Sparse representation has been in a widespread use in hyperspectral image (HSI) classification task. The samples to be classified can be linearly represented with a few samples from the same class. However, when samples from different classes are highly correlated with each other, it makes the classification task challenging. To solve this problem, we take the Euclidean distance information between the training samples and testing samples into consideration to construct a new dictionary for sparse representation. That is, we propose a locality-constrained sparse representation classifier (LSRC) in this paper. First, the K-nearest neighbour (KNN) algorithm is applied to the training data set to form a locality-constrained dictionary by excluding the samples separated from testing samples in the Euclidean space. Then, the sparse coding is applied to the testing sample with the formed dictionary via class dependent orthogonal matching pursuit (OMP) algorithm which utilizes the class label information. Finally, by using the minimal residual rule within all catergories, we can obtain class label of the testing sample. Experiments based on the chosen three hyperspectral datasets prove that our proposed LSRC outperforms other popular classifiers.

稀疏表示已在高光谱图像（HSI）分类任务中得到广泛使用。可以使用来自同一类别的一些样本来线性表示要分类的样本。然而，当来自不同类别的样本彼此高度相关时，这使分类任务具有挑战性。为了解决这个问题，我们考虑了训练样本和测试样本之间的欧几里得距离信息，以构建一个新的稀疏表示字典。也就是说，本文提出了一种局域约束的稀疏表示分类器（LSRC）。首先，通过排除与欧氏空间中的测试样本分离的样本，将K最近邻（KNN）算法应用于训练数据集，以形成局域性受限的字典。然后，通过利用类别标签信息的类别相关正交匹配追踪（OMP）算法，将稀疏编码应用于具有形成的字典的测试样本。最后，通过使用所有类别中的最小残差规则，我们可以获得测试样本的类别标签。基于所选的三个高光谱数据集的实验证明，我们提出的LSRC优于其他流行的分类器。