Seminar Machine Learning for Remote Sensing (ML4RS)

  • Semester:

    Winter semester 2021/22

  • Date and Time:

    Friday 10:00 - 12:00

  • SWS/Credits Points (ECTS)

    2 SWS/3 ECTS

  • Location

    MAR 0.002

  • Participants

    16

Register for course on ISIS

About Course

In this seminar, students will review the current state of the art in the field of machine learning applied to remote sensing image analysis in the framework of different Earth observation applications.
For the details about the course content, please visit the Moses page.

If you have any questions regarding the organization of the course, do not hesitate to contact us at: sekr@rsim.tu-berlin.de.

Contact Persons:
Course Schedule:
Date [Paper ID] Paper Info
14/01/2022 [2] Gong, Maoguo, Yingying Duan, and Hao Li. “Group self-paced learning with a time-varying regularizer for unsupervised change detection.” IEEE Transactions on Geoscience and Remote Sensing 58.4 (2019): 2481-2493.
21/01/2022 [27] Lloyd, D. T., Abela, A., Farrugia, R. A., Galea, A., & Valentino, G. (2021). Optically Enhanced Super-Resolution of Sea Surface Temperature Using Deep Learning. IEEE Transactions on Geoscience and Remote Sensing.

[22] Zheng, Aihua, Ming Wang, Chenglong Li, Jin Tang, and Bin Luo. “Entropy Guided Adversarial Domain Adaptation for Aerial Image Semantic Segmentation.” IEEE Transactions on Geoscience and Remote Sensing (2021).Early access
28/01/2022 [1] H. Chen, Z. Qi and Z. Shi, “Remote Sensing Image Change Detection With Transformers,” in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3095166, 2021, in press.

[10] X. Zheng, T. Gong, X. Li and X. Lu, “Generalized Scene Classification From Small-Scale Datasets With Multitask Learning,” IEEE Transactions on Geoscience and Remote Sensing, 2021, in press.
04/02/2022 [4] S. Saha, F. Bovolo and L. Bruzzone, “Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 3, pp. 1917-1929, 2021.

[20] Wang, J., Zhong, Y., Zheng, Z., Ma, A., & Zhang, L. (2020). RSNet: The search for remote sensing deep neural networks in recognition tasks. IEEE Transactions on Geoscience and Remote Sensing, 59(3), 2520-2534.
11/02/2022 [14] Ma, Xiaorui, Mou, X., Wang, J., Liu, X., Geng, J., & Wang, H. “Cross-Dataset Hyperspectral Image Classification Based on Adversarial Domain Adaptation.” IEEE Transactions on Geoscience and Remote Sensing 2020, pp 4179-4190.

[19] Dong, R., Fang, W., Fu, H., Gan, L., Wang, J., & Gong, P. (2021). High-Resolution Land Cover Mapping Through Learning With Noise Correction. IEEE Transactions on Geoscience and Remote Sensing.
18/02/2022 [18] Paris, C., Bruzzone, L. (2020). A novel approach to the unsupervised extraction of reliable training samples from thematic products. IEEE Transactions on Geoscience and Remote Sensing, 59(3), 1930-1948.

[5] Zhao, Wenzhi, Lichao Mou, Jiage Chen, Yanchen Bo, and William J. Emery. “Incorporating metric learning and adversarial network for seasonal invariant change detection.” IEEE Transactions on Geoscience and Remote Sensing 58, no. 4 (2019): 2720-2731.
25/02/2022 [21] Xu, Qingsong, Xin Yuan, and Chaojun Ouyang. “Class-Aware Domain Adaptation for Semantic Segmentation of Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing,2020.

[3] Q. Shi, M. Liu, S. Li, X. Liu, F. Wang and L. Zhang, “A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection,” in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3085870, 2021, in press.
04/03/2022 [16] X. Li, D. Shi, X. Diao and H. Xu, “SCL-MLNet: Boosting Few-Shot Remote Sensing Scene Classification via Self-Supervised Contrastive Learning,” in IEEE Transactions on Geoscience and Remote Sensing, 2021, in press.