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Open AccessArticle

Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures

Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milano, Italy
Department of Civil, Chemical and Environmental Engineering, Università degli Studi di Genova, 16145 Genova, Italy
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(8), 315;
Received: 14 June 2018 / Revised: 16 July 2018 / Accepted: 30 July 2018 / Published: 3 August 2018
(This article belongs to the Special Issue Applications and Potential of UAV Photogrammetric Survey)
This paper focuses on the use of ultra-high resolution Unmanned Aircraft Systems (UAS) imagery to classify tree species. Multispectral surveys were performed on a plant nursery to produce Digital Surface Models and orthophotos with ground sample distance equal to 0.01 m. Different combinations of multispectral images, multi-temporal data, and texture measures were employed to improve classification. The Grey Level Co-occurrence Matrix was used to generate texture images with different window sizes and procedures for optimal texture features and window size selection were investigated. The study evaluates how methods used in Remote Sensing could be applied on ultra-high resolution UAS images. Combinations of original and derived bands were classified with the Maximum Likelihood algorithm, and Principal Component Analysis was conducted in order to understand the correlation between bands. The study proves that the use of texture features produces a significant increase of the Overall Accuracy, whose values change from 58% to 78% or 87%, depending on components reduction. The improvement given by the introduction of texture measures is highlighted even in terms of User’s and Producer’s Accuracy. For classification purposes, the inclusion of texture can compensate for difficulties of performing multi-temporal surveys. View Full-Text
Keywords: UAS; texture; vegetation; classification; multispectral; accuracy UAS; texture; vegetation; classification; multispectral; accuracy
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Gini, R.; Sona, G.; Ronchetti, G.; Passoni, D.; Pinto, L. Improving Tree Species Classification Using UAS Multispectral Images and Texture Measures. ISPRS Int. J. Geo-Inf. 2018, 7, 315.

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