Multi-Structure KELM With Attention Fusion Strategy for Hyperspectral Image Classification

Hyperspectral image (HSI) classification refers to accurately corresponding each pixel in an HSI to a land-cover label. Recently, the successful application of multiscale and multifeature methods has greatly improved the performance of HSI classification due to their enhanced utilization of the available spectral-spatial information. However, as the number of scales and the number of features increases, it becomes more difficult to achieve an optimal degree of fusion for multiple classifiers [e.g., kernel extreme learning machine (KELM)]. On the other hand, a limited sample size of the HSI may cause overfitting problems, which seriously affects the classification accuracy. Therefore, in this article, a novel multi-structure KELM with attention fusion strategy (MSAF-KELM) is proposed to achieve accurate fusion of multiple classifiers for effective HSI classification with ultrasmall sample rates. First, a multi-structure network is built, which combines multiple scales and multiple features to extract abundant spectral-spatial information. Second, a fast and efficient KELM is employed to enable rapid classification. Finally, a weighted self-attention fusion strategy (WSAFS) is introduced, which combines the output weights of each KELM subbranch and the self-attention mechanism to achieve an efficient fusion result on multi-structure networks. We conducted experiments on four types of HSI datasets with different evaluation methods and compared them with several classical and state-of-the-art methods, which demonstrate the excellent performance of our method on ultrasmall sample rates. The code is available at https://github.com/Fang666666/MSAF-KELM for reproducibility.