Spectral-Spatial Discriminant Feature Learning for Hyperspectral Image Classification
Spectral-Spatial Discriminant Feature Learning for Hyperspectral Image Classification
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DOI：
10.3390/rs11131552
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Chunhua Dong;M. Naghedolfeizi;D. Aberra;Xiangyan Zeng
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文献类型：
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作者：
Chunhua Dong;M. Naghedolfeizi;D. Aberra;Xiangyan Zeng
稀疏表示分类(SRC)被广泛应用于高光谱图像的目标检测。然而，由于HSI中高维数据包含冗余信息的问题，即使有大量的光谱波段，SRC方法也可能无法达到高的分类性能。在高维空间中选择预测特征子集是高光谱图像分类中的一个重要且具有挑战性的问题。在本文中，我们提出了一种新的鉴别特征学习(DFL)方法，它将光谱和空间信息结合到超图拉普拉斯算子中。首先，选择一组区分特征，它保留了数据的谱结构和对已标记训练样本的类间和类内约束。利用超图拉普拉斯算子进行半监督学习，得到特征估值器。其次，通过拉普拉斯矩阵的广义特征分解，将所选特征映射到更低维的特征空间。最后将最终提取的判别特征用于联合稀疏模型算法。在基准数据集和不同的实验设置下进行的实验表明，我们提出的方法提高了分类准确率，并优于现有的HSI分类方法。
Sparse representation classification (SRC) is being widely applied to target detection in hyperspectral images (HSI). However, due to the problem in HSI that high-dimensional data contain redundant information, SRC methods may fail to achieve high classification performance, even with a large number of spectral bands. Selecting a subset of predictive features in a high-dimensional space is an important and challenging problem for hyperspectral image classification. In this paper, we propose a novel discriminant feature learning (DFL) method, which combines spectral and spatial information into a hypergraph Laplacian. First, a subset of discriminative features is selected, which preserve the spectral structure of data and the inter- and intra-class constraints on labeled training samples. A feature evaluator is obtained by semi-supervised learning with the hypergraph Laplacian. Secondly, the selected features are mapped into a further lower-dimensional eigenspace through a generalized eigendecomposition of the Laplacian matrix. The finally extracted discriminative features are used in a joint sparsity-model algorithm. Experiments conducted with benchmark data sets and different experimental settings show that our proposed method increases classification accuracy and outperforms the state-of-the-art HSI classification methods.