  Clouds and snow have similar spectral features in the visible and
near-infrared (VNIR) range and are thus difficult to distinguish from each
other in high resolution VNIR images. We address this issue by introducing a
shortwave-infrared (SWIR) band where clouds are highly reflective, and snow is
absorptive. As SWIR is typically of a lower resolution compared to VNIR, this
study proposes a multiresolution fully convolutional neural network (FCN) that
can effectively detect clouds and snow in VNIR images. We fuse the
multiresolution bands within a deep FCN and perform semantic segmentation at
the higher, VNIR resolution. Such a fusion-based classifier, trained in an
end-to-end manner, achieved 94.31% overall accuracy and an F1 score of 97.67%
for clouds on Resourcesat-2 data captured over the state of Uttarakhand, India.
These scores were found to be 30% higher than a Random Forest classifier, and
10% higher than a standalone single-resolution FCN. Apart from being useful for
cloud detection purposes, the study also highlights the potential of
convolutional neural networks for multi-sensor fusion problems.
