论文标题

结合摄影测量的计算机视觉和语义分割,以对气候变化下的珊瑚礁生长进行细粒度的了解

Combining Photogrammetric Computer Vision and Semantic Segmentation for Fine-grained Understanding of Coral Reef Growth under Climate Change

论文作者

Zhong, Jiageng, Li, Ming, Zhang, Hanqi, Qin, Jiangying

论文摘要

珊瑚是支持海洋中四分之一的礁石上的主要栖息地生命形式。珊瑚礁生态系统通常由珊瑚礁组成,每个礁​​石都像任何城市中的高大建筑。这些珊瑚礁珊瑚分泌硬质钙质外骨骼,使它们具有结构性刚度,也是我们使用先进的摄影计算机视觉和机器学习的准确3D建模和语义映射的先决条件。水下摄影作为现代水下遥感工具是一项高分辨率的珊瑚栖息地调查和地图技术。在本文中,从收集的珊瑚图像和水下控制点产生了详细的3D网格模型,数字表面模型和珊瑚栖息地的正踪。同时,高级深度学习可以进行正尾的新型语义分割方法。最后,语义图被映射到3D空间。第一次以毫米(mm)精度完成了3D细粒的语义建模和珊瑚礁的皱纹评估。这提供了一种新的强大方法,可以理解在气候变化下高空间和时间分辨率下珊瑚礁变化的过程和特征。

Corals are the primary habitat-building life-form on reefs that support a quarter of the species in the ocean. A coral reef ecosystem usually consists of reefs, each of which is like a tall building in any city. These reef-building corals secrete hard calcareous exoskeletons that give them structural rigidity, and are also a prerequisite for our accurate 3D modeling and semantic mapping using advanced photogrammetric computer vision and machine learning. Underwater videography as a modern underwater remote sensing tool is a high-resolution coral habitat survey and mapping technique. In this paper, detailed 3D mesh models, digital surface models and orthophotos of the coral habitat are generated from the collected coral images and underwater control points. Meanwhile, a novel pixel-wise semantic segmentation approach of orthophotos is performed by advanced deep learning. Finally, the semantic map is mapped into 3D space. For the first time, 3D fine-grained semantic modeling and rugosity evaluation of coral reefs have been completed at millimeter (mm) accuracy. This provides a new and powerful method for understanding the processes and characteristics of coral reef change at high spatial and temporal resolution under climate change.

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