  Detection of surface water in natural environment via multi-spectral imagery
has been widely utilized in many fields, such land cover identification.
However, due to the similarity of the spectra of water bodies, built-up areas,
approaches based on high-resolution satellites sometimes confuse these
features. A popular direction to detect water is spectral index, often
requiring the ground truth to find appropriate thresholds manually. As for
traditional machine learning methods, they identify water merely via
differences of spectra of various land covers, without taking specific
properties of spectral reflection into account. In this paper, we propose an
automatic approach to detect water bodies based on Dempster-Shafer theory,
combining supervised learning with specific property of water in spectral band
in a fully unsupervised context. The benefits of our approach are twofold. On
the one hand, it performs well in mapping principle water bodies, including
little streams and branches. On the other hand, it labels all objects usually
confused with water as `ignorance', including half-dry watery areas, built-up
areas and semi-transparent clouds and shadows. `Ignorance' indicates not only
limitations of the spectral properties of water and supervised learning itself
but insufficiency of information from multi-spectral bands as well, providing
valuable information for further land cover classification.
