  We present a novel multimodal neural network for classifying astronomical
sources in multiband ground-based observations, from optical to near infrared,
to separate sources in stars, galaxies and quasars. Our approach combines a
convolutional neural network branch for learning morphological features from
$r$-band images with an artificial neural network branch for extracting
spectral energy distribution (SED) information. Specifically, we have used
9-band optical ($ugri$) and NIR ($ZYHJK_s$) data from the Kilo-Degree Survey
(KiDS) Data Release 5. The two branches of the network are concatenated and
feed into fully-connected layers for final classification. We train the network
on a spectroscopically confirmed sample from the Sloan Digital Sky Survey
cross-matched with KiDS. The trained model achieves 98.76\% overall accuracy on
an independent testing dataset, with F1 scores exceeding 95\% for each class.
Raising the output probability threshold, we obtain higher purity at the cost
of a lower completeness. We have also validated the network using external
catalogs cross-matched with KiDS, correctly classifying 99.74\% of a pure star
sample selected from Gaia parallaxes and proper motions, and 99.74\% of an
external galaxy sample from the Galaxy and Mass Assembly survey, adjusted for
low-redshift contamination. We apply the trained network to 27,334,751 KiDS DR5
sources with $r \leqslant 23$ mag to generate a new classification catalog.
This multimodal neural network successfully leverages both morphological and
SED information to enable efficient and robust classification of stars,
quasars, and galaxies in large photometric surveys.
