Xiao Ke

PhD Researcher

I am currently a researcher at the School of Computer Science and Informatics, University of Liverpool. My research focuses on computer vision applied to remote sensing, with a particular emphasis on Synthetic Aperture Radar (SAR) ship detection, classification, and instance segmentation.

Latest News

[Oct 2025] We will release sea-land segmentation SAR Ship Detection Dataset (SL-SSDD), the first synergistic sea-land segmentation dataset for SAR ship detection. Link

[Aug 2025] Our paper "Scene-aware SAR ship detection guided by unsupervised sea-land segmentation" is published by 2025 IEEE 6th International Conference on Pattern Recognition and Machine Learning (PRML).

[Jan 2025] Our paper "Mambashadowdet: A high-speed and high-accuracy moving target shadow detection network for video SAR" is published by Remote Sensing.

Selected Publications

1. Ke H, Ke X, Zhang Z, et al. SLA-Net: A Novel Sea–Land Aware Network for Accurate SAR Ship Detection Guided by Hierarchical Attention Mechanism[J]. Remote Sensing, 2025, 17(21): 3576.

Journal Article | Remote Sensing

2. Zhang T, Zhang X, Ke X, et al. LS-SSDD-v1.0: A deep learning dataset dedicated to small ship detection from large-scale Sentinel-1 SAR images[J]. Remote Sensing, 2020, 12(18): 2997.

Journal Article | Remote Sensing

3. Ke X, Zhang X, Zhang T. GCBANet: A global context boundary-aware network for SAR ship instance segmentation[J]. Remote Sensing, 2022, 14(9): 2165.

Journal Article | Remote Sensing

4. Zhang T, Zhang X, Ke X, et al. HOG-ShipCLSNet: A novel deep learning network with hog feature fusion for SAR ship classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-22.

Journal Article | IEEE TGRS

5. Ke X, Zhang X, Zhang T, et al. Sar ship detection based on swin transformer and feature enhancement feature pyramid network[C]//IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022: 2163-2166.

Conference Paper | IGARSS

Service

Journal Reviewer

IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Aerospace and Electronic Systems
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IEEE Geoscience and Remote Sensing Letters
Remote Sensing
Sensors
Electronics
Applied Science
Sustainability
Mathematics

Teaching

Software Engineering I (UML)

School of Computer Science and Informatics, University of Liverpool | Undergraduate Course

Teach fundamental concepts of software engineering, focusing on Unified Modeling Language (UML) for software design and documentation.
Cover core UML diagrams: Use Case Diagrams, Class Diagrams, Sequence Diagrams, Activity Diagrams, and State Machine Diagrams.
Guide students to apply UML in practical software development projects, combining requirements analysis and system design.
Assess students through project assignments and practical exams to enhance their hands-on software modeling capabilities.

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