Tree detection and counting have been performed using conventional methods or high costly remote sensing data. In the past few years, deep learning techniques have gained significant progress in the remote sensing area. Namely, convolutional neural networks (CNNs) have been recognized as one of the most successful and widely used deep learning approaches and they have been used for object detection. In this paper, we employed a Mask R-CNN model and feature pyramid network (FPN) for tree extraction from high-resolution RGB unmanned aerial vehicle (UAV) data. The main aim of this paper is to explore the employed method in images with different scales and tree contents. For this purpose, UAV images from two different areas were acquired and three big-scale test images were created for experimental analysis and accuracy assessment. According to the accuracy analyses, despite the scale and the content changes, the proposed model maintains its detection accuracy to a large extent. To our knowledge, this is the first time a Mask R-CNN model with FPN has been used with UAV data for tree extraction.

已经使用常规方法或高成本的遥感数据执行了树木检测和计数。在过去的几年中，深度学习技术在遥感领域取得了重大进展。即，卷积神经网络（CNN）被公认为是最成功且使用最广泛的深度学习方法之一，并且已被用于对象检测。在本文中，我们采用了Mask R-CNN模型和特征金字塔网络（FPN）从高分辨率RGB无人机（UAV）数据中提取树。本文的主要目的是探索在具有不同比例和树内容的图像中采用的方法。为此，获取了来自两个不同区域的无人机图像，并创建了三个大型测试图像以进行实验分析和准确性评估。根据精度分析，尽管规模和内容有所变化，但该模型在很大程度上保持了其检测精度。据我们所知，这是第一次将带有FPN的Mask R-CNN模型与UAV数据一起用于树提取。