利用航空多光谱图像和激光雷达数据进行半监督多类树冠划分

IF 10.6 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL ISPRS Journal of Photogrammetry and Remote Sensing Pub Date : 2024-08-08 DOI:10.1016/j.isprsjprs.2024.07.032
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引用次数: 0

摘要

与传统方法相比,基于深度学习的单棵树分割更为精确。然而,要发挥基于深度学习的方法的准确性潜力,必须有足够数量的训练数据。相比之下,半监督学习技术可以帮助简化耗时的标记过程。在本研究中,我们引入了一种新的半监督树划分方法,利用预先聚类的树训练标签,对单棵树进行精确划分和分类。具体来说,我们将实例分割面具 R-CNN 与归一化切割聚类方法相结合,并将其应用于激光雷达点云。研究区域位于德国东南部的巴伐利亚森林国家公园,那里的树木组成包括针叶林、落叶林和混交林。重要树种有欧洲山毛榉()、挪威云杉()和银杉()。2017 年 6 月获取了地面采样距离为 10 的多光谱图像数据和点密度约为 55 的激光扫描数据。根据激光扫描数据生成了分辨率为 10 的三通道图像。这些模型在国家公园的七个参考地块中进行了测试,共实地测量了 516 棵树。实验表明,使用基于激光雷达聚类生成的树木标签训练的 Mask R-CNN 模型在使用彩色红外图像时,平均 F1 分数为 79%,比归一化剪切基线方法高出 18%,因此得到了显著提高。同样,针叶树、落叶树和枯木树分类结果的平均总体准确率为 96%,与基线分类方法相比提高了 6%。基于激光雷达图像的实验在分割和分类方面的结果都略差(1-2%)。我们的研究证明了这种简化的训练数据准备方法的实用性,与人工标注方法相比,这种方法可以使用更多的数据来训练模型。从 F1 分数来看,准确率提高了 18%,这进一步证明了它的优势。
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Semi-supervised multi-class tree crown delineation using aerial multispectral imagery and lidar data

The segmentation of individual trees based on deep learning is more accurate than conventional meth- ods. However, a sufficient amount of training data is mandatory to leverage the accuracy potential of deep learning-based approaches. Semi-supervised learning techniques, by contrast, can help simplify the time-consuming labelling process. In this study, we introduce a new semi-supervised tree segmen- tation approach for the precise delineation and classification of individual trees that takes advantage of pre-clustered tree training labels. Specifically, the instance segmentation Mask R-CNN is combined with the normalized cut clustering method, which is applied to lidar point clouds. The study areas were located in the Bavarian Forest National Park, southeast Germany, where the tree composition includes coniferous, deciduous and mixed forest. Important tree species are European beech (Fagus sylvatica), Norway spruce (Picea abies) and silver fir (Abies alba). Multispectral image data with a ground sample distance of 10 cm and laser scanning data with a point density of approximately 55 points/m2 were acquired in June 2017. From the laser scanning data, three-channel images with a resolution of 10 cm were generated. The models were tested in seven reference plots in the national park, with a total of 516 trees measured on the ground. When the color infrared images were used, the experiments demonstrated that the Mask R-CNN models, trained with the tree labels generated through lidar-based clustering, yielded mean F1 scores of 79 % that were up to 18 % higher than those of the normalized cut baseline method and thus significantly improved. Similarly, the mean over- all accuracy of the classification results for the coniferous, deciduous, and standing deadwood tree groups was 96 % and enhanced by up to 6 % compared with the baseline classification approach. The experiments with lidar-based images yielded slightly worse (1–2 %) results both for segmentation and for classification. Our study demonstrates the utility of this simplified training data preparation pro- cedure, which leads to models trained with significantly larger amounts of data than is feasible with with manual labelling. The accuracy improvement of up to 18 % in terms of the F1 score is further evidence of its advantages.

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来源期刊
ISPRS Journal of Photogrammetry and Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing 工程技术-成像科学与照相技术
CiteScore
21.00
自引率
6.30%
发文量
273
审稿时长
40 days
期刊介绍: The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive. P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields. In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.
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