A5088063261- Remote-Sensing Image Classification
- Advanced Vision and Imaging
- Domain Adaptation and Few-Shot Learning
- Infrared Target Detection Methodologies
- Human Pose and Action Recognition
- Advanced SAR Imaging Techniques
- Video Surveillance and Tracking Methods
- Advanced Neural Network Applications
National Space Science Center
2022
Chinese Academy of Sciences
2022
University of Chinese Academy of Sciences
2022
Aircraft classifiers in remotely sensed images based on deep convolutional neural networks play a significant role military. However, practical applications, there is lack of remote sensing fine-grained aircraft data. In this study, we demonstrate that few-shot learning (FSL) can be effectively used for identification and propose new classifier-adaptive earth mover's distance (Adap-EMD) recognition few-sample aircraft. Adap-EMD consists an efficient block attention mechanism (EBAM) adaptive...
The availability of many commercial satellites has created favorable conditions for tracking typical objects in remote sensing sequences, making them widely useful numerous applications. However, small objects, multiple similar disruptors, background clutter, and occlusion are significant challenges to this field. This study proposes the novel tracker-temporal motion compensation Siamese network (Siam-TMC) tracking. Our method relies on a multidimensional information-aware module temporal...
Interpreting airborne remote sensing images plays an important role in aviation control and battlefield situational awareness. However, highly dynamic aircraft detection remains challenging, owing to variable object sizes, flexible attitudes, motion blur. This study develops a multi-scale dataset benchmark overcome challenges, such as high intraclass variance, multiple scales angles, blur, partial occlusion. We also propose trained-from-scratch detector, the local feature correlation single...