Comparison of pixel and object-based image classification based on very high spatial resolution UAV-borne RGB imagery - Baroch case study

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of pixel and object-based image classification based on very high spatial resolution UAV-borne RGB imagery - Baroch case study

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Comparison of pixel and object-based image classification based on very high spatial resolution UAV-borne RGB imagery - Baroch case study

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 IEEE Zooming Innovation in Consumer Technologies Conference, ZINC 2025

ISBN

979-8-3315-1152-4

ISSN

2995-2689

e-ISSN

2995-2689

Počet stran výsledku

5

Strana od-do

"131 "- 135

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

New York

Místo konání akce

Novi Sad

Datum konání akce

28. 5. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001562509500025