Transfomer Based Hyperspectral Dimensionality Reduction with Gabor Kernel CNN for Feature Extraction

Dimensionality Reduction (DR) algorithms are used to identify sparse representation of given dataset. The proposed MT-CGF (Multiheaded Transformer with CNN and Gabor Filter) model uses the Multi-head Transformer-based Attention technique [1] for channel attention, which helps identify the most important bands in the dataset. The attention mask generated by the Transformer model is then used to select the top 'n' optimal bands. CNN with Gabor Filter [2], extract complex spatial and spectral features from the dimensionality reduced dataset. Gabor Filters are linear filters that closely resemble the human visual system and possess optimal localization properties in both spatial and frequency domains. Overall, the proposed MT-CGF model combines the strengths of multiple techniques to perform dimensionality reduction and feature extraction from hyperspectral data. By identifying the most important bands it reduces the dataset's dimensionality without significantly compromising its accuracy. It becomes more manageable and easier to analyze, and it can also help to reduce the risk of overfitting.