IEEE Trans Image Process. 2017 Jun;26(6):2918-2928. doi: 10.1109/TIP.2017.2687128. Epub 2017 Mar 24.
In hyperspectral image analysis, the classification task has generally been addressed jointly with dimensionality reduction due to both the high correlation between the spectral features and the noise present in spectral bands, which might significantly degrade classification performance. In supervised classification, limited training instances in proportion with the number of spectral features have negative impacts on the classification accuracy, which is known as Hughes effects or curse of dimensionality in the literature. In this paper, we focus on dimensionality reduction problem, and propose a novel feature-selection algorithm, which is based on the method called high dimensional model representation. The proposed algorithm is tested on some toy examples and hyperspectral datasets in comparison with conventional feature-selection algorithms in terms of classification accuracy, stability of the selected features and computational time. The results show that the proposed approach provides both high classification accuracy and robust features with a satisfactory computational time.
在高光谱图像分析中，由于光谱特征之间存在高度相关性以及光谱波段中存在噪声，分类任务通常与降维一起解决，这可能会显著降低分类性能。在监督分类中，与光谱特征数量成比例的有限训练实例对分类准确性有负面影响，这在文献中被称为 Hughes 效应或维度灾难。在本文中，我们专注于降维问题，并提出了一种新的特征选择算法，该算法基于称为高维模型表示的方法。与传统的特征选择算法相比，我们在一些玩具示例和高光谱数据集上测试了该算法，从分类准确性、所选特征的稳定性和计算时间等方面进行了评估。结果表明，该方法提供了高分类准确性和稳健的特征，并且计算时间令人满意。
