Engineering Geology at Xidian University, China
Dr. Xi Cheng is a doctoral candidate in Instrument Science and Technology at Xidian University, specializing in remote sensing, hyperspectral data, image processing, and deep learning. He holds a Master’s degree in Control Theory and Control Engineering and a Bachelor’s degree in Automation from Zhengzhou University of Light Industry. With an impressive publication record of 15 papers, including several in high-impact IEEE journals, his research focuses on advanced hyperspectral anomaly detection techniques. His work has been recognized as “Hot Papers” and “ESI Papers,” underscoring its significance in the field. Dr. Cheng is also an active member of the IEEE community and serves as a reviewer for various academic journals, demonstrating his commitment to advancing knowledge in his area. His academic and research endeavors position him as a promising researcher with the potential to make substantial contributions to technology and applied sciences.
Professional profile
Education📚
Dr. Xi Cheng is currently pursuing his Ph.D. in Instrument Science and Technology at Xidian University, where he has been enrolled since September 2021, with an expected graduation in July 2025. Prior to this, he completed his Master’s degree in Control Theory and Control Engineering at Zhengzhou University of Light Industry, graduating in July 2021. During his master’s program, he gained foundational knowledge in control systems and automation, which laid the groundwork for his current research in advanced image processing and deep learning techniques for remote sensing applications. Dr. Cheng also earned his Bachelor’s degree in Automation from Zhengzhou University of Light Industry in July 2018, where he developed essential skills in automation and control technologies. His educational background reflects a strong emphasis on engineering principles and research methodologies, equipping him with the expertise needed to excel in his doctoral studies and future research endeavors.
Professional Experience🏛️
Dr. Xi Cheng has gained valuable professional experience through his research endeavors in the field of remote sensing, hyperspectral data analysis, and deep learning. Currently, as a doctoral candidate at Xidian University, he is actively involved in several research projects focused on hyperspectral anomaly detection, where he applies advanced image processing techniques to enhance remote sensing data interpretation. Dr. Cheng has also contributed significantly to the academic community by publishing multiple research papers in esteemed journals, demonstrating his expertise and commitment to advancing the field. Additionally, he serves as a reviewer for prominent journals, such as IEEE Transactions on Geoscience and Remote Sensing, further showcasing his engagement with current research trends and methodologies. His involvement in the MCCC Lab and as an IEEE Student Member highlights his dedication to collaborative research and professional development in the aerospace and technology sectors, positioning him as a rising scholar in his field.
Research Interest🏆
Dr. Xi Cheng’s research interests lie at the intersection of remote sensing, hyperspectral data analysis, image processing, and deep learning. He focuses on developing innovative methodologies for hyperspectral anomaly detection, utilizing advanced algorithms to extract meaningful information from complex datasets. By leveraging deep learning techniques, Dr. Cheng aims to enhance the accuracy and efficiency of remote sensing applications, particularly in the context of environmental monitoring and resource management. His work often involves the integration of low-rank and sparse representation methods, as well as deep self-representation learning frameworks, to address challenges in hyperspectral data analysis. Dr. Cheng is committed to advancing the understanding of how hyperspectral imagery can be utilized for various applications, including co-saliency detection and image denoising. Through his research, he seeks to contribute significantly to the fields of remote sensing and image processing, ultimately benefiting various industries reliant on accurate data interpretation and analysis.
Award and Honor
Dr. Xi Cheng has garnered recognition for his significant contributions to the field of remote sensing and hyperspectral data analysis. His research has been published in esteemed journals such as the IEEE Transactions on Instrumentation and Measurement, IEEE Geoscience and Remote Sensing Letters, and Remote Sensing, among others. Several of his papers have been distinguished as “Hot Papers” and “ESI Papers,” reflecting their high impact and relevance in the academic community. Dr. Cheng’s innovative methodologies, particularly in hyperspectral anomaly detection, have received accolades from peers, establishing him as a thought leader in his field. Additionally, he is an active member of professional organizations, including IEEE, and serves as a reviewer for various prestigious journals, further underscoring his commitment to advancing scientific knowledge. Through his research excellence, Dr. Cheng continues to influence the development of remote sensing technologies and their applications, making him a deserving candidate for various academic honors and awards.
Conclusion
Overall, Xi Cheng is a strong candidate for the Best Researcher Award due to his solid academic foundation, innovative research contributions, and active participation in the research community. While he has several strengths, focusing on broader applications of his research and increasing public engagement will enhance his profile and impact. With continued dedication to these areas, Xi has the potential to make significant contributions to his field and the broader community.
- Title: Two-stream encoder GAN with progressive training for co-saliency detection
Authors: X. Qian, X. Cheng, G. Cheng, X. Yao, L. Jiang
Year: 2021
Citations: 37
- Title: Hyperspectral anomaly detection via dual dictionaries construction guided by two-stage complementary decision
Authors: S. Lin, M. Zhang, X. Cheng, L. Wang, M. Xu, H. Wang
Year: 2022
Citations: 28
- Title: Dynamic low-rank and sparse priors constrained deep autoencoders for hyperspectral anomaly detection
Authors: S. Lin, M. Zhang, X. Cheng, L. Shi, P. Gamba, H. Wang
Year: 2023
Citations: 27
- Title: Two-stream isolation forest based on deep features for hyperspectral anomaly detection
Authors: X. Cheng, M. Zhang, S. Lin, K. Zhou, S. Zhao, H. Wang
Year: 2023
Citations: 27
- Title: Hyperspectral anomaly detection via sparse representation and collaborative representation
Authors: S. Lin, M. Zhang, X. Cheng, K. Zhou, S. Zhao, H. Wang
Year: 2022
Citations: 27
- Title: Deep self-representation learning framework for hyperspectral anomaly detection
Authors: X. Cheng, M. Zhang, S. Lin, Y. Li, H. Wang
Year: 2023
Citations: 26
- Title: Dual collaborative constraints regularized low-rank and sparse representation via robust dictionaries construction for hyperspectral anomaly detection
Authors: S. Lin, M. Zhang, X. Cheng, K. Zhou, S. Zhao, H. Wang
Year: 2022
Citations: 26
- Title: Deep feature aggregation network for hyperspectral anomaly detection
Authors: X. Cheng, Y. Huo, S. Lin, Y. Dong, S. Zhao, M. Zhang, H. Wang
Year: 2024
Citations: 12
- Title: Multiscale superpixel guided discriminative forest for hyperspectral anomaly detection
Authors: X. Cheng, M. Zhang, S. Lin, K. Zhou, L. Wang, H. Wang
Year: 2022
Citations: 11
- Title: Arbitrary-oriented ellipse detector for ship detection in remote sensing images
Authors: K. Zhou, M. Zhang, H. Zhao, R. Tang, S. Lin, X. Cheng, H. Wang
Year: 2023
Citations: 10
- Title: Dual-GAN complementary learning for real-world image denoising
Authors: S. Zhao, S. Lin, X. Cheng, K. Zhou, M. Zhang, H. Wang
Year: 2023
Citations: 9
- Title: Memory-augmented Autoencoder with Adaptive Reconstruction and Sample Attribution Mining for Hyperspectral Anomaly Detection
Authors: Y. Huo, X. Cheng, S. Lin, M. Zhang, H. Wang
Year: 2024
Citations: 8
- Title: Hyperspectral anomaly detection using spatial–spectral-based union dictionary and improved saliency weight
Authors: S. Lin, M. Zhang, X. Cheng, S. Zhao, L. Shi, H. Wang
Year: 2023
Citations: 5
- Title: Low-Rank and Sparse Representation Inspired Interpretable Network for Hyperspectral Anomaly Detection
Authors: S. Lin, X. Cheng, Y. Zeng, Y. Huo, M. Zhang, H. Wang
Year: 2024
Citations: 4
- Title: Hyperspectral Anomaly Detection via Low-Rank Representation with Dual Graph Regularizations and Adaptive Dictionary
Authors: X. Cheng, R. Mu, S. Lin, M. Zhang, H. Wang
Year: 2024
Citations: 1
