Laser & Optoelectronics Progress, Volume. 55, Issue 10, 103002(2018)
Hyperspectral Band Selection Based on Affinity Propagation Clustering
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.
Get Citation
Copy Citation Text
Ren Zhiwei, Wu Lingda. Hyperspectral Band Selection Based on Affinity Propagation Clustering[J]. Laser & Optoelectronics Progress, 2018, 55(10): 103002
Category: Spectroscopy
Received: Mar. 3, 2018
Accepted: --
Published Online: Oct. 14, 2018
The Author Email: Zhiwei Ren (juimer@foxmail.com)