摘要
随着遥感技术和成像光谱仪的发展,高光谱遥感图像的应用越来越广泛,但其自身的特点给高光谱图像的分类、识别等带来了很大的困难。如何快速地从高达数百个波段的高光谱图像中选择出具有较好分类识别能力的波段组合是亟待解决的问题。针对上述问题分析了已有的波段选择方法,提出一种结合遗传算法和蚁群算法的高光谱图像波段选择方法。该算法首先利用遗传算法以较快的寻优能力获得几组较优解,以此来初始化蚁群算法的初始信息素列表,然后用蚁群算法以较高的求精解能力获得最优解,并且在遗传算法部分中采用四进制的编码方式,使得算法编/译码简单、遗传算子操作简捷、且处理时所占空间小,同时在蚁群算法部分中巧妙地对预处理图像进行子空间划分来缩小蚂蚁搜索的范围,提高了算法的搜索效率,减小了输出波段组合的相关性和冗余度。由于该算法充分地吸取遗传算法和蚁群算法的优点、克服各自的缺陷,是一种计算耗时少、收敛性能好的波段选择方法。利用AVIRIS(airborne visible infrared imaging spectrometer)图像对提出的算法进行实验,实验结果表明,本文算法在所选波段性能和计算耗时方面都获得令人满意的效果。
With the development of remote sensing technology and imaging spectrometer, hyperspectral remote sensing images are widely used. However, the features of hyperspectral images have brought great difficulties for its classification and identification. One important research question is ＂ How to select a group of bands from hundreds of bands of hyperspeetral images, which are good for classification and identification？＂ In view of the above question, the existing band selection methods are analyzed, and a new method of hyperspectral imagery band selection is proposed, which is combined with genetic algorithm and ant colony algorithm. In the algorithm, the genetic algorithm is used to search for some better solutions quickly which initialize the information list of the ant colony algorithm. Then, the ant colony algorithm can effectively search for the best solution. In the part of the genetic algorithm, quaternary encoding is used, which makes encoding/decoding and genetic operation simple and uses less memory. In the part of the ant colony algorithm, subspace division is used to deal with hyperspectral images, reducing the search range of the ants. Which improves the search efficiency, and reduces the correlation and redundancy of the output band of hyperspectral image. The algorithm makes good use of the advantages of both genetic algorithm and ant colony algorithm and overcomes their defects, by consuming less time and outperfoming restraining method for band selection. An AVIRIS image was used for experiment with the proposed algorithm, which proves that this algorithm of hyperspectal dimension reduction is effective in terms of band selection performance and execution time consumption.
出处
《中国图象图形学报》
CSCD
北大核心
2013年第2期235-242,共8页
Journal of Image and Graphics
基金
国家自然科学基金项目(60802059
61275010)
教育部博士点新教师基金项目(200802171003)
关键词
高光谱图像
波段选择
遗传算法
蚁群算法
hyperspectral imagery
band selection
genetic algorithm
ant colony algorithm
