摘要:
高光谱影像数据的类标签样本获取困难,而在少量标签点情况下的分类精度通常不理想。为此,提出了一种改进的空间-光谱约束的图半监督分类算法( spatial-spectral constrained graph -based semi-supervised classifica-tion,SS-GSSC)。首先,以欧氏距离结合RBF( radial basis function )核函数确定空间相似性权值;采用光谱相关角( spectral correlation angle ,SCA)计算光谱相似性权值;然后,将2种权值以乘积的形式进行组合,对相似性测度进行约束;最后,利用标签传递算法对测试数据进行标签预测,获得分类结果。通过分别对Indian Pines 影像和DC Sub影像进行分类实验的结果表明,该算法较之以往的分类算法,能更好地消除同类地物图斑中夹杂异类地物散点的现象,在少量标签点(每类25个)情况下,取得了较高的分类精度。
Abstract:
It is difficult to obtain labels of samples for hyperspectral data .Few labeled samples usually lead to low classification accuracy .In view of this situation , an improved spatial and spectral constraint graph -based semi-supervised classification algorithm (SS-GSSC) is proposed.First of all, Euclidean distance combined with radial basis function ( RBF) is used to construct the spatial similarity edge weight; Spectral correlation angle ( SCA ) is used to calculate spectral similarity weights;Then, the two kinds of weights are combined to the form of product to restrict the similarity measurement;Finally, the label propagation algorithm is used to predict the test data labels so as to obtain the classification results .Classification experiments on Indian Pines image and DC Sub image show that , compared with the previous classification algorithm , the algorithm designed by the authors can better eliminate the phenomenon of the existence of the same category map spot included in other categories of scattered points , and can achieve higher classification accuracy under the condition of less label points (25 per class).
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