Deformable convolution and transformer hybrid network for hyperspectral image classification

Key Points

• The hybrid network achieves a notable accuracy improvement in hyperspectral image classification.
• Key evidence shows an accuracy increase to 92.3% in testing against standard benchmarks.
• Analyzed hyperspectral data using a deformable convolution and transformer architecture to enhance classification performance.
• The findings may enable better applications in remote sensing and environmental monitoring.