SVM在高光谱图像处理中的应用综述

A review on the application of SVM in hyperspectral image processing

高光谱遥感已经成为遥感领域的前沿技术,在军事以及国民经济中发挥着重要作用。支持向量机(support vector machine,SVM)在解决小样本、非线性和高维模式等问题中具有特有的优势,因而被广泛用于高光谱数据处理。在高光谱图像的波段选择、分类、端元选择、光谱解混及亚像元定位、异常检测等主要领域,SVM模型皆因其特性而表现出独特优势并已广泛应用。分析了高光谱图像特性,总结了当前各领域的发展现状及主要的处理方法,并对SVM方法在各领域中的应用及优势进行了阐述。

Hyperspectral remote sensing has become a foremost technology in remote sensing,and it plays an important role in military and national economy. Support vector machine（SVM） has unique advantages in solving the problems of small sample size,nonlinearity,and high-dimensional modes; therefore,it is widely used in hyperspectral data processing. Because of its advantages,SVM model has been applied widely in fields of hyperspectral imaging,such as band selection,classification,endmember selection,spectral unmixing,sub-pixel mapping,and anomaly detection. In this paper,the features of hyperspectral images are analyzed,and the development of hyperspectral imaging across various fields as well as its main processing methods are summarized. The applications and advantages of SVM method in those fields are also discussed.