期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2006
卷号：XXXVI Part 7
出版社：Copernicus Publications
摘要：The improved spectral resolution of modern hyperspectral sensors provides effective means for discrimination of subtly different classes and objects. However, in order to obtain statistically reliable classification results, the number of required training samples increases exponentially as the number of spectral bands increases. However, in many situations, acquisition of the large number of training samples for these high-dimensional datasets may not be possible or so easy. This problem may be overcome by using multiple classifiers. In this paper, we describe a weighted combination of multiple classifiers based on the genetic algorithm. Practical examinations on the AVIRIS data for discrimination of different land use/cover classes demonstrate the effectiveness of the proposed approach
