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中文摘要：
针对传统字典学习算法难以有效保持极化SAR图像的空间结构以及难以处理大规模数据的问题,提出了一种基于空域和极化域的联合域字典学习和稀疏表示的分类方法.该方法采用基于联合域流形距离的快速AP聚类进行字典学习.利用局部线性编码对极化SAR图像进行空域和极化域的联合域稀疏表示,充分利用了极化SAR数据集潜在的信息,有效保持极化SAR数据结构的同时降低了算法的时间复杂度.试验结果表明：所提算法适应性强,收敛速度快,能够提高极化SAR图像的分类精度.
英文摘要：
T raditional dictionary learning （DL ） algorithms only consider the global sparsity of data , yet ignore the spatial structure of data .Moreover ,its high computational complexity leads to the dif‐ficulty of dealing with large‐scale image data .Considering the information of PolSAR image in the spatial‐polarimetric domain , a novel combined DL based sparse representation （SR ） classification method （CDL‐SRC） was proposed for PolSAR image classification in this paper .First ,the spatial‐po‐larimetric manifold based fast affinity propagation （AP） clustering was employed to learn an over‐complete dictionary .Then locality‐constrained linear coding method was adopted to extract the spatial and polarimetric features of PolSAR respectively .Finally ,the PolSAR image was classified by the lin‐ear support vector machine （SVM ） .Compared with traditional methods ,experimental results demon‐strate that the proposed method can improve the classification accuracy ,w hich has the advantages of strong adaptability ,efficient convergence rate and low computational complexity .
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