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微粒度3D建模和珊瑚礁的语义映射使用光度计算机视觉和机器学习的珊瑚礁

Jiageng Zhong¹, Ming Li^1,2, Hanqi Zhang¹

¹State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Luoyu Road No. 129, Wuhan 430079, China.

Sensors (Basel, Switzerland)

|August 12, 2023

概括

先进的3D建模和机器学习精确地绘制了珊瑚礁的地图,帮助了保护工作. 这项技术提供了对珊瑚礁健康状况和气候变化带来的变化的高分辨率见解.

科学领域:

海洋生物学海洋生物学
地理空间分析是什么?
人工智能的人工智能是人工智能.

背景情况:

珊瑚礁是重要的海洋息地,受到气候变化威胁.
有效管理需要对珊瑚礁结构有详细的了解.
目前的方法缺乏高空间和时间分辨率来监测珊瑚礁的变化.

研究的目的:

开发精确的3D模型和珊瑚礁的语义地图.
为了能够对珊瑚礁结构和粗度进行细粒度分析.
为监测气候变化下的珊瑚礁健康提供工具.

主要方法:

使用水下录像,光度计算机视觉和机器学习.
从珊瑚图像生成3D网状模型,数字表面模型和orthomosaics.
使用多模式深度神经网络进行语义细分和3D投影.

主要成果:

详细的3D网状模型,数字表面模型和珊瑚息地的orthomosaics被生成.
一个新的深度神经网络实现了对orthomosaics的像素智能语义细分.
完成了含义细粒度3D建模和粗度评估,精确度达到毫米级.

结论:

这项研究展示了一种有效的方法,用于了解珊瑚礁的变化.
现在可以对珊瑚礁进行高空间和时间分辨率的监测.
这项技术支持有效管理和保护脆弱的珊瑚生态系统.

关键词:

在3D分析中,我们使用3D分析.珊瑚礁中的珊瑚礁深度学习是一种深度学习.语义细分语义细分语义细分语义细分水下摄影仪测量 (Photogrammetry) 是一种水下摄影仪.

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