基于块近邻的边界约束标签平滑高光谱图像分类方案

摘要

In order to improve the accuracy of hyperspectral image classification,combined with spectral information,neighborhood information and boundary information,this paper proposes a hyperspectral image classification scheme.The method takes the Local Fisher Discriminant Analysis(LFDA) algorithm to reduce the dimension and get the boundary information.The proposed Block Nearest Classifier(BNC) algorithm is used to get the discriminant information with the spectral feature and neighbor feature.The boundary information is used to smooth the classification label obtained from BNC algorithm.Experiment is carried out on hyperspectral dataset of 3 real ground objects.Results show that the proposed scheme improves the classification accuracy of hyperspectral imag effectively and robust.%为提高高光谱图像分类精度,结合光谱信息、邻域信息和边界信息提出一种高光谱图像分类方案.利用局部费希尔判别分析算法进行降维操作并获取边界信息.根据块近邻分类器算法结合光谱和邻域2个维度获得判决信息.采用边界信息对块近邻分类器算法获得的分类标签进行标签平滑操作.在3个真实地物高光谱数据集上进行实验,结果表明该方案稳定有效地提高了高光谱图像的分类精度.