Document quand l'auteur est "Ghamisi, Pedram"

Ghorbanzadeh, Omid ORCID: https://orcid.org/0000-0002-9664-8770; Shahabi, Hejar ORCID: https://orcid.org/0000-0002-3275-8436; Piralilou, Sepideh Tavakkoli; Crivellari, Alessandro ORCID: https://orcid.org/0009-0008-7020-5374; Laura Elena, Cué la Rosa ORCID: https://orcid.org/0000-0002-6284-9494; Atzberger, Clement; Li, Jonathan ORCID: https://orcid.org/0000-0001-7899-0049 et Ghamisi, Pedram ORCID: https://orcid.org/0000-0003-1203-741X (2024). Contrastive Self-Supervised Learning for Globally Distributed Landslide Detection. IEEE Access , vol. 12 . pp. 118453-118466. DOI: 10.1109/ACCESS.2024.3449447.

Rajabi, Roozbeh; Zehtabian, Amin; Singh, Keshav D.; Tabatabaeenejad, Alireza; Ghamisi, Pedram et Homayouni, Saeid ORCID: https://orcid.org/0000-0002-0214-5356 (2023). Editorial: Hyperspectral imaging in environmental monitoring and analysis. Frontiers in Environmental Science , vol. 11 . p. 1353447. DOI: 10.3389/fenvs.2023.1353447.

Ghorbanzadeh, Omid; Shahabi, Hejar; Crivellari, Alessandro; Homayouni, Saeid ORCID: https://orcid.org/0000-0002-0214-5356; Blaschke, Thomas et Ghamisi, Pedram (2022). Landslide detection using deep learning and object-based image analysis. Landslides , vol. 19 , nº 4. pp. 929-939. DOI: 10.1007/s10346-021-01843-x.

Shahabi, Hejar; Rahimzad, Maryam; Tavakkoli Piralilou, Sepideh; Ghorbanzadeh, Omid; Homayouni, Saeid ORCID: https://orcid.org/0000-0002-0214-5356; Blaschke, Thomas; Lim, Samsung et Ghamisi, Pedram (2021). Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery. Remote Sensing , vol. 13 , nº 22. p. 4698. DOI: 10.3390/rs13224698.

Sheykhmousa, Mohammadreza; Mahdianpari, Masoud; Ghanbari, Hamid; Mohammadimanesh, Fariba; Ghamisi, Pedram et Homayouni, Saeid ORCID: https://orcid.org/0000-0002-0214-5356 (2020). Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 13 . pp. 6308-6325. DOI: 10.1109/JSTARS.2020.3026724.