  In this paper, we introduce a framework for segmenting instances of a common
object class by multiple active contour evolution over semantic segmentation
maps of images obtained through fully convolutional networks. The contour
evolution is cast as an energy minimization problem, where the aggregate energy
functional incorporates a data fit term, an explicit shape model, and accounts
for object overlap. Efficient solution neighborhood operators are proposed,
enabling optimization through metaheuristics such as simulated annealing. We
instantiate the proposed framework in the context of segmenting individual
fallen stems from high-resolution aerial multispectral imagery. We validated
our approach on 3 real-world scenes of varying complexity. The test plots were
situated in regions of the Bavarian Forest National Park, Germany, which
sustained a heavy bark beetle infestation. Evaluations were performed on both
the polygon and line segment level, showing that the multi-contour segmentation
can achieve up to 0.93 precision and 0.82 recall. An improvement of up to 7
percentage points (pp) in recall and 6 in precision compared to an iterative
sample consensus line segment detection was achieved. Despite the simplicity of
the applied shape parametrization, an explicit shape model incorporated into
the energy function improved the results by up to 4 pp of recall. Finally, we
show the importance of using a deep learning based semantic segmentation method
as the basis for individual stem detection. Our method is a step towards
increased accessibility of automatic fallen tree mapping, due to higher cost
efficiency of aerial imagery acquisition compared to laser scanning. The
precise fallen tree maps could be further used as a basis for plant and animal
habitat modeling, studies on carbon sequestration as well as soil quality in
forest ecosystems.
