Despite its robust performance under various illumination conditions, multispectral scene analysis has not been widely deployed due to two strong practical limitations: 1) thermal cameras, especially high-resolution ones are much more expensive than conventional visible cameras; 2) the most commonly adopted multispectral architectures, twostream neural networks, nearly double the inference time of a regular mono-spectral model which makes them impractical in embedded environments. In this work, we aim to tackle these two limitations by proposing a novel knowledge distillation framework named Modality Distillation (MD). The proposed framework distils the knowledge from a high thermal resolution two-stream network with featurelevel fusion to a low thermal resolution one-stream network with image-level fusion. We show on different multispectral scene analysis benchmarks that our method can effectively allow the use of low-resolution thermal sensors with more compact one-stream networks.