Tree extraction from multi-scale UAV images using Mask R-CNN with FPN

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.