  Clouds classification is a great challenge in meteorological research. The
different types of clouds, currently known and present in our skies, can
produce radioactive effects that impact on the variation of atmospheric
conditions, with the consequent strong dominance over the earth's climate and
weather. Therefore, identifying their main visual features becomes a crucial
aspect. In this paper, the goal is to adopt a pretrained deep neural networks
based architecture for clouds image description, and subsequently,
classification. The approach is pyramidal. Proceeding from the bottom up, it
partially extracts previous knowledge of deep neural networks related to
original task and transfers it to the new task. The updated knowledge is
integrated in a voting context to provide a classification prediction. The
framework trains the neural models on unbalanced sets, a condition that makes
the task even more complex, and combines the provided predictions through
statistical measures. Experimental phase on different cloud image datasets is
performed and results achieved show the effectiveness of the proposed approach
with respect to state of the art competitors.
