Dr. ZhikangZhao, Changchun Institute of Optics,Fine Mechanicsand Physics,Chinese Academy of Sciences, Β China.
Dr.Zhikang Zhao, a Ph.D. candidate at the Chinese Academy of Sciences, pioneers research in remote sensing image processing. His expertise lies in developing advanced algorithms employing deep learning for super-resolution reconstruction, vital for enhancing low-resolution remote sensing images. His method, featured in Image and Vision Computing, revolutionizes unsupervised super-resolution by simulating degradation mechanisms, leading to superior image quality. With ongoing projects focused on innovative reconstruction networks, Zhao’s contributions significantly advance remote sensing technology, promising accurate data for diverse scientific applications.Β 
Education
Dr.Zhikang Zhao pursued his Ph.D. degree at the prestigious Changchun Institute of Optics, Fine Mechanics and Physics, affiliated with the Chinese Academy of Sciences. Immersed in advanced research in remote sensing image processing, Zhao honed his expertise in developing groundbreaking super-resolution algorithms leveraging deep learning techniques. His academic journey reflects a commitment to pushing the boundaries of knowledge in his field, evident in his innovative contributions to the realm of remote sensing technology. With a solid educational foundation and a passion for research, Zhao is poised to continue making significant strides in advancing the capabilities of remote sensing technology. 
Research Focus
Dr.Zhikang Zhao’s research primarily centers on remote sensing image processing, with a specific emphasis on developing advanced super-resolution reconstruction algorithms. Through his work, he aims to address the challenges associated with low-resolution and low-quality remote sensing images by leveraging cutting-edge deep learning techniques. By focusing on innovative algorithmic developments, Zhao endeavors to enhance the resolution and quality of remote sensing data, thereby unlocking its full potential for various applications. His dedication to pushing the boundaries of remote sensing technology reflects a commitment to advancing scientific knowledge and contributing to the broader scientific community.
Publication Top Notes
- Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances by Zhao, T. et al. (2024)Β
- Published in Remote Sensing, cited by 0.
- Hyperspectral Image Classification Framework Based on Multichannel Graph Convolutional Networks and Class-Guided Attention Mechanism by Feng, H. et al. (2024)Β
- Published in IEEE Transactions on Geoscience and Remote Sensing, cited by 0.
- Remote Sensing Hyperspectral Image Super-Resolution via Multidomain Spatial Information and Multiscale Spectral Information Fusion by Chen, C. et al. (2024)Β
- Published in IEEE Transactions on Geoscience and Remote Sensing, cited by 0.
- Context Feature Integration and Balanced Sampling Strategy for Small Weak Object Detection in Remote Sensing Imagery by Li, Z. et al. (2024)Β
- Published in IEEE Geoscience and Remote Sensing Letters, cited by 2.
- A Review of Hyperspectral Image Super-Resolution Based on Deep Learning by Chen, C. et al. (2023)Β
- Published in Remote Sensing, cited by 9.
- RoI Fusion Strategy With Self-Attention Mechanism for Object Detection in Remote Sensing Images by Zhang, Y. et al. (2023)Β
- Published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, cited by 4.
- Multi-scale unsupervised network for infrared and visible image fusion based on joint attention mechanism by Xu, D. et al. (2022)Β
- Published in Infrared Physics and Technology, cited by 10.
- Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey by Li, Z. et al. (2022)Β
