【摘要】 由于高光谱数据的海量高维特征,对其进行降维处理成为高光谱遥感研究的一个重要问题。波段选择算法由于能够有效地保留原始数据的信息,在高光谱数据降维及后续的遥感识别与分类等方面具有明显的优越性。文章提出了一种基于正交投影散度(OPD)的波段选择方法,该方法继承了正交子空间投影(OSP)算法的特点,通过把原始数据投影到特征空间,实现感兴趣目标与背景噪声的分离;通过最大化光谱向量之间的相似性测度以及顺序浮动前向搜索(SFFS)算法,实现快速的波段选择。利用HYDICE和HY-MAP高光谱数据进行实验验证,并与其他传统波段选择算法,如光谱角度匹配、欧式距离、光谱信息散度和LCMV-BCC等进行对比,结果表明该算法在高光谱数据波段选择方面具有较好的适用性和鲁棒性,能够有效地应用于高光谱遥感数据的降维研究。更多还原

【Abstract】 Due to the high data dimensionality of a hyperspectral image,dimensionality reduction algorithm has attracted much attention in hyperspectral image analysis.Band selection algorithm,which selects appropriate bands from the original set of spectral bands,can preserve original information from the data and is useful for image classification and recognition.In the present paper,a novel band selection algorithm based on orthogonal projection divergence(OPD) is proposed,it aims to discriminate the interesting objects from background and noise information,maximize the spectral similarity between different spectral vectors by projecting the original data to feature space.Two HYDICE Washington DC Mall images and an HYMAP Purdue campus image data were experimented,and support vector machine(SVM) classifier was used for classification.The selected band number varies from 5 to 40 in order to study the impacts of different band selection algorithms on different features.For the computation complex,the sequential floating forward search(SFFS) was used to get the appropriate bands.The experiments have proved that our proposed OPD algorithm can outperform other traditional band selection methods such as SAM,ED,SID,and LCMV-BCC for hyperspectral image analysis.It is proven that OPD band selection is effective and robust in hyperspectral remote sensing dimensionality reduction.更多还原