Zhejiang University of Technology | China
Yuchao Feng is an emerging researcher in remote sensing, computer vision, and multimedia computing, known for his significant contributions to change detection, high-resolution image reconstruction, and hyperspectral image classification. With an expanding academic footprint and a series of publications in leading IEEE and ACM journals, he focuses on developing advanced, efficient, and high-accuracy deep learning models for analyzing complex visual and geospatial data. His work addresses key challenges in Earth observation and multimedia imaging by proposing innovative neural network architectures that improve performance, reduce computational complexity, and enhance the interpretability of spatial–temporal information. Yuchao’s research spans multiple interconnected domains, including bitemporal change detection, spatial–temporal feature representation, MRI reconstruction, reference-based image super-resolution, and hyperspectral image processing. A distinctive aspect of his work lies in the integration of multi-scale feature learning, contrastive attention, latent-space modeling, and cross-temporal interaction mechanisms to extract meaningful patterns and improve generalization in real-world applications. His influential papers published in the IEEE Transactions on Geoscience and Remote Sensing (TGRS) highlight his contributions to the remote sensing community, particularly his 2023 and 2022 works that introduced novel architectures for multitemporal change detection and cross-interaction feature fusion, offering advancements for urban development monitoring, environmental change analysis, and land observation management. In addition, he has contributed to the ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) through impactful studies in spatial–temporal learning and feature registration for image super-resolution. His conference papers presented at ICASSP and ACM events further demonstrate his interdisciplinary approach that bridges geospatial analytics, multimedia computing, and medical imaging, including a lightweight collective-attention network for change detection and a latent-space unfolding method for MRI reconstruction. Beyond his research publications, Yuchao actively contributes to the scientific community as a peer reviewer for several prestigious journals, including IEEE Transactions on Geoscience and Remote Sensing, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, International Journal of Remote Sensing, Digital Earth, and ISPRS Journal of Photogrammetry and Remote Sensing, reflecting his expertise and recognition in the global research ecosystem. Driven by a commitment to advancing intelligent Earth observation and data-driven decision-making, his research aims to create scalable, efficient, and high-performance AI-based solutions for remote sensing applications. With a strong foundation in deep learning for visual and geospatial data analysis, Yuchao is poised to make continued contributions that influence academia, industry, and applied Earth science research. His growing scholarly record, technical innovation, and interdisciplinary perspective highlight his potential as a promising research leader in next-generation remote sensing intelligence, AI-powered geospatial solutions, and high-performance multimedia systems.
Featured Publications
Feng, Y., Qin, M., Jiang, J., Lai, J., & Zheng, J. (2025). Axial-shunted spatial-temporal conversation for change detection. ACM Transactions on Multimedia Computing, Communications, and Applications.
Zheng, J., Liu, Y., Feng, Y., Xu, H., & Zhang, M. (2024). Contrastive attention-guided multi-level feature registration for reference-based super-resolution. ACM Transactions on Multimedia Computing, Communications, and Applications.
Feng, Y., Jiang, J., Xu, H., & Zheng, J. (2023). Change detection on remote sensing images using dual-branch multilevel intertemporal network. IEEE Transactions on Geoscience and Remote Sensing.
Feng, Y., Shao, Y., Xu, H., Xu, J., & Zheng, J. (2023). A lightweight collective-attention network for change detection. ACM Conference Paper.