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Heliport Detection Using Artificial Neural Networks
Date
2020-09-01
Author
Baseski, Emre
Metadata
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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