A Quantitative and Comparative Assessment of Unmixing-Based Feature Extraction Techniques for Hyperspectral Image Classification
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.