Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms

• URL: http://arxiv.org/abs/2504.14337v1
• Date: Sat, 19 Apr 2025 16:03:49 GMT
• Title: Multispectral airborne laser scanning for tree species classification: a benchmark of machine learning and deep learning algorithms
• Authors: Josef Taher, Eric Hyyppä, Matti Hyyppä, Klaara Salolahti, Xiaowei Yu, Leena Matikainen, Antero Kukko, Matti Lehtomäki, Harri Kaartinen, Sopitta Thurachen, Paula Litkey, Ville Luoma, Markus Holopainen, Gefei Kong, Hongchao Fan, Petri Rönnholm, Antti Polvivaara, Samuli Junttila, Mikko Vastaranta, Stefano Puliti, Rasmus Astrup, Joel Kostensalo, Mari Myllymäki, Maksymilian Kulicki, Krzysztof Stereńczak, Raul de Paula Pires, Ruben Valbuena, Juan Pedro Carbonell-Rivera, Jesús Torralba, Yi-Chen Chen, Lukas Winiwarter, Markus Hollaus, Gottfried Mandlburger, Narges Takhtkeshha, Fabio Remondino, Maciej Lisiewicz, Bartłomiej Kraszewski, Xinlian Liang, Jianchang Chen, Eero Ahokas, Kirsi Karila, Eugeniu Vezeteu, Petri Manninen, Roope Näsi, Heikki Hyyti, Siiri Pyykkönen, Peilun Hu, Juha Hyyppä,
• Abstract summary: Multispectral airborne laser scanning (ALS) has shown promise in automated point cloud processing and tree segmentation.<n>This study addresses these gaps by conducting a benchmark of machine learning and deep learning methods for tree species classification.
• Score: 3.9167717582896793
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Climate-smart and biodiversity-preserving forestry demands precise information on forest resources, extending to the individual tree level. Multispectral airborne laser scanning (ALS) has shown promise in automated point cloud processing and tree segmentation, but challenges remain in identifying rare tree species and leveraging deep learning techniques. This study addresses these gaps by conducting a comprehensive benchmark of machine learning and deep learning methods for tree species classification. For the study, we collected high-density multispectral ALS data (>1000 pts/m$^2$) at three wavelengths using the FGI-developed HeliALS system, complemented by existing Optech Titan data (35 pts/m$^2$), to evaluate the species classification accuracy of various algorithms in a test site located in Southern Finland. Based on 5261 test segments, our findings demonstrate that point-based deep learning methods, particularly a point transformer model, outperformed traditional machine learning and image-based deep learning approaches on high-density multispectral point clouds. For the high-density ALS dataset, a point transformer model provided the best performance reaching an overall (macro-average) accuracy of 87.9% (74.5%) with a training set of 1065 segments and 92.0% (85.1%) with 5000 training segments. The best image-based deep learning method, DetailView, reached an overall (macro-average) accuracy of 84.3% (63.9%), whereas a random forest (RF) classifier achieved an overall (macro-average) accuracy of 83.2% (61.3%). Importantly, the overall classification accuracy of the point transformer model on the HeliALS data increased from 73.0% with no spectral information to 84.7% with single-channel reflectance, and to 87.9% with spectral information of all the three channels.

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