR图像
Riemannian Nearest-Regularized Subspace Classification for Polarimetric SAR images
论文作者
论文摘要
作为表示方法，最近的正规子空间（NRS）算法是获得POLSAR图像分类的精度和速度的有效工具。但是，现有的NRS方法使用偏光特征向量，但Polsar原始协方差矩阵（称为Hermitian阳性定位（HPD）矩阵）作为输入。如果不考虑矩阵结构，现有的基于NRS的方法无法学习频道之间的相关性。如何利用原始协方差矩阵到NRS方法是一个关键问题。为了解决此限制，提出了Riemannian NRS方法，该方法考虑了Riemannian空间中的HPD矩阵。首先，为了利用Polsar原始数据，提出了Riemannian NRS方法（RNR），是通过构造HPD字典和HPD距离度量标准提出的。其次，新的Tikhonov正则化项旨在减少同一类中的差异。最后，开发了最佳方法，并推断出一阶导数。在实验测试期间，仅在提出的方法中使用t矩阵，而多个特征用于比较方法。实验结果表明，所提出的方法甚至使用更少的功能也可以胜过最先进的算法。
As a representation learning method, nearest regularized subspace(NRS) algorithm is an effective tool to obtain both accuracy and speed for PolSAR image classification. However, existing NRS methods use the polarimetric feature vector but the PolSAR original covariance matrix(known as Hermitian positive definite(HPD)matrix) as the input. Without considering the matrix structure, existing NRS-based methods cannot learn correlation among channels. How to utilize the original covariance matrix to NRS method is a key problem. To address this limit, a Riemannian NRS method is proposed, which consider the HPD matrices endow in the Riemannian space. Firstly, to utilize the PolSAR original data, a Riemannian NRS method(RNRS) is proposed by constructing HPD dictionary and HPD distance metric. Secondly, a new Tikhonov regularization term is designed to reduce the differences within the same class. Finally, the optimal method is developed and the first-order derivation is inferred. During the experimental test, only T matrix is used in the proposed method, while multiple of features are utilized for compared methods. Experimental results demonstrate the proposed method can outperform the state-of-art algorithms even using less features.
