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Potential evaluation of visible-thermal UAV image fusion for individual tree detection based on convolutional neural network
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-05 , DOI: 10.1016/j.jag.2022.103011
Fatemeh Moradi, Farzaneh Dadrass Javan, Farhad Samadzadegan

Unmanned aerial vehicles (UAVs) outfitted with thermal and visible sensors are already a popular platform in precision agriculture thanks to recent advances in remote sensing. Many researchers have studied integrating data from sensors with different spectral characteristics to achieve higher-level properties and, consequently, detect the trees accurately. In this research, visible and thermal images, as well as normalized digital surface models resulting from UAVs with high spatial resolution, are employed to accurately extract trees from two studied urban areas with complex backgrounds. In the thermal image, trees can be detected in hidden areas based on their brightness temperature difference compared to other features. In contrast, the visible image has a higher spatial resolution, and fusing this data with thermal images can resolve the complexity of the problem. In the proposed method, first, a deep learning network based on visible-thermal data is evaluated in terms of detecting trees with various data approaches. These evaluations include comparison tests on four types of data input to the convolutional network of the visible images, thermal images, fusing visible-thermal images, and also fusing visible-thermal- normalized digital surface model images. Results of evaluation parameters indicate maximum precision in the fourth approach (intersection-over-union = 91.72, F-score = 95.67). Then, the output binary map with the highest accuracy approach and Canny edge detection operator is utilized to accurately identify tree boundaries, count, and estimate the area and diameter of the tree canopy. Finally, the findings revealed the root mean square error (RMSE) first and second areas are 0.21 m2, 0.08 m and 0.24 m2, 0.11 m respectively for the area and diameter of the tree crown.



中文翻译:

基于卷积神经网络的可见热无人机图像融合对单棵树检测的潜力评估

得益于遥感技术的最新进展,配备热传感器和可见光传感器的无人机 (UAV) 已经成为精准农业领域的热门平台。许多研究人员已经研究了整合来自具有不同光谱特性的传感器的数据以实现更高级别的特性,从而准确地检测树木。在这项研究中,可见光和热图像,以及由具有高空间分辨率的无人机产生的归一化数字表面模型,被用来准确地从两个具有复杂背景的研究城市区域中提取树木。在热图像中,可以根据与其他特征相比的亮度温差来检测隐藏区域中的树木。相比之下,可见图像具有更高的空间分辨率,并将这些数据与热图像融合可以解决问题的复杂性。在所提出的方法中,首先,基于可见热数据的深度学习网络在使用各种数据方法检测树木方面进行了评估。这些评估包括对输入到可见图像、热图像、融合可见热图像以及融合可见热归一化数字表面模型图像的卷积网络的四种类型的数据进行比较测试。评估参数的结果表明第四种方法的最大精度(intersection-over-union = 91.72,F-score = 95.67)。然后,利用最高精度方法和 Canny 边缘检测算子输出的二值图来准确识别树木边界、计数和估计树冠的面积和直径。最后,2 , 0.08 m 和 0.24 m 2 , 0.11 m 分别为树冠的面积和直径。

更新日期:2022-09-05
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