Hyperspectral image classification based on clustering dimensionality reduction and multi-scale feature fusion

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.