Comparison of Deep Learning and Object-Based Image Classification Methods: Identification of Horses from RGB Imagery

The paper describes the utilisation of remotely sensed RGB data to support routine monitoring of horses in a natural environment on demand. Data are sensed using an unmanned aerial vehicle (UAV). UAVs provide very high spatial resolution data sensed at a low altitude on demand. Sensing is limited by weather conditions and legal rules only. Terrain does not need to be accessible. The article provides a comparison of several classification methods, namely object-based classification methods and Deep Learning classification. Namely Maximum Likelihood, Random Trees, Support Vector Machine (SVM), K-Nearest Neighbour (K-NN) and Deep Learning models U-Net and Deep Lab version 3. Manual classification is used as the reference method.

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Publication Details

Journal
Procedia Computer Science
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procs.2025.07.026
Primary Topic
Image Processing and 3D Reconstruction
Type
article
Field-Weighted Citation Impact
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article

Comparison of Deep Learning and Object-Based Image Classification Methods: Identification of Horses from RGB Imagery

Pavel Sedlák, Jakub Jech, Martin Krátký, Tomáš Brunclík
Procedia Computer Science
Image Processing and 3D Reconstruction
article

Comparison of Deep Learning and Object-Based Image Classification Methods: Identification of Horses from RGB Imagery

Pavel Sedlák, Jakub Jech, Martin Krátký, Tomáš Brunclík
article en

Abstract

The paper describes the utilisation of remotely sensed RGB data to support routine monitoring of horses in a natural environment on demand. Data are sensed using an unmanned aerial vehicle (UAV). UAVs provide very high spatial resolution data sensed at a low altitude on demand. Sensing is limited by weather conditions and legal rules only. Terrain does not need to be accessible. The article provides a comparison of several classification methods, namely object-based classification methods and Deep Learning classification. Namely Maximum Likelihood, Random Trees, Support Vector Machine (SVM), K-Nearest Neighbour (K-NN) and Deep Learning models U-Net and Deep Lab version 3. Manual classification is used as the reference method.

Procedia Computer ScienceVol. 263
University of Pardubice (CZ)
Openalex Percentile: Top 81%
Image Processing and 3D Reconstruction
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