Comparison of pixel and object-based image classification based on very high spatial resolution UAV-borne RGB imagery - Baroch case study
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F25%3A39923202" target="_blank" >RIV/00216275:25410/25:39923202 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/11103546" target="_blank" >https://ieeexplore.ieee.org/document/11103546</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ZINC65316.2025.11103546" target="_blank" >10.1109/ZINC65316.2025.11103546</a>
Alternative languages
Result language
angličtina
Original language name
Comparison of pixel and object-based image classification based on very high spatial resolution UAV-borne RGB imagery - Baroch case study
Original language description
This case study compares supervised pixel-based and object-based image classification approaches applied to very high spatial resolution RGB imagery acquired by an unmanned aerial vehicle (UAV) for land cover classification. The analysis focuses on the Baroch Nature Reserve in the Pardubice region of the Czech Republic, using RGB images captured by a DJI Mavic 2 DUAL Enterprise UAV at a spatial resolution of 2 cm per pixel. Two classification methods available in ArcGIS Pro, Maximum Likelihood (ML) and Support Vector Machine (SVM), were applied using both pixel-based and object-based classification techniques. Four land cover classes were distinguished: high vegetation, low vegetation, bare soil, and shadows. Classification accuracy was evaluated using 1,000 randomly distributed validation points, with performance quantified by the Kappa coefficient. The results indicate that pixel-based classification achieves higher accuracy than object-based classification, particularly for vegetated areas. These findings suggest that pixel-based approaches are more suitable for high spatial resolution UAV imagery when classifying vegetation-rich environments.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2025
ISBN
979-8-3315-1152-4
ISSN
2995-2689
e-ISSN
2995-2689
Number of pages
5
Pages from-to
"131 "- 135
Publisher name
IEEE (Institute of Electrical and Electronics Engineers)
Place of publication
New York
Event location
Novi Sad
Event date
May 28, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001562509500025