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引用本文:

DOI:

10.11834/jrs.20221613

收稿日期:

2021-09-17

修改日期:

2022-05-12

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结合 RGB-DSM 图像和深度学习的城市樟树树冠检测 研究
王昊, 夏凯, 杨垠晖, 冯海林
浙江农林大学信息工程学院
摘要:

当前,结合遥感图像和深度学习进行单木树冠检测已经成为一种趋势。RGB图像是检测中最常用的数据类型,但由于树冠的颜色和纹理一般比较接近,在树冠密度较高的区域,仅使用RGB图像的颜色和纹理信息难以区分不同个体的树冠。对此,本研究在RGB图像的基础上,叠加了高程信息,以提高单木树冠检测的精度。实验采用RGB图像(彩色图像)和DSM(Digital Surface Model,数字表面模型)作为数据源,并分别利用波段组合和双源检测网络模型两种方法结合RGB和DSM进行单木树冠检测。在前一种方法中,对RGB和DSM进行波段组合,生成GBD、RGD和RBD三类图像,并使用这三类图像分别进行网络的训练和测试。在后一种方法中,将RGB和DSM输入双源检测网络模型,并得到检测结果。本文使用FPN-Faster-R-CNN和Yolov3进行实验,相比于RGB方案(仅使用地物的颜色和纹理信息进行单木树冠检测,是对照方案),FPN-Faster-R-CNN在GBD方案、RBD方案和双源检测网络方案中的平均精度分别上升了3.36%、2.45%和7.77%,在RGD方案中的平均精度下降了0.17%,Yolov3在GBD方案、RBD方案和双源检测网络方案中的平均精度分别上升了0.72%、0.14%和5.71%,在RGD方案中的平均精度下降了0.98%。在两个网络下,双源检测网络方案都在各方案中取得了最佳的检测结果。并且相对于RGB方案,双源检测网络方案在平均精度上的提升幅度随着树冠密度的上升呈现出上升的趋势。对比分析实验结果可知,在基于深度学习的城市单木树冠检测任务中,妥善结合并利用地物的颜色、纹理信息和高程信息有利于提高任务性能。

Urban Cinnamomum Camphora crown detection research using RGB-DSM images and deep learning
Abstract:

At present, it has become a trend to combine remote sensing image and deep learning to detect individual tree crown. RGB image is the most commonly used data type in detection, but since the color and texture of the tree crowns are generally close, it is difficult to distinguish the crowns of different individuals by using only the color and texture information of RGB image in areas with high density of crowns. In this study, on the basis of RGB images, the elevation information is superimposed to improve the accuracy of individual tree crown detection. In the experiment, RGB image (color image) and DSM (Digital Surface Model) were used as data sources, and band combination and double source detection network model were used to combine RGB and DSM for individual tree crown detection. In the former method, band combination of RGB and DSM was carried out to generate GBD, RGD and RBD images, and these three kinds of images were used for network training and testing respectively. In the latter method, RGB and DSM were input into the double source detection network model and the detection results were obtained. FPN-Faster-R-CNN and Yolov3 were used for experiments in this paper. Compared with RGB scheme (which uses only the color and texture information of ground objects for individual tree crown detection, it was the control scheme), The average accuracy of FPN-Faster-R-CNN in GBD scheme, RBD scheme and double source detection network scheme increased by 3.36%, 2.45% and 7.77%, respectively, and decreased by 0.17% in RGD scheme. The average accuracy of Yolov3 in GBD scheme, RBD scheme and double source detection network scheme increased by 0.72%, 0.14% and 5.71%, respectively, and decreased by 0.98% in RGD scheme. Under the two networks, the double source detection network scheme achieved the best detection result in each scheme. Compared with RGB scheme, the improvement of average accuracy of double source detection network scheme showed a rising trend with the increase of forest density. Comparative analysis of the experimental results shows that in the urban individual tree crown detection task based on deep learning, proper combination and utilization of ground objects’ color, texture and elevation information is beneficial to improve the task performance.

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