基于马尔科夫判别谱聚类的极化SAR图像分类方法

该文针对现有的谱聚类方法用于极化SAR图像分类时精度较低的问题，提出一种基于马尔科夫的判别谱聚类方法(MDSC)，具有低秩和稀疏分解的特点。该方法首先恢复一个真实的低秩概率转移矩阵，将其作为标准马尔科夫谱聚类方法的输入，以减少噪声对分类结果的影响；然后在目标函数中引入判别信息，使极化SAR图像的数据信息能够得到更加充分地利用；最后采用增广拉格朗日乘子法来解决低秩和概率单纯形约束下的目标函数优化问题。在荷兰小农田、德国、西安和荷兰大农田4个不同数据集上的实验证明，该方法具有较好的准确率，且参数敏感性较低，表现出了良好的分类性能。 

PolSAR Image Classification Method Based on Markov Discriminant Spectral Clustering

Due to the existing spectral clustering methods have low accuracy for PolSAR image classification, a Markov-based Discriminative Spectral Clustering(MDSC) method is proposed, which has the characteristics of low-rank and sparse decomposition. Firstly, a real low-rank probability transfer matrix is restored as an input to the standard Markov spectral clustering method to reduce the influence of noise on the classification result. Then the discriminative information is introduced into the objective function to make the polarimetric SAR image data can be more fully used. Finally, the augmented Lagrangian multiplier method is used to solve the objective function optimization problem under low-rank and probability simplex constraints. Experiments on three different data sets of Flevoland, Oberpfaffenhofen, and Xi’an show that our method has good accuracy and low sensitivity, which having a good classification performance.