Heliport Detection Using Artificial Neural Networks

Date

2020-09-01

Author

Baseski, Emre

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Automatic image exploitation is a critical technology for quick content analysis of high-resolution remote sensing images. The presence of a heliport on an image usually implies an important facility, such as military facilities. Therefore, detection of heliports can reveal critical information about the content of an image. In this article, two learning-based algorithms are presented that make use of artificial neural networks to detect H-shaped, light-colored heliports. The first algorithm is based on shape analysis of the heliport candidate segments using classical artificial neural networks. The second algorithm uses deep-learning techniques. While deep learning can solve difficult problems successfully, classical-learning approaches can be tuned easily to obtain fast and reasonable results. Therefore, although the main objective of this article is heliport detection, it also compares a deep-learning based approach with a classical learning-based approach and discusses advantages and disadvantages of both techniques.

Subject Keywords

Computers in Earth Sciences

URI

https://hdl.handle.net/11511/63607

Journal

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING

DOI

https://doi.org/10.14358/pers.86.9.541

Collections

Department of Computer Engineering, Article

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Citation Formats

E. Baseski, “Heliport Detection Using Artificial Neural Networks,” PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, pp. 541–546, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/63607.