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

• URL: http://arxiv.org/abs/2106.00799v1
• Date: Tue, 1 Jun 2021 21:10:10 GMT
• Title: Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data
• Authors: Laura Elena Cu\'e La Rosa, Camile Sothe, Raul Queiroz Feitosa, Cl\'audia Maria de Almeida, Marcos Benedito Schimalski, Dario Augusto Borges Oliveira
• Abstract summary: This work proposes a multi-task fully convolutional architecture for tree species mapping in dense forests. Our model implements a partial loss function that enables dense tree semantic labeling outcomes from non-dense training samples. A distance regression complementary task that enforces tree crown boundary constraints substantially improves the model performance.
• Score: 0.9646880519252493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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