Abstract
Band selection is a commonly used approach for dimensionality reduction in hyperspectral imagery. Affinity propagation (AP), a new clustering algorithm, is addressed in many fields, and it can be used for hyperspectral band selection. However, this algorithm cannot get a fixed number of exemplars during the message-passing procedure, which limits its uses to a great extent. This paper proposes an adaptive AP (AAP) algorithm for semi-supervised hyperspectral band selection and investigates the effectiveness of distance metrics for improving band selection. Specifically, the exemplar number determination algorithm and bisection method are addressed to improve AP procedure, and the relations between selected exemplar numbers and preferences are established. Experiments are conducted to evaluate the proposed AAP-based band selection algorithm, and the results demonstrate that the proposed method outperforms other popular methods, with lower computational cost and robust results.
©2012 Optical Society of America
Full Article | PDF ArticleMore Like This
Haitao Gan, Nong Sang, and Rui Huang
J. Opt. Soc. Am. A 31(1) 1-6 (2014)
Yao Liu, Hong Xie, Liguo Wang, and Kezhu Tan
Appl. Opt. 55(3) 462-472 (2016)
Shigeki Nakauchi, Ken Nishino, and Takuya Yamashita
Opt. Express 20(2) 986-1000 (2012)