Abstract
Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the practical migration application of ship detection in large-scene space-borne SAR images. Thus, to solve this problem, this paper releases a Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) from Sentinel-1, for small ship detection under large-scale backgrounds. LS-SSDD-v1.0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from the Automatic Identification System (AIS) and Google Earth. To facilitate network training, the large-scale images are directly cut into 9000 sub-images without bells and whistles, providing convenience for subsequent detection result presentation in large-scale SAR images. Notably, LS-SSDD-v1.0 has five advantages: (1) large-scale backgrounds, (2) small ship detection, (3) abundant pure backgrounds, (4) fully automatic detection flow, and (5) numerous and standardized research baselines. Last but not least, combined with the advantage of abundant pure backgrounds, we also propose a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images. Experimental results of ablation study can verify the effectiveness of the PBHT-mechanism. LS-SSDD-v1.0 can inspire related scholars to make extensive research into SAR ship detection methods with engineering application value, which is conducive to the progress of SAR intelligent interpretation technology.
Highlights
Synthetic aperture radar (SAR) is an active microwave imaging sensor whose all-day and all-weather working capacity give it an important place in marine exploration [1,2,3,4,5,6,7]
Deep learning was applied in various parts of synthetic aperture radar (SAR) ship detection, e.g., land masking [28], region of interest (ROI) extraction, and ship discrimination [28,72]
Artificial interference is incorporated to select ship candidate regions from the raw large-scale images, which is troublesome and insufficiently intelligent, but if using LS-SAR ship detection dataset (SSDD)-v1.0, one can directly evaluate models’ migration capability because we keep the original status of large-scale space-borne SAR images, and the final detection results can be better presented by simple sub-image stitching without complex coordinate transformation or other post-processing means, so LS-SSDD-v1.0 can enable a fully automatic detection flow without any human involvement that is closer to the engineering applications of deep learning
Summary
Synthetic aperture radar (SAR) is an active microwave imaging sensor whose all-day and all-weather working capacity give it an important place in marine exploration [1,2,3,4,5,6,7]. Artificial interference is incorporated to select ship candidate regions from the raw large-scale images, which is troublesome and insufficiently intelligent, but if using LS-SSDD-v1.0, one can directly evaluate models’ migration capability because we keep the original status of large-scale space-borne SAR images (i.e., the pure background samples without ships are not discarded manually and the ship candidate regions are not selected by human involvement.), and the final detection results can be better presented by simple sub-image stitching without complex coordinate transformation or other post-processing means, so LS-SSDD-v1.0 can enable a fully automatic detection flow without any human involvement that is closer to the engineering applications of deep learning.
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