Hyperspectral images (HSI) contain rich ground object information, which has great potential in classification. However, the large amount of data and noise also pose a challenge to HSI classification. In this paper, a new framework based on band selection and multi-scale structure features is proposed, which mainly consists of the following steps. Firstly, the spectral dimension of the HSI is reduced with the clustering average method based on information divergence. Secondly, the detailed multi-scale structure features of HSI are extracted by using multi-parameter relative total variation. Thirdly, in order to reduce noise and highlight structural features, bilateral filtering is used to fine-tune the extracted structural features. Finally, the improved quantum particle swarm optimization algorithm is proposed to optimize the parameters of SVM. A lot of experiment results on two hyperspectral datasets show that the proposed method performs better than several state-of-the-art methods.

高光谱图像（HSI）包含丰富的地物信息，具有很大的分类潜力。然而，大量的数据和噪声也对 HSI 分类提出了挑战。本文提出了一种基于波段选择和多尺度结构特征的新框架，主要包括以下步骤。首先，利用基于信息散度的聚类平均方法降低HSI的谱维数。其次，利用多参数相对总变差提取HSI详细的多尺度结构特征。第三，为了降低噪声和突出结构特征，使用双边滤波对提取的结构特征进行微调。最后，提出了改进的量子粒子群优化算法来优化支持向量机的参数。