ABSTRACT

Informative feature extraction from hyperspectral image (HSI) is the primary and most challenging task in the hyperspectral data processing. The rich source of HSI information provides effective ground cover analysis which requires high computational cost and, using the original, classification accuracy suffers from the curse of dimensionality. Therefore, feature reduction has been applied through feature extraction and feature selection. The popularly used unsupervised feature extraction method, Minimum Noise Fraction (MNF), has been applied but the computational cost is high. This paper proposed a band grouping technique using Normalized Mutual Information (NMI) and applies MNF to each individual group called BgMNF. Feature selection can be done with NMI. The extracted feature can be classified using kernel Support Vector Machine (SVM) for performance analysis. Two real HSI is used in experimentation that demonstrates the proposed technique significantly improves the classification accuracy as well as computational cost as compared with the studied methods.

摘要

从高光谱图像（HSI）中提取信息特征是高光谱数据处理中的主要和最具挑战性的任务。丰富的 HSI 信息源提供了有效的地表覆盖分析，这需要高计算成本，并且使用原始的分类精度受到维数灾难的影响。因此，通过特征提取和特征选择来应用特征约简。已经应用了普遍使用的无监督特征提取方法最小噪声分数（MNF），但计算成本很高。本文提出了一种使用归一化互信息 (NMI) 的波段分组技术，并将 MNF 应用于称为 BgMNF 的每个单独的组。可以使用 NMI 完成特征选择。提取的特征可以使用内核支持向量机 (SVM) 进行分类以进行性能分析。实验中使用了两个真实的 HSI，这表明与所研究的方法相比，所提出的技术显着提高了分类精度和计算成本。