FusionNet: Advancing Multi-Spectral and Thermal Data Analysis with Physics-Informed AI

This research explores a novel approach to multi-spectral and thermal data analysis by integrating physics-based priors into the representation learning process. The use of trainable signal-processing priors offers a promising avenue for improving the accuracy and robustness of AI models in this domain.

• •FusionNet integrates physics-aware principles into representation learning.
• •The approach focuses on multi-spectral and thermal data.
• •Trainable signal-processing priors are a key component of the model.