Hyperspectral image (HSI) classification is a research hotspot in the field of remote sensing image processing. Deep learning-based methods have gradually become one of the mainstream in the field of HSI classification. However, deep learning-based HSI classification methods still face the challenge of insufficient training samples. Transfer learning is regarded as an effective method to alleviate the problem of insufficient samples. However, hyperspectral image data is scarce, lacking the foundation for pre-training high-quality models. In this paper, a Hybrid Transfer Semantic Segmentation Architecture (HTSSA) is proposed, which transfers knowledge from different datasets by adopting different network structures. The proposed model adopts a triple branch network architecture. The three branches respectively use the vision transformer (ViT) classification model pre-trained on ImageNet, the Deeplabv3 semantic segmentation model pre-trained on the PASCAL VOC 2012 dataset, and the convolutional neural network (CNN) model pre-trained on the source hyperspectral image dataset. The three branch network models were fine-tuned on the target hyperspectral image dataset. The mapping modules were designed to handle the problem of heterogeneous data migration. The ViT branch utilizes the Transformer to extract spatial global context features. The Deeplabv3 branch utilizes the feature pyramid to extract spatial local multi-scale features. The CNN branch uses 3D-CNN to extract the spectral features of hyperspectral images. Finally, the final classification result is obtained by using the fusion features of the three branches. Extensive experiments on public datasets have verified that the Hybrid Transfer Semantic Segmentation Architecture proposed in this paper has alleviated the negative impact of sample scarcity to a certain extent, enhanced the representation ability of the model, and improved the final classification performance.
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