【摘要】 高光谱遥感数据具有波段数目多、数据量庞大等特点.针对传统方法应用于高光谱图像分类中存在波段选择时计算量大、运行时间长,以及图像分类精度不高等问题,首先利用差分演化算法进行波段选择,有效地降低了信息的冗余和数据的维度,然后对波段选择后的结果成图,并对要识别地物的典型区域进行取样,最后采用基因表达式编程算法构建分类器进行图像分类.在波段选择中,与完全搜索的结果相比,差分演化算法可以在很快的时间里取得了较好的搜索结果,基因表达式编程在遥感图像分类中,分类结果优于传统的KNN算法.更多还原

【Abstract】 Hyperspectral data has the features of multi-band,large amounts of data,etc.For hyperspectral remote sensing image classification,traditional methods spend a long operation time in band selection,and image classification accuracy is not high,so first we use differential evolution(DE) for band selection,effectively reduce the redundancy of information and data dimensions,then show the image based on the result of band selection,sample the typical area of the surface feature to identify,finally use gene expression programming(GEP) to bulid classifier for image classification.In band selection,compared with full search algorithm,DE obtains relative good results in a fast time,in GEP image classification,the classification results superior to the traditional KNN algorithm.更多还原