Locality-constrained sparse representation for hyperspectral image classification
Locality-constrained sparse representation for hyperspectral image classification
复制标题
高光谱图像分类的局部约束稀疏表示
DOI：
10.1016/j.ins.2020.09.009
复制
发表时间：
2020
影响因子：
8.1
通讯作者：
Jiayi Ma
中科院分区：
文献类型：
--
作者：
Yuanshu Zhang;Yong Ma;Xiaobing Dai;Hao Li;Xiaoguang Mei;Jiayi Ma
稀疏表示在高光谱图像分类中得到了广泛的应用。待分类的样本可以用来自同一类的几个样本线性表示。然而，当来自不同类别的样本彼此高度相关时，这使得分类任务具有挑战性。为了解决这个问题，我们考虑到训练样本和测试样本之间的欧氏距离信息，构造一个新的字典稀疏表示。也就是说，我们提出了一个局部约束稀疏表示分类器（LSRC）在本文中。首先，K-最近邻（KNN）算法被应用到训练数据集，形成一个局部约束的字典，排除从测试样本分离的样本在欧氏空间。然后，稀疏编码应用于测试样本与形成的字典通过类相关的正交匹配追踪（OMP）算法，利用类标签信息。最后，利用所有类别内的最小残差准则，得到测试样本的类标号。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。
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.