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 10% reaching an overall F1 score of 87.5% and an overall accuracy of 85.9%, achieving state-of-art performance for tree species classification in tropical forests.
翻译:这项工作提出一个多任务全演化结构,用于利用超光谱无人机载运的数据,从稀有和稀有多边形图示中绘制密林树种图,利用高光谱无人机载数据绘制。 我们的模型采用部分损耗功能,使非密集训练样本中的稠密树语义标签结果得以实现,并执行一项远距回归互补任务,以强制执行树冠边界限制并大大改进模型性能。 我们的多任务架构使用一个共享的主干网,以学习任务和两个任务特定解剖器的共同表述,一个用于语义分解输出,另一个用于远距地图回归。 我们报告,引入互补任务可以提升语义分解功能,与10%的单一任务对应方相比,达到87.5%的总F1分和85.9%的总精度,在热带森林树种分类中达到最先进的性能。