The Information Engineering University | China
Dr. Tian Gao is a distinguished researcher in the field of remote sensing, specializing in multimodal image matching, Arctic sea ice motion analysis, and image registration for optical and SAR imagery. He completed his graduate studies at PLA Information Engineering University, Zhengzhou, China, focusing on geospatial information and advanced computational methods for Earth observation.Gao has authored 11 peer-reviewed publications, including in top-tier journals such as IEEE Sensors Journal, ISPRS Journal of Photogrammetry and Remote Sensing, and the International Journal of Applied Earth Observation and Geoinformation. His notable contributions include the development of SFA-Net, a SAM-guided focused attention network for multimodal remote sensing image matching, and innovative approaches to sharpened side phase fusion and self-similar adjacent self-convolutional feature registration. Gao’s work also encompasses keypoint-free feature tracking for Arctic sea ice motion retrieval, DEM super-resolution using attention-based and relative depth-guided methods, and GNSS-denied UAV geolocalization. These efforts have advanced both methodological innovation and practical applications in environmental monitoring, geospatial intelligence and disaster response.His research demonstrates extensive collaboration with domestic and international scholars, reflecting interdisciplinary engagement across remote sensing, UAV imaging, and geospatial data analysis. Gao’s publications have collectively received 51 citations, highlighting the growing impact of his work in the scientific community.Beyond methodological contributions Gao’s work has significant societal and environmental relevance enabling improved monitoring of polar ice dynamics, enhancing emergency response through UAV-assisted image stitching and supporting sustainable geospatial intelligence applications. With expertise spanning optical and SAR imagery multimodal data fusion and image registration, Tian Gao continues to contribute to cutting-edge research that bridges academic innovation with real-world solutions in Earth observation and remote sensing.
Featured Publications
1.Wang, Y., Lan, C., Gao, T., Yao, F., & Mu, Z. (2025). Multimodal image matching using sharpened side phase fusion method. IEEE Sensors Journal.
2.Gao, T., Lan, C., Lv, L., Shi, Q., Huang, W., Wang, Y., & Mu, Z. (2025). Robust registration of multimodal remote sensing images using self-similar adjacent self-convolutional feature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
3.Gao, T., Lan, C., Zhou, C., Zhang, Y., Huang, W., Wang, L., & Wang, Y. (2025). Arctic sea ice motion retrieval from multisource SAR images using a keypoint-free feature tracking algorithm. ISPRS Journal of Photogrammetry and Remote Sensing. Cited By: 1
4.Huang, W., Sun, Q., Guo, W., Xu, Q., Wen, B., Gao, T., & Yu, A. (2025). Multi-modal DEM super-resolution using relative depth: A new benchmark and beyond. International Journal of Applied Earth Observation and Geoinformation.
5.Gao, T., Lan, C., Huang, W., & Wang, S. (2025). SFA-Net: A SAM-guided focused attention network for multimodal remote sensing image matching. ISPRS Journal of Photogrammetry and Remote Sensing.
Tian Gao’s research advances remote sensing and multimodal image analysis, enabling precise monitoring of Arctic sea ice, GNSS-denied UAV navigation, and environmental changes. His work bridges scientific innovation with practical applications, supporting disaster response, geospatial intelligence, and sustainable environmental management globally.