Learnable Channel Converter for Multi-Spectral Image to RGB Visualization using a Vision-Text Model

Multi-spectral images capture critical information beyond the visible spectrum, enabling powerful applications such as flood detection and burned-scar analysis. However, visually interpreting these bands for explainability is challenging because human perception is limited to visible light. To address this gap, we propose a learnable channel conversion mechanism that transforms multi-spectral data into visually interpretable RGB images. By training the channel converter with a pre-trained vision-text model, our method ensures that the final RGB visualization highlights important objects or regions of interest and improves interpretability, enabling more intuitive coloring and easier distinction of regions of interest.