Active learning with prediction vector diversity for crop classification in western Inner Mongolia
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-022-13768-1.pdf
Reference34 articles.
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3. Amor IBSB, Chehata N, Bailly J, Farah IR, Lagacherie P (2018) Parcel-based active learning for large extent cultivated area mapping. IEEE J Sel Top Appl Earth Obs Remote Sens 11(1):79–88. https://doi.org/10.1109/JSTARS.2017.2751148
4. Belgiu M, Csillik O (2018) Sentinel-2 cropland mapping using pixel-based and object-based timeweighted dynamic time warping analysis. Remote Sens Environ 204:509–523. https://doi.org/10.1016/j.rse.2017.10.005
5. Belgiu M, Dragut L (2016) Random Forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
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