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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >A Coarse-to-Fine Optimization for Hyperspectral Band Selection
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A Coarse-to-Fine Optimization for Hyperspectral Band Selection

机译：高光谱频带选择的粗略优化

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摘要

Hyperspectral band selection is a feature selection method that selects a most representative set of bands to achieve a good performance in several tasks such as classification and anomaly detection. It reduces the burden of storage, transmission, and computation. In this letter, a two-stage band selection algorithm is introduced. It selects bands and refines the result using a linear reconstruction error criterion. Then a coarse-tofine band selection (CFBS) strategy is applied to the two-stage band selection in order to achieve a better result. CFBS selects bands group by group. Each group is selected based on bands that are not well represented by the previous groups, trying to minimize the linear reconstruction error. Experiments show that the proposed method has a significant advancement compared with other competitors.
机译：高光谱频带选择是一种特征选择方法，可选择最代表性的频带组，以在诸如分类和异常检测之类的若干任务中实现良好的性能。它降低了存储，传输和计算的负担。在这封信中，介绍了一种两级频带选择算法。它选择频带并使用线性重建误差标准改进结果。然后将粗豆类频带选择（CFB）策略应用于两级频带选择以实现更好的结果。 CFBS按组选择频带组。基于前一组不得很好地表示的频段选择每个组，尝试最小化线性重建误差。实验表明，与其他竞争对手相比，该方法具有重要进步。

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