基于聚类和单层神经网络的无监督高光谱波段选择

Mateus Habermann, V. Fremont, E. H. Shiguemori
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引用次数: 3

摘要

高光谱图像通过利用连续波段提供观测场景丰富的光谱细节。但是,由于这些图像的高维数,使其处理变得非常繁重。因此,波段选择是在进行任何进一步处理之前采用的一种做法。为此,本文提出了一种基于聚类和神经网络的无监督波段选择方法。与其他六种波段选择框架的比较表明了该方法的优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsupervised Hyperspectral Band Selection using Clustering and Single-Layer Neural Network
Hyperspectral images provide rich  spectral details of the observed scene by exploiting contiguous bands.But, the processing of such images becomes heavy, due to the high dimensionality.Thus, band selection is a practice that has been adopted before any further processing takes place.Therefore, in this paper, a new unsupervised method for band selection based on clustering and neural network is proposed. A comparison with six other band selection frameworks shows the strength of the proposed method.
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