Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination
CoRR(2023)
摘要
LiDAR (Light Detection and Ranging) has become an essential part of the
remote sensing toolbox used for biosphere monitoring. In particular, LiDAR
provides the opportunity to map forest leaf area with unprecedented accuracy,
while leaf area has remained an important source of uncertainty affecting
models of gas exchanges between the vegetation and the atmosphere. Unmanned
Aerial Vehicles (UAV) are easy to mobilize and therefore allow frequent
revisits to track the response of vegetation to climate change. However,
miniature sensors embarked on UAVs usually provide point clouds of limited
density, which are further affected by a strong decrease in density from top to
bottom of the canopy due to progressively stronger occlusion. In such a
context, discriminating leaf points from wood points presents a significant
challenge due in particular to strong class imbalance and spatially irregular
sampling intensity. Here we introduce a neural network model based on the
Pointnet ++ architecture which makes use of point geometry only (excluding any
spectral information). To cope with local data sparsity, we propose an
innovative sampling scheme which strives to preserve local important geometric
information. We also propose a loss function adapted to the severe class
imbalance. We show that our model outperforms state-of-the-art alternatives on
UAV point clouds. We discuss future possible improvements, particularly
regarding much denser point clouds acquired from below the canopy.
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关键词
sparse irregular point clouds,semantic segmentation,leaf/wood
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