Band Selection for Hyperspectral Imagery Using Affinity Propagation
Band Selection for Hyperspectral Imagery Using Affinity Propagation
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DOI：
10.1109/dicta.2008.42
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发表时间：
2008-12
期刊：
影响因子：
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通讯作者：
Sen Jia;Y. Qian;Z. Ji
中科院分区：
文献类型：
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作者：
Sen Jia;Y. Qian;Z. Ji
由于有数百个光谱波段，高光谱图像通常包含大量数据。为了减少计算量，加速后续分类分析的知识发现，通常先进行波段选择。最近，提出了一种新的聚类算法，称为“亲和传播”。与流行的k中心聚类技术不同，亲和传播通过同时将所有数据点视为潜在的聚类中心（称为“样本”）并在数据点之间交换消息，直到出现一组良好的样本和聚类。在本文中，我们采用仿射传播的波段选择的高光谱数据。实验结果表明，与一些相关的和最近的波段选择方法相比，从像素图像分类的角度来看，通过亲和传播选择的波段最能代表高光谱图像。
Hyperspectral imagery generally contains enormous amounts of data due to hundreds of spectral bands. Band selection is often adopted firstly to reduce computational cost and accelerate knowledge discovery of subsequent classificationand analysis. Recently, a new clustering algorithm, named "affinity propagation," is proposed. Different from the popular k-centers clustering technique, affinity propagation operates by simultaneously considering all data points as potential cluster centers (called "exemplars") and exchanging messages between data points until a good set of exemplars and clusters emerges. In this paper, we apply affinity propagation for band selection of hyperspectral data. Experimental results demonstrate that, compared with some relevant and recent methods for band selection, the bands chosen by affinity propagation best represent the hyperspectral imagery from the pixel image classification standpoint.