Publication detail

Aerial Landscape Recognition via Multi-Input Neural Network

KOPEČNÝ, L. HNIDKA, J.

Original Title

Aerial Landscape Recognition via Multi-Input Neural Network

Type

conference paper

Language

English

Original Abstract

Throughout the last decade, the advancements in the hardware allow use for wider applications of the unmanned aerial vehicles (UAV). UAVs feature significant advantages in autonomous aerial landscape mapping and recognition (ALR) over traditional methods due to their high level of operationality and mission repeatability, along with a simple alteration of e.g., on board remote sensors. ALR system based on convolutional neural networks is proposed. The system is designed with real-time capabilities. Data classification based on histogram and Gabor filter is explored on commercially available aerial images. The research roadmap designed to offload the dependency of the process on flight testing to improve the cost-efficiency of the development is proposed as well.

Keywords

aerial landscape recognition; Gabor Filter; histogram; Multi-input neural networks; Principal Component Analysis; Unmanned Aerial Vehicles

Authors

KOPEČNÝ, L.; HNIDKA, J.

Released

11. 6. 2021

Publisher

Institute of Electrical and Electronics Engineers Inc.

ISBN

978-1-6654-3724-0

Book

2021 International Conference on Military Technologies (ICMT)

Pages from

1

Pages to

5

Pages count

5

URL

https://ieeexplore.ieee.org/document/9502749

BibTex

@inproceedings{BUT176844,
  author="Ladislav {Kopečný} and Jakub {Hnidka}",
  title="Aerial Landscape Recognition via Multi-Input Neural Network",
  booktitle="2021 International Conference on Military Technologies (ICMT)",
  year="2021",
  pages="1--5",
  publisher="Institute of Electrical and Electronics Engineers Inc.",
  doi="10.1109/ICMT52455.2021.9502749",
  isbn="978-1-6654-3724-0",
  url="https://ieeexplore.ieee.org/document/9502749"
}