Cross-Scale Attention-based Tree Crown Detection via UAV imagery

Wei Yuan, Xiaodan Shi, Zhiling Guo, Zipei Fan, Jianya Gong, Ryosuke Shibasaki

Abstract: This paper introduces a cross-scale attention based end-to-end learning framework for tree crown detection via UAV imagery. Given that UAV images covered a large forests, the illumination variations, shadow obstacles and texture repetition always lead to inaccurate tree crown detection results. We introduce a cross-scale attention based mechanism to address the above issues, enabling the tree crown detection framework to reason about the RGB texture information and depth information introduced by the automatically generated depth map jointly. Compared to traditional image based tree crown detection methods, our approach learns prior over geometrical structure information from the real 3D world, which is robust to the texture repetition and small tree crowns. The experimental results demonstrated that the proposed approach outperforms the traditional CNN based method.