ABSTRACT

The information in hyperspectral images usually has a strong correlation, a large number of bands, which lead to the “curse of dimensionality”. So, band selection is usually used to address this issue. However, problems remain for band selection, such as how to search for the most informative bands, and how many bands should be selected. In this paper, a cuckoo search (CS)-based adaptive band selection framework is proposed to simultaneously select bands and determine the optimal number of bands to be selected. The proposed framework includes two “cuckoo search”, i.e. the outer one for estimating the optimal number of bands and the inner one for the corresponding band selection. To avoid employing an actual classifier within CS so as to greatly reduce computational cost, minimum estimated abundance covariance (MEAC) and Jeffreys-Matusita (JM) distance are adopted as criterion functions, which measures class separability. For the experiments, two widely used hyperspectral images, which acquired by the Hyperspectral digital imagery collection experiment (HYDICE) and the airborne Hyperspectral Mapper (HYMAP) system, are adopted for performance evaluation. The experimental results show that the two-CS-based algorithm outperforms the popular sequential forward selection (SFS), sequential floating forward search (SFFS), and other similar algorithms for hyperspectral band selection.

摘要

高光谱图像中的信息通常具有很强的相关性，具有大量的波段，从而导致“维数诅咒”。因此，频段选择通常用于解决此问题。但是，频段选择仍然存在问题，例如如何搜索信息最丰富的频段以及应选择多少个频段。本文提出了一种基于杜鹃搜索（CS）的自适应频段选择框架，以同时选择频段并确定要选择的最佳频段数。所提出的框架包括两个“布谷鸟搜索”，即用于估计最佳频段数量的外部一个和用于相应频段选择的内部一个。为了避免在CS中使用实际的分类器，从而大大降低了计算成本，最小估计丰度协方差（MEAC）和Jeffreys-Matusita（JM）距离用作标准函数，用于衡量类的可分离性。对于实验，采用通过高光谱数字影像收集实验（HYDICE）和机载高光谱测绘仪（HYMAP）系统获取的两个广泛使用的高光谱图像进行性能评估。实验结果表明，基于两个CS的算法优于流行的顺序前向选择（SFS），顺序浮动前向搜索（SFFS）和其他类似的高光谱波段选择算法。通过高光谱数字影像采集实验（HYDICE）和机载高光谱测绘仪（HYMAP）系统获取的数据进行性能评估。实验结果表明，基于两个CS的算法优于流行的顺序前向选择（SFS），顺序浮动前向搜索（SFFS）和其他类似的高光谱波段选择算法。通过高光谱数字影像采集实验（HYDICE）和机载高光谱测绘仪（HYMAP）系统获取的数据进行性能评估。实验结果表明，基于两个CS的算法优于流行的顺序前向选择（SFS），顺序浮动前向搜索（SFFS）和其他类似的高光谱波段选择算法。