I published two papers in international conferences - the 26th International Conference on Pattern Recognition (ICPR) and the International Conference on Image Processing (ICIP) in 2022. 

The first paper [1], presented a novel approach to designing a unified spectral-spatial Transformer for hyperspectral image classification. Specifically, I proposed a cascaded integration of the spectral vision Transformer with the spatial pyramid vision Transformer, along with a cross-scale fusion module. Moreover, I introduced a local-global encoder in the spatial domain, which validates the effectiveness of incorporating local features into the Transformer model. Overall, my paper contributed to the advancement and practicality of using a pure vision Transformer-based model for hyperspectral image classification.

  The second paper [2] proposed a new approach for addressing hyperspectral image classification by leveraging the 3D configuration of a vision Transformer, which enabled simultaneous correlation of spectral and spatial features. To this end, I introduced a novel 3D coordinate positional embedding method that distinguished the relative distances among all hyper-cubes resulting from the 3D partition operation. I also designed a local-global feature combination approach that seamlessly integrates with the 3D configuration of the vision Transformer. Furthermore, we presented our research at two conferences and received positive feedback.