NOVEL SINGLE TREE DETECTION BY TRANSFORMERS USING UAV-BASED MULTISPECTRAL IMAGERY
- 1Dept. of Geoinformatics, Munich University of Applied Sciences, 80333 Munich, Germany
- 2Faculty of Environment and Natural Resources, University of Freiburg, Germany
- 3Bavarian Forest National Park, Dept. of Visitor Management and National Park Monitoring, 94481 Grafenau, Germany
- 4Dept. of Electrical Engineering and Information Technology, Munich University of Applied Sciences, 80333 Munich, Germany
- 5Institute for Applications of Machine Learning and Intelligent Systems, Munich University of Applied Sciences, 80333 Munich, Germany
Keywords: neural networks, transformer, single tree detection, multispectral imagery, UAV
Abstract. Single tree detection has been a major research topic concerning automatic forest inventory using remote sensing data. Recently, deep learning-based approaches in remote sensing forestry have gained attention because of the prospect of improved accuracy. In this study, we present a novel tree detection method based on the detection transformer (DETR), which applies a transformer in combination with a pre-trained convolutional neural network to detect individual trees using high-resolution multispectral imagery. The test site (Kranzberg Forest Roof Experiment - KROOF) is located in Bavaria, north of Munich, and is characterised by a mixed forest which consists of large groups of European beeches (Fagus sylvatica) surrounded by Norway spruces (Picea abies). The image data were acquired with a MicaSense RedEdge-MX Dual camera mounted to UAV. Two flight mission were conducted at an altitude of around 85 m with a flight speed of 5 m/sec, resulting in a ground resolution of about 5 cm. 125 trees were surveyed by tacheometric means in the field for testing, and 1390 trees were labelled by visual interpretation of the multispectral imagery for training and validation. The novel tree detection method based on DETR shows promising results and outperforms the standard, well-known object detection method YOLOv4 in mixed and deciduous test plots. More detailed, F1-scores were evaluated for coniferous plot at 83%, for mixed plot at 86% and for deciduous plot at 71%. The corresponding figures for YOLOv4 are 87% coniferous, 65% mixed and 67% deciduous. In terms of accuracy, DETR is inferior by 6% in coniferous plot, however superior by 28% and 5% in mixed and deciduous plot, respectively. Compared to YOLOv4, we found that DETR sometimes failed to detect small coniferous trees. Moreover, both deep learning-based methods tend to over-detect single trees in deciduous test areas. In sum, transformer-based tree detection shows great potential to improve single tree detection.