Task-Driven Dictionary Learning for Hyperspectral Image Classification With Structured Sparsity Constraints
Task-Driven Dictionary Learning for Hyperspectral Image Classification With Structured Sparsity Constraints
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DOI：
10.1109/tgrs.2015.2399978
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发表时间：
2015-02
影响因子：
8.2
通讯作者：
Xiaoxia Sun;N. Nasrabadi;T. Tran
中科院分区：
文献类型：
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作者：
Xiaoxia Sun;N. Nasrabadi;T. Tran
稀疏表示将信号建模为少量字典原子的线性组合。作为一种生成模型，它要求字典是高度冗余的，以确保信号的稳定的高稀疏水平和低重构误差。然而，在实践中，这一要求通常由于缺乏标记的训练样本而受到损害。幸运的是，之前的研究表明，如果采用同时稀疏逼近，则对冗余字典的要求可能会不那么严格，这可以通过对邻近像素的稀疏代码实施各种结构化稀疏约束来实现。此外，大量的工作表明，应用各种字典学习方法的稀疏表示模型也可以提高分类性能。在本文中，我们重点介绍了任务驱动的字典学习（TSTO）算法，这是监督字典学习方法的一般框架。为了提高高光谱分类的性能，我们建议在Tspiral方法上强制执行结构化稀疏先验。我们的方法能够受益于同时稀疏表示和监督字典学习的优点。我们强制执行两个不同的结构稀疏先验，联合和拉普拉斯稀疏，Tendon方法，并提供相应的优化算法的细节。大量流行的高光谱图像上的实验表明，我们的方法的分类性能是上级的稀疏表示分类器与结构化的先验或Tdish方法。
Sparse representation models a signal as a linear combination of a small number of dictionary atoms. As a generative model, it requires the dictionary to be highly redundant in order to ensure both a stable high sparsity level and a low reconstruction error for the signal. However, in practice, this requirement is usually impaired by the lack of labeled training samples. Fortunately, previous research has shown that the requirement for a redundant dictionary can be less rigorous if simultaneous sparse approximation is employed, which can be carried out by enforcing various structured sparsity constraints on the sparse codes of the neighboring pixels. In addition, numerous works have shown that applying a variety of dictionary learning methods for the sparse representation model can also improve the classification performance. In this paper, we highlight the task-driven dictionary learning (TDDL) algorithm, which is a general framework for the supervised dictionary learning method. We propose to enforce structured sparsity priors on the TDDL method in order to improve the performance of the hyperspectral classification. Our approach is able to benefit from both the advantages of the simultaneous sparse representation and those of the supervised dictionary learning. We enforce two different structured sparsity priors, the joint and Laplacian sparsities, on the TDDL method and provide the details of the corresponding optimization algorithms. Experiments on numerous popular hyperspectral images demonstrate that the classification performance of our approach is superior to that of the sparse representation classifier with structured priors or the TDDL method.