Spatial–Spectral Enhancement and Fusion Network for Hyperspectral Image Classification With Few Labeled Samples

Deep learning has shown great potential in hyperspectral image (HSI) classification. However, training these models usually requires a large amount of labeled data. Since the collection of pixel-level annotations for HSIs is laborious and time-consuming, developing algorithms that can yield good performance in a small sample size situation is of great significance. Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples. However, prevalent solutions are unsatisfactory in feature discrimination and model overfitting, which greatly limits their performance. To remedy these drawbacks, we propose a novel spatial-spectral enhancement and fusion network for hyperspectral image classification with few labeled samples, named SSEFN. Specifically, we design a spatial-spectral enhancement strategy (SSES) to boost the feature discrimination from spatial and spectral perspectives, which enables the model to learn more easily with fewer samples. In addition, we propose an adaptive decision fusion (ADF) module to fuse the decisions of all enhanced features. Since each decision prediction may have a different trend of overfitting, combining multiple predictions alleviates the overfitting. Extensive experiments are conducted on four diverse hyperspectral image datasets. The results show that our method significantly outperforms the state-of-the-art approaches on all datasets and demonstrates the effectiveness and superiority of the proposed model for hyperspectral image classification with few labeled samples. Codes are available at https://github.com/liushuang963/SSEFN.