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Xian Sun
Researcher at Chinese Academy of Sciences
Publications - 246
Citations - 7546
Xian Sun is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 31, co-authored 194 publications receiving 3697 citations. Previous affiliations of Xian Sun include Nanjing University of Posts and Telecommunications & Karlsruhe Institute of Technology.
Papers
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Proceedings ArticleDOI
SCRDet: Towards More Robust Detection for Small, Cluttered and Rotated Objects
TL;DR: A sampling fusion network is devised which fuses multi-layer feature with effective anchor sampling, to improve the sensitivity to small objects, and the IoU constant factor is added to the smooth L1 loss to address the boundary problem for the rotating bounding box.
Journal ArticleDOI
Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images
TL;DR: A novel supervised change detection method based on a deep siamese convolutional network for optical aerial images that is comparable, even better, with the two state-of-the-art methods in terms of F-measure.
Journal ArticleDOI
Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks
TL;DR: Wang et al. as mentioned in this paper proposed a framework called Rotation Dense Feature Pyramid Networks (R-DFPN), which can effectively detect ships in different scenes including ocean and port.
Journal ArticleDOI
Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks
TL;DR: This work proposes a framework called Rotation Dense Feature Pyramid Networks (R-DFPN) which can effectively detect ships in different scenes including ocean and port and proposes multiscale region of interest (ROI) Align for the purpose of maintaining the completeness of the semantic and spatial information.
Journal ArticleDOI
A Densely Connected End-to-End Neural Network for Multiscale and Multiscene SAR Ship Detection
TL;DR: A densely connected multiscales neural network based on faster-RCNN framework to solve multiscale and multiscene SAR ship detection and a training strategy to reduce the weight of easy examples in the loss function so that the training process more focus on the hard examples to reduce false alarm.