Airbus ship detection from satellite imagery using frequency domain learning

Mina Al-Saad, Nour Aburaed, Alavikunhu Panthakkan, Saeed Al Mansoori, Hussain Al Ahmad, Stephen Marshall

Research output: Contribution to journalConference Contributionpeer-review

7 Citations (Scopus)
70 Downloads (Pure)


Ship detection from remote sensing images has been a topic of interest that gradually gained attention over the years due to the wide variety of its applications in the field of maritime surveillance, such as oil discharge control, sea pollution monitoring, and harbour management. Even though there is an extensive amount of methods developed for ship detection, there are still several challenges that remain unsolved, especially in complex environments. These challenges include occlusions due to shadows, clouds, and fog. Nowadays, deep learning algorithms, especially Deep Convolutional Neural Networks (DCNNs), are considered as a powerful approach for automatically detecting ships in satellite imagery. In this paper, enhanced Faster R-CNN (FRCNN) model will be used to overcome the aforementioned unsolved challenges. The enhanced FRCNN, which combines high level features with low level features, will be trained and tested in the frequency domain using the publicly available satellite imagery dataset, Airbus Ship Detection, provided by Kaggle. The performance will be compared to the original FRCNN based on their Overall Accuracy (OA) and Mean Average Precision (mAP) metrics.
Original languageEnglish
Article number1186210
JournalProceedings of SPIE
Publication statusPublished - 12 Sept 2021
EventSPIE Remote Sensing 2021 - Online
Duration: 13 Sept 202118 Sept 2021


  • Deep Learning
  • Discrete Wavelet Transform
  • Remote Sensing
  • Ship Detection


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