面向高光谱图像分类的半监督Laplace鉴别嵌入

为有效提取出高光谱遥感图像数据的鉴别特征，该文阐述一种融合标记样本中鉴别信息和无标记样本中局部结构信息的半监督Laplace鉴别嵌入(SSLDE)算法。该算法利用标记样本的类别信息来保持样本集的可分性，并通过构建标记样本和无标记样本的Laplace矩阵来发现样本集中局部流形结构，实现半监督的流形鉴别。在KSC 和Urban数据集上的实验结果说明:该算法具有更高的分类精度，可以有效地提取出鉴别特征信息。在总体分类精度上，该算法比半监督最大边界准则(SSMMC)算法提升了6.3%~7.4%，比半监督流形保持嵌入(SSSMPE)算法提升了1.6%~4.4%。

Semi-supervised Laplace Discriminant Embedding for Hyperspectral Image Classification

In order to extract effectively the discriminant characteristics of hyperspectral remote sensing image data, this paper presents a Semi-Supervised Laplace Discriminant Embedding (SSLDE) algorithm based on the discriminant information of labeled samples and the local structural information of unlabeled samples. The proposed algorithm makes use of the class information of labeled samples to maintain the separability of sample set, and discovers the local manifold structure in sample set by constructing Laplace matrix of labeled and unlabeled samples, which can achieve semi-supervised manifold discriminant. The experimental results on KSC and Urban database show that the algorithm has higher classification accuracy and can effectively extract the information of discriminant characteristics. In the overall classification accuracy, this algorithm is improved by 6.3%~7.4% compared with Semi-Supervised Maximum Margin Criterion (SSMMC) algorithm and increased by 1.6%~4.4% compared with Semi-Supervised Sub-Manifold Preserving Embedding (SSSMPE) algorithm.