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A Fast Neighborhood Grouping Method for Hyperspectral Band Selection

机译：高光谱频段选择的快速邻域分组方法

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摘要

Hyperspectral images can provide dozens to hundreds of continuous spectral bands, so the richness of information has been greatly improved. However, these bands lead to increasing complexity of data processing, and the redundancy of adjacent bands is large. Recently, although many band selection methods have been proposed, this task is rarely handled through the context information of the whole spectral bands. Moreover, the scholars mainly focus on the different numbers of selected bands to explain the influence by accuracy measures, neglecting how many bands to choose is appropriate. To tackle these issues, we propose a fast neighborhood grouping method for hyperspectral band selection (FNGBS). The hyperspectral image cube in space is partitioned into several groups using coarse-fine strategy. By doing so, it effectively mines the context information in a large spectrum range. Compared with most algorithms, the proposed method can obtain the most relevant and informative bands simultaneously as subset in accordance with two factors, such as local density and information entropy. In addition, our method can also automatically determine the minimum number of recommended bands by determinantal point process. Extensive experimental results on benchmark data sets demonstrate the proposed FNGBS achieves satisfactory performance against state-of-the-art algorithms.
机译：高光谱图像可以提供数十到数百个连续光谱带，因此信息的丰富性得到了大大提高。然而，这些频带导致数据处理的复杂性增加，并且相邻频带的冗余大。最近，尽管已经提出了许多频带选择方法，但是通过整个光谱频带的上下文信息很少处理该任务。此外，学者主要关注不同数量的选择乐队，以解释精度措施的影响，忽略了多少频段是合适的。为了解决这些问题，我们提出了一种快速的Hyperspectral频带选择（FNGB）的邻域分组方法。空间中的高光谱图像立方体使用粗细策略将空间中的若干组分成几个组。通过这样做，它有效地将上下文信息进行了大的频谱范围。与大多数算法相比，所提出的方法可以根据两个因素同时获得最相关和信息频带，例如局部密度和信息熵。此外，我们的方法还可以通过决定点过程自动确定推荐频段的最小数量。基准数据集的广泛实验结果展示了建议的FNGBS实现了对最先进的算法的令人满意的性能。

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