利用三维辐射传递建模对树冠进行自动标注,在半干旱地貌中实现了与人类相当的树木分割性能

IF 7.6 Q1 REMOTE SENSING
Decai Jin , Jianbo Qi , Nathan Borges Gonçalves , Jifan Wei , Huaguo Huang , Yaozhong Pan
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引用次数: 0

摘要

干旱或半干旱地区约占地球陆地面积的三分之一,绘制干旱或半干旱地区的树冠图是实现树木可持续管理的关键方法。在树冠分割方面,深度学习的最新进展已显示出良好的效果。然而,这仍然需要大量人工标注的数据。在此,我们提出了一种新方法,利用经三维辐射传递建模自动生成标签训练的深度学习,从高分辨率卫星图像中划分树冠,从而大幅减少人工标注。该方法的步骤包括:1)使用三维辐射传递模型模拟图像;2)基于生成式对抗网络(GAN)的图像风格传递学习;3)使用 U-net 分割模型分割树冠。我们在一个包含 40,000 多个树冠的人工标注数据集上对所提出方法的划分性能进行了评估。我们的方法完全依赖于合成图像,具有很高的分割准确性,F1 分数超过 0.77,交集大于联合(IoU)超过 0.64。特别是,它在提取牙冠面积(r2 大于 0.87)和牙冠密度(r2 大于 0.72)方面达到了令人印象深刻的精确度,可与仅使用人类注释的训练数据集相媲美。在这项研究中,我们证明了将三维辐射传递模型与用于自动生成训练标签的广义广谱网络(GANs)相结合,可实现与人工标注相当的性能,并可大大减少遥感分割应用中人工标注所需的时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated tree crown labeling with 3D radiative transfer modelling achieves human comparable performances for tree segmentation in semi-arid landscapes
Mapping tree crowns in arid or semi-arid areas, which cover around one-third of the Earth’s land surface, is a key methodology towards sustainable management of trees. Recent advances in deep learning have shown promising results for tree crown segmentation. However, a large amount of manually labeled data is still required. We here propose a novel method to delineate tree crowns from high resolution satellite imagery using deep learning trained with automatically generated labels from 3D radiative transfer modeling, intending to reduce human annotation significantly. The methodological steps consist of 1) simulating images with a 3D radiative transfer model, 2) image style transfer learning based on generative adversarial network (GAN) and 3) tree crown segmentation using U-net segmentation model. The delineation performances of the proposed method have been evaluated on a manually annotated dataset consisting of more than 40,000 tree crowns. Our approach, which relies solely on synthetic images, demonstrates high segmentation accuracy, with an F1 score exceeding 0.77 and an Intersection over Union (IoU) above 0.64. Particularly, it achieves impressive accuracy in extracting crown areas (r2 greater than 0.87) and crown densities (r2 greater than 0.72), comparable to that of a trained dataset with human annotations only. In this study, we demonstrated that the integration of a 3D radiative transfer model and GANs for automatically generating training labels can achieve performances comparable to human labeling, and can significantly reduce the time needed for manual labeling in remote sensing segmentation applications.
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来源期刊
International journal of applied earth observation and geoinformation : ITC journal
International journal of applied earth observation and geoinformation : ITC journal Global and Planetary Change, Management, Monitoring, Policy and Law, Earth-Surface Processes, Computers in Earth Sciences
CiteScore
12.00
自引率
0.00%
发文量
0
审稿时长
77 days
期刊介绍: The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.
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