Multi-feature fusion network based on wavelet transform and multi-scale cross-response for hyperspectral image classification

Key Points

• Classification accuracy improved significantly with a novel multi-feature fusion network—achieving an accuracy increase of 15%.
• The approach utilizes wavelet transform and multi-scale analysis to process hyperspectral images effectively.
• Employing advanced algorithms for feature extraction demonstrates a critical role in enhancing image classification performance.
• These findings indicate potential advancements in hyperspectral image applications; further validation in diverse settings is necessary.