Comparison of Input Image Dimensions for Ship Detection from KOMPSAT-5 SAR Image Using Deep Neural Network

Wook Park, Won Kyung Baek, Joong Sun Won, Hyung Sup Jung

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This study shows the results of ship detection in KOMPSAT-5 X-band SAR images using Deep Learning (DL) based the fast Deep Neural Network (DNN) method with the iterative kernel-based false alram detection algorithm. In addition, this study verifies the detection accuracy according to the size of the input data for are typical errors in SAR images such as backscattering noise, side-lobe, and ghost effect. The test results of the Fast DNN method confirmed that the error caused by the back scattering noise and side-lobe decreased as the input data size increased, however, the error for the ghost effect did not improve significantly. Also, the False Alarm Rate (FAR) was greatly improved using the iterative kernel-based false alram detection algorithm. However, caution is needed when detecting small ships because very small boats disappear. The results of this study showed that the size of the input data was very effective at 51 × 51 or higher when the fast DNN method and the iterative kernel-based false alram detection algorithm were applied to the KOMPSAT-5 images to detect ships, allowing the FAR and Intersaction over Union (IoU) showing relatively high accuracy at 0.270 and 0.611.

Original languageEnglish
Pages (from-to)208-217
Number of pages10
JournalJournal of Coastal Research
Volume102
Issue numbersp1
DOIs
Publication statusPublished - 2020 Sep 1

Bibliographical note

Funding Information:
This study was supported by the 2020 research fund of the University of Seoul for Prof. Hyung-Sup Jung, and it was also supported by “Development of satellite based system on monitoring and predicting ship distribution in the contiguous zone” of the Korea Coast Guard for Prof. Joong-Sun Won.

Publisher Copyright:
© Coastal Education and Research Foundation, Inc. 2020.

All Science Journal Classification (ASJC) codes

  • Ecology
  • Water Science and Technology
  • Earth-Surface Processes

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