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

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

Abstract: 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.