A Quantitative and Comparative Assessment of Unmixing-Based Feature Extraction Techniques for Hyperspectral Image Classification
A Quantitative and Comparative Assessment of Unmixing-Based Feature Extraction Techniques for Hyperspectral Image Classification
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DOI：
10.1109/jstars.2011.2176721
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发表时间：
2012-04
影响因子：
5.5
通讯作者：
I. Dopido;A. Villa;A. Plaza;P. Gamba
中科院分区：
文献类型：
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作者：
I. Dopido;A. Villa;A. Plaza;P. Gamba
在过去的几年中，许多特征提取技术已被集成在处理链打算用于高光谱图像分类。在监督分类的上下文中，已经表明，机器学习技术（诸如支持向量机（SVM））的良好泛化能力仍然可以通过在分类之前充分提取特征来增强，从而减轻由休斯效应引入的维数灾难。最近，一种新的策略，用于特征提取之前的分类的基础上光谱分解的概念已经推出。当高光谱图像的空间分辨率不足以在子像素级分离不同的光谱成分时，这种策略已经显示出成功。与主成分分析（PCA）或最小噪声分数（MNF）等统计变换相比，另一个优点是基于解混的特征具有物理意义，因为它们可以被解释为光谱成分的丰度。反过来，以前开发的基于解混的特征提取链不包括空间信息。本文提出了两个新的贡献。首先，我们开发了一种新的基于解混的特征提取技术，它集成了空间和光谱信息，使用无监督聚类和部分光谱解混的组合。其次，我们进行了定量和比较评估的解混为基础的与传统的（监督和无监督）的高光谱图像分类的背景下的特征提取技术。我们的研究，使用不同的仪器收集的各种高光谱场景进行，提供了实用的观察和不同的分类方案所需的特征提取技术的类型。
Over the last years, many feature extraction techniques have been integrated in processing chains intended for hyperspectral image classification. In the context of supervised classification, it has been shown that the good generalization capability of machine learning techniques such as the support vector machine (SVM) can still be enhanced by an adequate extraction of features prior to classification, thus mitigating the curse of dimensionality introduced by the Hughes effect. Recently, a new strategy for feature extraction prior to classification based on spectral unmixing concepts has been introduced. This strategy has shown success when the spatial resolution of the hyperspectral image is not enough to separate different spectral constituents at a sub-pixel level. Another advantage over statistical transformations such as principal component analysis (PCA) or the minimum noise fraction (MNF) is that unmixing-based features are physically meaningful since they can be interpreted as the abundance of spectral constituents. In turn, previously developed unmixing-based feature extraction chains do not include spatial information. In this paper, two new contributions are proposed. First, we develop a new unmixing-based feature extraction technique which integrates the spatial and the spectral information using a combination of unsupervised clustering and partial spectral unmixing. Second, we conduct a quantitative and comparative assessment of unmixing-based versus traditional (supervised and unsupervised) feature extraction techniques in the context of hyperspectral image classification. Our study, conducted using a variety of hyperspectral scenes collected by different instruments, provides practical observations regarding the utility and type of feature extraction techniques needed for different classification scenarios.