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Authors: Kevin Qiu ; Dimitri Bulatov and Lukas Lucks

Affiliation: Fraunhofer IOSB Ettlingen, Gutleuthaus Str. 1, 76275 Ettlingen, Germany

Keyword(s): DeepLab, Machine Learning, Remote Sensing, Markov Random Fields.

Abstract: Convolutional neural networks are often trained on RGB images because it is standard practice to use transfer learning using a pre-trained model. Satellite and aerial imagery, however, usually have additional bands, such as infrared or elevation channels. Especially when it comes to detection of small objects, like cars, this additional information could provide a significant benefit. We developed a semantic segmentation model trained on the combined optical and elevation data. Moreover, a post-processing routine using Markov Random Fields was developed and compared to a sequence of pixel-wise and object-wise filtering steps. The models are evaluated on the Potsdam dataset on the pixel and object-based level, whereby accuracies around 90% were obtained.

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Paper citation in several formats:
Qiu, K., Bulatov, D. and Lucks, L. (2022). Improving Car Detection from Aerial Footage with Elevation Information and Markov Random Fields. In Proceedings of the 19th International Conference on Signal Processing and Multimedia Applications - SIGMAP; ISBN 978-989-758-591-3; ISSN 2184-9471, SciTePress, pages 112-119. DOI: 10.5220/0011335900003289

@conference{sigmap22,
author={Kevin Qiu and Dimitri Bulatov and Lukas Lucks},
title={Improving Car Detection from Aerial Footage with Elevation Information and Markov Random Fields},
booktitle={Proceedings of the 19th International Conference on Signal Processing and Multimedia Applications - SIGMAP},
year={2022},
pages={112-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011335900003289},
isbn={978-989-758-591-3},
issn={2184-9471},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Signal Processing and Multimedia Applications - SIGMAP
TI - Improving Car Detection from Aerial Footage with Elevation Information and Markov Random Fields
SN - 978-989-758-591-3
IS - 2184-9471
AU - Qiu, K.
AU - Bulatov, D.
AU - Lucks, L.
PY - 2022
SP - 112
EP - 119
DO - 10.5220/0011335900003289
PB - SciTePress

- Science and Technology Publications, Lda.
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