Locality Adaptive Discriminant Analysis for Spectral–Spatial Classification of Hyperspectral Images
Locality Adaptive Discriminant Analysis for Spectral–Spatial Classification of Hyperspectral Images
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DOI：
10.1109/lgrs.2017.2751559
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发表时间：
2017-09
影响因子：
4.8
通讯作者：
Qi Wang;Zhao-Xi Meng;Xuelong Li
中科院分区：
文献类型：
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作者：
Qi Wang;Zhao-Xi Meng;Xuelong Li
线性判别分析（LDA）是一种用于有监督降维的常用技术，但对局部数据结构关注较少。这使得LDA不适用于许多实际情况，例如高光谱图像（HSI）分类。在本文中，我们提出了一种新的降维算法，即局部自适应判别分析（LADA）用于HSI分类。所提出的算法旨在学习数据的一个代表性子空间，并关注在光谱域和空间域中具有紧密关系的数据点。一个直观的动机是，同一类的数据点具有相似的光谱特征，并且空间邻域内的数据点通常与同一类相关。与传统的LDA及其变体相比，LADA能够自适应地利用数据的局部流形结构。在几个真实的高光谱数据集上进行的实验证明了所提方法的有效性。
Linear discriminant analysis (LDA) is a popular technique for supervised dimensionality reduction, but with less concern about a local data structure. This makes LDA inapplicable to many real-world situations, such as hyperspectral image (HSI) classification. In this letter, we propose a novel dimensionality reduction algorithm, locality adaptive discriminant analysis (LADA) for HSI classification. The proposed algorithm aims to learn a representative subspace of data, and focuses on the data points with close relationship in spectral and spatial domains. An intuitive motivation is that data points of the same class have similar spectral feature and the data points among spatial neighborhood are usually associated with the same class. Compared with traditional LDA and its variants, LADA is able to adaptively exploit the local manifold structure of data. Experiments carried out on several real hyperspectral data sets demonstrate the effectiveness of the proposed method.