Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification
Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification
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DOI：
10.1109/tgrs.2018.2862899
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发表时间：
2019-02-01
影响因子：
8.2
通讯作者：
Li, Xuelong
中科院分区：
文献类型：
--
作者：
Wang, Qi;He, Xiang;Li, Xuelong
高光谱图像分类是为高光谱像元分配一个准确的标签的一种方法，近年来引起了人们的极大兴趣。虽然低秩表示（LRR）已被用于HSI分类，但其从整个HSI数据中分割每个类的能力尚未得到充分利用。LRR具有良好的捕获嵌入在原始数据中的底层低维子空间的能力。然而，LRR仍然存在两个缺点。首先，LRR没有考虑数据内部的局部几何结构，这使得相邻数据之间的局部相关性容易被忽略。其次，通过求解LRR获得的表示不足以区分不同的数据。本文提出了一种新的局部和结构正则化LRR（LSLRR）模型用于HSI分类。为了克服上述局限性，我们提出了局部约束准则和结构保持策略来改进经典的LRR。具体来说，我们引入了一个新的距离度量，它结合了空间和光谱特征，探索像素的局部相似性。因此，HSI数据的全局和局部结构可以被充分利用。此外，我们提出了一个结构约束，使表示具有近块对角结构。这有助于直接确定最终的分类标签。广泛的实验已经进行了三个流行的HSI数据集。实验结果表明，所提出的LSLRR优于其他国家的最先进的方法。
Hyperspectral image (HSI) classification, which aims to assign an accurate label for hyperspectral pixels, has drawn great interest in recent years. Although low-rank representation (LRR) has been used to classify HSI, its ability to segment each class from the whole HSI data has not been exploited fully yet. LRR has a good capacity to capture the underlying low-dimensional subspaces embedded in original data. However, there are still two drawbacks for LRR. First, the LRR does not consider the local geometric structure within data, which makes the local correlation among neighboring data easily ignored. Second, the representation obtained by solving LRR is not discriminative enough to separate different data. In this paper, a novel locality- and structure-regularized LRR (LSLRR) model is proposed for HSI classification. To overcome the above-mentioned limitations, we present locality constraint criterion and structure preserving strategy to improve the classical LRR. Specifically, we introduce a new distance metric, which combines both spatial and spectral features, to explore the local similarity of pixels. Thus, the global and local structures of HSI data can be exploited sufficiently. In addition, we propose a structural constraint to make the representation have a near-block-diagonal structure. This helps to determine the final classification labels directly. Extensive experiments have been conducted on three popular HSI data sets. And the experimental results demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.