Optimizing Hyperspectral Image Classification Through Swin Transformer Integration and CNN Feature Extraction

Due to the multidimensional structure of hyperspectral imaging, deep learning methods—in particular, vision transformer models—have been incorporated into classification systems. This article presents a novel use of Swin Transformer Network for hyperspectral image classification. The three layers that make up the suggested technique extract, embed, and merge image patches to provide a framework for classification. Our method uses patch-extraction, patch-embedding, and patch-merging approaches to improve feature representation. Hyperspectral features are preserved when window size, window shift, focus head, and input dimensions are taken into account. We demonstrate the effectiveness of our suggested method with a testing accuracy of 99.97% on hyperspectral datasets such as the Salinas public HSI dataset. The superiority of our approach is shown by comparing it with competing dimensionality reduction strategies. This work provides encouraging progress in the use of deep learning methods for classification of hyperspectral data.