Spectral–Spatial Classification of Hyperspectral Data Using 3-D Morphological Profile
Spectral–Spatial Classification of Hyperspectral Data Using 3-D Morphological Profile
复制标题
DOI：
10.1109/lgrs.2015.2476498
复制
发表时间：
2015-10
影响因子：
4.8
通讯作者：
B. Hou;T. Huang;L. Jiao
中科院分区：
文献类型：
--
作者：
B. Hou;T. Huang;L. Jiao
提出了一种新的基于三维形态剖面的高光谱数据光谱-空间分类方法。作为以前的方法的扩展，所提出的方法使用的光谱和空间信息进行分类。首先，随机投影（RP）用于降维的高光谱数据。在谱域RP之后，提出了一种新的3D-MP方法来利用数据之间的相关性。最后，使用广泛使用的支持向量机分类器进行分类。我们的实验表明，该方法利用3-D光谱空间特征，提供国家的最先进的分类结果，为不同的高光谱数据集。
A new spectral-spatial method based on a 3-D morphological profile (3D-MP) is proposed for hyperspectral data classification. As an extension of a previous approach, the proposed method uses both the spectral and spatial information for classification. First, random projection (RP) is used for dimensionality reduction of hyperspectral data. After RP in spectral domain, a novel 3D-MP method is proposed to exploit the dependence between data. Finally, the classification is performed by the widely used support vector machine classifier. Our experiments reveal that the proposed approach exploits the 3-D spectral-spatial feature to provide the state-of-the-art classification results for different hyperspectral data sets.