  Multispectral (MS) snapshot cameras equipped with a MS filter array (MSFA),
capture multiple spectral bands in a single shot, resulting in a raw mosaic
image where each pixel holds only one channel value. The fully-defined MS image
is estimated from the raw one through $\textit{demosaicing}$, which inevitably
introduces spatio-spectral artifacts. Moreover, training on fully-defined MS
images can be computationally intensive, particularly with deep neural networks
(DNNs), and may result in features lacking discrimination power due to
suboptimal learning of spatio-spectral interactions. Furthermore, outdoor MS
image acquisition occurs under varying lighting conditions, leading to
illumination-dependent features. This paper presents an original approach to
learn discriminant and illumination-robust features directly from raw images.
It involves: $\textit{raw spectral constancy}$ to mitigate the impact of
illumination, $\textit{MSFA-preserving}$ transformations suited for raw image
augmentation to train DNNs on diverse raw textures, and $\textit{raw-mixing}$
to capture discriminant spatio-spectral interactions in raw images. Experiments
on MS image classification show that our approach outperforms both handcrafted
and recent deep learning-based methods, while also requiring significantly less
computational effort.
