I am an ELLIIT Assistant Professor at Linköping University, affiliated with the Computer Vision Laboratory (CVL) at the Department of Electrical Engineering (ISY). Before joining Linköping University, I was a post-doctoral researcher at the Institute of Advanced Research in Artificial Intelligence (IARAI) from 2021 to 2023. I received my Ph.D. degree in Photogrammetry and Remote Sensing from Wuhan University in 2021. My research interests include machine learning, computer vision, and their applications in remote sensing.
Vacancies:
Multiple Ph.D. and PostDoc positions are available at CVL from time to time. Please check the vacancies page.
Selected Publications
Vision and Language Models for Remote Sensing
Y. Xu, W. Yu, P. Ghamisi, M. Kopp, and S. Hochreiter, “Txt2Img-MHN: Remote sensing image generation from text using modern hopfield networks,” IEEE Trans. Image Process., vol. 32, pp. 5737-5750, 2023. [Paper][Code][Video]
Trustworthy Remote Sensing Models
Y. Xu, T. Bai, W. Yu, S. Chang, P. M. Atkinson, and P. Ghamisi, “AI security for geoscience and remote sensing: Challenges and future trends,” IEEE Geosci. Remote Sens. Mag., vol. 11, no. 2, pp. 60-85, 2023. [Paper]
N. Dräger, Y. Xu, and P. Ghamisi, “Backdoor attacks for remote sensing data with wavelet transform,” IEEE Trans. Geosci. Remote Sens., vol. 61, pp. 1-15, 2023. [Paper][Code]
Y. Xu and P. Ghamisi, “Universal adversarial examples in remote sensing: Methodology and benchmark,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1-15, 2022. (ESI Highly Cited Paper) [Paper][Data][Code][Video]
Y. Xu, B. Du, and L. Zhang, “Self-attention context network: Addressing the threat of adversarial attacks for hyperspectral image classification,” IEEE Trans. Image Process., vol. 30, pp. 8671-8685, 2021. [Paper][Code]
Y. Xu, B. Du, and L. Zhang, “Assessing the threat of adversarial examples on deep neural networks for remote sensing scene classification: Attacks and defenses,” IEEE Trans. Geosci. Remote Sens., vol. 59, no. 2, pp. 1604-1617, 2021. (ESI Highly Cited Paper) [Paper]
AI for Environmental Monitoring
Y. Xu, A. Berg, and L. Haglund, “Sen2Fire: A challenging benchmark dataset for wildfire detection using Sentinel data,” in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2024. [Paper][Data & Code]
O. Ghorbanzadeh, Y. Xu, P. Ghamisi, M. Kopp, and D. Kreil, “Landslide4sense: Reference benchmark data and deep learning models for landslide detection,” IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1-17, 2022. [Paper][Data][Code]
Intelligent Remote Sensing Data Interpretation
Y. Xu, B. Du, and L. Zhang, “Robust self-ensembling network for hyperspectral image classification,” IEEE Trans. Neural Netw. Learn. Syst., vol. 35, no. 3, pp. 3780-3793, 2024. [Paper][Code]
Y. Xu and P. Ghamisi, “Consistency-regularized region-growing network for semantic segmentation of urban scenes with point-level annotations,” IEEE Trans. Image Process., vol. 31, pp. 5038-5051, 2022. [Paper][Code]
Y. Xu, B. Du, L. Zhang, D. Cerra, M. Pato, E. Carmona, S. Prasad, N. Yokoya, R. Hansch, and B. Le Saux, “Advanced multi-sensor optical remote sensing for urban land use and land cover classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest,” in IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 12, no. 6, pp. 1709-1724, 2019. (ESI Highly Cited Paper) [Paper][Data]
Y. Xu, B. Du, F. Zhang, and L. Zhang, “Hyperspectral image classification via a random patches network,” ISPRS J. Photogram. Remote Sens., vol. 142, no. 10, pp. 344-357, 2018. (ESI Highly Cited Paper) [Paper][Code]
Y. Xu, L. Zhang, B. Du, and F. Zhang, “Spectral-spatial unified networks for hyperspectral image classification,” IEEE Trans. Geosci. Remote Sens., vol. 56, no. 10, pp. 5893-5909, 2018. (ESI Highly Cited Paper) [Paper][Code]
Cross-Domain Semantic Segmentation of Urban Scenes
Y. Xu, F. He, B. Du, D. Tao, and L. Zhang, “Self-ensembling GAN for cross-domain semantic segmentation,” IEEE Trans. Multimedia, vol. 25, pp. 7837-7850, 2023. [Paper][Code]
L. Song, Y. Xu, L. Zhang, B. Du, Q. Zhang, and X. Wang, “Learning from synthetic images via active pseudo-labeling,” IEEE Trans. Image Process., vol. 29, pp. 6452-6465, 2020. [Paper][Code]
Y. Xu, B. Du, L. Zhang, Q. Zhang, G. Wang, and L. Zhang, “Self-ensembling attention networks: Addressing domain shift for semantic segmentation,” in Proc. AAAI Conf. Artif. Intell., vol. 33, pp. 5581-5588, 2019. [Paper][Code]
