A two-branch multiscale spectral-spatial feature extraction network for hyperspectral image classification

In the field of hyperspectral image (HSI) classification in remote sensing, the combination of spectral and spatial features has gained considerable attention. In addition, the multiscale feature extraction approach is very effective at improving the classification accuracy for HSIs, capable of capturing a large amount of intrinsic information. However, some existing methods for extracting spectral and spatial features can only generate low-level features and consider limited scales, leading to low classification results, and dense-connection based methods enhance the feature propagation at the cost of high model complexity. This paper presents a two-branch multiscale spectral-spatial feature extraction network (TBMSSN) for HSI classification. We design the multiscale spectral feature extraction (MSEFE) and multiscale spatial feature extraction (MSAFE) modules to improve the feature representation, and a spatial attention mechanism is applied in the MSAFE module to reduce redundant information and enhance the representation of spatial features at multiscale. Then we densely connect series of MSEFE or MSAFE modules respectively in a two-branch framework to balance efficiency and effectiveness, alleviate the vanishing-gradient problem and strengthen the feature propagation. To evaluate the effectiveness of the proposed method, the experimental results were carried out on bench mark HSI datasets, demonstrating that TBMSSN obtained higher classification accuracy compared with several state-of-the-art methods.