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Open AccessArticle

Computer Vision and Deep Learning Techniques for the Analysis of Drone-Acquired Forest Images, a Transfer Learning Study

1
Faculty of Agriculture, Yamagata University, Tsuruoka, Yamagata 997-8555, Japan
2
Eurecat, Centre Tecnològic de Catalunya, 08005 Barcelona, Spain
3
Faculty of Science, Yamagata University, Yamagata 990-8560, Japan
*
Authors to whom correspondence should be addressed.
Remote Sens. 2020, 12(8), 1287; https://doi.org/10.3390/rs12081287
Received: 13 March 2020 / Revised: 15 April 2020 / Accepted: 16 April 2020 / Published: 18 April 2020
Unmanned Aerial Vehicles (UAV) are becoming an essential tool for evaluating the status and the changes in forest ecosystems. This is especially important in Japan due to the sheer magnitude and complexity of the forest area, made up mostly of natural mixed broadleaf deciduous forests. Additionally, Deep Learning (DL) is becoming more popular for forestry applications because it allows for the inclusion of expert human knowledge into the automatic image processing pipeline. In this paper we study and quantify issues related to the use of DL with our own UAV-acquired images in forestry applications such as: the effect of Transfer Learning (TL) and the Deep Learning architecture chosen or whether a simple patch-based framework may produce results in different practical problems. We use two different Deep Learning architectures (ResNet50 and UNet), two in-house datasets (winter and coastal forest) and focus on two separate problem formalizations (Multi-Label Patch or MLP classification and semantic segmentation). Our results show that Transfer Learning is necessary to obtain satisfactory outcome in the problem of MLP classification of deciduous vs evergreen trees in the winter orthomosaic dataset (with a 9.78% improvement from no transfer learning to transfer learning from a a general-purpose dataset). We also observe a further 2.7% improvement when Transfer Learning is performed from a dataset that is closer to our type of images. Finally, we demonstrate the applicability of the patch-based framework with the ResNet50 architecture in a different and complex example: Detection of the invasive broadleaf deciduous black locust (Robinia pseudoacacia) in an evergreen coniferous black pine (Pinus thunbergii) coastal forest typical of Japan. In this case we detect images containing the invasive species with a 75% of True Positives (TP) and 9% False Positives (FP) while the detection of native trees was 95% TP and 10% FP. View Full-Text
Keywords: deep learning; mixed forest; multi-label segmentation; semantic segmentation; unmanned aerial vehicles deep learning; mixed forest; multi-label segmentation; semantic segmentation; unmanned aerial vehicles
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MDPI and ACS Style

Kentsch, S.; Lopez Caceres, M.L.; Serrano, D.; Roure, F.; Diez, Y. Computer Vision and Deep Learning Techniques for the Analysis of Drone-Acquired Forest Images, a Transfer Learning Study. Remote Sens. 2020, 12, 1287.

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