基于AP聚类的高光谱波段选择

Hyperspectral Band Selection Based on Affinity Propagation Clustering

波段选择在降维的同时能够保留高光谱数据的物理意义,在很多方面具有一定应用。近邻传播(AP)算法根据数据点之间的相关性进行聚类,将所有数据点视为潜在的聚类中心。提出了一种基于AP聚类的波段选择方法,利用光谱信息散度和光谱相关角(SID-SCA)与光谱信息散度和光谱梯度角(SID-SGA)改进AP算法中相似度的计算。将降维结果输入支持向量机(SVM)分类器进行分类,计算分类准确性,并通过数据集Indiana Pines进行验证。实验结果表明:所提方法能够更好地提取波段的信息,得到更高的分类精度。

Band selection can preserve the physical meaning of hyperspectral data while reducing dimension, and has application in many aspects. The cluster of affinity propagation （AP） algorithm is according to the correlation of data points, and the AP algorithm regards all data points as potential clustering centers. We propose a band selection method based on AP clustering, which uses spectral information divergence and spectral correlation angle （SID-SCA）, and spectral information divergence and spectral gradient angle （SID-SGA） to improve the similarity calculation in AP algorithm. The reducing dimension results are input into the support vector machine （SVM） classifier to classify, and the classification accuracy is calculated and verified using the data set Indiana Pines. The experimental results reveal that the proposed method can better extract the information of the band and obtain a high classification accuracy.