Paper Info
Title
Sar Ship Detection Dataset (Ssdd): Official Release And Comprehensive Data Analysis
Abstract
SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL). According to our investigation, up to 46.59% of the total 161 public reports confidently select SSDD to study DL-based SAR ship detection. Undoubtedly, this situation reveals the popularity and great influence of SSDD in the SAR remote sensing community. Nevertheless, the coarse annotations and ambiguous standards of use of its initial version both hinder fair methodological comparisons and effective academic exchanges. Additionally, its single-function horizontal-vertical rectangle bounding box (BBox) labels can no longer satisfy the current research needs of the rotatable bounding box (RBox) task and the pixel-level polygon segmentation task. Therefore, to address the above two dilemmas, in this review, advocated by the publisher of SSDD, we will make an official release of SSDD based on its initial version. SSDD's official release version will cover three types: (1) a bounding box SSDD (BBox-SSDD), (2) a rotatable bounding box SSDD (RBox-SSDD), and (3) a polygon segmentation SSDD (PSeg-SSDD). We relabel ships in SSDD more carefully and finely, and then explicitly formulate some strict using standards, e.g., (1) the training-test division determination, (2) the inshore-offshore protocol, (3) the ship-size reasonable definition, (4) the determination of the densely distributed small ship samples, and (5) the determination of the densely parallel berthing at ports ship samples. These using standards are all formulated objectively based on the using differences of existing 75 (161 x 46.59%) public reports. They will be beneficial for fair method comparison and effective academic exchanges in the future. Most notably, we conduct a comprehensive data analysis on BBox-SSDD, RBox-SSDD, and PSeg-SSDD. Our analysis results can provide some valuable suggestions for possible future scholars to further elaborately design DL-based SAR ship detectors with higher accuracy and stronger robustness when using SSDD.
Year
DOI
Venue
2021
10.3390/rs13183690
REMOTE SENSING
Keywords
DocType
Volume
SAR Ship Detection Dataset (SSDD), Synthetic Aperture Radar (SAR), dataset, ship detection, deep learning (DL), data analysis
Journal
13
Issue
Citations 
PageRank 
18
1
0.35
References 
Authors
0
16
Name
Order
Citations
PageRank
Tianwen Zhang1115.94
Xiaoling Zhang2124.53
Jianwei Li330.75
Xiaowo Xu412.37
Baoyou Wang510.35
Xu Zhan632.44
Yanqin Xu710.35
Xiao Ke851.78
Tianjiao Zeng910.35
Hao Su1010.35
Israr Ahmad1110.68
Dece Pan1231.09
Chang Liu1315952.61
Yue Zhou1431.76
Jun Shi152713.21
Shunjun Wei16148.82