HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation

By Shunjun Wei, Xiangfeng Zeng, Qizhe Qu, Mou Wang, Hao Su, Jun Shi. IEEE Access, 2020

809 citations recorded by the source corpus.

With the development of satellite technology, up to date imaging mode of synthetic aperture radar (SAR) satellite can provide higher resolution SAR imageries, which benefits ship detection and instance segmentation. Meanwhile, object detectors based on convolutional neural network (CNN) show high performance on SAR ship detection even without land-ocean segmentation; but with respective shortcomings, such as the relatively small size of SAR images for ship detection, limited SAR training samples, and inappropriate annotations, in existing SAR ship datasets, related research is hampered. To promote the development of CNN based ship detection and instance segmentation, we have constructed a High-Resolution SAR Images Dataset (HRSID). In addition to object detection, instance segmentation can also be implemented on HRSID. As for dataset construction, under the overlapped ratio of 25%, 136 panoramic SAR imageries with ranging resolution from 1m to 5m are cropped to $800 \times 800$ pixels SAR images. To reduce wrong annotation and missing annotation, optical remote sensing imageries are applied to reduce the interferes from harbor constructions. There are 5604 cropped SAR images and 16951 ships in HRSID, and we have divided HRSID into a training set (65% SAR images) and test set (35% SAR images) with the format of Microsoft Common Objects in Context (MS COCO). 8 state-of-the-art detectors are experimented on HRSID to build the baseline; MS COCO evaluation metrics are applicated for comprehensive evaluation. Experimental results reveal that ship detection and instance segmentation can be well implemented on HRSID.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/301e66e0446933b9d2a5d12a40d0e27f3c34b199

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Citations in the corpus

EarthGPT: A Universal Multimodal Large Language Model for Multisensor Image Comprehension in Remote Sensing Domain (2024, methodology): “After mixed encoding, the comprehensive extraction of visual information is achieved, preparing for alignment with LLM.”
Transformers in Remote Sensing: A Survey (2022, methodology): “GCBANet [146] Segmentation SSDD [158], HRSID [159] AP Introduces a transformers-based approach with a global contextual block for capturing spatial holistic long-range dependencies and a boundary-aware prediction scheme for estimating the boundaries of ship.”
SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis (2021, background): “[63] HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation IEEE Access”
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS (2023, result): “The authors showed that these changes improved the accuracy of YOLO-SD compared with YOLOX in the HRSID dataset [136].”