智能系统学报Issue(2)：201-208,8.DOI:10.3969/j.issn.1673-4785.201410040
基于谱域－空域结合特征和图割原理的高光谱图像分类
Hyperspectral image classification based on spectral-spatial combination features and graph cut
摘要
Abstract
The high⁃dimension of the feature vs. small⁃size of training set is an unsolved problem in the hyperspectral image classification task. To solve this problem a two⁃step classification method is proposed. Firstly, a preliminary classification is performed by the support vector machine ( SVM) and the classification results are used to calculate the mean feature ( MF) of each class. Secondly, a classification based on the graph cut theory is applied with the MFs as an input of the energy function. The experimental results showed that spatially nearby pixels have large possibilities of having the same label and similar features. Therefore, a new feature called spectral⁃spatial combination ( SSC) is extracted that combines the spectral⁃based feature and spatial⁃based feature. The SSC feature contains the related spectral and spatial information of each pixel and provides better classification performance and robustness. Experi⁃ment results on the Indian Pine dataset and the Pavia University dataset demonstrated the effectiveness of the pro⁃posed method.关键词
高光谱/图像分类/谱域特征/空域特征/谱域-空域结合特征/均值特征/支持向量机/图割原理Key words
hyperspectral/image classification/spectral feature/spatial feature/spectral-spatial combination fea-ture/mean features/support vector machines/graph cut分类
信息技术与安全科学引用本文复制引用
尤雅萍,成运,苏松志,曹冬林,李绍滋..基于谱域－空域结合特征和图割原理的高光谱图像分类[J].智能系统学报,2015,(2):201-208,8.基金项目
国家自然科学基金资助项目（61202143）；福建省自然科学基金资助项目（2013J05100，2010J01345，2011J01367）；湖南省自然科学基金资助项目（12JJ2040）． （）
