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An Interpretable Explanation Approach for Signal Modulation Classification
Jing Bai;Yuheng Lian;Yiran Wang;Junjie Ren;Zhu Xiao;Huaji Zhou;Licheng Jiao
Signal modulation classification (SMC) has attracted extensive attention for its wide application in the military and civil fields. The current direction of combining deep-learning (DL) technology with wireless communication technology is developing hotly. DL models are riding high in the field of SMC with their highly abstract feature extraction capability. However, most DL models are decision-ag...Show More
Localizing From Classification: Self-Directed Weakly Supervised Object Localization for Remote Sensing Images
Jing Bai;Junjie Ren;Zhu Xiao;Zheng Chen;Chengxi Gao;Talal Ahmed Ali Ali;Licheng Jiao
In recent years, object localization and detection methods in remote sensing images (RSIs) have received increasing attention due to their broad applications. However, most previous fully supervised methods require a large number of time-consuming and labor-intensive instance-level annotations. Compared with those fully supervised methods, weakly supervised object localization (WSOL) aims to recog...Show More
Few-Shot SAR Ship Image Detection Using Two-Stage Cross-Domain Transfer Learning
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Year: 2022 | Conference Paper |
Cited by: Papers (4)
Synthetic Aperture Radar is superior to optical sensors in that it can identify ships at all hours and on all days. Deep learning-based object detection relies on huge amounts of data, yet SAR ship images are challenging to obtain and label. A few-shot cross-domain transfer learning approach for SAR image ship detection is used in this paper. It is divided into two stages: the first uses a large v...Show More
Object Detection in Large-Scale Remote-Sensing Images Based on Time-Frequency Analysis and Feature Optimization
Jing Bai;Junjie Ren;Yujia Yang;Zhu Xiao;Wentao Yu;Vincent Havyarimana;Licheng Jiao
Recently, optical remote-sensing images have been steadily growing in size, as they contain massive data and complex backgrounds. This trend presents several problems for object detection, for example, increased computation time and memory consumption and more false positives due to the complex backgrounds of large-scale images. Inspired by deep neural networks combined with time-frequency analysi...Show More
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