Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/92354
Type of publication: Straipsnis Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or / and Scopus (S1)
Field of Science: Miškotyra / Forestry (A004)
Author(s): Mozgeris, Gintautas;Juodkienė, Vytautė;Jonikavičius, Donatas;Straigytė, Lina;Gadal, Sébastien;Ouerghemmi, Walid
Title: Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment
Is part of: Remote sensing. Basel : MDPI AG, 2018, Vol. 10, iss. 10, Article 1668
Extent: p. 1-22
Date: 2018
Note: Art. no. 1668
Keywords: hyperspectral;colour infrared;ultra-light aircraft;urban trees;classification
Abstract: One may consider the application of remote sensing as a trade-off between the imaging platforms, sensors, and data gathering and processing techniques. This study addresses the potential of hyperspectral imaging using ultra-light aircraft for vegetation species mapping in an urban environment, exploring both the engineering and scientific aspects related to imaging platform design and image classification methods. An imaging system based on simultaneous use of Rikola frame format hyperspectral and Nikon D800E adopted colour infrared cameras installed onboard a Bekas X32 manned ultra-light aircraft is introduced. Two test imaging flight missions were conducted in July of 2015 and September of 2016 over a 4000 ha area in Kaunas City, Lithuania. Sixteen and 64 spectral bands in 2015 and 2016, respectively, in a spectral range of 500–900 nm were recorded with colour infrared images. Three research questions were explored assessing the identification of six deciduous tree species: (1) Pre-treatment of spectral features for classification, (2) testing five conventional machine learning classifiers, and (3) fusion of hyperspectral and colour infrared images. Classification performance was assessed by applying leave-one-out cross-validation at the individual crown level and using as a reference at least 100 field inventoried trees for each species. The best-performing classification algorithm—multilayer perceptron, using all spectral properties extracted from the hyperspectral images—resulted in a moderate classification accuracy. The overall classification accuracy was 63%, Cohen’s Kappa was 0.54, and the species-specific classification accuracies were in the range of 51–72%. Hyperspectral images resulted in significantly better tree species classification ability than the colour infrared images and simultaneous use of spectral properties extracted from hyperspectral and colour infrared image
Internet: https://www.vdu.lt/cris/bitstream/20.500.12259/92354/2/ISSN2072-4292_2018_V_10_10.PG_1-22.pdf
https://hdl.handle.net/20.500.12259/92354
https://doi.org/10.3390/rs10101668
Affiliation(s): Vytauto Didžiojo universitetas
Žemės ūkio akademija
Appears in Collections:1. Straipsniai / Articles
Universiteto mokslo publikacijos / University Research Publications

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