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.

这项工作提出了一种多任务全卷积架构，用于使用高光谱无人机携带数据从稀疏和稀缺的多边形级注释中对茂密森林中的树种进行映射。我们的模型实现了一个部分损失函数，可以从非密集训练样本中实现密集树语义标记结果，以及一个强制树冠边界约束并显着提高模型性能的距离回归补充任务。我们的多任务架构使用一个共享的骨干网络，该网络学习两个任务和两个特定于任务的解码器的共同表示，一个用于语义分割输出，另一个用于距离图回归。