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EXTRACTION OF OPTIMAL SPECTRAL BANDS USING HIERARCHICAL BAND MERGING OUT OF HYPERSPECTRAL DATA

机译:使用Hyperspectral数据分离频带的提取优化光谱带

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Spectral optimization consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspectral data huge dimensionality and can be applied to design specific superspectral sensors dedicated to specific land cover applications. Spectral optimization includes both band selection and band extraction. On the one hand, band selection aims at selecting an optimal band subset (according to a relevance criterion) among the bands of a hyperspectral data set, using automatic feature selection algorithms. On the other hand, band extraction defines the most relevant spectral bands optimizing both their position along the spectrum and their width. The approach presented in this paper first builds a hierarchy of groups of adjacent bands, according to a relevance criterion to decide which adjacent bands must be merged. Then, band selection is performed at the different levels of this hierarchy. Two approaches were proposed to achieve this task: a greedy one and a new adaptation of an incremental feature selection algorithm to this hierarchy of merged bands.
机译:光谱优化包括识别特定应用程序的最相关的频带子集。它是减少高光谱数据巨大维度的一种方法,可以应用于设计专用于特定地覆盖应用的特定超光传感器。光谱优化包括频带选择和频带提取。一方面,频带选择旨在使用自动特征选择算法选择高光谱数据集的频带之间的最佳频带子集(根据相关标准)。另一方面,频带提取定义了最相关的光谱带,优化其沿光谱的位置及其宽度。本文呈现的方法首先构建相邻频带组的层次结构,根据相关标准来确定必须合并哪个相邻频带。然后,在该层级的不同级别执行频带选择。提出了两种方法来实现这项任务:贪婪的一个和一个新的一个合并频带层次结构的增量特征选择算法的新改编。

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