  Skyline plays a pivotal role in mountainous visual geo-localization and
localization/navigation of planetary rovers/UAVs and virtual/augmented reality
applications. We present a novel mountainous skyline detection approach where
we adapt a shallow learning approach to learn a set of filters to discriminate
between edges belonging to sky-mountain boundary and others coming from
different regions. Unlike earlier approaches, which either rely on extraction
of explicit feature descriptors and their classification, or fine-tuning
general scene parsing deep networks for sky segmentation, our approach learns
linear filters based on local structure analysis. At test time, for every
candidate edge pixel, a single filter is chosen from the set of learned filters
based on pixel's structure tensor, and then applied to the patch around it. We
then employ dynamic programming to solve the shortest path problem for the
resultant multistage graph to get the sky-mountain boundary. The proposed
approach is computationally faster than earlier methods while providing
comparable performance and is more suitable for resource constrained platforms
e.g., mobile devices, planetary rovers and UAVs. We compare our proposed
approach against earlier skyline detection methods using four different data
sets. Our code is available at
\url{https://github.com/TouqeerAhmad/skyline_detection}.
