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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Hyperspectral Image Classification Using Principal Components-Based Smooth Ordering and Multiple 1-D Interpolation
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Hyperspectral Image Classification Using Principal Components-Based Smooth Ordering and Multiple 1-D Interpolation

机译：基于主成分的平滑排序和多重一维插值的高光谱图像分类

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摘要

This paper proposes a spectral-spatial classification algorithm based on principal components (PCs)-based smooth ordering and multiple 1-D interpolation, which can alleviate the general classification problems effectively. Because of the characteristics of hyperspectral image, there always exist easily separable samples (ESSs) and difficultly separable samples (DSSs) in view of the different sets of labeled samples. In this paper, the PC analysis is first used for reducing features and extracting the few first PCs of a hyperspectral image. Then, PC-based smooth ordering is designed for the separation of ESSs and DSSs, and multiple 1-D interpolation is used for the accurate classification of the ESSs. Next, the highly confident samples are selected from the ESSs by the spatial neighborhood information, which are added into the training set for the classification of DSSs. In the case of sufficient training samples, a supervised spectral-spatial method is used for classifying the DSSs by combining the spatial information built with popular extended multiattribute profiles. The proposed algorithm is compared with some state-of-the-art methods on three hyperspectral data sets. The results demonstrate that the presented algorithm achieves much better classification performance in terms of the accuracy and the computation time.
机译：提出了一种基于主成分平滑排序和一维多插值的光谱空间分类算法，可以有效地缓解一般分类问题。由于高光谱图像的特性，鉴于标记样品的不同集合，总是存在容易分离的样品（ESS）和难以分离的样品（DSS）。在本文中，PC分析首先用于减少特征并提取高光谱图像的少数几台PC。然后，基于PC的平滑排序被设计用于ESS和DSS的分离，并且多个一维插值用于ESS的准确分类。接下来，通过空间邻域信息从ESS中选择高度可信的样本，并将其添加到训练集中以进行DSS分类。在训练样本足够的情况下，通过将构建的空间信息与流行的扩展多属性配置文件相结合，可以使用监督频谱空间方法对DSS进行分类。将该算法与三个高光谱数据集上的一些最新方法进行了比较。结果表明，该算法在准确性和计算时间上均达到了更好的分类性能。

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