To improve the remote sensing image classification accuracy by incorporating labeled and unlabeled samples, this paper proposes a new manifold learning method called Semi-supervised Manifold Discriminant Embedding (SSMDE). This method uses data point labels to construct two relational graphs, within-class graph and between-class graph,they then are taken to encode the class relation information indicated in the labeled data points and to construct two weighted matrices. The labeled and unlabeled data points are utilized to construct the total scatter matrix to describe all the data points. Finally, the projection matrix of SSMDE is obtained by solving an optimization problem. The SSMDE method can not only take into account the discriminant information of labeled data, but also preserve the global structure of all data points. The experimental results on both synthetic and remote sensing images show that the proposed method can achieve the classification accuracy of 92. 32% and the error between the classification results by the SSMDE and the government statistics is less than 5 %, which demonstrates the effectiveness of SSMDE.%为了有效利用已标记与未标记样本提高遥感影像分类精度,提出了一种新的半监督流形学习方法-半监督流形鉴别嵌入法(SSMDE).该方法利用标记样本的类别信息构建类内图和类间图来表征样本数据的类别联系,并计算相应的权重矩阵；利用标记和未标记数据构建全局散度矩阵来表征数据的整体结构.在此基础上,通过优化目标函数得到投影矩阵,在保持特征空间中数据整体结构的前提下,使同类数据点之间保持近邻关系、不同类数据点的距离尽可能大.在人工数据集和遥感影像上的实验结果表明,SSMDE分类率为92.36％,且分类结果与政府统计数据之间的误差均小于5％.该方法通过有效利用少量标记样本和大量无标记样本实现半监督学习,有效提高了遥感影像的分类精度.
展开▼
