Heliport Detection Using Artificial Neural Networks

Baseski E.

PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, vol.86, no.9, pp.541-546, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 86 Issue: 9
  • Publication Date: 2020
  • Doi Number: 10.14358/pers.86.9.541
  • Journal Name: PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Geobase, Metadex, Pollution Abstracts, DIALNET, Civil Engineering Abstracts
  • Page Numbers: pp.541-546
  • Middle East Technical University Affiliated: No

Abstract

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.