Boosting Hyperspectral Image Classification with Gate-Shift-Fuse Mechanisms in a Novel CNN-Transformer Approach
<|reference_start|>Boosting Hyperspectral Image Classification with Gate-Shift-Fuse Mechanisms in a Novel CNN-Transformer Approach: During the process of classifying Hyperspectral Image (HSI), every pixel sample is categorized under a land-cover type. CNN-based techniques for HSI classification have notably advanced the field by their adept feature representation capabilities. However, acquiring deep features remains a challenge for these CNN-based methods. In contrast, transformer models are adept at extracting high-level semantic features, offering a complementary strength. This paper's main contribution is the introduction of an HSI classification model that includes two convolutional blocks, a Gate-Shift-Fuse (GSF) block and a transformer block. This model leverages the strengths of CNNs in local feature extraction and transformers in long-range context modelling. The GSF block is designed to strengthen the extraction of local and global spatial-spectral features. An effective attention mechanism module is also proposed to enhance the extraction of information from HSI cubes. The proposed method is evaluated on four well-known datasets (the Indian Pines, Pavia University, WHU-WHU-Hi-LongKou and WHU-Hi-HanChuan), demonstrating that the proposed framework achieves superior results compared to other models.<|reference_end|>
@article{guerri2024boosting,
    title={Boosting Hyperspectral Image Classification with Gate-Shift-Fuse
  Mechanisms in a Novel CNN-Transformer Approach},
    author={Mohamed Fadhlallah Guerri, Cosimo Distante, Paolo Spagnolo, Fares
  Bougourzi, Abdelmalik Taleb-Ahmed},
    journal={arXiv preprint arXiv:2406.14120},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14120},
    primaryClass={cs.CV}
}