Recognition of Urbanized Areas in UAV-Derived Very-High-Resolution Visible-Light Imagery

被引:0
|
作者
Puniach, Edyta [1 ]
Gruszczynski, Wojciech [1 ]
Cwiakala, Pawel [1 ]
Strzabala, Katarzyna [1 ]
Pastucha, Elzbieta [2 ]
机构
[1] AGH Univ Krakow, Fac Geodata Sci Geodesy & Environm Engn, Mickiewicza 30, PL-30059 Krakow, Poland
[2] Univ Southern Denmark, Maersk Mc Kinney Moller Inst, Campusvej 55, DK-5230 Odense, Denmark
关键词
image classification; neural networks; unmanned aerial vehicle (UAV); vegetation indices; visible-light imagery; VEGETATION INDEXES; CLASSIFICATION;
D O I
10.3390/rs16183444
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study compared classifiers that differentiate between urbanized and non-urbanized areas based on unmanned aerial vehicle (UAV)-acquired RGB imagery. The tested solutions included numerous vegetation indices (VIs) thresholding and neural networks (NNs). The analysis was conducted for two study areas for which surveys were carried out using different UAVs and cameras. The ground sampling distances for the study areas were 10 mm and 15 mm, respectively. Reference classification was performed manually, obtaining approximately 24 million classified pixels for the first area and approximately 3.8 million for the second. This research study included an analysis of the impact of the season on the threshold values for the tested VIs and the impact of image patch size provided as inputs for the NNs on classification accuracy. The results of the conducted research study indicate a higher classification accuracy using NNs (about 96%) compared with the best of the tested VIs, i.e., Excess Blue (about 87%). Due to the highly imbalanced nature of the used datasets (non-urbanized areas constitute approximately 87% of the total datasets), the Matthews correlation coefficient was also used to assess the correctness of the classification. The analysis based on statistical measures was supplemented with a qualitative assessment of the classification results, which allowed the identification of the most important sources of differences in classification between VIs thresholding and NNs.
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页数:21
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