Efficient Airport Detection Using Line Segment Detector and Fisher Vector Representation

Budak U., HALICI U., Sengur A., Karabatak M., Xiao Y.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, vol.13, no.8, pp.1079-1083, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 13 Issue: 8
  • Publication Date: 2016
  • Doi Number: 10.1109/lgrs.2016.2565706
  • Journal Name: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1079-1083
  • Keywords: Airport detection, Fisher vector (FV), line segment detector (LSD), remote sensing images (RSIs), scale-invariant feature transform (SIFT) features, support vector machines (SVMs), REMOTE-SENSING IMAGES
  • Middle East Technical University Affiliated: Yes

Abstract

In this letter, a two-stage method for airport detection on remote sensing images is proposed. In the first stage, a new algorithm composed of several line-based processing steps is used for extraction of candidate airport regions. In the second stage, the scale-invariant feature transformation and Fisher vector coding are used for efficient representation of the airport and nonairport regions and support vector machines employed for classification. In order to evaluate the performance of the proposed method, extensive experiments are conducted on airports around the world with different layouts. The measures used in the evaluation are accuracy, sensitivity, and specificity. The proposed method achieved an accuracy of 94.6%, which was benchmarked with two previous methods to prove its superiority.