| 基于可区分性字典学习模型的极化SAR图像分类 |
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| 引用本文： | 桑成伟,孙洪.基于可区分性字典学习模型的极化SAR图像分类[J].信号处理,2017,33(11):1405-1415. |
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| 作者姓名： | 桑成伟 孙洪 |
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| 作者单位： | 武汉大学电子信息学院 |
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| 基金项目： | 国家自然科学基金项目（61771014） |
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| 摘 要： | 极化SAR图像分类是一个高维非线性映射问题，稀疏表示(CS)对于解决此类问题具有很大潜力。字典学习在基于CS的分类中起到重要作用。本文提出了一种新的字典学习模型，用于增强字典的区分能力，使其更适合极化SAR图像分类。提出的模型根据字典中两类子字典在分类中的作用对其相应的表达系数施加不同的稀疏约束。为使共同子字典能够抓住所有类共享的特征，对其相应系数施加稀疏约束，为使类专属子字典能够抓住类内独享的局部和全局结构特征，对其相应系数同时施加稀疏和低秩约束。由于共同子字典表达所有类共享的特征，我们以测试样本在类专属子字典上的重建误差作为准则进行分类。本文在AIRSAR的Flevoland数据集上对此算法进行验证，实验结果验证了算法的有效性。
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| 关 键 词： | 极化SAR图像分类 超完备字典 稀疏表示 低秩表达 |
| 收稿时间： | 2017-03-15 |
Discriminative Dictionary Learning for Polarimetric SAR Image Classification |
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| Affiliation: | School of Information and Electronics, Wuhan University |
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| Abstract: | Polarimetric SAR image classification is a high-dimensional nonlinear mapping problem, sparse representation (CS) has shown great potential to tackle such problems. Dictionary training based-CS plays an important role in SAR image classification. In this paper, we propose a novel dictionary learning model to make dictionary more discriminative, thus such dictionary is very fit for SAR image classification. According to the role of two kinds of sub-dictionaries, different sparsity regularizations are imposed on their corresponding coefficients. Specifically, the coefficients corresponding a common sub-dictionary are imposed with sparse regularization, so that the common sub-dictionary obtains features shared by all classes; the coefficients corresponding to class-specific sub-dictionaries are imposed with both sparse and low rank regularization, so that the class-specific sub-dictionaries capture intra-class intrinsic local and globe structure features. Due to the common sub-dictionary represents features shared by all classes, we use the reconstruction error based on each class-specific sub-dictionary for classification. The results obtained with AIRSAR Flevoland data set confirm that the proposed method is validated. |
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