This work proposes a multi-task fully convolutional architecture for tree
species mapping in dense forests from sparse and scarce polygon-level
annotations using hyperspectral UAV-borne data. Our model implements a partial
loss function that enables dense tree semantic labeling outcomes from non-dense
training samples, and a distance regression complementary task that enforces
tree crown boundary constraints and substantially improves the model
performance. Our multi-task architecture uses a shared backbone network that
learns common representations for both tasks and two task-specific decoders,
one for the semantic segmentation output and one for the distance map
regression. We report that introducing the complementary task boosts the
semantic segmentation performance compared to the single-task counterpart in up
to 11% reaching an average user's accuracy of 88.63% and an average producer's
accuracy of 88.59%, achieving state-of-art performance for tree species
classification in tropical forests.

本研究提出了一种基于多任务全卷积架构的方法，利用超光谱无人机数据从稀疏和有限的多边形注释中进行密集森林的树种映射。通过实现局部损失函数和距离回归互补任务，该模型从非密集的训练样本中实现了密集的树木语义标记结果，并使用共享的骨干网络和两个任务特定解码器来学习两个任务的普遍表示，并取得了热带森林树种分类的最先进性能。