Deep Cascade Network for Noise-Robust SAR Ship Detection With Label Augmentation

Keunhoon Choi, Taeyong Song, Sunok Kim, Hyunsung Jang, Namkoo Ha, Kwanghoon Sohn

Research output: Contribution to journalArticlepeer-review


Deep learning has recently made an impressive advance in ship detection in Synthetic Aperture Radar (SAR) images. Despite this advancement, conventional deep detection networks often suffer from speckle noise that inherently occurs in SAR images. However, despeckling researches have focused only on improving the visual quality of the SAR images. Despeckling without considering subsequent task may cause loss of semantic information and result in performance degradation. In this letter, we propose a deep cascade framework for noise-robust SAR ship detection that sequentially performs despeckle and detection. We effectively train our cascade network using pseudo-SAR images with SAR-like structures and additional detection annotations. We also propose semantic conservative loss that allows these two tasks to cooperate with each other. Experimental results including comparisons to previous methods and extensive ablation studies show the effectiveness of our proposed method.

Original languageEnglish
Article number4514005
JournalIEEE Geoscience and Remote Sensing Letters
Publication statusPublished - 2022

Bibliographical note

Funding Information:
This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant by the Korean Government (MSIT) (Artificial Intelligence Innovation Hub) under Grant 2021-0-02068 and in part by the Yonsei University Research Fund of 2022 under Grant 2022-22-0002. The work of Sunok Kim was supported by the National Research Foundation of Korea (NRF) Grant by the Korean Government (MSIP) under Grant NRF-2021R1C1C2005202.

Publisher Copyright:
© 2004-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering


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