Clustering-based hyperspectral band selection using information measures
Clustering-based hyperspectral band selection using information measures
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DOI：
10.1109/tgrs.2007.904951
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发表时间：
2007-12-01
影响因子：
8.2
通讯作者：
Garcia-Sevilla, Pedro
中科院分区：
文献类型：
--
作者：
Martinez-Uso, Adolfo;Pla, Filiberto;Garcia-Sevilla, Pedro
高光谱成像涉及大量信息。提出了一种高光谱图像降维方法。该方法是基于一个层次聚类结构组波段，以最大限度地减少集群内的方差和最大限度地提高集群间的方差。这一目标是追求使用信息的措施，如基于互信息或Kullback-Leibler分歧的距离，以减少数据冗余和无用的信息之间的图像带。实验结果包括一些相关的和最近的方法之间的比较高光谱波段选择使用无标记的信息，显示其性能方面的像素图像分类任务。所提出的技术对于不同的图像数据集具有稳定的行为，并且主要在选择小的波段集时具有显著的准确性。
Hyperspectral imaging involves large amounts of information. This paper presents a technique for dimensionality reduction to deal with hyperspectral images. The proposed method is based on a hierarchical clustering structure to group bands to minimize the intracluster variance and maximize the intercluster variance. This aim is pursued using information measures, such as distances based on mutual information or Kullback-Leibler divergence, in order to reduce data redundancy and nonuseful information among image bands. Experimental results include a comparison among some relevant and recent methods for hyperspectral band selection using no labeled information, showing their performance with regard to pixel image classification tasks. The technique that is presented has a stable behavior for different image data sets and a noticeable accuracy, mainly when selecting small sets of bands.