Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data

Abstract

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.

Full version of preprint accepted at ISPRS Journal of Photogrammetry and Remote Sensing

References in corpus (6)

• Deep learning in remote sensing: a review
• Fully Convolutional Networks for Semantic Segmentation
• An Overview of Multi-Task Learning in Deep Neural Networks
• Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks
• Multi-Task Learning with Deep Neural Networks: A Survey
• A graph cut approach to 3D tree delineation, using integrated airborne LiDAR and hyperspectral imagery