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.

本文介绍了一种包括两个卷积块、一个Gate-Shift-Fuse（GSF）块和一个transformer块的高光谱图像分类模型，通过在局部特征提取方面利用CNN的优势以及在远程上下文建模方面利用transformer的优势，该模型提供了对高层语义特征的提取能力。同时，提出了一个强化局部和全局空间-光谱特征提取的GSF块以及一个有效的注意力机制模块，该方法在四个知名数据集上进行了评估，包括Indian Pines、Pavia University、WHU-WHU-Hi-LongKou和WHU-Hi-HanChuan，结果表明该框架相较于其他模型具有更好的效果。

一种新的CNN-Transformer方法中基于门控-移位-融合机制的高光谱图像分类增强